15 Repositories
Python regime-shifts Libraries
MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts (ICLR 2022)
MetaShift: A Dataset of Datasets for Evaluating Distribution Shifts and Training Conflicts This repo provides the PyTorch source code of our paper: Me
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters
CNN-Filter-DB An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters Paul Gavrikov, Janis Keuper Paper: htt
PyTorch implementation for the paper Visual Representation Learning with Self-Supervised Attention for Low-Label High-Data Regime
Visual Representation Learning with Self-Supervised Attention for Low-Label High-Data Regime Created by Prarthana Bhattacharyya. Disclaimer: This is n
Helps compare between New and Old Tax Regime.
Income-Tax-Calculator Helps compare between New and Old Tax Regime. Sample Console Input/Output saanika@gupta:~/Desktop$ python3 income_tax_calculator
Set of scripts that schedules employees for shifts throughout the week based on availability, shift times, and shift necessities
Automatic-Scheduler Set of scripts that schedules employees for shifts throughout the week based on availability, shift times, and shift necessities *
The best solution of the Weather Prediction track in the Yandex Shifts challenge
yandex-shifts-weather The repository contains information about my solution for the Weather Prediction track in the Yandex Shifts challenge https://re
SHIFT15M: multiobjective large-scale fashion dataset with distributional shifts
[arXiv] The main motivation of the SHIFT15M project is to provide a dataset that contains natural dataset shifts collected from a web service IQON, wh
Implementation of SSMF: Shifting Seasonal Matrix Factorization
SSMF Implementation of SSMF: Shifting Seasonal Matrix Factorization, Koki Kawabata, Siddharth Bhatia, Rui Liu, Mohit Wadhwa, Bryan Hooi. NeurIPS, 2021
Code accompanying the paper "How Tight Can PAC-Bayes be in the Small Data Regime?"
How Tight Can PAC-Bayes be in the Small Data Regime? This is the code to reproduce all experiments for the following paper: @inproceedings{Foong:2021:
Distributionally robust neural networks for group shifts
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization This code implements the g
CrossNorm and SelfNorm for Generalization under Distribution Shifts (ICCV 2021)
CrossNorm (CN) and SelfNorm (SN) (Accepted at ICCV 2021) This is the official PyTorch implementation of our CNSN paper, in which we propose CrossNorm
CrossNorm and SelfNorm for Generalization under Distribution Shifts (ICCV 2021)
CrossNorm (CN) and SelfNorm (SN) (Accepted at ICCV 2021) This is the official PyTorch implementation of our CNSN paper, in which we propose CrossNorm
PyTorch evaluation code for Delving Deep into the Generalization of Vision Transformers under Distribution Shifts.
Out-of-distribution Generalization Investigation on Vision Transformers This repository contains PyTorch evaluation code for Delving Deep into the Gen
Oncall is a calendar tool designed for scheduling and managing on-call shifts. It can be used as source of dynamic ownership info for paging systems like http://iris.claims.
Oncall See admin docs for information on how to run and manage Oncall. Development setup Prerequisites Debian/Ubuntu - sudo apt-get install libsasl2-d
A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.
WILDS is a benchmark of in-the-wild distribution shifts spanning diverse data modalities and applications, from tumor identification to wildlife monitoring to poverty mapping.