Search Results for "evolution neural"

...In addition to neural networks used for image classification, the project also references broader AI fields such as generative adversarial networks, natural language processing, and graph neural networks.

DeepCTR-Torch is an easy-to-use, Modular and Extendible package of deep-learning-based CTR models along with lots of core components layers that can be used to build your own custom model easily.It is compatible with PyTorch.You can use any complex model with model.fit() and model.predict(). With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical...

AtomAI is a Pytorch-based package for deep and machine-learning analysis of microscopy data that doesn't require any advanced knowledge of Python or machine learning. The intended audience is domain scientists with a basic understanding of how to use NumPy and Matplotlib. It was developed by Maxim Ziatdinov at Oak Ridge National Lab. The purpose of the AtomAI is to provide an environment that bridges the instrument-specific libraries and general physical analysis by enabling the seamless...

...It includes surveys, benchmark studies, and cutting-edge research that examine topics such as knowledge graph-guided prompting, retrieval-augmented generation, reasoning over structured data, and hybrid architectures combining symbolic and neural systems. By gathering these papers into a single organized repository, the project helps researchers quickly discover relevant literature and track the evolution of the field.

... - Testing: Accuracy calculation, error graphs, confusion matrix, misclassified sample montage. - Camera Dataset Builder: Capture and label images live via webcam. - Model Insight: Animated weight & bias evolution, interactive heatmaps. - Save & Load: Export models and configurations for reuse and sharing. 💸 Donateware Model MLP Network Configurator is distributed as donateware: - The program is fully functional

OculiX is the evolution of SikuliX, actively maintained with the full agreement of its original creator RaiMan. Automate any desktop application using image recognition (OpenCV) and OCR (Tesseract + PaddleOCR). No access to source code or DOM required — if you can see it, you can automate it. Key features: - Guided step-by-step recorder with live code preview - Image recognition via OpenCV 4.10 - Dual OCR: Tesseract (built-in) + PaddleOCR (neural, high precision) - Local and remote automation via integrated VNC - SSH tunnels via embedded JSch - Cross-platform: Windows, macOS (Apple Silicon M1-M4), Linux - Scripting: Jython, JRuby, Java, PowerShell, AppleScript - Java 17 recommended (Java 8+ supported) - Full CI/CD with automated builds for all platforms Used worldwide for test automation, RPA, and visual regression testing. ...

...It provides a graphical user interface (GUI) and a platform which simplifies integration of new algorithms as "Modules". Implemented Modules Evolutionary Algorithms: - CMA-ES - (1+1)-ES - Differential Evolution Deterministic optimization algorithm: - SIMPLEX Learning: - a simple Artificial Neural Net Optimization problems: - test functions - interface for executing other programs (solvers) - parallel execution of problems - distributed execution of problems via socket connection between computers Others: - data storage - data analyser and viewer

popt4jlib is an open-source parallel optimization library for the Java programming language supporting both shared memory and distributed message passing models. Implements a number of meta-heuristic algorithms for Non-Linear Programming, including Genetic Algorithms, Differential Evolution, Evolutionary Algorithms, Simulated Annealing, Particle Swarm Optimization, Firefly Algorithm, Monte-Carlo Search, Local Search algorithms, Gradient-Descent-based algorithms, as well as some well-known...

This project provides a set of Python tools for creating various kinds of neural networks, which can also be powered by genetic algorithms using grammatical evolution. MLP, backpropagation, recurrent, sparse, and skip-layer networks are supported.

...They can attack and repair other organisms. They can select mates, and they can gather and distribute food and material. This behavior is controlled by a genetically evolved neural net augmented with online back propagation learning. The back propagation learning uses a reward vector and plasticity matrix that is evolved as part of the genome. Long story short, the AI is pretty frickin' sophisticated. Players can take control of organisms, trade resources and organisms in a market, and aid evolution by selective breeding.

Library for creation of artificial neural networks in C#. Library supports Hopfield nets, Kohonen nets, feedforward networks, several learning techniques (differential evolution, back propagation, quick propagation, resilient propagation etc.)

The Mars Rover Simulator project is based on the evolutionary robotics paradigm where an artificial agent acquires its skills through the process of artificial evolution. This simulator can be useful to evolve neural network controllers for the rover

VitoshaTrade is Distributed Artificial Neural Network trained by Differential Evolution for prediction of Forex. Project development is in Sofia, Bulgaria. Vitosha is a mountain massif, on the outskirts of Sofia, the capital of Bulgaria.

IMPORTANT UPDATE! This project has migrated to https://github.com/polyworld/polyworld . This SourceForge repository is now obsolete. Polyworld is one of the earliest and most sophisticated artificial worlds developed to study Artificial Life and Artificial Intelligence, using computational genetics, physiology, metabolism, neural networks, learning, vision, and behavior.

Simulation of plants, prey, and predators, using sense->think->action paradigm with neural net processing. Reproduction and mutation implemented.

We experiment with Evolution of Artifical Neural Networks, combining the two fields of Evolutionary Computation and ANNs. Our methods are applied to a variety of interesting problems. To learn more, click on "Home Page", "Mail", or "Files".

Neural networks evolution (genetic algorithm) written in Java. Now neural networks try to playing Go, Tic-Tac-Toe.

ANJI (Another NEAT Java Implementation) Built on top of existing OpenSource projects, ANJI is an implementation of NEAT (Neuro-Evolution of Augmenting Topologies), an algorithm for evolving artificial neural networks.

Java based system for creating Bio-Musical Worlds. Investigating the use of AI (flocking,A-Life, Genetic Algorithms, Neural Networks) and other bio-modelling techniques for the creation and evolution of musical landscapes.

This is a artificial life project. the goal is to simulate the evolution of micro-organisms. It uses neural networks to simulate the brain of each of those micro-organisms. It's written in JAVA, and uses the Joone librarie

An OpenGL simulation of artificial life and artificial evolution using neural networks and genetic algorithms based on Larry Yaeger's PolyWorld.