A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation
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Updated
Oct 8, 2021 - Python
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation
Assignments for the course of Intelligent Systems for Pattern Recognition (university of Pisa)
Kaggle Kore 2022 - An autoregressive modeling approach to imitation learning
This is a repository for the paper: "Spontaneous variability in gamma dynamics described by a damped harmonic oscillator driven by noise" G Spyropoulos, M Saponati, JR Dowdall, ML Schölvinck, CA Bosman, B Lima, A Peter, I Onorato, J Klon-Lipok, R Roese, S Neuenschwander, W Singer, P Fries, M Vinck (2022, Nature Communications)
A enhanced Open Dialogue Context Generator supported by General Language Model Pretraining with Autoregressive Blank Infilling
PyTorch implementation for "Training and Inference on Any-Order Autoregressive Models the Right Way", NeurIPS 2022 Oral, TPM 2023 Best Paper Honorable Mention
PyTorch implementation for "Long Horizon Temperature Scaling", ICML 2023
Octree Transformer: Autoregressive 3D Shape Generation on Hierarchically Structured Sequences - CVPRW: StruCo3D, 2023
Symbolic music generation taking inspiration from NLP and human composition process
Forecast of Salado River level (Santa Fe, Arg.)
Generative model for sequential recommendation based on Convolution Neural Networks (CNN))
[GPT beats diffusion🔥] [scaling laws in visual generation📈] Official impl. of "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction". An *ultra-simple, user-friendly yet state-of-the-art* codebase for autoregressive image generation!
PDEBench: An Extensive Benchmark for Scientific Machine Learning
Time-series forecasting tecniques applied to the stock market
Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding (EMNLP 2023 Long)
Repository for the paper "Advancing Time Series Forecasting: Variance-Aware Loss Functions in Transformers"
dpart: General, flexible, and scalable framework for differentially private synthetic data generation, developed by hazy.
code for the paper "LayerDAG: A Layerwise Autoregressive Diffusion Model of Directed Acyclic Graphs"
Code repository for "Machine Learning Predictors for Min-Entropy Estimation" (arXiv:2406.19983). Implements RCNN and GPT-2 models for entropy prediction in RNGs, with data generation, training pipelines, and analysis scripts.
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