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AISTAT2018: Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure
Paper: "Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers", at 2018 IEEE Security and Privacy Workshops (SPW), co-located with the 39th IEEE Symposium on Security and Privacy)
ICLR16: DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
https://qdata.github.io/deep2Read/ This website includes a (growing) list of papers and lectures we read about deep learning and related.
VizSec17: Web-based visualization tool for adversarial machine learning / LiveDemo
AISTAT 2017 Paper: A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models
ICML18: JEEK - Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
ECML16: GaKCo: a Fast Gapped k-mer string Kernel using Counting
NIPS17: [AttentiveChrome] Attend and Predict: Using Deep Attention Model to Understand Gene Regulation by Selective Attention on Chromatin
Bioinformatics16: DeepChrome: Deep-learning for predicting gene expression from histone modifications
Benchmarking and Visualization Tool for Adversarial Machine Learning
Deep Motif (ICLR16)/ Deep Motif Dashboard (PSB17): Visualizing Genomic Sequence Classifications
Detecting Adversarial Examples in Deep Neural Networks
Transfer String Kernel for Cross-Context String Classification
A Deep-Learning based Multi-Task Framework for Protein Sequence Labeling / Local Structural Property Prediction on biological Sequences
MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction
An evolutionary framework for evading machine learning-based malware classifiers.
Code for Learning Dependency Graph between Latent Factors from Data
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