Topic: trustworthy-machine-learning Goto Github
Some thing interesting about trustworthy-machine-learning
Some thing interesting about trustworthy-machine-learning
trustworthy-machine-learning,Code from PLDI '21 paper "Provable Repair of Deep Neural Networks."
Organization: 95616arg
trustworthy-machine-learning,SyReNN: Symbolic Representations for Neural Networks
Organization: 95616arg
trustworthy-machine-learning,[ICCV2021 Oral] Fooling LiDAR by Attacking GPS Trajectory
Organization: ai4ce
Home Page: https://ai4ce.github.io/FLAT/
trustworthy-machine-learning,Birhanu Eshete is an Associate Professor of Computer Science at the University of Michigan, Dearborn. His main research focus is in trustworthy machine learning with emphasis on security, safety, privacy, interpretability, fairness, and the dynamics thereof. He also studies online cybercrime and advanced and persistent threats (APTs).
User: birhanu-eshete
Home Page: https://birhanu-eshete.github.io/
trustworthy-machine-learning,A list of research papers of explainable machine learning.
User: birkhoffg
trustworthy-machine-learning,Papers and online resources related to machine learning fairness
Organization: brandeis-machine-learning
trustworthy-machine-learning,Code for the paper "Approximating full conformal prediction at scale via influence functions""
Organization: cambridge-mlg
Home Page: https://ojs.aaai.org/index.php/AAAI/article/view/25814
trustworthy-machine-learning,Fair and explainable ML workshop
Organization: carpentries-incubator
Home Page: https://carpentries-incubator.github.io/fair-explainable-ml/
trustworthy-machine-learning,a tool for comparing the predictions of any text classifiers
User: crisp-unimib
trustworthy-machine-learning,MERLIN is a global, model-agnostic, contrastive explainer for any tabular or text classifier. It provides contrastive explanations of how the behaviour of two machine learning models differs.
User: crisp-unimib
Home Page: https://crispresearch.it/
trustworthy-machine-learning,My personal website.
User: csrzhang
Home Page: https://csrzhang.github.io/
trustworthy-machine-learning,A project to improve out-of-distribution detection (open set recognition) and uncertainty estimation by changing a few lines of code in your project! Perform efficient inferences (i.e., do not increase inference time) without repetitive model training, hyperparameter tuning, or collecting additional data.
User: dlmacedo
trustworthy-machine-learning,A project to add scalable state-of-the-art out-of-distribution detection (open set recognition) support by changing two lines of code! Perform efficient inferences (i.e., do not increase inference time) and detection without classification accuracy drop, hyperparameter tuning, or collecting additional data.
User: dlmacedo
trustworthy-machine-learning,A project to train your model from scratch or fine-tune a pretrained model using the losses provided in this library to improve out-of-distribution detection and uncertainty estimation performances. Calibrate your model to produce enhanced uncertainty estimations. Detect out-of-distribution data using the defined score type and threshold.
User: dlmacedo
trustworthy-machine-learning,Welcome to my Machine Learning repository, where you can find learning materials both from my studies and from various online courses.
User: dragoa
trustworthy-machine-learning,Open-source framework for uncertainty and deep learning models in PyTorch :seedling:
Organization: ensta-u2is-ai
Home Page: https://torch-uncertainty.github.io
trustworthy-machine-learning,[ICML 2024] TrustLLM: Trustworthiness in Large Language Models
User: howiehwong
Home Page: https://trustllmbenchmark.github.io/TrustLLM-Website/
trustworthy-machine-learning,PyTorch package to train and audit ML models for Individual Fairness
Organization: ibm
Home Page: https://ibm.github.io/inFairness
trustworthy-machine-learning,In the dynamic landscape of medical artificial intelligence, this study explores the vulnerabilities of the Pathology Language-Image Pretraining (PLIP) model, a Vision Language Foundation model, under targeted attacks like PGD adversarial attack.
User: jaiprakash1824
trustworthy-machine-learning,KDD 2023 tutorial "Trustworthy Transfer Learning: Transferability and Trustworthiness"
User: junwu6
trustworthy-machine-learning,[Findings of EMNLP 2022] Holistic Sentence Embeddings for Better Out-of-Distribution Detection
Organization: lancopku
trustworthy-machine-learning,Privacy-Preserving Machine Learning (PPML) Tutorial
User: leriomaggio
trustworthy-machine-learning,Trustworthy AI method based on Dempster-Shafer theory - application to fetal brain 3D T2w MRI segmentation
User: lucasfidon
trustworthy-machine-learning,Machine Learning Security Library
User: melihcatal
Home Page: https://melihcatal.github.io/advsecurenet/
trustworthy-machine-learning,Repository for the NeurIPS 2023 paper "Beyond Confidence: Reliable Models Should Also Consider Atypicality"
User: mertyg
trustworthy-machine-learning,Papers related to Federated Learning in all venue (dblp)
User: mtuann
Home Page: https://mtuann.shinyapps.io/research-papers/
trustworthy-machine-learning,Explainable Debugger for Black-box Machine Learning Models
User: peymanrasouli
trustworthy-machine-learning,This repo contains the codes, figures and datasets for the paper - U-Trustworthy Models. Reliability, Competence, and Confidence in Decision-Making.
User: ritwikvashistha
trustworthy-machine-learning,Data-SUITE: Data-centric identification of in-distribution incongruous examples (ICML 2022)
User: seedatnabeel
trustworthy-machine-learning,TRIAGE: Characterizing and auditing training data for improved regression (NeurIPS 2023)
User: seedatnabeel
trustworthy-machine-learning,The open-sourced Python toolbox for backdoor attacks and defenses.
User: thuyimingli
trustworthy-machine-learning,A School for All Seasons on Trustworthy Machine Learning
Organization: trustworthy-machine-learning
trustworthy-machine-learning,Trustworthy AI/ML course by Professor Birhanu Eshete, University of Michigan, Dearborn.
User: trustworthy-ml-course
Home Page: https://trustworthy-ml-course.github.io
trustworthy-machine-learning,Official implementation of NeurIPS 2023 paper "Trade-off Between Efficiency and Consistency for Removal-based Explanations" (https://arxiv.org/abs/2210.17426)
Organization: trusty-ai
Home Page: https://arxiv.org/abs/2210.17426
trustworthy-machine-learning,[ICML2022 Long Talk] Official Pytorch implementation of "To Smooth or Not? When Label Smoothing Meets Noisy Labels"
Organization: ucsc-real
trustworthy-machine-learning,Explanation-guided boosting of machine learning evasion attacks.
User: um-dsp
trustworthy-machine-learning,Morphence: An implementation of a moving target defense against adversarial example attacks demonstrated for image classification models trained on MNIST and CIFAR10.
User: um-dsp
trustworthy-machine-learning,DSPLab@UMich-Dearborn Website
User: um-dsp
Home Page: https://um-dsp.github.io
trustworthy-machine-learning,Neural Network Verification Software Tool
Organization: verivital
Home Page: http://www.verivital.com
trustworthy-machine-learning,Framework for Adversarial Malware Evaluation.
User: zrapha
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