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)
[NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection, NeurIPS2020
Learn fast, scalable, and calibrated measures of uncertainty using neural networks!
Personalized Federated Learning with Moreau Envelopes (pFedMe) using Pytorch (NeurIPS 2020)
The official PyTorch implementation for NCSNv2 (NeurIPS 2020)
Source code and dataset of the NeurIPS 2020 paper "Graph Random Neural Network for Semi-Supervised Learning on Graphs"
A list of the top 10 computer vision papers in 2020 with video demos, articles, code and paper reference.
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (NeurIPS 2020)
Codes for NeurIPS 2020 paper "Adversarial Weight Perturbation Helps Robust Generalization"
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.
[NeurIPS 2020] Official PyTorch Implementation of "Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis". NeurIPS 2020.
Embed strange attractors using a regularizer for autoencoders
Meta-Learning through Hebbian Plasticity in Random Networks: https://arxiv.org/abs/2007.02686
(NeurIPS 2020) Transductive Information Maximization for Few-Shot Learning https://arxiv.org/abs/2008.11297
Research Code for NeurIPS 2020 Spotlight paper "Large-Scale Adversarial Training for Vision-and-Language Representation Learning": UNITER adversarial training part
[NeurIPS 2020] Neural Manifold Ordinary Differential Equations (https://arxiv.org/abs/2006.10254)
Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)
Implementation of Neuron-level Structured Pruning using Polarization Regularizer
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