Code and data accompanying our SVRHM'21 paper.

Last update: Nov 17, 2021

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Deep Learning svrhm21_RNN_explain

Overview

Code and data accompanying our SVRHM'21 paper.

Requires tensorflow 1.13, python 3.7, scikit-learn, and pytorch 1.6.0 to be installed.

Python scripts included:

RNN_gen.py can be used to train the RNNs.
RNN_analyse_reprs_recurrence.py can be used to train linear classifiers for auxiliary variables and category decoding, on the layer activations and recurrent flows
RNN_perturb.py can be used to perform the perturbation analyses.

Two pretrained RNNs can be downloaded from https://osf.io/pf4u5/ to be assessed using the jupyter notebook 'SVRHM21_results.ipynb'. Alternatively, you can train your own RNN and assess it.

Owner

GitHub Repository

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Code and data accompanying our SVRHM'21 paper.

Related tags

Overview

Owner

Benchmarking Pipeline for Prediction of Protein-Protein Interactions

Code of 3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces

Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomaly Detection

MPViT:Multi-Path Vision Transformer for Dense Prediction

The code for Expectation-Maximization Attention Networks for Semantic Segmentation (ICCV'2019 Oral)

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Several simple examples for popular neural network toolkits calling custom CUDA operators.

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Code for paper "Context-self contrastive pretraining for crop type semantic segmentation"

Character Controllers using Motion VAEs

NAACL'2021: Factual Probing Is [MASK]: Learning vs. Learning to Recall

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Reference code for the paper "Cross-Camera Convolutional Color Constancy" (ICCV 2021)