this is the code for the paper "On Sample Based Explanation Methods for NLP: Efficiency, Faithfulness, and Semantic Evaluation
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Updated
Aug 4, 2021 - Python
this is the code for the paper "On Sample Based Explanation Methods for NLP: Efficiency, Faithfulness, and Semantic Evaluation
A deep learning approach to solar-irradiance forecasting in sky-videos
AI Explainability 360 Toolkit for Time-Series and Industrial Use Cases
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.
Reinforcement learning project that investigates different methods of learning skills that are beneficial for decision making
This repo includes code referenced in the paper A Rigorous Risk-aware Linear Approach to Extended Markov Ratio Decision Processes with Embedded Learning by Alexander Zadorojniy, Takayuki Osogami, and Orit Davidovich to appear in IJCAI 2023.
A Testing Framework for Decision-Optimization Model Learning Algorithms
Planning tasks succinctly represent labeled transition systems, with each ground action corresponding to a label. This granularity, however, is not necessary for solving planning tasks and can be harmful, especially for model-free methods. In this work, we propose automatic approach to reduce the label sets for planning domains.
code repo associated to the ACL 2023 paper "DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications"
Data for the ACL 2020 paper - Improving Segmentation for Technical Support Problems
Codes for reproducing robustness-accuracy analysis in "Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models", ECCV 2018
Forecasting mixed migration for the Danish Refugee Council.
Code accompanying the paper Sobolev GAN https://arxiv.org/abs/1711.04894
Source code for paper Mroueh, Sercu, Rigotti, Padhi, dos Santos, "Sobolev Independence Criterion", NeurIPS 2019
Codes for reproducing the white-box adversarial attacks in “EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples,” AAAI 2018
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