LibTraffic（阡陌） LibTraffic is a unified, flexible and comprehensive traffic prediction library, which provides researchers with a credibly experimental tool and a convenient development framework. Our library is implemented based on PyTorch, and includes all the necessary steps or components related to traffic prediction into a systematic pipeline. LibTraffic currently supports the following tasks: Traffic State Prediction Traffic Flow Prediction Traffic Speed Prediction On-Demand Service Prediction Trajectory Next-Location Prediction Features Unified: LibTraffic builds a systematic pipeline to implement, use and evaluate […]

ThermalControlLPBF-DRL Code implementation of the paper “Thermal Control of Laser Powder Bed Fusion using Deep Reinforcement Learning”. This repository is the implementation of the paper “Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning”, linked here. The project makes use of the Deep Reinforcement Library stable-baselines3 to derive a control policy that maximizes melt pool depth consistency. Simulation Framework The Repeated Usage of Stored Line Solutions (RUSLS) method proposed by Wolfer et al. is used to simulate the […]

Locus This repository is an open-source implementation of the ICRA 2021 paper: Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling. More information: https://research.csiro.au/robotics/locus-pr/ Paper Pre-print: https://arxiv.org/abs/2011.14497 Method overview. Locus is a global descriptor for large-scale place recognition using sequential 3D LiDAR point clouds. It encodes topological relationships and temporal consistency of scene components to obtain a discriminative and view-point invariant scene representation. Usage Set up environment This project has been tested on Ubuntu 18.04 (with Open3D 0.11, tensorflow 1.8.0, pcl […]

redunet_paper Deep Networks from the Principle of Rate ReductionThis repository is the official NumPy implementation of the paper Deep Networks from the Principle of Rate Reduction (2021) by Kwan Ho Ryan Chan* (UC Berkeley), Yaodong Yu* (UC Berkeley), Chong You* (UC Berkeley), Haozhi Qi (UC Berkeley), John Wright (Columbia), and Yi Ma (UC Berkeley). For PyTorch version of ReduNet, please visit https://github.com/ryanchankh/redunet. What is ReduNet? ReduNet is a deep neural network construcuted naturally by deriving the gradients of the Maximal […]

SCAAML SCAAML (Side Channel Attacks Assisted with Machine Learning) is a deep learning framwork dedicated to side-channel attacks. It is written in python and run on top of TensorFlow 2.x. Available compoments scaaml/: The SCAAML framework code. Its used by the various tools. scaaml_intro/: A Hacker Guide To Deep Learning Based Side Channel Attacks.Code, dataset and models used in our step by step tutorial on how to use deep-learning to perform AES side-channel attacks in practice. Install Dependencies To use […]

PySyft PySyft decouples private data from model training, using Federated Learning, Differential Privacy, and Encrypted Computation (like Multi-Party Computation (MPC) and Homomorphic Encryption (HE)) within the main Deep Learning frameworks like PyTorch and TensorFlow. Most software libraries let you compute over the information you own and see inside of machines you control. However, this means that you cannot compute on information without first obtaining (at least partial) ownership of that information. It also means that you cannot compute using machines […]

GitRec GitRec is the missing recommender system for GitHub repositories based on Gorse. Architecture The trending crawler crawls trending repositories and insert them into Gorse as new items. The user starred crawler crawls user starred repositories and insert them into Gorse as new fewdback. GitRec web service pulls recommendations from Gorse and show to users. It also submits a crawling request to the user starred crawler when a new user signed in. Quick Start First, clone the repository and enter […]

CvT This is an official implementation of CvT: Introducing Convolutions to Vision Transformers. We present a new architecture, named Convolutional vision Transformers (CvT), that improves Vision Transformers (ViT) in performance and efficienty by introducing convolutions into ViT to yield the best of both disignes. This is accomplished through two primary modifications: a hierarchy of Transformers containing a new convolutional token embedding, and a convolutional Transformer block leveraging a convolutional projection. These changes introduce desirable properties of convolutional neural networks (CNNs) […]