WebSearch.Net is an open-source research platform that provides uniform data source access, data modeling, feature calculation, data mining, etc.
algorithms research search visual-studio webVuvuzela 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.
vuvuzela metadata privacy messaging chat cryptography researchCARLA 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.
simulator autonomous-vehicles autonomous-driving research ai artificial-intelligence computer-vision deep-learning deep-reinforcement-learning imitation-learning self-driving-car ue4 unreal-engine-4 cross-platformBy 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.
awesome awesome-list unicorns list lists resources deeplearning deep-learning deep-neural-networks neural-network neural-networks music music-information-retrieval audio audio-processing article music-genre-classification bib machine-learning researchThis 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.
machine-learning research aiShared repository for open-sourced projects from the Google Research Language team. This is not an official Google product.
machine-learning natural-language-processing researchAcme 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.
research reinforcement-learning agentsOpenBot 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.
android education arduino research robot robotics smartphone deeplearning openbotThe 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.
react api search-engine community education research knowledge interactive curated resources mindmapsLingvo is a framework for building neural networks in Tensorflow, particularly sequence models. A list of publications using Lingvo can be found here.
nlp research translation tensorflow machine-translation speech distributed tts speech-synthesis mnist speech-recognition lm seq2seq speech-to-text gpu-computing language-model asrHabitat Lab is a modular high-level library for end-to-end development in embodied AI -- defining embodied AI tasks (e.g. navigation, instruction following, question answering), configuring embodied agents (physical form, sensors, capabilities), training these agents (via imitation or reinforcement learning, or no learning at all as in classical SLAM), and benchmarking their performance on the defined tasks using standard metrics. Habitat Lab currently uses Habitat-Sim as the core simulator, but is designed with a modular abstraction for the simulator backend to maintain compatibility over multiple simulators. For documentation refer here.
simulator research reinforcement-learning ai computer-vision deep-learning robotics deep-reinforcement-learning sim2realAccelerated deep learning R&D
infrastructure machine-learning natural-language-processing information-retrieval research reinforcement-learning computer-vision deep-learning text-classification distributed-computing image-processing pytorch image-classification metric-learning recommender-system object-detection image-segmentation reproducibility text-segmentationMlFinlab is a python package which helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools. This repo is public facing and exists for the sole purpose of providing users with an easy way to raise bugs, feature requests, and other issues.
finance machine-learning research trading investing portfolio-optimization quantitative-finance algorithmic-trading portfolio-management financial-machine-learningAirSim 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.
drones ai self-driving-car autonomous-vehicles autonomous-quadcoptor research computer-vision artificial-intelligence deeplearning deep-reinforcement-learning control-systems pixhawk cross-platform platform-independent airsim unreal-engine simulatorThis 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.
recurrent-neural-networks sequential-data time-series research rwa-model recurrent-weighted-average deep-memoryA list of computer-science related readings I'm planning on reading. Would love PR's!
reading science academia research computer-science compiler type-system concurrency parallel-computing operating-system static-analysis garbage-collectionDryad 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.
data repository science knowledge-sharing citation research analysisA 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.
awesome research awesome-list awesomeness listMany examples can be found on my Wikpedia Commons user page. You might also be interested in my Blog.
latex tex tikz mathematics science research
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