Reverse Dependencies of captum
The following projects have a declared dependency on captum:
- ADGT — Model interpretability for PyTorch
- AttentionMOI — A Denoised Multi-omics Integration Framework for Cancer Subtype Classification and Survival Prediction.
- AttentionOdorify — Attention based BiLSTM model for Olfactory Analysis
- azureml-automl-dnn-vision — AutoML DNN Vision Models
- beexai — BEExAI: Benchmark to Evaluate Explainable AI
- biome-text — Biome-text is a light-weight open source Natural Language Processing toolbox built with AllenNLP
- bpnet-lite — bpnet-lite is a minimal implementation of BPNet, a neural network aimed at interpreting regulatory activity of the genome.
- codeflare-torchx — TorchX SDK and Components
- coolstuff — ML-analysis
- DeepLocRNA — Predicting RNA localization based on RBP binding information
- DeepMuon — Interdisciplinary Deep Learning Platform
- dive-into-graphs — DIG: Dive into Graphs is a turnkey library for graph deep learning research.
- dnn-cool — DNN.Cool: Multi-task learning for Deep Neural Networks (DNN).
- dynamask — Dynamask - Explaining Time Series Predictions with Dynamic Masks
- ecco — Visualization tools for NLP machine learning models.
- eir-dl — no summary
- fastai — fastai simplifies training fast and accurate neural nets using modern best practices
- fastAIcourse — fastAIcourse
- ferret-xai — A python package for benchmarking interpretability approaches.
- foxai — Model Interpretability for PyTorch.
- giotto-deep — Toolbox for Deep Learning and Topological Data Analysis.
- helmet — no summary
- imgraph — Graph Neural Network Library Built On Top Of PyTorch and PyTorch Geometric
- inseq — Interpretability for Sequence Generation Models 🔍
- intel-xai — Intel® Explainable AI Tools
- joltml — joltml unravels the dark side of machine learning models
- lfxai — A framework to explain the latent representations of unsupervised black-box models with the help of usual feature importance and example-based methods.
- liver-ct-segmentation-package — Prediction package for U-Net models trained on the LiTS dataset.
- metaquantus — MetaQuantus is a XAI performance tool for identifying reliable metrics.
- miidl — A Python package for microbial biomarkers identification powered by interpretable deep learning
- mirzai — Prediction of Exchangeable Potassium in Soil through Mid-Infrared Spectroscopy and Deep Learning: from Prediction to Explainability, Albinet et al., 2022
- moleculex — MoleculeX: a new and rapidly growing suite of machine learning methods and software tools for molecule exploration
- neuralprophet — NeuralProphet is an easy to learn framework for interpretable time series forecasting.
- newAI — newAi
- Nubilum — An explainability library for instance segmentation in point clouds
- proteinworkshop — no summary
- pyg-nightly — Graph Neural Network Library for PyTorch
- pytorch-tabular — A standard framework for using Deep Learning for tabular data
- quantus — A metrics toolkit to evaluate neural network explanations.
- quarks2-fractal — Integrated image classification and semantic segmentation package
- rld — A development tool for evaluation and interpretability of reinforcement learning agents.
- root-tissue-seg-package — An mlf-core prediction package for root tissue segmentation.
- scCAMEL — scCAMEL: single cell Cross- Annotation and Multimodal Estimation on Lineage trajectory;License: GPL version 3;Developed by: Yizhou Hu, Patrik Ernfors lab, MBB, Karolinska Institutet;Tutorials and other informations in :https://sccamel.readthedocs.io/
- scce — a Single-cell method for predicting Chromatin Conformation based on gene Expression
- scReGAT — A GAT-based computational framework to predict long-range gene regulatory relationships
- seqexplainer — Interpreting sequence-to-function machine learning models
- simplexai — SimplEx - Explaining Latent Representations with a Corpus of Examples
- sportsml — ML for sports
- spotGUI — spotgui - GUI for the Sequential Parameter Optimization in Python
- spotPython — spotpython - Sequential Parameter Optimization in Python
- thermostat-datasets — Collection of NLP model explanations and accompanying analysis tools
- time-interpret — Model interpretability library for PyTorch with a focus on time series.
- TimeSeriesML — TimeSeriesML
- torch-geometric — Graph Neural Network Library for PyTorch
- torchx — TorchX SDK and Components
- torchx-applovin — TorchX SDK and Components
- torchx-nightly — TorchX SDK and Components
- trame-sandtank-xai — AI/XAI exploration in the context of ParFlow simulation code
- transformers-interpret — Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
- transformers-visualizer — Explain your 🤗 transformers without effort! Display the internal behavior of your model.
- transmep — Transfer learning for Mutation Effect Prediction
- TSInterpret — todo
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