A Python package for biomarkers identification powered by interpretable deep learning
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Updated
Oct 4, 2022 - Python
A Python package for biomarkers identification powered by interpretable deep learning
Official Implementation of TMLR's paper: "TabCBM: Concept-based Interpretable Neural Networks for Tabular Data"
neural network to learn paths in decision tree
Code for Surgical Skill Assessment via Video Semantic Aggregation (MICCAI 2022)
Facial emotion classification and modification using CNNs.
An unofficial version of the PyTorch implementation of CURE and Fast Adversarial training with FGSM.
Master's Thesis (Master's Degree in Artificial Intelligence and Robotics at Sapienza University of Rome) - 2023
Pytorch Implementation of bmvc 2022 paper "Beyong the CLS Token: Image Reranking using Pretrained Vision Transformers"
ICCV2021 paper: Interpretable Image Recognition by Constructing Transparent Embedding Space (TesNet)
The source code for the journal paper: Spatio-Temporal Perturbations for Video Attribution, TCSVT-2021
a module to obtain diverse real-world-grounded features for sentences for large-scale benchmarking
Official Implementation of ARACHNET: INTERPRETABLE SUB-ARACHNOID SPACE SEGMENTATION USING AN ADDITIVE CONVOLUTIONAL NEURAL NETWORK
Code used in the paper `Explanatory Paradigms in Neural Networks', published in the Signal Processing Magazine
Code and Datasets for the paper "DG-Viz: Deep Visual Analytics with Domain Knowledge Guided Recurrent Neural Networks on Electronic Health Records", published on Journal of Medical Internet Research (JMIR) in 2020.
Explainable deep networks that are not only as accurate as their black-box deep-learning counterparts but also as interpretable as state-of-the-art explanation techniques.
✂️ Repository for our ICLR 2019 paper: Discovery of Natural Language Concepts in Individual Units of CNNs
Code and Datasets for the paper "An Interpretable Risk Prediction Model for Healthcare with Pattern Attention", published on BMC Medical Informatics and Decision Making.
Code and Datasets for the paper "TransICD: Transformer Based Code-wise Attention Model for Explainable ICD Coding", accepted by AIME 2021.
Attribution (or visual explanation) methods for understanding video classification networks. Demo codes for WACV2021 paper: Towards Visually Explaining Video Understanding Networks with Perturbation.
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