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Releasing Research Code ⭐ 2,306

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

Awesome Computer Vision Paper List ⭐ 399

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

Attentionwalk ⭐ 299

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

Gradient Descent The Ultimate Optimizer ⭐ 240

Code for our NeurIPS 2022 paper

Optimal Sparse Decision Trees

Machine Learning Research

Dyffusion ⭐ 58

[NeurIPS 2023] A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting

Eeg Gcnn ⭐ 56

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.

Source code for NeurIPS 2020 paper "Meta-Learning with Adaptive Hyperparameters"

Awesome Neurips 2023 ⭐ 23

Conference schedule, top papers, and analysis of the data for NeurIPS 2023!

Patient Instructions ⭐ 20

Code for "Retrieve, Reason, and Refine: Generating Accurate and Faithful Patient Instructions" (NeurIPS 2022)

This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help of a corpus of examples. For more details, please read our NeurIPS 2021 paper: 'Explaining Latent Representations with a Corpus of Examples'.

[NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.

Statistics, Visualizations and Paper Links of Machine Learning Conferences

Web interface for browsing arXiv papers

Targeted Adversarial Perturbations Monocular Depth ⭐ 9

PyTorch implementation of Targeted Adversarial Perturbations for Monocular Depth Predictions (in NeurIPS 2020)

The official implementation of the paper "Rethinking Data Augmentation for Tabular Data in Deep Learning"

This is the code of NeurIPS'21 paper "Towards Enabling Meta-Learning from Target Models".

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