COLLECTED BY
Organization:
Internet Archive
Focused crawls are collections of frequently-updated webcrawl data from narrow (as opposed to broad or wide) web crawls, often focused on a single domain or subdomain.
The Wayback Machine - https://web.archive.org/web/20200827071505/https://github.com/topics/interpretable-deep-learning
#
interpretable-deep-learning
Here are
53 public repositories
matching this topic...
A curated list of awesome machine learning interpretability resources.
Public facing deeplift repo
Updated
Aug 18, 2020
Python
Tensorflow tutorial for various Deep Neural Network visualization techniques
Updated
Aug 22, 2020
Jupyter Notebook
A Simple pytorch implementation of GradCAM and GradCAM++
Updated
Apr 23, 2019
Jupyter Notebook
Pytorch Implementation of recent visual attribution methods for model interpretability
Updated
Feb 27, 2020
Jupyter Notebook
A repository for explaining feature attributions and feature interactions in deep neural networks.
Updated
May 1, 2020
Jupyter Notebook
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM)
Updated
Aug 26, 2020
Python
Tools for training explainable models using attribution priors.
Updated
Apr 26, 2020
Jupyter Notebook
Protein-compound affinity prediction through unified RNN-CNN
Updated
Aug 22, 2020
Python
Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers
Updated
Aug 21, 2018
Python
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
Updated
Jul 23, 2020
Jupyter Notebook
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting
Updated
Aug 12, 2020
Python
Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks
Updated
Aug 8, 2019
Python
Multislice PHATE for tensor embeddings
Updated
Mar 10, 2020
Python
All about explainable AI, algorithmic fairness and more
Updated
Apr 16, 2018
Python
Code for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''
Updated
Sep 9, 2019
Python
✂️ Repository for our ICLR 2019 paper: Discovery of Natural Language Concepts in Individual Units of CNNs
Updated
Mar 9, 2019
Python
Interpretable Image Search by Priyam Tejaswin and Akshay Chawla
Updated
Mar 30, 2020
Python
NeurIPS17: [AttentiveChrome] Attend and Predict: Using Deep Attention Model to Understand Gene Regulation by Selective Attention on Chromatin
Interpreting DNNs, Relative attributing propagation
Updated
Nov 11, 2019
Python
Unsupervised Representation Learning for Singing Voice Separation
Updated
Jul 7, 2020
Python
💡 A curated list of awesome adversarial interpretable machine learning resources
ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
Updated
Jan 27, 2020
Jupyter Notebook
On the importance of single directions for generalization(Morcos et al, ICLR 2018)
Updated
Jul 23, 2018
Shell
Official Pytorch implementation of (Roles and Utilization of Attention Heads in Transformer-based Neural Language Models), ACL 2020
Updated
Jul 11, 2020
Python
Quantitative Testing with Concept Activation Vectors in PyTorch
Updated
Mar 18, 2019
Python
Interpretability of Machine Learning-Visualizations
Updated
Jul 9, 2018
Python
Enabling interactive plotting of the visualizations from the SHAP project.
Updated
Jan 15, 2020
Python
Attribution (or visual explanation) methods for understanding video classification networks
Updated
Jul 7, 2020
Python
Improve this page
Add a description, image, and links to the
interpretable-deep-learning
topic page so that developers can more easily learn about it.
Curate this topic
Add this topic to your repo
To associate your repository with the
interpretable-deep-learning
topic, visit your repo's landing page and select "manage topics."
Learn more
You can’t perform that action at this time.
You signed in with another tab or window. Reload to refresh your session.
You signed out in another tab or window. Reload to refresh your session.