Library for Semi-Automated Data Science
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Updated
Jul 3, 2024 - Python
Library for Semi-Automated Data Science
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
The prime repository for state-of-the-art Multilingual Question Answering research and development.
Regression Transformer (2023; Nature Machine Intelligence)
Interpretability and explainability of data and machine learning models
Natural Language (NL) to Linear Temporal Logic (LTL)
Semantic Search for Sustainable Development is experimental code for searching documents for text that "semantically" corresponds to any of the UN's Sustainable development goals/targets. For example, it can be used to mine the national development plan documents of a country and identify pieces of text that correspond to any of the SDGs in orde…
code repo associated to the ACL 2023 paper "DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications"
Reinforcement learning project that investigates different methods of learning skills that are beneficial for decision making
Quality Controlled Paraphrase Generation (ACL 2022)
Codes for reproducing the adversarial attacks on image captioning systems in “Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning,” ACL 2018
this is the code for the paper "On Sample Based Explanation Methods for NLP: Efficiency, Faithfulness, and Semantic Evaluation
Codes for reproducing the results of the paper "Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness" published at ICLR 2020
Codes for reproducing query-efficient black-box attacks in “AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks” , published at AAAI 2019
Source code for paper Mroueh, Sercu, Rigotti, Padhi, dos Santos, "Sobolev Independence Criterion", NeurIPS 2019
Source code for paper Choromanska et al. -- Beyond Backprop: Online Alternating Minimization with Auxiliary Variables -- http://proceedings.mlr.press/v97/choromanska19a.html
Codes for reproducing the black-box adversarial attacks in “ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models,” ACM CCS Workshop on AI-Security, 2017
This code is written in Python and implements a goal-oriented dialog system which takes as input a conversation history as well as the underlying database, and predicts the best next utterance.
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