Search Results for "algorithms framework"

An open source software-defined Global Navigation Satellite Systems (GNSS) receiver written in C++ and based on the GNU Radio framework.

FREVO is an open-source framework developed in Java to help engineers and scientists in evolutionary design or optimization tasks. The major feature of FREVO is the componentwise decomposition and separation of the key building blocks for each optimization tasks. We identify these as the problem definition, solution representation and the optimization method. This structure enables the components to be designed separately allowing the user to easily swap and evaluate different configurations...

The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. If you are working or plan to work on research in graph deep learning, DIG enables you to develop your own methods within our extensible framework, and compare with current baseline methods using common datasets and evaluation metrics without extra efforts. It includes unified implementations of data interfaces, common algorithms, and evaluation metrics for several advanced tasks. Our goal is to enable researchers to easily implement and benchmark 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).

Learning to Learn, created by Google DeepMind, is an experimental framework that implements meta-learning—training neural networks to learn optimization strategies themselves rather than relying on manually designed algorithms like Adam or SGD. The repository provides code for training and evaluating learned optimizers that can generalize across different problem types, such as quadratic functions and image classification tasks (MNIST and CIFAR-10).

StochKit is an extensible stochastic simulation framework developed in C++ that aims to make stochastic simulation accessible to practicing biologists and chemists, while remaining open to extension via new stochastic and multiscale algorithms. StochKit is part of the StochSS project [http://www.stochss.org/], and we are relying on continued funding for sustained development. Please consider registering to show your support.

...To learn for library please try to use test examples at OpenCezeriLibrary\test\test. It is originally developed at el-cezeri laboratory of Siirt University, in order to establish generic framework of reusable components and software tools for machine vision, machine learning, AI and robotic applications. Currently, it holds following main concepts 1- Vision: It can access web cams, imaging source industrial cameras for manuel settings and advanced issues. Studies on accesing Leapmotion and Kinect is still under-development. 2- Machine learning: It uses Weka Software tool and some personel coded ML algorithms 3- CMatrix: Special matrix library called as CMatrix meaning Cezeri Maztrix Class. ...

S-Match is a semantic matching framework. S-Match takes any two tree like structures (such as database schemas, classifications, lightweight ontologies) and returns a set of correspondences between those tree nodes which semantically correspond to one another. S-Match contains implementations of the semantic matching, minimal semantic matching and structure preserving semantic matching algorithms.

Perfect to explore in the classroom: * Choose different problems, for different representations * Choose different genetic operators * Configure parameters * Visualization of the results on-line * Stop, pause or resume the evolutionary process * Explore how the genetic operators work * Save the results for future analysis

Lightweight and extensible Java framework for evolutionary (genetic) algorithms.

TraSim is a simple framework for the development of traffic algorithms, and the observation of their behaviour. It comes with an algorithm that handles traffic lights dynamically, based on the presence of cars and pedestrians. Both cars and pedestrians can be spawned randomly; however, user interaction is also supported for both entities (e.g. to control destination, crossing decisions).

Currently, all existing formal tools are designed to serve as formal verifiers, using one implementation or another. NO tool is providing a global framework to develop algorithms. Silicis is a new formal framework for designing [verification] algorithms.