ibm-research-ai
There are 5 repositories under ibm-research-ai topic.
Trusted-AI / AIF360
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.
Language:Python 2304Interpretability and explainability of data and machine learning models
Language:Python 1527primeqa / primeqa
The prime repository for state-of-the-art Multilingual Question Answering research and development.
Language:Python 697IBM / FfDL
Fabric for Deep Learning (FfDL, pronounced fiddle) is a Deep Learning Platform offering TensorFlow, Caffe, PyTorch etc. as a Service on Kubernetes
Language:Go 687- Language:Python 320
Regression Transformer (2023; Nature Machine Intelligence)
Language:Python 126- Language:Java 91
- commonsense-rl
Knowledge-Aware RL agents with Commonsense Reasoning
Language:Inform 7 73 Quality Controlled Paraphrase Generation (ACL 2022)
Language:Python 65Codes 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
Language:Python 54Codes for reproducing the contrastive explanation in “Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives”
Language:Python 53Codes 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
Language:Python 51- Language:Python 45
Codes for reproducing the robustness evaluation scores in “Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach,” ICLR 2018
Language:Python 44Codes 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
Language:Python 37- Language:Python 36
CROWN: A Neural Network Verification Framework for Networks with General Activation Functions
Language:Python 35- Language:Java 28
- Language:C++ 28
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 order to verify alignment of the plan with the SDGs.
Language:Python 27Codes for reproducing the results of the paper "Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness" published at ICLR 2020
Language:Python 26Source code for paper Choromanska et al. -- Beyond Backprop: Online Alternating Minimization with Auxiliary Variables -- http://proceedings.mlr.press/v97/choromanska19a.html
Language:Python 24- Language:Python 23
Codes for reproducing the white-box adversarial attacks in “EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples,” AAAI 2018
Language:Python 22- Language:Java 16
- Language:Python 14
Source code for paper Mroueh, Sercu, Rigotti, Padhi, dos Santos, "Sobolev Independence Criterion", NeurIPS 2019
Language:Python 14- Language:Jupyter Notebook 13
tomsercu / SobolevGAN-SSL
Code accompanying the paper Sobolev GAN https://arxiv.org/abs/1711.04894
Language:Python 12Forecasting mixed migration for the Danish Refugee Council.
Language:Jupyter Notebook 10Codes 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
Language:Python 9kushalchauhan98 / ticket-segmentation
Data for the ACL 2020 paper - Improving Segmentation for Technical Support Problems
A Testing Framework for Decision-Optimization Model Learning Algorithms
Language:Jupyter Notebook 4code repo associated to the ACL 2023 paper "DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications"
Language:Python 4This 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.
Language:Jupyter Notebook 4Planning 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.
Language:PDDL 4
