APPNP A PyTorch implementation of Predict then Propagate: Graph Neural Networks meet Personalized PageRank (ICLR 2019). Abstract Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success. However, these methods only consider nodes that are a few propagation steps away and the size of this utilized neighborhood cannot be easily extended. In this paper, we use the relationship between graph convolutional networks (GCN) and PageRank to derive an improved propagation scheme based on personalized PageRank. We […]

DiffAI v3 DiffAI is a system for training neural networks to be provably robust and for proving that they are robust. The system was developed for the 2018 ICML paper and the 2019 ArXiV Paper. Background By now, it is well known that otherwise working networks can be tricked by clever attacks. For example Goodfellow et al. demonstrated a network with high classification accuracy which classified one image of a panda correctly, and a seemingly identical attack pictureincorrectly. Many defenses […]

Character Based CNN This repo contains a PyTorch implementation of a character-level convolutional neural network for text classification. The model architecture comes from this paper: https://arxiv.org/pdf/1509.01626.pdf There are two variants: a large and a small. You can switch between the two by changing the configuration file. This architecture has 6 convolutional layers: Layer Large Feature Small Feature Kernel Pool 1 1024 256 7 3 2 1024 256 7 3 3 1024 256 3 N/A 4 1024 256 3 N/A 5 […]

loss-landscape This repository contains the PyTorch code for the paper Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer and Tom Goldstein. Visualizing the Loss Landscape of Neural Nets. NIPS, 2018. An interactive 3D visualizer for loss surfaces has been provided by telesens. Given a network architecture and its pre-trained parameters, this tool calculates and visualizes the loss surface along random direction(s) near the optimal parameters. The calculation can be done in parallel with multiple GPUs per node, and multiple nodes. […]