Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)
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Updated
May 19, 2023
Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)
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)
[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning
A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.
This repository contains all the papers accepted in top conference of computer vision, with convenience to search related papers.
Attention over nodes in Graph Neural Networks using PyTorch (NeurIPS 2019)
A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (NeurIPS 2020)
This repository is a paper digest of recent advances in collaborative / cooperative / multi-agent perception for V2I / V2V / V2X autonomous driving scenario.
Fetch Academic Research Papers from different sources
Resources for the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with an oral spotlight!) at ML4H Workshop, NeurIPS 2020.
📚 List of Top-tier Conference Papers on Reinforcement Learning (RL),including: NeurIPS, ICML, AAAI, IJCAI, AAMAS, ICLR, ICRA, etc.
This is our implementation of ENMF: Efficient Neural Matrix Factorization (TOIS. 38, 2020). This also provides a fair evaluation of existing state-of-the-art recommendation models.
Code for our NeurIPS 2022 paper
The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"
This repository is a paper digest of Transformer-related approaches in visual tracking tasks.
Official PyTorch implementation of TSDiff models presented in the NeurIPS 2023 paper "Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting"
Code and Data artifact for NeurIPS 2023 paper - "Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context". `multispy` is a lsp client library in Python intended to be used to build applications around language servers.
[NeurIPS 2019] Deep Set Prediction Networks
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