Variational Dropout Sparsifies Deep Neural Networks (Molchanov et al. 2017) by Chainer

Added transformation any tables, including cells, spanning as many columns or rows. Also added transformation preserves any text inlines and their combinations: bold, italic, superscript and subscript. Added transformation specific to the original descriptions stored in the database fields in html format.

A semantical DOCX to ICML converter

Get Deep Learning Related Statistics(CNN,RNN,RL) from Publications. Including NIPS, ICML, ICLR, CVPR, MICCAI.

(ICML-W, 2018) Text to image synthesis, by distilling concepts from multiple captions.

Crawler used to crawl papers

Reinforcement Learning Papers at ICML

Code for ICML 2019 paper titled "On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning"

Community Regularization of Visually Grounded Dialog https://arxiv.org/abs/1808.04359

ICML2019 Time Series Workshop Poster on BreizhCrops dataset

Poster of ICML AI for Social Good submission

AutoLearn, a domain independent regression-based feature learning algorithm.

This repository is no longer maintained.

Repository and website for the ICML 2019 tutorial "A Primer on PAC-Bayesian Learning"

Sparse Variational Dropout, ICML 2017

Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction. ICML 2020 Graph Representation Learning and Beyond (GRL+) Workshop

This project contains the code for the paper accepted at NeurIPS 2020 - Robust Meta-learning for Mixed Linear Regression with Small Batches.

Official PyTorch implementation of Time-aware Large Kernel (TaLK) Convolutions (ICML 2020)

ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels

⚡️ A framework that investigates the scaling limit of ResNets and compares it to Neural ODEs. Tested on synthetic and standardized datasets. 📈