StemGNN This repository is the official implementation of Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting. Requirements Recommended version of OS & Python: To install python dependencies, virtualenv is recommended, sudo apt install python3.7-venv to install virtualenv for python3.7. All the python dependencies are verified for pip==20.1.1 and setuptools==41.2.0. Run the following commands to create a venv and install python dependencies: python3.7 -m venv venv source venv/bin/activate pip install –upgrade pip pip install -r requirements.txt Datasets PEMS03,PEMS04,PEMS07,PEMS08,METR-LA,PEMS-BAY,Solar,Electricity,ECG5000,COVID-19 We can […]

Authors: Anadi Chaman and Ivan Dokmanić Convolutional neural networks were always assumed to be shift invariant, until recently when it was shown that the classification accuracy of a trained CNN can take a serious hit with merely a 1-pixel shift in input image. One of the primary reasons for this problem is the use of downsampling (popularly known as stride) layers in the networks. In this work, we present Adaptive Polyphase Sampling (APS), an easy-to-implement non-linear downsampling scheme that completely […]

This repository contains the official PyTorch implementation of the following paper to appear at IEEE Security and Privacy 2022: SoK: How Robust is Deep Neural Network Image Classification Watermarking? Nils Lukas, Edward Jiang,Xinda Li, Florian Kerschbaum https://arxiv.org/abs/2108.04974 Abstract: Deep Neural Network (DNN) watermarking is a method for provenance verification of DNN models. Watermarking should be robust against watermark removal attacks that derive a surrogate model that evades provenance verification. Many watermarking schemes that claim robustness have been proposed, but their […]

A-CNN Created by Artem Komarichev, Zichun Zhong, Jing Hua from Department of Computer Science, Wayne State University. Introduction Our paper (arXiV) proposes a new approach to define and compute convolution directly on 3D point clouds by the proposed annular convolution. To appear, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019 A-CNN usage We provide the code of A-CNN model that was tested with Tensorflow 1.3.0, CUDA 8.0, and python 3.6 on Ubuntu 16.04. We […]

This repository provides the code of our paper: Blockwisely Supervised Neural Architecture Search with Knowledge Distillation. Illustration of DNA. Each cell of the supernet is trained independently to mimic the behavior of the corresponding teacher block. Comparison of model ranking for DNA vs. DARTS, SPOS and MnasNet under two different hyper-parameters. Our Trained Models Usage 1. Requirements Install PyTorch (pytorch.org) Install third-party requirements Download the ImageNet dataset and move validation images to labeled subfolders 2. Searching The code for supernet […]

anatome Ἀνατομή is a PyTorch library to analyze internal representation of neural networks This project is under active development and the codebase is subject to change. Installation anatome requires Python>=3.9.0 PyTorch>=1.9.0 torchvision>=0.10.0 After the installation of PyTorch, install anatome as follows: pip install -U git+https://github.com/moskomule/anatome Representation Similarity To measure the similarity of learned representation, anatome.SimilarityHook is a useful tool. Currently, the followingmethods are implemented. from anatome import SimilarityHook model = resnet18() hook1 = SimilarityHook(model, “layer3.0.conv1”) hook2 = SimilarityHook(model, “layer3.0.conv2”) model.eval() […]

FlashTorch A Python visualization toolkit, built with PyTorch, for neural networks in PyTorch. Neural networks are often described as “black box”. The lack of understanding on how neural networks make predictions enables unpredictable/biased models, causing real harm to society and a loss of trust in AI-assisted systems. Feature visualization is an area of research, which aims to understand how neural networks perceive images. However, implementing such techniques is often complicated. FlashTorch was created to solve this problem! You can apply […]

Empirical Experiments in “Feature Learning in Infinite-width Neural Networks” This repo contains code to replicate our experiments (Word2Vec, MAML) in our paper Feature Learning in Infinite-Width Neural NetworksGreg Yang, Edward Hu In short, the code here will allow you to train feature learning infinite-width neural networks on Word2Vec and on Omniglot (via MAML). Our results on Word2Vec: Our Results on MAML: Please see the README in individual folders for more details. This is the 4th paper in the Tensor Programs […]

ASWS This codebase is being actively maintained, please create and issue if you have issues using it Code to use Augmented Shapiro Wilks Stopping, as well as code for the paper “Statistically Signifigant Stopping of Neural Network Training” Basics All data files are included under losses and each folder. The main Augmented Shapiro-Wilk Stopping criterion is implemented in analysis.py, along with several helper functions and wrappers. The other comparison heuristics are also included in analysis.py, along with their wrappers. grapher.py […]

UIS-RNN This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization. This algorithm was originally proposed in the paperFully Supervised Speaker Diarization. The work has been introduced byGoogle AI Blog. Disclaimer This open source implementation is slightly different than the internal onewhich we used to produce the results in thepaper, due to dependencies onsome internal libraries. We CANNOT share the data, code, or model for the speaker recognition system(d-vector […]