Displaying 1 to 20 from 135 results

WebSearch.Net

  •    CSharp

WebSearch.Net is an open-source research platform that provides uniform data source access, data modeling, feature calculation, data mining, etc.

StarWars

  •    Swift

This component implements transition animation to crumble view-controller into tiny pieces. Check this project on dribbble.

vuvuzela - Private messaging system that hides metadata

  •    Go

Vuvuzela is a messaging system that protects the privacy of message contents and message metadata. Users communicating through Vuvuzela do not reveal who they are talking to, even in the presence of powerful nation-state adversaries. Our SOSP 2015 paper explains the system, its threat model, performance, limitations, and more. Our SOSP 2015 slides give a more graphical overview of the system. Vuvuzela is the first system that provides strong metadata privacy while scaling to millions of users. Previous systems that hide metadata using Tor (such as Pond) are prone to traffic analysis attacks. Systems that encrypt metadata using techniques like DC-nets and PIR don't scale beyond thousands of users.

carla - Open-source simulator for autonomous driving research.

  •    C++

CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. If you want to benchmark your model in the same conditions as in our CoRL’17 paper, check out Benchmarking.




awesome-deep-learning-music - List of articles related to deep learning applied to music

  •    TeX

By Yann Bayle (Website, GitHub) from LaBRI (Website, Twitter), Univ. Bordeaux (Website, Twitter), CNRS (Website, Twitter) and SCRIME (Website). The role of this curated list is to gather scientific articles, thesis and reports that use deep learning approaches applied to music. The list is currently under construction but feel free to contribute to the missing fields and to add other resources! To do so, please refer to the How To Contribute section. The resources provided here come from my review of the state-of-the-art for my PhD Thesis for which an article is being written. There are already surveys on deep learning for music generation, speech separation and speaker identification. However, these surveys do not cover music information retrieval tasks that are included in this repository.

google-research - Google Research

  •    Jupyter

This repository contains code released by Google Research. All datasets in this repository are released under the CC BY 4.0 International license, which can be found here: https://creativecommons.org/licenses/by/4.0/legalcode. All source files in this repository are released under the Apache 2.0 license, the text of which can be found in the LICENSE file.

language - Shared repository for open-sourced projects from the Google AI Language team.

  •    Python

Shared repository for open-sourced projects from the Google Research Language team. This is not an official Google product.

acme - A library of reinforcement learning components and agents

  •    Python

Acme is a library of reinforcement learning (RL) building blocks that strives to expose simple, efficient, and readable agents. These agents first and foremost serve both as reference implementations as well as providing strong baselines for algorithm performance. However, the baseline agents exposed by Acme should also provide enough flexibility and simplicity that they can be used as a starting block for novel research. Finally, the building blocks of Acme are designed in such a way that the agents can be written at multiple scales (e.g. single-stream vs. distributed agents). The quickest way to get started is to take a look at the detailed working code examples found in the examples subdirectory. These show how to instantiate a a number of different agents and run them within a variety of environments. See the quickstart notebook for an even quicker dive into using a single agent. Even more detail on the internal construction of an agent can be found inside our tutorial notebook. Finally, a full description Acme and its underlying components can be found by referring to the documentation. More background information and details behind the design decisions can be found in our technical report.


OpenBot - OpenBot leverages smartphones as brains for low-cost robots

  •    Java

OpenBot leverages smartphones as brains for low-cost robots. We have designed a small electric vehicle that costs about $50 and serves as a robot body. Our software stack for Android smartphones supports advanced robotics workloads such as person following and real-time autonomous navigation. Please read the contribution guidelines. If you are not sure where to start have a look at the open issues.

learn-anything - Organize world's knowledge, explore connections and curate learning paths

  •    TypeScript

The vision of project is to create the best platform possible for learning any topic as well as finding & building novel ideas together. Please read our roadmap where we outline the problems we want to solve with building this platform and how we plan to solve them.

lingvo - Lingvo

  •    Python

Lingvo is a framework for building neural networks in Tensorflow, particularly sequence models. A list of publications using Lingvo can be found here.

AirSim - Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research

  •    C++

AirSim is a simulator for drones (and soon other vehicles) built on Unreal Engine. It is open-source, cross platform and supports hardware-in-loop with popular flight controllers such as PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped in to any Unreal environment you want.

rwa - Machine Learning on Sequential Data Using a Recurrent Weighted Average

  •    Python

This repository holds the code to a new kind of RNN model for processing sequential data. The model computes a recurrent weighted average (RWA) over every previous processing step. With this approach, the model can form direct connections anywhere along a sequence. This stands in contrast to traditional RNN architectures that only use the previous processing step. A detailed description of the RWA model has been published in a manuscript at https://arxiv.org/pdf/1703.01253.pdf. Because the RWA can be computed as a running average, it does not need to be completely recomputed with each processing step. The numerator and denominator can be saved from the previous step. Consequently, the model scales like that of other RNN models such as the LSTM model.

Dryad - Repository for data underlying scientific publications, based on the DSpace platform

  •    Java

Dryad is a repository for data underlying scientific literature. Dryad allows investigators to validate published findings, explore new analysis methodologies and repurpose the data for research questions unanticipated by the original authors. The core code of Dryad comes from the DSpace repository software. It is a general-purpose repository that makes the data underlying scientific publications discoverable, freely reusable, and citable.

awesome-research - :seedling: a curated list of tools to help you with research/life

  •    

A list of tools for research. First things first, everyone should know version control systems. Version control is one of the most useful tools for scientists.

LaTeX-examples - Examples for the usage of LaTeX

  •    TeX

Many examples can be found on my Wikpedia Commons user page. You might also be interested in my Blog.

GibsonEnv - Gibson Environments: Real-World Perception for Embodied Agents

  •    C

You shouldn't play video games all day, so shouldn't your AI! We built a virtual environment simulator, Gibson, that offers real-world experience for learning perception. I. being from the real-world and reflecting its semantic complexity through virtualizing real spaces, II. having a baked-in mechanism for transferring to real-world (Goggles function), and III. embodiment of the agent and making it subject to constraints of space and physics via integrating a physics engine (Bulletphysics).






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