Topic: interpretable-deep-learning Goto Github
Some thing interesting about interpretable-deep-learning
Some thing interesting about interpretable-deep-learning
interpretable-deep-learning,Tensorflow tutorial for various Deep Neural Network visualization techniques
User: 1202kbs
interpretable-deep-learning,A Simple pytorch implementation of GradCAM and GradCAM++
User: 1konny
interpretable-deep-learning,PyTorch reimplementation of computing Shapley values via Truncated Monte Carlo sampling from "What is your data worth? Equitable Valuation of Data" by Amirata Ghorbani and James Zou [ICML 2019]
User: ajsanjoaquin
interpretable-deep-learning,CausaLM: Causal Model Explanation Through Counterfactual Language Models
User: amirfeder
Home Page: https://amirfeder.github.io/CausaLM/
interpretable-deep-learning,All about explainable AI, algorithmic fairness and more
User: andreysharapov
interpretable-deep-learning,Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers
User: atulshanbhag
interpretable-deep-learning,Genetic programming method for explaining complex black-box models
User: benjaminpatrickevans
Home Page: https://dl.acm.org/citation.cfm?id=3321707.3321726
interpretable-deep-learning,[ICCV 2023] Learning Support and Trivial Prototypes for Interpretable Image Classification
User: cwangrun
interpretable-deep-learning,
User: ducminhkhoi
interpretable-deep-learning,Extracting knowledge graphs from language models as a diagnostic benchmark of model performance (NeurIPS XAI 2021).
Organization: epfml
Home Page: https://arxiv.org/pdf/2111.08546.pdf
interpretable-deep-learning,Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)
User: frgfm
Home Page: https://frgfm.github.io/torch-cam/
interpretable-deep-learning,Pytorch implementation of various neural network interpretability methods
User: hans66hsu
interpretable-deep-learning,Can we use explanations to improve hate speech models? Our paper accepted at AAAI 2021 tries to explore that question.
Organization: hate-alert
interpretable-deep-learning,Implementation of the paper "Shapley Explanation Networks"
Organization: inouye-lab
interpretable-deep-learning,ICCV2021 paper: Interpretable Image Recognition by Constructing Transparent Embedding Space (TesNet)
User: jackeywang96
interpretable-deep-learning,Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
User: jacobgil
Home Page: https://jacobgil.github.io/pytorch-gradcam-book
interpretable-deep-learning,Official repository of cross-modal transformer for interpretable automatic sleep stage classification. https://arxiv.org/abs/2208.06991
User: jathurshan0330
interpretable-deep-learning,Code for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''
User: jialinwu17
interpretable-deep-learning,This repository contains the implementation of Label-Free XAI, a new framework to adapt explanation methods to unsupervised models. For more details, please read our ICML 2022 paper: 'Label-Free Explainability for Unsupervised Models'.
User: jonathancrabbe
interpretable-deep-learning,Unsupervised Representation Learning for Singing Voice Separation
User: js-mim
interpretable-deep-learning,Public facing deeplift repo
Organization: kundajelab
interpretable-deep-learning,In-silico Saturation Mutagenesis implementation with 10x or more speedup for certain architectures.
Organization: kundajelab
interpretable-deep-learning,Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584
User: laura-rieger
interpretable-deep-learning,PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)
User: m-nauta
interpretable-deep-learning,ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
User: m-nauta
interpretable-deep-learning,This repository provides an app for exploring the predictions of an image classification network using several deep learning visualization techniques. Using the app, you can: explore network predictions with occlusion sensitivity, Grad-CAM, and gradient attribution methods, investigate misclassifications using confusion and t-SNE plots, visualize layer activations, and many more techniques to help you understand and explain your deep network’s predictions.
Organization: matlab-deep-learning
interpretable-deep-learning,Time series explainability via self-supervised model behavior consistency
Organization: mims-harvard
Home Page: https://zitniklab.hms.harvard.edu/projects/TimeX
interpretable-deep-learning,A curated list of trustworthy deep learning papers. Daily updating...
User: minghuichen43
interpretable-deep-learning,Clinically-Interpretable Radiomics [MICCAI'22, CMPB'21]
Organization: nadeemlab
interpretable-deep-learning,PyTorch Explain: Interpretable Deep Learning in Python.
User: pietrobarbiero
interpretable-deep-learning,Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks
Organization: pkuai26
interpretable-deep-learning,Enabling interactive plotting of the visualizations from the SHAP project.
User: prashantsaikia
interpretable-deep-learning,Interpretable Image Search by Priyam Tejaswin and Akshay Chawla
User: priyamtejaswin
Home Page: http://35.227.39.159:5050/
interpretable-deep-learning,NeurIPS17: [AttentiveChrome] Attend and Predict: Using Deep Attention Model to Understand Gene Regulation by Selective Attention on Chromatin
Organization: qdata
Home Page: http://deepchrome.org
interpretable-deep-learning,Official NumPy Implementation of Deep Networks from the Principle of Rate Reduction (2021)
User: ryanchankh
interpretable-deep-learning,Multislice PHATE for tensor embeddings
User: scottgigante
Home Page: https://arxiv.org/abs/1908.02831
interpretable-deep-learning,:scissors: Repository for our ICLR 2019 paper: Discovery of Natural Language Concepts in Individual Units of CNNs
User: seilna
interpretable-deep-learning,Protein-compound affinity prediction through unified RNN-CNN
User: shen-lab
interpretable-deep-learning,[ECCV 2020] QAConv: Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting, and [CVPR 2022] GS: Graph Sampling Based Deep Metric Learning
User: shengcailiao
Home Page: https://arxiv.org/abs/1904.10424
interpretable-deep-learning,[NeurIPS 2021] TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification
User: shengcailiao
Home Page: https://arxiv.org/abs/2105.14432
interpretable-deep-learning,Attribution (or visual explanation) methods for understanding video classification networks. Demo codes for WACV2021 paper: Towards Visually Explaining Video Understanding Networks with Perturbation.
User: shinkyo0513
interpretable-deep-learning,Tools for training explainable models using attribution priors.
Organization: suinleelab
interpretable-deep-learning,A repository for explaining feature attributions and feature interactions in deep neural networks.
Organization: suinleelab
interpretable-deep-learning,Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."
User: wanyu-lin
interpretable-deep-learning,ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
User: williamcaicedo
interpretable-deep-learning,Interpreting DNNs, Relative attributing propagation
User: wjnam
interpretable-deep-learning,[ICCV 2021] Towards Interpretable Deep Metric Learning with Structural Matching
User: wl-zhao
interpretable-deep-learning,Pytorch Implementation of recent visual attribution methods for model interpretability
User: yulongwang12
interpretable-deep-learning,∂B nets: learning discrete, boolean-valued functions by gradient descent
User: z80coder
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