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DeepLearning Note

DeepLearning note

SoA

AI for Time Series

GNN

[Tensorflow GNN v1.0]

Google 에서 몇년 전부터 개발해오던 Tensorflow GNN 의 v1.0 이 몇일전에 릴리즈되었습니다.

GPU 및 TPU에서 작동하며, Tutorial 을 위한 colab 또한 제공됩니다.

https://github.com/tensorflow/gnn https://pypi.org/project/tensorflow-gnn/

Main Recent Update Note

  • [Jul. 05, 2023] Add papers accepted by KDD'23!
  • [Jun. 20, 2023] Add papers accepted by ICML'23!
  • [Feb. 07, 2023] Add papers accepted by ICLR'23 and AAAI'23!
  • [Sep. 18, 2022] Add papers accepted by NeurIPS'22!
  • [Jul. 14, 2022] Add papers accepted by KDD'22!
  • [Jun. 02, 2022] Add papers accepted by ICML'22, ICLR'22, AAAI'22, IJCAI'22!

Table of Contents

Tutorials and Surveys

Tutorials

  • Robust Time Series Analysis and Applications: An Industrial Perspective, in KDD 2022. [Link]
  • Time Series in Healthcare: Challenges and Solutions, in AAAI 2022. [Link]
  • Time Series Anomaly Detection: Tools, Techniques and Tricks, in DASFAA 2022. [Link]
  • Modern Aspects of Big Time Series Forecasting, in IJCAI 2021. [Link]
  • Explainable AI for Societal Event Predictions: Foundations, Methods, and Applications, in AAAI 2021. [Link]
  • Physics-Guided AI for Large-Scale Spatiotemporal Data, in KDD 2021. [Link]
  • Deep Learning for Anomaly Detection, in KDD & WSDM 2020. [Link1] [Link2] [Link3]
  • Building Forecasting Solutions Using Open-Source and Azure Machine Learning, in KDD 2020. [Link]
  • Interpreting and Explaining Deep Neural Networks: A Perspective on Time Series Data, KDD 2020. [Link]
  • Forecasting Big Time Series: Theory and Practice, KDD 2019. [Link]
  • Spatio-Temporal Event Forecasting and Precursor Identification, KDD 2019. [Link]
  • Modeling and Applications for Temporal Point Processes, KDD 2019. [Link1] [Link2]

Surveys

General Time Series Survey

  • Transformers in Time Series: A Survey, in IJCAI 2023. [paper] [GitHub Repo]
  • Time series data augmentation for deep learning: a survey, in IJCAI 2021. [paper]
  • Neural temporal point processes: a review, in IJCAI 2021. [paper]
  • Causal inference for time series analysis: problems, methods and evaluation, in KAIS 2022. [paper]
  • Survey and Evaluation of Causal Discovery Methods for Time Series, in JAIR 2022. [paper]
  • Deep learning for spatio-temporal data mining: A survey, in TKDE 2020. [paper]
  • Generative Adversarial Networks for Spatio-temporal Data: A Survey, in TIST 2022. [paper]
  • Spatio-Temporal Data Mining: A Survey of Problems and Methods, in CSUR 2018. [paper]
  • A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series, in NeurIPS Workshop 2020. [paper]
  • Count Time-Series Analysis: A signal processing perspective, in SPM 2019. [paper]
  • Wavelet transform application for/in non-stationary time-series analysis: a review, in Applied Sciences 2019. [paper]
  • Granger Causality: A Review and Recent Advances, in Annual Review of Statistics and Its Application 2014. [paper]
  • A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data, in arXiv 2020. [paper]
  • Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data, in arXiv 2022. [paper]
  • A Survey on Time-Series Pre-Trained Models, in arXiv 2023. [paper] [link]
  • Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects, in arXiv 2023. [paper] [Website]
  • A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection, in arXiv 2023. [paper] [Website]

Time Series Forecasting Survey

  • Forecasting: theory and practice, in IJF 2022. [paper]
  • Time-series forecasting with deep learning: a survey, in Philosophical Transactions of the Royal Society A 2021. [paper]
  • Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions, in TITS 2022. [paper]
  • Event prediction in the big data era: A systematic survey, in CSUR 2022. [paper]
  • A brief history of forecasting competitions, in IJF 2020. [paper]
  • Neural forecasting: Introduction and literature overview, in arXiv 2020. [paper]
  • Probabilistic forecasting, in Annual Review of Statistics and Its Application 2014. [paper]

Time Series Anomaly Detection Survey

  • A review on outlier/anomaly detection in time series data, in CSUR 2021. [paper]
  • Anomaly detection for IoT time-series data: A survey, in IEEE Internet of Things Journal 2019. [paper]
  • A Survey of AIOps Methods for Failure Management, in TIST 2021. [paper]
  • Sequential (quickest) change detection: Classical results and new directions, in IEEE Journal on Selected Areas in Information Theory 2021. [paper]
  • Outlier detection for temporal data: A survey, TKDE'13. [paper]
  • Anomaly detection for discrete sequences: A survey, TKDE'12. [paper]
  • Anomaly detection: A survey, CSUR'09. [paper]

Time Series Classification Survey

  • Deep learning for time series classification: a review, in Data Mining and Knowledge Discovery 2019. [paper]
  • Approaches and Applications of Early Classification of Time Series: A Review, in IEEE Transactions on Artificial Intelligence 2020. [paper]

Papers 2023

NeurIPS 2023

ICML 2023

Time Series Forecasting

  • Learning Deep Time-index Models for Time Series Forecasting [paper]
  • Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts [paper]
  • Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting [paper]
  • Feature Programming for Multivariate Time Series Prediction [paper]
  • Non-autoregressive Conditional Diffusion Models for Time Series Prediction [paper]

Time Series Anomaly Detection, Classification, Imputation, and XAI

  • Prototype-oriented unsupervised anomaly detection for multivariate time series [paper]
  • Probabilistic Imputation for Time-series Classification with Missing Data [paper]
  • Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation [paper]
  • Self-Interpretable Time Series Prediction with Counterfactual Explanations [paper]
  • Learning Perturbations to Explain Time Series Predictions [paper]

Other Time Series Analysis

  • Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion [paper]
  • Neural Stochastic Differential Games for Time-series Analysis [paper]
  • Sequential Monte Carlo Learning for Time Series Structure Discovery [paper]
  • Context Consistency Regularization for Label Sparsity in Time Series [paper]
  • Sequential Predictive Conformal Inference for Time Series [paper]
  • Improved Online Conformal Prediction via Strongly Adaptive Online Learning [paper]
  • Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series [paper]
  • SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series [paper]
  • Domain Adaptation for Time Series Under Feature and Label Shifts [paper]
  • Deep Latent State Space Models for Time-Series Generation [paper]
  • Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series [paper]
  • Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting [paper]
  • Generalized Teacher Forcing for Learning Chaotic Dynamics [paper]
  • Learning the Dynamics of Sparsely Observed Interacting Systems [paper]
  • Markovian Gaussian Process Variational Autoencoders [paper]
  • ClimaX: A foundation model for weather and climate [paper]

ICLR 2023

Time Series Forecasting

Time Series Anomaly Detection and Classification

Other Time Series Analysis

KDD 2023

Time Series Anomaly Detection

  • DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection [paper] [official code]
  • Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models [paper] [official code]
  • Precursor-of-Anomaly Detection for Irregular Time Series [paper]

Time Series Forecasting

  • When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
  • TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting [paper]
  • Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting
  • Sparse Binary Transformers for Multivariate Time Series Modeling [paper] [official code]
  • Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting

Time Series Forecasting (Traffic)

  • Frigate: Frugal Spatio-temporal Forecasting on Road Networks [paper] [official code]
  • Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities
  • Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training
  • Pattern Expansion and Consolidation on Evolving Graphs for Continual Traffic Prediction

Time Series Imputation

  • Source-Free Domain Adaptation with Temporal Imputation for Time Series Data [paper] [official code]
  • Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders
  • An Observed Value Consistent Diffusion Model for Imputing Missing Values in Multivariate Time Series

Others

  • Online Few-Shot Time Series Classification for Aftershock Detection [paper] [official code]
  • Self-supervised Classification of Clinical Multivariate Time Series using Time Series Dynamics
  • Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series
  • Parameter-free Spikelet: Discovering Different Length and Warped Time Series Motifs using an Adaptive Time Series Representation
  • FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework
  • WHEN: A Wavelet-DTW Hybrid Attention Network for Heterogeneous Time Series Analysis

AAAI 2023

Time Series Forecasting

Other Time Series Analysis

Papers 2022

NeurIPS 2022

Time Series Forecasting

  • FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting [paper] [official code]

  • SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction [paper] [official code]

  • Non-stationary Transformers: Rethinking the Stationarity in Time Series Forecasting [paper]

  • Earthformer: Exploring Space-Time Transformers for Earth System Forecasting [paper]

  • Generative Time Series Forecasting with Diffusion, Denoise and Disentanglement

  • Learning Latent Seasonal-Trend Representations for Time Series Forecasting

  • WaveBound: Dynamically Bounding Error for Stable Time Series Forecasting

  • Time Dimension Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting

  • Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks

  • C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting

  • Meta-Learning Dynamics Forecasting Using Task Inference [paper]

  • Conformal Prediction with Temporal Quantile Adjustments

Other Time Series Analysis

  • Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency, [paper] [official code]

  • Causal Disentanglement for Time Series

  • BILCO: An Efficient Algorithm for Joint Alignment of Time Series

  • Dynamic Sparse Network for Time Series Classification: Learning What to “See”

  • AutoST: Towards the Universal Modeling of Spatio-temporal Sequences

  • GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks

  • Efficient learning of nonlinear prediction models with time-series privileged information

  • Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models

ICML 2022

Time Series Forecasting

  • FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting [paper] [official code]
  • TACTiS: Transformer-Attentional Copulas for Time Series [paper] [official code]
  • Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes [paper] [official code]
  • Domain Adaptation for Time Series Forecasting via Attention Sharing [paper]
  • DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting [paper] [official code]

Time Series Anomaly Detection

  • Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection [paper]

Other Time Series Analysis

  • Adaptive Conformal Predictions for Time Series [paper] [official code]
  • Modeling Irregular Time Series with Continuous Recurrent Units [paper] [official code]
  • Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion [paper]
  • Reconstructing nonlinear dynamical systems from multi-modal time series [paper] [official code]
  • Utilizing Expert Features for Contrastive Learning of Time-Series Representations [paper] [official code]
  • Learning of Cluster-based Feature Importance for Electronic Health Record Time-series [paper]

ICLR 2022

Time Series Forecasting

  • Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting [paper] [official code]
  • DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting [paper] [official code]
  • CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting [paper] [official code]
  • Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift [paper] [official code]
  • TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting [paper] [official code]
  • Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [paper] [official code]
  • On the benefits of maximum likelihood estimation for Regression and Forecasting [paper]
  • Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting [paper] [official code]

Time Series Anomaly Detection

Time Series Classification

  • T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis [paper]
  • Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification [paper]

Other Time Series Analysis

  • Graph-Guided Network for Irregularly Sampled Multivariate Time Series [paper]
  • Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series [paper]
  • Transformer Embeddings of Irregularly Spaced Events and Their Participants [paper]
  • Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks [paper]
  • PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series [paper]
  • Huber Additive Models for Non-stationary Time Series Analysis [paper]
  • LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations [paper]
  • Imbedding Deep Neural Networks [paper]
  • Coherence-based Label Propagation over Time Series for Accelerated Active Learning [paper]
  • Long Expressive Memory for Sequence Modeling [paper]
  • Autoregressive Quantile Flows for Predictive Uncertainty Estimation [paper]
  • Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks [paper]
  • Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification [paper]
  • Explaining Point Processes by Learning Interpretable Temporal Logic Rules [paper]

KDD 2022

Time Series Forecasting

  • Learning to Rotate: Quaternion Transformer for Complicated Periodical Time Series Forecasting [code]
  • Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting
  • Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
  • Multi-Variate Time Series Forecasting on Variable Subset
  • Greykite: Deploying Flexible Forecasting at Scale in LinkedIn

Time Series Anomaly Detection

  • Local Evaluation of Time Series Anomaly Detection Algorithms
  • Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams

Other Time-Series/Spatio-Temporal Analysis

  • Task-Aware Reconstruction for Time-Series Transformer
  • Towards Learning Disentangled Representations for Time Series
  • ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences
  • Non-stationary Time-aware Kernelized Attention for Temporal Event Prediction
  • MSDR: Multi-Step Dependency Relation Networks for Spatial Temporal Forecasting
  • Graph2Route: A Dynamic Spatial-Temporal Graph Neural Network for Pick-up and Delivery Route Prediction
  • Beyond Point Prediction: Capturing Zero-Inflated & Heavy-Tailed Spatiotemporal Data with Deep Extreme Mixture Models
  • Robust Event Forecasting with Spatiotemporal Confounder Learning
  • Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning
  • Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer
  • Characterizing Covid waves via spatio-temporal decomposition

AAAI 2022

Time Series Forecasting

  • CATN: Cross Attentive Tree-Aware Network for Multivariate Time Series Forecasting [paper]
  • Reinforcement Learning based Dynamic Model Combination for Time Series Forecasting [paper]
  • DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation [paper] official code]
  • PrEF: Probabilistic Electricity Forecasting via Copula-Augmented State Space Model [paper]
  • LIMREF: Local Interpretable Model Agnostic Rule-Based Explanations for Forecasting, with an Application to Electricity Smart Meter Data [paper]
  • Learning and Dynamical Models for Sub-Seasonal Climate Forecasting: Comparison and Collaboration [paper] [official code]
  • CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting [paper]
  • Conditional Local Convolution for Spatio-Temporal Meteorological Forecasting [paper] [official code]
  • Graph Neural Controlled Differential Equations for Traffic Forecasting [paper] [official code]
  • STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction [paper] [official code]

Time Series Anomaly Detection

  • Towards a Rigorous Evaluation of Time-Series Anomaly Detection [paper]
  • AnomalyKiTS-Anomaly Detection Toolkit for Time Series [Demo paper]

Other Time Series Analysis

  • TS2Vec: Towards Universal Representation of Time Series [paper] [official code]
  • I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding [paper]
  • Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis [paper]
  • Conditional Loss and Deep Euler Scheme for Time Series Generation [paper]
  • Clustering Interval-Censored Time-Series for Disease Phenotyping [paper]

IJCAI 2022

Time Series Forecasting

  • Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting [paper]
  • Coherent Probabilistic Aggregate Queries on Long-horizon Forecasts [paper] [official code]
  • Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting
  • DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data [paper] [official code]
  • Memory Augmented State Space Model for Time Series Forecasting
  • Physics-Informed Long-Sequence Forecasting From Multi-Resolution Spatiotemporal Data
  • Long-term Spatio-Temporal Forecasting via Dynamic Multiple-Graph Attention [paper] [official code]
  • FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting

Time Series Anomaly Detection

  • Neural Contextual Anomaly Detection for Time Series [paper]
  • GRELEN: Multivariate Time Series Anomaly Detection from the Perspective of Graph Relational Learning

Time Series Classification

  • A Reinforcement Learning-Informed Pattern Mining Framework for Multivariate Time Series Classification [paper]
  • T-SMOTE: Temporal-oriented Synthetic Minority Oversampling Technique for Imbalanced Time Series Classification

SIGMOD VLDB ICDE 2022

Time Series Forecasting

  • METRO: A Generic Graph Neural Network Framework for Multivariate Time Series Forecasting, VLDB'22. [paper] [official code]
  • AutoCTS: Automated Correlated Time Series Forecasting, VLDB'22. [paper]
  • Towards Spatio-Temporal Aware Traffic Time Series Forecasting, ICDE'22. [paper] [official code]

Time Series Anomaly Detection

  • Sintel: A Machine Learning Framework to Extract Insights from Signals, SIGMOD'22. [paper] [official code]
  • TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection, VLDB'22. [paper] [official code]
  • TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data, VLDB'22. [paper] [official code]
  • Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles, VLDB'22. [paper]
  • Robust and Explainable Autoencoders for Time Series Outlier Detection, ICDE'22. [paper]
  • Anomaly Detection in Time Series with Robust Variational Quasi-Recurrent Autoencoders, ICDE'22.

Time Series Classification

  • IPS: Instance Profile for Shapelet Discovery for Time Series Classification, ICDE'22. [paper]
  • Towards Backdoor Attack on Deep Learning based Time Series Classification, ICDE'22. [paper]

Other Time Series Analysis

  • OnlineSTL: Scaling Time Series Decomposition by 100x, VLDB'22. [paper]
  • Efficient temporal pattern mining in big time series using mutual information, VLDB'22. [paper]
  • Learning Evolvable Time-series Shapelets, ICDE'22.

Misc 2022

Time Series Forecasting

  • CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting, WWW'22. [paper] [official code]
  • Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction, WWW'22. [paper]
  • RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph, WWW'22. [paper]
  • Robust Probabilistic Time Series Forecasting, AISTATS'22. [paper] [official code]
  • Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting, AISTATS'22. [paper]

Time Series Anomaly Detection

  • TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis, CIKM'22. [paper] [official code]
  • Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection, AISTATS'22. [paper] [official code]
  • A Semi-Supervised VAE Based Active Anomaly Detection Framework in Multivariate Time Series for Online Systems, WWW'22. [paper]

Other Time Series Analysis

  • Decoupling Local and Global Representations of Time Series, AISTATS'22. [paper] [official code]
  • LIMESegment: Meaningful, Realistic Time Series Explanations, AISTATS'22. [paper]
  • Using time-series privileged information for provably efficient learning of prediction models, AISTATS'22. [paper] [official code]
  • Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation, AISTATS'22. [paper] [official code]
  • EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting, WWW'22. [paper]

Papers 2021

NeurIPS 2021

Time Series Forecasting

  • Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting [paper] [official code]
  • MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data [paper]
  • Conformal Time-Series Forecasting [paper] [official code]
  • Probabilistic Forecasting: A Level-Set Approach [paper]
  • Topological Attention for Time Series Forecasting [paper]
  • When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting [paper] [official code]
  • Monash Time Series Forecasting Archive [paper] [official code]

Time Series Anomaly Detection

  • Revisiting Time Series Outlier Detection: Definitions and Benchmarks [paper] [official code]
  • Online false discovery rate control for anomaly detection in time series [paper]
  • Detecting Anomalous Event Sequences with Temporal Point Processes [paper]

Other Time Series Analysis

ICML 2021

Time Series Forecasting

Time Series Anomaly Detection

Other Time Series Analysis

ICLR 2021

Time Series Forecasting

Other Time Series Analysis

KDD 2021

Time Series Forecasting

  • ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting [paper] [official code]
  • Graph Deep Factors for Forecasting with Applications to Cloud Resource Allocation [paper]
  • Quantifying Uncertainty in Deep Spatiotemporal Forecasting [paper]
  • Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting [paper] [official code]
  • TrajNet: A Trajectory-Based Deep Learning Model for Traffic Prediction [paper]
  • Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting [paper]

Time Series Anomaly Detection

  • Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding [paper] [official code]
  • Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization [paper] [official code]
  • Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering [paper] [official code]
  • Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection [paper] [official code]

Other Time Series Analysis

  • Representation Learning of Multivariate Time Series using a Transformer Framework [paper] [official code]
  • Causal and Interpretable Rules for Time Series Analysis [paper]
  • MiniRocket: A Fast (Almost) Deterministic Transform for Time Series Classification [paper] [official code]
  • Statistical models coupling allows for complex localmultivariate time series analysis [paper]
  • Fast and Accurate Partial Fourier Transform for Time Series Data [paper] [official code]
  • Deep Learning Embeddings for Data Series Similarity Search [paper] [link]

AAAI 2021

Time Series Forecasting

  • Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting [paper] [official code]
  • Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting [paper] [official code]
  • Dynamic Gaussian Mixture Based Deep Generative Model for Robust Forecasting on Sparse Multivariate Time Series [paper] [official code]
  • Temporal Latent Autoencoder: A Method for Probabilistic Multivariate Time Series Forecasting [paper]
  • Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting [paper]
  • Meta-Learning Framework with Applications to Zero-Shot Time-Series Forecasting [paper]
  • Attentive Neural Point Processes for Event Forecasting [paper] [official code]
  • Forecasting Reservoir Inflow via Recurrent Neural ODEs [paper]
  • Hierarchical Graph Convolution Network for Traffic Forecasting [paper]
  • Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network [paper] [official code]
  • Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting [paper] [official code]
  • FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting [paper] [official code]
  • Fairness in Forecasting and Learning Linear Dynamical Systems [paper]
  • A Multi-Step-Ahead Markov Conditional Forward Model with Cube Perturbations for Extreme Weather Forecasting [paper]
  • Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances [paper]

Time Series Anomaly Detection

  • Graph Neural Network-Based Anomaly Detection in Multivariate Time Series [paper] [official code]
  • Time Series Anomaly Detection with Multiresolution Ensemble Decoding [paper]
  • Outlier Impact Characterization for Time Series Data [paper]

Time Series Classification

  • Correlative Channel-Aware Fusion for Multi-View Time Series Classification [paper]
  • Learnable Dynamic Temporal Pooling for Time Series Classification [paper] [official code]
  • ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification [paper]
  • Joint-Label Learning by Dual Augmentation for Time Series Classification [paper]

Other Time Series Analysis

  • Time Series Domain Adaptation via Sparse Associative Structure Alignment [paper] [official code]
  • Learning Representations for Incomplete Time Series Clustering [paper]
  • Generative Semi-Supervised Learning for Multivariate Time Series Imputation [paper] [official code]
  • Second Order Techniques for Learning Time-Series with Structural Breaks [paper]

IJCAI 2021

Time Series Forecasting

  • Two Birds with One Stone: Series Saliency for Accurate and Interpretable Multivariate Time Series Forecasting [paper]
  • Residential Electric Load Forecasting via Attentive Transfer of Graph Neural Networks [paper]
  • Hierarchical Adaptive Temporal-Relational Modeling for Stock Trend Prediction [paper]
  • TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning [paper] [official code]

Other Time Series Analysis

  • Time Series Data Augmentation for Deep Learning: A Survey [paper]
  • Time-Series Representation Learning via Temporal and Contextual Contrasting [paper] [official code]
  • Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation [paper] [official code]
  • Time-Aware Multi-Scale RNNs for Time Series Modeling [paper]
  • TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data [paper]

SIGMOD VLDB ICDE 2021

Time Series Forecasting

  • AutoAI-TS:AutoAI for Time Series Forecasting, SIGMOD'21. [paper]
  • FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data, VLDB'21. [paper]
  • MDTP: a multi-source deep traffic prediction framework over spatio-temporal trajectory data, VLDB'21. [paper]
  • EnhanceNet: Plugin Neural Networks for Enhancing Correlated Time Series Forecasting, ICDE'21. [paper] [slides]
  • An Effective Joint Prediction Model for Travel Demands and Traffic Flows, ICDE'21. [paper]

Time Series Anomaly Detection

  • Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series, VLDB'21. [paper] [official code]
  • SAND: Streaming Subsequence Anomaly Detection, VLDB'21. [paper]

Other Time Series Analysis

  • RobustPeriod: Robust Time-Frequency Mining for Multiple Periodicity Detection, SIGMOD'21. [paper] [code]
  • ORBITS: Online Recovery of Missing Values in Multiple Time Series Streams, VLDB'21. [paper] [official code]
  • Missing Value Imputation on Multidimensional Time Series, VLDB'21. [paper]

Misc 2021

Time Series Forecasting

  • DeepFEC: Energy Consumption Prediction under Real-World Driving Conditions for Smart Cities, WWW'21. [paper] [official code]
  • AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph, WWW'21. [paper] [official code]
  • REST: Reciprocal Framework for Spatiotemporal-coupled Predictions, WWW'21. [paper]
  • Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series, AISTATS'21. [paper]
  • SSDNet: State Space Decomposition Neural Network for Time Series Forecasting, ICDM'21. [paper]
  • AdaRNN: Adaptive Learning and Forecasting of Time Series, CIKM'21. [paper] [official code]
  • Learning to Learn the Future: Modeling Concept Drifts in Time Series Prediction, CIKM'21. [paper]
  • Stock Trend Prediction with Multi-Granularity Data: A Contrastive Learning Approach with Adaptive Fusion, CIKM'21. [paper]
  • DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction, CIKM'21. [paper] [official code1] [official code2]
  • Long Horizon Forecasting With Temporal Point Processes, WSDM'21. [paper] [official code]
  • Time-Series Event Prediction with Evolutionary State Graph, WSDM'21. [paper] [official code].

Time Series Anomaly Detection

  • SDFVAE: Static and Dynamic Factorized VAE for Anomaly Detection of Multivariate CDN KPIs, WWW'21. [paper]
  • Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding, WWW'21. [paper] [official code]
  • FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection, WSDM'21. [paper]
  • Weakly Supervised Temporal Anomaly Segmentation with Dynamic Time Warping, ICCV'21. [paper] [official code]
  • Jump-Starting Multivariate Time Series Anomaly Detection for Online Service Systems, ATC'21. [paper]

Other Time Series Analysis

  • Network of Tensor Time Series, WWW'21. [paper] [official code]
  • Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series, WWW'21. [paper] [official code]
  • SrVARM: State Regularized Vector Autoregressive Model for Joint Learning of Hidden State Transitions and State-Dependent Inter-Variable Dependencies from Multi-variate Time Series, WWW'21. [paper]
  • Deep Fourier Kernel for Self-Attentive Point Processes, AISTATS'21. [paper]
  • Differentiable Divergences Between Time Series, AISTATS'21. [paper] [official code]
  • Aligning Time Series on Incomparable Spaces, AISTATS'21. [paper] [official code]
  • Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions, ICDM'21. [paper]
  • Towards Generating Real-World Time Series Data, ICDM'21. [paper] [official code]
  • Learning Saliency Maps to Explain Deep Time Series Classifiers, CIKM'21. [paper] [official code]
  • Actionable Insights in Urban Multivariate Time-series, CIKM'21. [paper]
  • Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals, WSDM'21. [paper]

Papers 201X-2020 Selected

NeurIPS 201X-2020

Time Series Forecasting

  • Adversarial Sparse Transformer for Time Series Forecasting, NeurIPS'20. [paper] [official code]
  • Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting, NeurIPS'20. [paper] [official code]
  • Deep Rao-Blackwellised Particle Filters for Time Series Forecasting, NeurIPS'20. [paper]
  • Probabilistic Time Series Forecasting with Shape and Temporal Diversity, NeurIPS'20. [paper] [official code]
  • Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting, NeurIPS'20. [paper] [official code]
  • Interpretable Sequence Learning for Covid-19 Forecasting, NeurIPS'20. [paper]
  • Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting, NeurIPS'19. [paper] [code]
  • Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting, NeurIPS'19. [paper] [official code]
  • High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes, NeurIPS'19. [paper] [official code]
  • Deep State Space Models for Time Series Forecasting, NeurIPS'18. [paper]
  • Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction, NeurIPS'16. [paper]

Time Series Anomaly Detection

  • Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network, NeurIPS'20. [paper]
  • PIDForest: Anomaly Detection via Partial Identification, NeurIPS'19. [paper] [official code]
  • Precision and Recall for Time Series, NeurIPS'18. [paper] [official code]

Time Series Classification

  • Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices, NeurIPS'19. [paper]

Time Series Clustering

  • Learning Representations for Time Series Clustering, NeurIPS'19. [paper] [official code]
  • Learning low-dimensional state embeddings and metastable clusters from time series data, NeurIPS'19. [paper]

Time Series Imputation

Time Series Neural xDE

General Time Series Analysis

  • High-recall causal discovery for autocorrelated time series with latent confounders, NeurIPS'20. [paper] [paper2] [official code]
  • Benchmarking Deep Learning Interpretability in Time Series Predictions, NeurIPS'20. [paper] [official code]
  • What went wrong and when? Instance-wise feature importance for time-series black-box models, NeurIPS'20. [paper] [official code]
  • Normalizing Kalman Filters for Multivariate Time Series Analysis, NeurIPS'20. [paper]
  • Unsupervised Scalable Representation Learning for Multivariate Time Series, NeurIPS'19. [paper] [official code]
  • Time-series Generative Adversarial Networks, NeurIPS'19. [paper] [official code]
  • U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging, NeurIPS'19. [paper] [official code]
  • Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders, NeurIPS'18. [paper]
  • Safe Active Learning for Time-Series Modeling with Gaussian Processes, NeurIPS'18. [paper]

ICML 201X-2020

General Time Series Analysis

  • Learning from Irregularly-Sampled Time Series: A Missing Data Perspective, ICML'20. [paper] [official code]
  • Set Functions for Time Series, ICML'20. [paper] [official code]
  • Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders, ICML'20. [paper] [official code]
  • Spectral Subsampling MCMC for Stationary Time Series, ICML'20. [paper]
  • Learnable Group Transform For Time-Series, ICML'20. [paper]
  • Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models, ICML'19. [paper] [official code]
  • Discovering Latent Covariance Structures for Multiple Time Series, ICML'19. [paper]
  • Autoregressive convolutional neural networks for asynchronous time series, ICML'18. [paper] [official code]
  • Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series, ICML'18. [paper]
  • Soft-DTW: a Differentiable Loss Function for Time-Series, ICML'17. [paper] [official code]

Time Series Forecasting

  • Forecasting Sequential Data Using Consistent Koopman Autoencoders, ICML'20. [paper] [official code]
  • Adversarial Attacks on Probabilistic Autoregressive Forecasting Models, ICML'20. [paper] [official code]
  • Influenza Forecasting Framework based on Gaussian Processes, ICML'20. [paper]
  • Deep Factors for Forecasting, ICML'19. [paper]
  • Coherent Probabilistic Forecasts for Hierarchical Time Series, ICML'17. [paper]

ICLR 201X-2020

General Time Series Analysis

Time Series Forecasting

  • N-BEATS: Neural basis expansion analysis for interpretable time series forecasting, ICLR'20. [paper] [official code]
  • Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting, ICLR'18. [paper] [official code]
  • Automatically Inferring Data Quality for Spatiotemporal Forecasting, ICLR'18. [paper]

KDD 201X-2020

General Time Series Analysis

  • Fast RobustSTL: Efficient and Robust Seasonal-Trend Decomposition for Time Series with Complex Patterns, KDD'20. [paper] [code]
  • Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data, KDD'20. [paper] [official code]
  • Online Amnestic DTW to allow Real-Time Golden Batch Monitoring, KDD'19. [paper]
  • Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis, KDD'18. [paper]
  • Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data, KDD'17. [paper]

Time Series Forecasting

  • Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks, KDD'20. [paper] [official code]
  • Attention based Multi-Modal New Product Sales Time-series Forecasting, KDD'20. [paper]
  • Forecasting the Evolution of Hydropower Generation, KDD'20. [paper]
  • Modeling Extreme Events in Time Series Prediction, KDD'19. [paper]
  • Multi-Horizon Time Series Forecasting with Temporal Attention Learning, KDD'19. [paper]
  • Regularized Regression for Hierarchical Forecasting Without Unbiasedness Conditions, KDD'19. [paper]
  • Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Units, KDD'19. [paper] [official code]
  • Dynamic Modeling and Forecasting of Time-evolving Data Streams, KDD'19. [paper] [official code]
  • DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events, KDD'19. [paper] [official code]
  • Stock Price Prediction via Discovering Multi-Frequency Trading Patterns, KDD'17. [paper] [official code]

Time Series Anomaly Detection

  • USAD: UnSupervised Anomaly Detection on Multivariate Time Series, KDD'20. [paper] [official code]
  • RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks, KDD'20 MiLeTS. [paper]
  • Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network, KDD'19. [paper] [official code]
  • Time-Series Anomaly Detection Service at Microsoft, KDD'19. [paper]
  • Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding, KDD'18. [paper] [official code]
  • Anomaly Detection in Streams with Extreme Value Theory, KDD'17. [paper]

AAAI 201X-2020

General Time Series Analysis

  • Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets, AAAI'20. [paper] [official code]
  • DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series, AAAI'20. [paper]
  • Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series, AAAI'20. [paper] [official code]
  • Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series, AAAI'20. [paper] [official code]
  • Relation Inference among Sensor Time Series in Smart Buildings with Metric Learning, AAAI'20. [paper]
  • TapNet: Multivariate Time Series Classification with Attentional Prototype Network, AAAI'20. [paper] [official code]
  • RobustSTL: A Robust Seasonal-Trend Decomposition Procedure for Long Time Series, AAAI'19. [paper] [code]
  • Estimating the Causal Effect from Partially Observed Time Series, AAAI'19. [paper]
  • Adversarial Unsupervised Representation Learning for Activity Time-Series, AAAI'19. [paper]
  • Fourier Feature Approximations for Periodic Kernels in Time-Series Modelling, AAAI'18. [paper]

Time Series Forecasting

  • Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values, AAAI'20. [paper]
  • Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting, AAAI'20. [paper] [official code]
  • Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting, AAAI'20. [paper] [official code]
  • Self-Attention ConvLSTM for Spatiotemporal Prediction, AAAI'20. [paper]
  • Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting, AAAI'20. [paper]
  • Spatio-Temporal Graph Structure Learning for Traffic Forecasting, AAAI'20. [paper]
  • GMAN: A Graph Multi-Attention Network for Traffic Prediction, AAAI'20. [paper] [official code]
  • Cogra: Concept-drift-aware Stochastic Gradient Descent for Time-series Forecasting, AAAI'19. [paper]
  • Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting, AAAI'19. [paper]
  • Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting, AAAI'19. [paper] [official code]
  • MRes-RGNN: A Novel Deep Learning based Framework for Traffic Prediction, AAAI'19. [paper]
  • DeepSTN+: Context-aware Spatial Temporal Neural Network for Crowd Flow Prediction in Metropolis, AAAI'19. [paper] [official code]
  • Incomplete Label Multi-task Deep Learning for Spatio-temporal Event Subtype Forecasting, AAAI'19. [paper]
  • Learning Heterogeneous Spatial-Temporal Representation for Bike-sharing Demand Prediction, AAAI'19. [paper]
  • Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting, AAAI'19. [paper]

Time Series Anomaly Detection

  • A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data, AAAI'19. [paper]
  • Non-parametric Outliers Detection in Multiple Time Series A Case Study: Power Grid Data Analysis, AAAI'18. [paper]

IJCAI 201X-2020

General Time Series Analysis

  • RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering, IJCAI'19. [paper]
  • E2GAN: End-to-End Generative Adversarial Network for Multivariate Time Series Imputation, IJCAI'19. [paper]
  • Causal Inference in Time Series via Supervised Learning, IJCAI'18. [paper]

Time Series Forecasting

  • PewLSTM: Periodic LSTM with Weather-Aware Gating Mechanism for Parking Behavior Prediction, IJCAI'20. [paper] [official code]
  • LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks, IJCAI'20. [paper]
  • Cross-Interaction Hierarchical Attention Networks for Urban Anomaly Prediction, IJCAI'20. [paper]
  • Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting, IJCAI'19. [paper]
  • Explainable Deep Neural Networks for Multivariate Time Series Predictions, IJCAI'19. [paper]
  • Periodic-CRN: A Convolutional Recurrent Model for Crowd Density Prediction with Recurring Periodic Patterns. [paper]
  • Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. [paper] [official code]
  • LC-RNN: A Deep Learning Model for Traffic Speed Prediction. [paper]
  • GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction, IJCAI'18. [paper] [official code]
  • Hierarchical Electricity Time Series Forecasting for Integrating Consumption Patterns Analysis and Aggregation Consistency, IJCAI'18. [paper]
  • NeuCast: Seasonal Neural Forecast of Power Grid Time Series, IJCAI'18. [paper] [official code]
  • A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction, IJCAI'17. [paper] [code]
  • Hybrid Neural Networks for Learning the Trend in Time Series, IJCAI'17. [paper]

Time Series Anomaly Detection

  • BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series, IJCAI'19. [paper] [official code]
  • Outlier Detection for Time Series with Recurrent Autoencoder Ensembles, IJCAI'19. [paper] [official code]
  • Stochastic Online Anomaly Analysis for Streaming Time Series, IJCAI'17. [paper]

Time Series Clustering

  • Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest, IJCAI'19. [paper]
  • Similarity Preserving Representation Learning for Time Series Clustering, IJCAI'19. [paper]

Time Series Classification

  • A new attention mechanism to classify multivariate time series, IJCAI'20. [paper]

SIGMOD VLDB ICDE 201X-2020

General Time Series Analysis

  • Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures, SIGMOD'20. [paper] [official code]
  • Database Workload Capacity Planning using Time Series Analysis and Machine Learning, SIGMOD'20. [paper]
  • Mind the gap: an experimental evaluation of imputation of missing values techniques in time series, VLDB'20. [paper] [official code]
  • Active Model Selection for Positive Unlabeled Time Series Classification, ICDE'20. [paper] [official code]
  • ExplainIt! -- A declarative root-cause analysis engine for time series data, SIGMOD'19. [paper]
  • Cleanits: A Data Cleaning System for Industrial Time Series, VLDB'19. [paper]
  • Matrix Profile X: VALMOD - Scalable Discovery of Variable-Length Motifs in Data Series, SIGMOD'18. [paper]
  • Effective Temporal Dependence Discovery in Time Series Data, VLDB'18. [paper]

Time Series Anomaly Detection

  • Series2Graph: graph-based subsequence anomaly detection for time series, VLDB'20. [paper] [official code]
  • Neighbor Profile: Bagging Nearest Neighbors for Unsupervised Time Series Mining, ICDE'20. [paper]
  • Automated Anomaly Detection in Large Sequences, ICDE'20. [paper] [official code]
  • User-driven error detection for time series with events, ICDE'20. [paper]

Misc 201X-2020

General Time Series Analysis

  • STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks, WWW'19. [paper] [official code]
  • GP-VAE: Deep probabilistic time series imputation, AISTATS'20. [paper] [official code]
  • DYNOTEARS: Structure Learning from Time-Series Data, AISTATS'20. [paper]
  • Personalized Imputation on Wearable-Sensory Time Series via Knowledge Transfer, CIKM'20. [paper]
  • Order-Preserving Metric Learning for Mining Multivariate Time Series, ICDM'20. [paper]
  • Learning Periods from Incomplete Multivariate Time Series, ICDM'20. [paper]
  • Foundations of Sequence-to-Sequence Modeling for Time Series, AISTATS'19. [paper]

Time Series Forecasting

  • Hierarchically Structured Transformer Networks for Fine-Grained Spatial Event Forecasting, WWW'20. [paper]
  • HTML: Hierarchical Transformer-based Multi-task Learning for Volatility Prediction, WWW'20. [paper] [official code]
  • Traffic Flow Prediction via Spatial Temporal Graph Neural Network, WWW'20. [paper]
  • Towards Fine-grained Flow Forecasting: A Graph Attention Approach for Bike Sharing Systems, WWW'20. [paper]
  • Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting, WWW'20. [paper]
  • Spatiotemporal Hypergraph Convolution Network for Stock Movement Forecasting, ICDM'20. [paper]
  • Probabilistic Forecasting with Spline Quantile Function RNNs, AISTATS'19. [paper]
  • DSANet: Dual self-attention network for multivariate time series forecasting, CIKM'19. [paper]
  • RESTFul: Resolution-Aware Forecasting of Behavioral Time Series Data, CIKM'18. [paper]
  • Forecasting Wavelet Transformed Time Series with Attentive Neural Networks, ICDM'18. [paper]
  • A Flexible Forecasting Framework for Hierarchical Time Series with Seasonal Patterns: A Case Study of Web Traffic, SIGIR'18. [paper]
  • Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks, SIGIR'18. [paper] [official code]

Time Series Anomaly Detection

  • Multivariate Time-series Anomaly Detection via Graph Attention Network, ICDM'20. [paper] [code]
  • MERLIN: Parameter-Free Discovery of Arbitrary Length Anomalies in Massive Time Series Archives, ICDM'20. [paper] [official code]
  • Cross-dataset Time Series Anomaly Detection for Cloud Systems, ATC'19. [paper]
  • Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications, WWW'18. [paper] [official code]

mook

  • Stanford CS229: Machine Learning
  • Applied Machine Learning
  • Practical Deep Learning for Coders (2020)
  • Machine Learning with Graphs (Stanford)
  • Probabilistic Machine Learning
  • Introduction to Deep Learning (MIT)
  • Deep Learning: CS 182
  • Deep Unsupervised Learning
  • NYU Deep Learning SP21
  • CS224N: Natural Language Processing with Deep Learning
  • CMU Neural Networks for NLP
  • CS224U: Natural Language Understanding
  • CMU Advanced NLP
  • Multilingual NLP
  • Advanced NLP
  • Deep Learning for Computer Vision
  • Deep Reinforcement Learning
  • Full Stack Deep Learning
  • AMMI Geometric Deep Learning Course (2021)

Pytorch

Christian Perone  - 발표자료: https://speakerdeck.com/perone/pytorch-under-the-hood

  1. Making your RL Projects in 20 Minutes : https://www.edyoda.com/course/1421

  2. Style Transfer, Face Generation using GANs in 20 minutes : https://www.edyoda.com/course/1418

  3. Language and Machine Learning in 20 minutes : https://www.edyoda.com/course/1419

  4. AI Project - Web application for Object Identification : https://www.edyoda.com/course/1185

  5. Dog Breed Prediction : https://www.edyoda.com/course/1336

DL papers

  • ast-SCNN: Fast Semantic Segmentation Network.
  • Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs (https://arxiv.org/pdf/1606.00915.pdf) Source code: https://github.com/vietnguyen91/Deeplab-pytorch
  • Correlational Neural Network. CV, TL, RPL.
  • Reasoning With Neural Tensor Networks for Knowledge Base Completion. NLP, ML. Blog-post
  • Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks. NLP, DL, CQA. Code
  • Common Representation Learning Using Step-based Correlation Multi-Modal CNN. CV, TL, RPL. Code
  • ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs. NLP, AT, DL, STS. Code
  • Combining Neural, Statistical and External Features for Fake News Stance Identification. NLP, IR, DL. Code
  • WIKIQA: A Challenge Dataset for Open-Domain Question Answering. NLP, DL, CQA. Code
  • Siamese Recurrent Architectures for Learning Sentence Similarity. NLP, STS, DL. Code
  • Teaching Machines to Read and Comprehend. NLP, AT, DL. Code
  • Improved Representation Learning for Question Answer Matching. NLP, AT, DL, CQA. Code
  • Map-Reduce for Machine Learning on Multicore]. map-reduce, hadoop, ML.. MR, ML. Code
  • Convolutional Neural Tensor Network Architecture for Community Question Answering. NLP, DL, CQA. Code
  • External features for community question answering. NLP, DL, CQA. Code
  • Language Identification and Disambiguation in Indian Mixed-Script. NLP, IR, ML. Blog-post
  • Construction of a Semi-Automated model for FAQ Retrieval via Short Message Service. NLP, IR, ML. Code

Table of Contents

AI Tutorials

Tutorials 2023

  • Everything You Need to Know about Transformers: Architectures, Optimization, Applications, and Interpretation, AAAI 2023. [Link]
  • On Explainable AI: From Theory to Motivation, Industrial Applications, XAI Coding & Engineering Practices, AAAI 2023. [Link]

Tutorials 2022

  • Causality and deep learning: synergies, challenges& opportunities for research, ICML 2022. [Link TBD]
  • Bridging Learning and Decision Making, ICML 2022. [Link TBD]
  • Facilitating a smoother transition to Renewable Energy with AI (AI4Renewables), ICLR 2022 Social. [Link] [slides]
  • Optimization in ML and DL - A discussion on theory and practice, ICLR 2022 Social. [slides]
  • Beyond Convolutional Neural Networks, CVPR 2022. [Link]
  • Evaluating Models Beyond the Textbook: Out-of-distribution and Without Labels, CVPR 2022. [Link]
  • Sparsity Learning in Neural Networks and Robust Statistical Analysis, CVPR 2022. [Link]
  • Denoising Diffusion-based Generative Modeling: Foundations and Applications, CVPR 2022. [Link]
  • On Explainable AI: From Theory to Motivation, Industrial Applications, XAI Coding & Engineering Practices, AAAI 2022. [Link]
  • Deep Learning on Graphs for Natural Language Processing, AAAI 2022. [Link]
  • Bayesian Optimization: From Foundations to Advanced Topics, AAAI 2022. [Link]

Tutorials 2021

  • The Art of Gaussian Processes: Classic and Contemporary, NeurIPS 2021. [Link] [slides]
  • Self-Supervised Learning: Self-Prediction and Contrastive Learning, , NeurIPS 2021. [slides] [vedio]
  • Self-Attention for Computer Vision, ICML 2021. [Link]
  • Continual Learning with Deep Architectures, ICML 2021. [Link]
  • Responsible AI in Industry: Practical Challenges and Lessons Learned, ICML 2021. [Link]
  • Self-Supervision for Learning from the Bottom Up, ICLR 2021 Talk. [Link]
  • Geometric Deep Learning: the Erlangen Programme of ML, ICLR 2021 Talk. [Link]
  • Moving beyond the fairness rhetoric in machine learning, ICLR 2021 Talk. [Link]
  • Is My Dataset Biased, ICLR 2021 Talk. [Link]
  • Interpretability with skeptical and user-centric mind, ICLR 2021 Talk. [Link]
  • AutoML: A Perspective where Industry Meets Academy, KDD 2021. [Link]
  • Automated Machine Learning on Graph, KDD 2021. [Link]
  • Toward Explainable Deep Anomaly Detection, KDD 2021. [Link]
  • Fairness and Explanation in Clustering and Outlier Detection, KDD 2021. [Link]
  • Real-time Event Detection for Emergency Response, KDD 2021. [Link]
  • Machine Learning Explainability and Robustness: Connected at the Hip, KDD 2021. [Link]
  • Machine Learning Robustness, Fairness, and their Convergence, KDD 2021. [Link]
  • Counterfactual Explanations in Explainable AI: A Tutorial, KDD 2021. [Link]
  • Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber, KDD 2021. [Link]
  • Normalization Techniques in Deep Learning: Methods, Analyses, and Applications, CVPR 2021. [Link]
  • Normalizing Flows and Invertible Neural Networks in Computer Vision, CVPR 2021. [Link]
  • Theory and Application of Energy-Based Generative Models, CVPR 2021. [Link]
  • Adversarial Machine Learning in Computer Vision, CVPR 2021. [Link]
  • Practical Adversarial Robustness in Deep Learning: Problems and Solutions, CVPR 2021. [Link]
  • Leave those nets alone: advances in self-supervised learning, CVPR 2021. [Link]
  • Interpretable Machine Learning for Computer Vision, CVPR 2021. [Link]
  • Learning Representations via Graph-structured Networks, CVPR 2021. [Link]
  • Reviewing the Review Process, ICCV 2021. [Link]
  • Meta Learning and Its Applications to Natural Language Processing, ACL 2021. [Link]
  • Deep generative modeling of sequential data with dynamical variational autoencoders, ICASSP 2021. [Link]

Tutorials 2020

  • Deep Implicit Layers - Neural ODEs, Deep Equilibirum Models, and Beyond, NeurIPS 2020. [Link]
  • Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning, NeurIPS 2020. [Link]
  • Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities, NeurIPS 2020. [Link]
  • Advances in Approximate Inference, NeurIPS 2020. [Link]
  • There and Back Again: A Tale of Slopes and Expectations, NeurIPS 2020. [Link]
  • Federated Learning and Analytics: Industry Meets Academia, NeurIPS 2020. [Link]
  • Machine Learning with Signal Processing, ICML 2020. [Link]
  • Bayesian Deep Learning and a Probabilistic Perspective of Model Construction, ICML 2020. [slides] [video]
  • Representation Learning Without Labels, ICML 2020. [slides] [video]
  • Recent Advances in High-Dimensional Robust Statistics, ICML 2020. [Link]
  • Submodular Optimization: From Discrete to Continuous and Back, ICML 2020. [Link]
  • Deep Learning for Anomaly Detection, in KDD 2020. [Link] [video]
  • Learning with Small Data, in KDD 2020. [Link]

Tutorials 201X

  • Adversarial Machine Learning, ICLR 2019 Keynote. [slides]
  • Introduction to GANs, CVPR 2018. [slides]
  • Which Anomaly Detector should I use, ICDM 2018. [slides]

AI Surveys

General

  • Deep learning, in Nature 2015. [paper]
  • Deep learning in neural networks: An overview, in Neural networks 2015. [paper]

Transformer and Attention

  • A survey on visual transformer, in IEEE TPAMI 2022. [paper]
  • Transformers in vision: A survey, in ACM Computing Surveys 2021. [paper]
  • Efficient transformers: A survey, in arXiv 2022. [paper]
  • A General Survey on Attention Mechanisms in Deep Learning, in IEEE TKDE 2022. [paper]
  • Attention, please! A survey of neural attention models in deep learning, in Artificial Intelligence Review 2022. [paper]
  • An attentive survey of attention models, in ACM TIST 2021. [paper]
  • Attention in natural language processing, in IEEE TNNLS 2020. [paper]

Self-Supervised Learning

  • Self-supervised visual feature learning with deep neural networks: A survey, in IEEE TPAMI 2020. [paper]
  • Self-supervised Learning: Generative or Contrastive, TKDE'21. [paper]
  • Self-Supervised Representation Learning: Introduction, advances, and challenges, SPM'22. [paper]

Graph Neural Networks

  • A comprehensive survey on graph neural networks, TNNLS'20. [paper]
  • Deep learning on graphs: A survey, TKDE'20. [paper]
  • Graph neural networks: A review of methods and applications, AI Open'20. [paper]
  • Self-Supervised Learning of Graph Neural Networks: A Unified Review, TPAMI'22. [paper]
  • Graph Self-Supervised Learning: A Survey, TKDE'22. [paper]
  • Self-supervised learning on graphs: Contrastive, generative, or predictive, TKDE'21. [paper]

Federated Learning

  • Federated machine learning: Concept and applications, TIST'19. [paper]
  • Advances and open problems in federated learning, now'21. [paper]
  • A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection, TKDE'21. [paper]
  • A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions, CSUR'21. [paper]
  • A survey on federated learning, Knowledge-Based Systems'21. [paper]
  • A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond, JIOT'20. [paper]
  • Federated learning: Challenges, methods, and future directions, SPM'20. [paper]

XAI

  • Explaining deep neural networks and beyond: A review of methods and applications, PIEEE'21. [paper]
  • Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI, Information Fusion'20. [paper]
  • A survey on the explainability of supervised machine learning, JAIR'21. [paper]
  • Techniques for Interpretable Machine Learning, CACM'19. [paper]

AutoML

  • AutoML: A survey of the state-of-the-art, Knowledge-Based Systems'21. [paper]
  • Benchmark and survey of automated machine learning frameworks, JAIR'21. [paper]
  • AutoML to Date and Beyond: Challenges and Opportunities, CSUR'22. [paper]
  • Automated Machine Learning on Graphs: A Survey, IJCAI'21. [paper]
  • Others: awesome-automl-papers. [repo]

Deep Generative Models

  • NIPS 2016 Tutorial: Generative Adversarial Networks, arXiv'17. [paper]
  • Generative adversarial networks: An overview, SPM'18. [paper]
  • A review on generative adversarial networks: Algorithms, theory, and applications, TKDE'21. [paper]
  • A survey on generative adversarial networks: Variants, applications, and training, CSUR'22. [paper]
  • An Introduction to Variational Autoencoders, now'19. [paper]
  • Dynamical Variational Autoencoders: A Comprehensive Review, now'21. [paper]
  • Advances in variational inference, TPAMI'19. [paper]
  • Normalizing flows: An introduction and review of current methods, TPAMI'20. [paper]
  • Normalizing Flows for Probabilistic Modeling and Inference, JMLR'21. [paper]

N-Shot Learning

  • A survey of zero-shot learning: Settings, methods, and applications, in TIST 2019. [paper]
  • Generalizing from a few examples: A survey on few-shot learning, in CSUR 2020. [paper] [Link]
  • What Can Knowledge Bring to Machine Learning?—A Survey of Low-shot Learning for Structured Data, in TIST 2022. [paper]
  • A Survey of Few-Shot Learning: An Effective Method for Intrusion Detection, in SCN 2022. [paper]
  • Few-Shot Learning on Graphs: A Survey, in arXiv 2022. [paper]
  • A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities, in arXiv 2022. [paper]

Anomaly Detection and OOD

  • A unifying review of deep and shallow anomaly detection, PIEEE'21. [paper]
  • Deep learning for anomaly detection: A review, CSUR'20. [paper]
  • A Comprehensive Survey on Graph Anomaly Detection with Deep Learning, TKDE'21. [paper]
  • Graph based anomaly detection and description: a survey, DMKD'15. [paper]
  • Anomaly detection in dynamic networks: a survey, WICS'15. [paper]
  • Anomaly detection: A survey, CSUR'09. [paper]
  • A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges, arXiv'21. [paper]
  • Self-Supervised Anomaly Detection: A Survey and Outlook, arXiv'21. [paper]

Label-noise Learning

  • A Survey of Label-noise Representation Learning: Past, Present and Future, arXiv'21. [paper] [link]
  • Learning from Noisy Labels with Deep Neural Networks: A Survey, TNNLS'22. [paper] [link]
  • Classification in the presence of label noise: a survey, TNNLS'13. [paper]

Imbalanced-data Learning

  • Learning from imbalanced data, TKDE'09. [paper]
  • A Systematic Review on Imbalanced Data Challenges in Machine Learning: Applications and Solutions, CSUR'20. [paper]
  • Imbalance problems in object detection: A review, TPAMI'20. [paper]

Deep Reinforcement Learning

Domain Adaptation

  • Generalizing to unseen domains: A survey on domain generalization, TKDE'22. [paper]
  • A survey of unsupervised deep domain adaptation, TIST'21. [paper]
  • A review of domain adaptation without target labels, TPAMI'19. [paper]

Others

  • A continual learning survey: Defying forgetting in classification tasks, in IEEE TPAMI 2021. [paper]
  • Learning under concept drift: A review, in IEEE TKDE 2018. [paper]
  • Learning in nonstationary environments: A survey, MCI'15. [paper]
  • Online learning: A comprehensive survey, Neucom'21. [paper]
  • A survey on transfer learning, TKDE'09. [paper]
  • A Comprehensive Survey on Transfer Learning, PIEEE'21. [paper]
  • A survey on multi-task learning, TKDE'21. [paper]
  • Bayesian statistics and modelling, Nature Reviews Methods Primers'21. [paper]
  • Meta-learning in neural networks: A survey, arXiv'21. [paper]
  • Deep Long-Tailed Learning: A Survey, arXiv'21. [paper] [link]
  • Learning to optimize: A primer and a benchmark, arXiv'21. [paper]

data science tutorial

Tracking Bird Migration Using Python 3 Source Code & Tutorial: https://goo.gl/BS4rQc

Data Science Tutorial Read Here: https://goo.gl/ZPyZBX

Deep Learning (CS 1470)

http://cs.brown.edu/courses/cs1470/index.html

Deep Learning Book

https://www.deeplearningbook.org/ [GitHub] https://github.com/janishar/mit-deep-learning-book-pdf [tutorial] http://www.iro.umontreal.ca/~bengioy/talks/lisbon-mlss-19juillet2015.pdf [videos] https://www.youtube.com/channel/UCF9O8Vj-FEbRDA5DcDGz-Pg/videos

Dive into Deep Learning

https://d2l.ai/ [GitHub] https://github.com/d2l-ai/d2l-en [pdf] https://en.d2l.ai/d2l-en.pdf [STAT 157] http://courses.d2l.ai/berkeley-stat-157/index.html

Neural Network Design

http://hagan.okstate.edu/nnd.html [pdf] http://hagan.okstate.edu/NNDesign.pdf

Neural Networks and Deep Learning

http://neuralnetworksanddeeplearning.com/ [GitHub] https://github.com/mnielsen/neural-networks-and-deep-learning [pdf] http://static.latexstudio.net/article/2018/0912/neuralnetworksanddeeplearning.pdf [solutions] https://github.com/reachtarunhere/nndl/blob/master/2016-11-22-ch1-sigmoid-2.md

Theories of Deep Learning (STATS 385)

https://stats385.github.io/ [videos] https://www.researchgate.net/project/Theories-of-Deep-Learning

Theoretical Principles for Deep Learning (IFT 6085)

http://mitliagkas.github.io/ift6085-dl-theory-class-2019/

A collection of links of videos(youtube) by course

https://github.com/kmario23/deep-learning-drizzle/blob/master/README.md

A collection of tutorial Jupyter notebooks

https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks

the matrix calculus

https://explained.ai/matrix-calculus/index.html

etc

https://fleuret.org/ee559/ http://deep-learning-phd-course-2018-xb.s3-website-ap-southeast-1.amazonaws.com/ https://www.fast.ai/

refe. https://www.reddit.com/r/MachineLearning/comments/anrams/d_sharing_my_personal_resource_list_for_deep/

Annotation detect

  • Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series

Tensorflow

The TenSorFlow is an Open Soruce Software Library for Machine Intellience. This repository are many jupyter note-pad like TesroFlow turorials, step books, and others.

DeepMind's WaveNet

DeepVoice

Deep Voice: Real-Time Neural Text-to-Speech for Production

  • Sercan O. Arik, Mike Chrzanowski, Adam Coates, Gregory Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Jonathan Raiman, Shubho Sengupta, Mohammad Shoeybi
  • [paper]
  • [Ref.code

Very simple TensorFlow examples

GAN

Style-based GAN

Pytorch a2c

  • Advantage Actor Critic (A2C), a synchronous deterministic version of A3C
    • Volodymyr Mnih1
    • Adria Puigdomenech Badia1
    • Mehdi Mirza1,2
    • Alex Graves1
    • Tim Harley1
    • Timothy P. Lillicrap1
    • David Silver1
    • Koray Kavukcuoglu1
  • code

Sequence to Sequence -- Video to Text

  • Subhashini Venugopalan, Marcus Rohrbach, Jeff Donahue, Raymond Mooney, Trevor Darrell, Kate Saenko, arxiv, 2015
  • [code]
  • [paper]

Sequence to Sequence -- chatbot

  • Oriol Vinyals, Quoc V. Le, arxiv, 2015
  • [code]
  • [paper]

Show and Tell: A Neural Image Caption Generator

  • Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan, arxiv, 2015
  • [code]
  • [paper]

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

  • Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio, ICLR, 2014
  • [code]
  • [paper]

Learning Deep Features for Discriminative Localization

  • Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba, CVPR, 2016
  • [code]
  • [paper]

Deep Visual Analogy-Making

  • Scott Reed, Yi Zhang, Yuting Zhang, Honglak Lee, NIPS, 2015
  • [code]
  • [paper]

Deep Convolutional Generative Adversarial Networks

  • Alec Radford, Luke Metz, Soumith Chintala, arxiv, 2015
  • [code]
  • [paper]

End-To-End Memory Networks

  • Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus, NIPS, 2015
  • [code]
  • [paper]

Character-Aware Neural Language Models

  • Yoon Kim, Yacine Jernite, David Sontag, Alexander M. Rush, AAAI, 2016
  • [code]
  • [paper]

Deep Reinforcement Learning

Human-level control through deep reinforcement learning

  • Volodymyr Mnih, et al, 2014
  • [code1], not trained on atari
  • [code2]
  • [paper]

Deep Reinforcement Learning with Double Q-learning

  • Hado van Hasselt, Arthur Guez, David Silver, 2015
  • [code]
  • [paper]

Using Deep Q-Network to Learn How To Play Flappy Bird

Semi-Supervised Learning with Ladder Network

Convolutional Neural Networks for Sentence Classification

Black-Box Adversarial Perturbations

Implementation of Simple Black-Box Adversarial Perturbations for Deep Networks in Keras fork from link

  • python cifar100.py to train a basic CNN for cifar100 and save that file.
  • python find_better.py <model> to go through cifar100 test dataset and find a good image (as defined in the paper).
  • python per.py <KERAS_MODEL> <IMAGE_in_NUMPY> : currently works for cifar images only.

Deep Residual Learning for Image Recognition

colornet - Neural Network to colorize grayscale images

DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

  • Shuchang Zhou, Zekun Ni, Xinyu Zhou, He Wen, Yuxin Wu, Yuheng Zou, 2016
  • [code]
  • [paper]

A Neural Algorithm of Artistic Style

콘크리트 구조물 균열 탐지 및 분석

csv ttols

https://github.com/eBay/tsv-utils.git

Sequence Generative Adversarial Networks

https://github.com/shawLyu/HR-Depth

Reference

  • CV | Computer Vision
  • TL | Transfer Learning
  • RPL | Representation Learning
  • CQA | Community Question Answering
  • STS | Sentence Text Similarity
  • IR | Information Retrieval
  • AT | Attention
  • MR | Map Reduce
  • ASR | Acoustic Scene Recognition
  • DL | Deep Learning
  • NLP | Natural Language Processing
  • ML | Machine Learning.

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