A PyTorch Implementation of GGNN This is a PyTorch implementation of the Gated Graph Sequence Neural Networks (GGNN) as described in the paper Gated Graph Sequence Neural Networks by Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel. This implementation gets 100% accuracy on node-selection bAbI task 4, 15, and 16. Their official implementation are available in the yujiali/ggnn repo on GitHub. What is GGNN? Solve graph-structured data and problems A gated propagation model to compute node representations Unroll recurrence […]

TilinGNN TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network (SIGGRAPH 2020) About The goal of our research problem is illustrated below: given a tile set (a) and a 2D region to be filled (b), we aim to produce a tiling (c) that maximally covers the interior of the given region without overlap or hole between the tile instances. Dependencies: This project is implemented in Python 3.7. You need to install the following packages to run our program. Pytorch (tested […]

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. […]

GPRGNN This is the source code for our ICLR2021 paper: Adaptive Universal Generalized PageRank Graph Neural Network. Hidden state feature extraction is performed by a neural networks using individual node features propagated via GPR. Note that both the GPR weights and parameter set of the neural network are learned simultaneously in an end-to-end fashion (as indicated in red). The learnt GPR weights of the GPR-GNN on real world datasets. Cora is homophilic while Texas is heterophilic (Here, H stands for […]

AnalyticMesh Analytic Marching is an exact meshing solution from neural networks. Compared to standard methods, it completely avoids geometric and topological errors that result from insufficient sampling, by means of mathematically guaranteed analysis. This repository gives an implementation of Analytic Marching algorithm. This algorithm is initially proposed in our conference paper Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks, then finally improved in our journal paper: Learning and Meshing from Deep Implicit Surface Networks Using an Efficient […]

stox A Module to predict the “close price” for the next day and give “technical analysis”. It uses a Neural Network and the LSTM algorithm to predict the price. It uses a technical indicator algorithm developed by the Stox team for technical analysis. Installation Get it from PyPi: pip3 install stox Clone it from github: git clone https://github.com/dopevog/stox.git cd stox python3 setup.py install Usage Arguments: stock (str): stock ticker symbol output (str): ‘list’ or ‘message’ (Format Of Output) years (int […]

neural-g2p-portuguese Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written form. It has a highly essential role for natural language processing, text-to-speech synthesis and automatic speech recognition systems. This project was adapted from https://github.com/hajix/G2P. Dependencies The following libraries are used:pytorchtqdmmatplotlib Install dependencies using pip: pip3 install -r requirements.txt Dataset The dataset used here was taken from site http://www.portaldalinguaportuguesa.org/, as well as some insertions made by me so that the dataset would give more coverage […]