Code accompanying the paper Fisher GAN: https://arxiv.org/abs/1705.09675
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Updated
Nov 3, 2017 - Python
Code accompanying the paper Fisher GAN: https://arxiv.org/abs/1705.09675
Code accompanying the paper Sobolev GAN https://arxiv.org/abs/1711.04894
Intu is a Cognitive Embodiment Middleware for AI on the edge.
Codes for reproducing the contrastive explanation in “Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives”
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
Codes for reproducing the robustness evaluation scores in “Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach,” ICLR 2018
Codes for reproducing the white-box adversarial attacks in “EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples,” AAAI 2018
CROWN: A Neural Network Verification Framework for Networks with General Activation Functions
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.
A deep learning approach to solar-irradiance forecasting in sky-videos
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
Source code for paper Choromanska et al. -- Beyond Backprop: Online Alternating Minimization with Auxiliary Variables -- http://proceedings.mlr.press/v97/choromanska19a.html
Source code for paper Mroueh, Sercu, Rigotti, Padhi, dos Santos, "Sobolev Independence Criterion", NeurIPS 2019
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
Codes for reproducing the results of the paper "Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness" published at ICLR 2020
Data for the ACL 2020 paper - Improving Segmentation for Technical Support Problems
this is the code for the paper "On Sample Based Explanation Methods for NLP: Efficiency, Faithfulness, and Semantic Evaluation
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