Search Results for "algorithms framework"
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Showing 140 open source projects for "algorithms framework"
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OpenSpiel
Environments and algorithms for research in general reinforcement
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games, as well as traditional multiagent environments such as (partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel also includes tools to... -
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FATE
An industrial grade federated learning framework
FATE (Federated AI Technology Enabler) is the world's first industrial grade federated learning open source framework to enable enterprises and institutions to collaborate on data while protecting data security and privacy. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). Supporting various federated learning scenarios, FATE now provides a host of federated learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning. ... -
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DomainBed
DomainBed is a suite to test domain generalization algorithms
DomainBed is a PyTorch-based research suite created by Facebook Research for benchmarking and evaluating domain generalization algorithms. It provides a unified framework for comparing methods that aim to train models capable of performing well across unseen domains, as introduced in the paper In Search of Lost Domain Generalization. The library includes a wide range of well-known domain generalization algorithms, from classical baselines such as Empirical Risk Minimization (ERM) and Invariant Risk Minimization (IRM) to more advanced techniques like Domain Adversarial Neural Networks (DANN), Adaptive Risk Minimization (ARM), and Invariance Principle Meets Information Bottleneck (IB-ERM/IB-IRM). ... -
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Python Outlier Detection
A Python toolbox for scalable outlier detection
...PyOD contains multiple models that also exist in scikit-learn. It is possible to train and predict with a large number of detection models in PyOD by leveraging SUOD framework. A benchmark is supplied for select algorithms to provide an overview of the implemented models. In total, 17 benchmark datasets are used for comparison, which can be downloaded at ODDS. -
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CryptoSwift
Collection of standard and secure cryptographic algorithms
The master branch follows the latest currently released version of Swift. If you need an earlier version for an older version of Swift, you can specify its version in your Podfile or use the code on the branch for that version. Older branches are unsupported. Swift Package Manager uses debug configuration for debug Xcode build, that may result in significant (up to x10000) worse performance. Performance characteristic is different in Release build. XCFrameworks require Xcode 11 or later and... -
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Evolutionary Computation Framework
C++ framework for application of any type of evolutionary computation.
ECF is a framework intended for application of any type of evolutionary computation (GA/GP, DE, Clonalg, ES, PSO, ABC, GAn, local search...). It offers simplicity for the end-user (parameterless usage, tutorial) and customization for experienced EC practicioners. -
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DualPipe
A bidirectional pipeline parallelism algorithm
DualPipe is a bidirectional pipeline parallelism algorithm open-sourced by DeepSeek, introduced in their DeepSeek-V3 technical framework. The main goal of DualPipe is to maximize overlap between computation and communication phases during distributed training, thus reducing idle GPU time (i.e. “pipeline bubbles”) and improving cluster efficiency. Traditional pipeline parallelism methods (e.g. 1F1B or staggered pipelining) leave gaps because forward and backward phases can’t fully overlap... -
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This is the official repository for the CUSTOMHyS-Qt software. CUSTOMHyS-Qt is a GUI for the CUSTOMHyS framework, which is an interactive tool for customysing heuristic-based algorithms. The CUSTOMHyS-Qt is written in Python and uses the PyQt5 library for the GUI. Further references about the backend can be found in the CUSTOMHyS repository.
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MOA - Massive Online Analysis
Big Data Stream Analytics Framework.
A framework for learning from a continuous supply of examples, a data stream. Includes classification, regression, clustering, outlier detection and recommender systems. Related to the WEKA project, also written in Java, while scaling to adaptive large scale machine learning. -
Total Network Visibility for Network Engineers and IT ManagersThis means every device on your network, and every interface on every device is automatically analyzed for performance, errors, QoS, and configuration.
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GNSS-SDR
An open source software-defined GNSS receiver
An open source software-defined Global Navigation Satellite Systems (GNSS) receiver written in C++ and based on the GNU Radio framework. -
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CloudI is an open-source private cloud computing framework for efficient, secure, and internal data processing. CloudI provides scaling for previously unscalable source code with efficient fault-tolerant execution of ATS, C/C++, Erlang/Elixir, Go, Haskell, Java, JavaScript/node.js, OCaml, Perl, PHP, Python, Ruby, or Rust services. The bare essentials for efficient fault-tolerant processing on a cloud!
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YACS (Yet Another Constraint Solver) is a Java library and an object-oriented framework for constraint solvers. It supports propagating and solving of constraint satisfaction problems with finite and infinite domains (discrete values and real intervals).
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FRODO 2
Open-Source Framework for Distributed Constraint Optimization (DCOP)
FRODO is a Java platform to solve Distributed Constraint Satisfaction Problems (DisCSPs) and Optimization Problems (DCOPs). It provides implementations for a variety of algorithms, including DPOP (and its variants), ADOPT, SynchBB, DSA... -
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DifferenceKit
A fast and flexible O(n) difference algorithm framework
A fast and flexible O(n) difference algorithm framework for Swift collection. The algorithm is optimized based on the Paul Heckel’s algorithm. This is a diffing algorithm developed for Carbon, works stand alone. The algorithm optimized based on the Paul Heckel’s algorithm. See also his paper A technique for isolating differences between files released in 1978. It allows all kind of diffs to be calculated in linear time O(n). RxDataSources and IGListKit are also implemented based on his... -
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Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. ... -
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The Practice of Parallel Programming
A book on parallel programming and a C++ library
An online edition of the book "The Practice of Parallel Programming" and examples from it. The examples include a parallel programming framework of production quality, that has been used in another project, Triceps. -
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frontend-interview
The strongest front-end interview guide in the universe
This is a review summary project about preparing for front-end interviews that I have summarized, and the project is updated from time to time. This is not only a strategy for job interviews, but also a collection of front-end ers to examine themselves and achieve breakthroughs. I hope that through this guide, everyone can open up their own lines of Ren and Du, and go further on the road to the front. This warehouse uses a large number of diagrams to convey knowledge. The so-called picture... -
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Coach
Enables easy experimentation with state of the art algorithms
...It exposes a set of easy-to-use APIs for experimenting with new RL algorithms and allows simple integration of new environments to solve. Coach collects statistics from the training process and supports advanced visualization techniques for debugging the agent being trained. Coach supports many state-of-the-art reinforcement learning algorithms, which are separated into three main classes - value optimization, policy optimization, and imitation learning. -
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Dopamine
Framework for prototyping of reinforcement learning algorithms
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research). This first version focuses on supporting the state-of-the-art, single-GPU Rainbow agent (Hessel et al., 2018) applied to Atari 2600 game-playing (Bellemare et al., 2013). -
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Active Learning
Framework and examples for active learning with machine learning model
Active Learning is a Python-based research framework developed by Google for experimenting with and benchmarking various active learning algorithms. It provides modular tools for running reproducible experiments across different datasets, sampling strategies, and machine learning models. The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. ... -
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Code Catalog in Python
Algorithms and data structures for review for coding interview
code-catalog-python serves as a grab-bag of small, readable Python examples that illustrate common algorithms, data structures, and utility patterns. Each snippet aims to be self-contained and easy to study, with clear inputs, outputs, and the essential logic on display. The catalog format lets you scan for an example, copy it, and adapt it to your use case without wading through a large framework. It favors clarity over micro-optimizations so learners can grasp the idea before worrying about edge performance. ... -
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Physhun is a Java framework for building and executing finite state machines in J2SE and J2EE environments. State models are defined as collections of Spring beans, and do not use proprietary modeling languages. The framework is simple and flexible.
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TEESer
Tool for Engineering Emergent Selforganizing bEhavioR
...Designers can continue to use their domain tools such as favorable simulator and by pluging it into the framework can take advantage of distributed simulations in the grid, various analyzers, optimizers (particle swarm optimization, genetic algorithms, ...). See Wiki pages for more information. -
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FastFlow: programming multi-core
Pattern-based multi/many-core parallel programming framework
FastFlow is a C/C++ programming framework supporting the development of pattern-based parallel programs on multi/many-core, GPUs and distributed platforms. FastFlow run-time is built upon non-blocking threads and lock-free queues. Thanks to its very efficient CAS-free communication/synchronization support (e.g. few clock cycles core-to-core latency), FastFlow effectively supports the exploitation of fine grain parallelism, e.g. parallel codes managing very high frequency streams on... -
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FUMOLA - Functional Mock-up Laboratory
An FMI-based co-simulation framework.
FUMOLA is a co-simulation framework specifically designed to support the features offered by the FMI specification. It provides a flexible platform that allows to configure and execute co-simulation setups in an easy way. FUMOLA is developed on top of the Ptolemy II framework (https://ptolemy.eecs.berkeley.edu) and the FMI++ library (http://fmipp.sourceforge.net). The online documentation of FUMOLA is available here: https://sourceforge.net/p/fumola/wiki/Home/
