ibm-research-ai

There are 43 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:Python2.3k90248818

  • Trusted-AI/AIX360

    Interpretability and explainability of data and machine learning models

    Language:Python1.5k5676307

  • primeqa/primeqa

    The prime repository for state-of-the-art Multilingual Question Answering research and development.

    Language:Python7072832658

  • IBM/FfDL

    Fabric for Deep Learning (FfDL, pronounced fiddle) is a Deep Learning Platform offering TensorFlow, Caffe, PyTorch etc. as a Service on Kubernetes

    Language:Go6888364185

  • IBM/lale

    Library for Semi-Automated Data Science

    Language:Python324236183

  • IBM/regression-transformer

    Regression Transformer (2023; Nature Machine Intelligence)

    Language:Python1296821

  • IBM/science-result-extractor

    Language:Java9210517
  • commonsense-rl

    IBM/commonsense-rl

    Knowledge-Aware RL agents with Commonsense Reasoning

    Language:Inform 77413121

  • IBM/quality-controlled-paraphrase-generation

    Quality Controlled Paraphrase Generation (ACL 2022)

    Language:Python67877

  • IBM/Autozoom-Attack

    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

    Language:Python55121923

  • IBM/Contrastive-Explanation-Method

    Codes for reproducing the contrastive explanation in “Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives”

    Language:Python5414414

  • IBM/ZOO-Attack

    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

    Language:Python5314622

  • IBM/nl2ltl

    Natural Language (NL) to Linear Temporal Logic (LTL)

    Language:Python515138

  • IBM/CLEVER-Robustness-Score

    Codes for reproducing the robustness evaluation scores in “Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach,” ICLR 2018 ​​​​​​​

    Language:Python4411321

  • IBM/Image-Captioning-Attack

    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 ​​​​​​​

    Language:Python3914012

  • IBM/CROWN-Robustness-Certification

    CROWN: A Neural Network Verification Framework for Networks with General Activation Functions

    Language:Python3713111

  • IBM/graph_ensemble_learning

    Graph Ensemble Learning

    Language:Python371437

  • IBM/cc-dbp

    A dataset for knowledge base population research using Common Crawl and DBpedia.

    Language:Java2810219

  • watson-intu/self

    Intu is a Cognitive Embodiment Middleware for AI on the edge.

    Language:C++28173327

  • IBM/Semantic-Search-for-Sustainable-Development

    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:Python2710116

  • IBM/model-sanitization

    Codes for reproducing the results of the paper "Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness" published at ICLR 2020

    Language:Python26738

  • IBM/online-alt-min

    Source code for paper Choromanska et al. -- Beyond Backprop: Online Alternating Minimization with Auxiliary Variables -- http://proceedings.mlr.press/v97/choromanska19a.html

    Language:Python249116

  • tomsercu/FisherGAN

    Code accompanying the paper Fisher GAN: https://arxiv.org/abs/1705.09675

    Language:Python23608

  • IBM/EAD-Attack

    Codes for reproducing the white-box adversarial attacks in “EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples,” AAAI 2018

    Language:Python2213216

  • HumanBehaviourChangeProject/Info-extract

    Repository of the HBCP project.

    Language:Java181235

  • IBM/bridging-resolution

    Language:Python14727

  • IBM/SIC

    Source code for paper Mroueh, Sercu, Rigotti, Padhi, dos Santos, "Sobolev Independence Criterion", NeurIPS 2019

    Language:Python1411012

  • IBM/sensitive-subspace-robustness

    Language:Jupyter Notebook135116

  • tomsercu/SobolevGAN-SSL

    Code accompanying the paper Sobolev GAN https://arxiv.org/abs/1711.04894

    Language:Python12412

  • IBM/mixed-migration-forecasting

    Forecasting mixed migration for the Danish Refugee Council.

    Language:Jupyter Notebook1112215

  • IBM/ImageNet-Robustness

    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

    Language:Python91307

  • IBM/domain-adaptive-attribution-robustness

    code repo associated to the ACL 2023 paper "DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications"

    Language:Python5

  • kushalchauhan98/ticket-segmentation

    Data for the ACL 2020 paper - Improving Segmentation for Technical Support Problems

    5302

  • IBM/doframework

    A Testing Framework for Decision-Optimization Model Learning Algorithms

    Language:Jupyter Notebook4401

  • IBM/IBM-Extended-Markov-Ratio-Decision-Process

    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.

    Language:Jupyter Notebook430

  • IBM/Parameter-Seed-Set

    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.

    Language:PDDL4510