Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)
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Updated
Jun 2, 2021
Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight)
Code accompanying the NeurIPS 2021 paper "Generating High-Quality Explanations for Navigation in Partially-Revealed Environments"
Interactive Label Cleaning with Example-based Explanations
CircleNLU: A Tool for building Natural Language Understanding system. This paper was accepted at NeurIPS Data-centric AI workshop 2021.
Official implementation of "FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective".
Code of the NeurIPS 2021 paper: Language and Visual Entity Relationship Graph for Agent Navigation
[NeurIPS 2021] Unsupervised Foreground Extraction via Deep Region Competition
[NeurIPS 2021] Source code for the paper "Qu-ANTI-zation: Exploiting Neural Network Quantization for Achieving Adversarial Outcomes"
Code for the NeurIPS 2021 paper: Fairness in Ranking under Uncertainty
Official implementation of the NeurIPS 2021 paper Online Learning Of Neural Computations From Sparse Temporal Feedback
[NeurIPS 2021 Spotlight] Aligning Pretraining for Detection via Object-Level Contrastive Learning
Code for "Credal Self-Supervised Learning" as published at NeurIPS 2021.
NeurIPS 2021 papers and code focus on adversarial attacks and defense
The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021
Dense Unsupervised Learning for Video Segmentation (NeurIPS*2021)
Robust Contrastive Learning Using Negative Samples with Diminished Semantics (NeurIPS 2021)
[NeurIPS 2021] ORL: Unsupervised Object-Level Representation Learning from Scene Images
Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)
Hyperbolic Busemann Learning with Ideal Prototypes, NeurIPS2021
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