Neural Network

Visualizing the Loss Landscape of Neural Nets

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. […]

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Machine Learning

A Neural Network Approach to Fast Graph Similarity Computation

SimGNN A PyTorch implementation of SimGNN: A Neural Network Approach to Fast Graph Similarity Computation (WSDM 2019). Abstract Graph similarity search is among the most important graph-based applications, e.g. finding the chemical compounds that are most similar to a query compound. Graph similarity/distance computation, such as Graph Edit Distance (GED) and Maximum Common Subgraph (MCS), is the core operation of graph similarity search and many other applications, but very costly to compute in practice. Inspired by the recent success of […]

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Graph

TuckER: Tensor Factorization for Knowledge Graph Completion

TuckER TuckER: Tensor Factorization for Knowledge Graph Completion This codebase contains PyTorch implementation of the paper: TuckER: Tensor Factorization for Knowledge Graph Completion.Ivana Balažević, Carl Allen, and Timothy M. Hospedales.Empirical Methods in Natural Language Processing (EMNLP), 2019.[Paper] TuckER: Tensor Factorization for Knowledge Graph Completion.Ivana Balažević, Carl Allen, and Timothy M. Hospedales.ICML Adaptive & Multitask Learning Workshop, 2019.[Short Paper] Link Prediction Results Running a model To run the model, execute the following command: CUDA_VISIBLE_DEVICES=0 python main.py –dataset FB15k-237 –num_iterations 500 –batch_size […]

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Transformer

State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow

transformers Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX. 🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation and more in over 100 languages. Its aim is to make cutting-edge NLP easier to use for everyone. 🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the […]

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Machine Learning

A PyTorch implementation of Graph Classification Using Structural Attention

GAM A PyTorch implementation of Graph Classification Using Structural Attention (KDD 2018). Abstract Graph classification is a problem with practical applications in many different domains. To solve this problem, one usually calculates certain graph statistics (i.e., graph features) that help discriminate between graphs of different classes. When calculating such features, most existing approaches process the entire graph. In a graphlet-based approach, for instance, the entire graph is processed to get the total count of different graphlets or subgraphs. In many […]

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Network

A PyTorch implementation of Scalable Incomplete Network Embedding

Scalable Incomplete Network Embedding A PyTorch implementation of Scalable Incomplete Network Embedding (ICDM 2018). Abstract Attributed network embedding aims to learn low-dimensional vector representations for nodes in a network, where each node contains rich attributes/features describing node content. Because network topology structure and node attributes often exhibit high correlation, incorporating node attribute proximity into network embedding is beneficial for learning good vector representations. In reality, large-scale networks often have incomplete/missing node content or linkages, yet existing attributed network embedding algorithms […]

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Tool

A forensic tool to look for signs of infection in smartphone devices

Mobile Verification Toolkit (MVT) is a collection of utilities to simplify and automate the process of gathering forensic traces helpful to identify a potential compromise of Android and iOS devices. It has been developed and released by the Amnesty International Security Lab in July 2021 in the context of the Pegasus project along with a technical forensic methodology and forensic evidences. Installation First you need to install dependencies, on Linux sudo apt install python3 python3-pip libusb-1.0-0 or on MacOS brew […]

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Network

A PyTorch implementation of Signed Graph Convolutional Network

SGCN A PyTorch implementation of Signed Graph Convolutional Network (ICDM 2018). Abstract Due to the fact much of today’s data can be represented as graphs, there has been a demand for generalizing neural network models for graph data. One recent direction that has shown fruitful results, and therefore growing interest, is the usage of graph convolutional neural networks (GCNs). They have been shown to provide a significant improvement on a wide range of tasks in network analysis, one of which […]

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Machine Learning

A PyTorch implementation of Capsule Graph Neural Network

CapsGNN A PyTorch implementation of Capsule Graph Neural Network (ICLR 2019). Abstract The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance. However, when applying node embeddings learned from GNNs to generate graph embeddings, the scalar node representation may not suffice to preserve the node/graph properties efficiently, resulting in sub-optimal graph embeddings. Inspired by the Capsule Neural Network (CapsNet), we […]

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Machine Learning

Watch Your Step: Learning Node Embeddings via Graph Attention

Attention Walk A PyTorch Implementation of Watch Your Step: Learning Node Embeddings via Graph Attention (NIPS 2018). Abstract Graph embedding methods represent nodes in a continuous vector space, preserving different types of relational information from the graph. There are many hyper-parameters to these methods (e.g. the length of a random walk) which have to be manually tuned for every graph. In this paper, we replace previously fixed hyper-parameters with trainable ones that we automatically learn via backpropagation. In particular, we […]

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