code released for our ICML 2020 paper "Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation"
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Updated
Feb 22, 2024 - Python
code released for our ICML 2020 paper "Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation"
Benchmarking continual learning techniques for Human Activity Recognition data. We offer interesting insights on how the performance techniques vary with a domain other than images.
Implementations of few-shot object detection benchmarks
Code for generating data in ICML 2020 paper "PackIt: A Virtual Environment for Geometric Planning"
Code for reproducing results in ICML 2020 paper "PackIt: A Virtual Environment for Geometric Planning"
Code for "Adversarial Robustness via Runtime Masking and Cleansing" (ICML 2020)
Soft Threshold Weight Reparameterization for Learnable Sparsity
Code base for the precision, recall, density, and coverage metrics for generative models. ICML 2020.
A Systematic Comparison of Robustness in Bayesian Deep Learning on Diabetic Retinopathy Diagnosis Tasks
Official implementation of the paper Stochastic Latent Residual Video Prediction
A list of the top 10 computer vision papers in 2020 with video demos, articles, code and paper reference.
Reproducing RigL (ICML 2020) as a part of ML Reproducibility Challenge 2020
Official Pytorch implementation of ReBias (Learning De-biased Representations with Biased Representations), ICML 2020
Repository of the ICML 2020 paper "Set Functions for Time Series"
Code-repository for the ICML 2020 paper Fairwashing explanations with off-manifold detergent
Official PyTorch implementation of Time-aware Large Kernel (TaLK) Convolutions (ICML 2020)
[ICML 2020] Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. https://arxiv.org/abs/2007.12678, https://icml.cc/virtual/2020/poster/5797
Code for "Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources". (ICML 2020)
This project contains the code for the paper accepted at NeurIPS 2020 - Robust Meta-learning for Mixed Linear Regression with Small Batches.
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