neurips

Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)

[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning

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)

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.

A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).

Code for our NeurIPS 2022 paper

Attention over nodes in Graph Neural Networks using PyTorch (NeurIPS 2019)

Fetch Academic Research Papers from different sources

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.

Official implementation of CATs

[NeurIPS 2022] Official PyTorch implementation of Optimizing Relevance Maps of Vision Transformers Improves Robustness. This code allows to finetune the explainability maps of Vision Transformers to enhance robustness.

Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology (LMRL Workshop, NeurIPS 2021)

[NeurIPS 2019] Deep Set Prediction Networks

This repository is a paper digest of recent advances in collaborative / cooperative / multi-agent perception for V2I / V2V / V2X autonomous driving scenario.

Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]

[NeurIPS 2018] [JSAIT] PacGAN: The power of two samples in generative adversarial networks

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.

Official implementation of NeurIPS 2021 paper "One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective"

This repository is a paper digest of Transformer-related approaches in visual tracking tasks.