This is the code of NeurIPS'21 paper "Towards Enabling Meta-Learning from Target Models".
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Updated
Jun 8, 2022 - Python
This is the code of NeurIPS'21 paper "Towards Enabling Meta-Learning from Target Models".
Code accompanying the NeurIPS 2021 paper "Generating High-Quality Explanations for Navigation in Partially-Revealed Environments"
Fork of Official Implementation of Meta-Learning to Improve Pre-Training, NeurIPS'21 Poster. (https://arxiv.org/abs/2111.01754)
Unofficial pytorch implementation of the paper "Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding", NeurIPS 2021.
Official implementation of Set Prediction in the Latent Space (LSP)
[Open Sourced] NeurIPS 2021: Dynamical Inference with Neural Interpreters by N. Rahaman, M. Gondal, S. Joshi, P. Gehler, Y. Bengio, F. Locatello, B. Schölkopf
Code for "Credal Self-Supervised Learning" as published at NeurIPS 2021.
The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021
Interactive Label Cleaning with Example-based Explanations
Code of "Not Too Close and Not Too Far: Enforcing Monotonicity Requires Penalizing The Right Points"
Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models
[NeurIPS 2021] Source code for the paper "Qu-ANTI-zation: Exploiting Neural Network Quantization for Achieving Adversarial Outcomes"
[NeurIPS 2021] Self-Supervised GANs with Label Augmentation
[NeurIPS 2021] Unsupervised Foreground Extraction via Deep Region Competition
Hyperbolic Busemann Learning with Ideal Prototypes, NeurIPS2021
LearningByDoing NeurIPS 2021 Competition: Standalone Code and Results
Resources for the paper titled "Domain-guided Self-supervision of EEG Data Improves Downstream Classification Performance and Generalizability". Accepted at ML4H Symposium 2021 with an oral spotlight!
(NeurIPS 2021) Realistic Evaluation of Transductive Few-Shot Learning
Code of the NeurIPS 2021 paper: Language and Visual Entity Relationship Graph for Agent Navigation
[NeurIPS 2021] ORL: Unsupervised Object-Level Representation Learning from Scene Images
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