Alternatives and similar repositories for mitxharvard-wai-resources:

Users that are interested in mitxharvard-wai-resources are comparing it to the libraries listed below

• microsoft / augmented-interpretable-models
  Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.
  ☆39Updated 9 months ago
• Yu-Group / clinical-rule-vetting
  Learning clinical-decision rules with interpretable models.
  ☆20Updated last year
• gregdeon / spotlight
  Implementation of the spotlight: a method for discovering systematic errors in deep learning models
  ☆11Updated 3 years ago
• benbo / interactive-weak-supervision
  Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling
  ☆31Updated 3 years ago
• peterbhase / ExplanationRoles
  Code for paper "When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data"
  ☆14Updated 3 years ago
• BatsResearch / nplm
  A weak supervision framework for (partial) labeling functions
  ☆15Updated 6 months ago
• YilunZhou / optimal-active-learning
  Code repository for the AISTATS 2021 paper "Towards Understanding the Optimal Behaviors of Deep Active Learning Algorithms"
  ☆15Updated 3 years ago
• swabhs / notebooks_for_aflite
  IPython notebook with synthetic experiments for AFLite, based on the ICML 2020 paper, "Adversarial Filters of Dataset Biases".
  ☆16Updated 4 years ago
• JeremyAlain / imitation_learning_from_language_feedback
  This repository contains some of the code used in the paper "Training Language Models with Langauge Feedback at Scale"
  ☆27Updated last year
• uvavision / Double-Hard-Debias
  [ACL 2020] Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation
  ☆27Updated 4 years ago
• MLforHealth / MIMIC_Generalisation
  Code to study the generalisability of benchmark models on non-stationary EHRs.
  ☆14Updated 5 years ago
• szhang42 / Uneven_training_data
  ☆9Updated 3 years ago
• peterbhase / SLAG-Belief-Updating
  Code for paper "Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs"
  ☆28Updated 2 years ago
• autonlab / KeyClass
  Code for "Classifying Unstructured Clinical Notes via Automatic Weak Supervision", MLHC 2022.
  ☆17Updated last year
• peterbhase / ExplanationSearch
  Code for paper "Search Methods for Sufficient, Socially-Aligned Feature Importance Explanations with In-Distribution Counterfactuals"
  ☆17Updated 2 years ago
• HazyResearch / liger
  Liger: Fusing Weak Supervision and Model Embeddings
  ☆9Updated 2 years ago
• ruiqi-zhong / Meta-tuning
  EMNLP 2021 Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections
  ☆50Updated 3 years ago
• CEBaBing / CEBaB
  CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior
  ☆12Updated 2 years ago
• samiraabnar / Bridge
  Making a bridge between NLP models and Brain data
  ☆18Updated 4 years ago
• allenai / contrastive-explanations
  Explaining neural decisions contrastively to alternative decisions.
  ☆23Updated 3 years ago
• acmi-lab / counterfactually-augmented-data
  Learning the Difference that Makes a Difference with Counterfactually-Augmented Data
  ☆169Updated 3 years ago
• ykwon0407 / beta_shapley
  Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)
  ☆40Updated 2 years ago
• amit-sharma / chatgpt-causality-pairs
  Solving the causality pairs challenge (does A cause B) with ChatGPT
  ☆75Updated 8 months ago
• elehman16 / exposing_patient_data_release
  ☆48Updated 3 years ago
• martiansideofthemoon / relic-retrieval
  Official codebase accompanying our ACL 2022 paper "RELiC: Retrieving Evidence for Literary Claims" (https://relic.cs.umass.edu).
  ☆20Updated 2 years ago
• csinva / iprompt
  Finding semantically meaningful and accurate prompts.
  ☆46Updated last year
• copenlu / xai-benchmark
  A Diagnostic Study of Explainability Techniques for Text Classification
  ☆66Updated 4 years ago
• petezh / OpenD5
  Tasks for describing differences between text distributions.
  ☆16Updated 5 months ago
• allenai / EmbeddingRecycling
  Embedding Recycling for Language models
  ☆38Updated last year
• xiamengzhou / training_trajectory_analysis
  [ACL 2023]: Training Trajectories of Language Models Across Scales https://arxiv.org/pdf/2212.09803.pdf
  ☆22Updated last year