Icml Collection

◆

◆

◆

◆

📦Normal

★★★

“This repository is no longer maintained.”

◆

◆

◆

◆

🔮Psychic

★★

“My Python scripts to make high-quality figures for publications in top AI conferences and journals.”

◆

◆

◆

◆

🔮Psychic

★★

“pip install antialiased-cnns to improve stability and accuracy”

◆

◆

◆

◆

🔥Flame

★★

“For deep RL and the future of AI. ”

◆

◆

◆

◆

📦Normal

★★

“Official Pytorch Implementation for "MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation" presenting "MultiDiffusion" (ICML 2023)”

◆

◆

◆

◆

🔮Psychic

★★

“[ICML 2021, Long Talk] Delving into Deep Imbalanced Regression”

◆

◆

◆

◆

📦Normal

★★

“深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)”

◆

◆

◆

◆

🔮Psychic

★★

“This repository contains all the papers accepted in top conference of computer vision, with convenience to search related papers.”

◆

◆

◆

◆

🔮Psychic

★

“FedScale is a scalable and extensible open-source federated learning (FL) platform.”

◆

◆

◆

◆

🔮Psychic

★

“[ICML 2026] ByteDance's All-in-One Video Generation Model for Human-Object Interaction Video Generation”

◆

◆

◆

◆

📦Normal

★

“📚 List of Top-tier Conference Papers on Reinforcement Learning (RL)，including: NeurIPS, ICML, AAAI, IJCAI, AAMAS, ICLR, ICRA, etc.”

◆

◆

◆

◆

📦Normal

★

“A vue-based project page template for academic papers. (in development) https://junyaohu.github.io/academic-project-page-template-vue”

◆

◆

◆

◆

📦Normal

★

“Sparse Variational Dropout, ICML 2017”

◆

◆

◆

◆

🔮Psychic

★

“Download papers and supplemental materials from open-access paper website, such as AAAI, AAMAS, AISTATS, COLT, CORL, CVPR, ECCV, ICCV, ICLR, ICML, IJCAI, JMLR, NIPS, RSS, WACV.”

◆

◆

◆

◆

🔮Psychic

★

“Code base for the precision, recall, density, and coverage metrics for generative models. ICML 2020.”

◆

◆

◆

◆

📦Normal

★

“A list of paper templates in the area of machine learning.”

◆

◆

◆

◆

📦Normal

★

“All graph/GNN papers accepted at the International Conference on Machine Learning (ICML) 2024.”

◆

◆

◆

◆

🔮Psychic

★

“Implementation of ICML'2021:Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces”