Search Results for "probabilistic neural network"

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed...

Implementation of Imagen, Google's Text-to-Image Neural Network that beats DALL-E2, in Pytorch. It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pre-trained T5 model (attention network). It also contains dynamic clipping for improved classifier-free guidance, noise level conditioning, and a memory-efficient unit design. It appears neither CLIP nor prior network...

Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2. The main goal of this project is to provide a simple but flexible framework for creating graph neural networks (GNNs). You can use Spektral for classifying the users of a social network, predicting molecular properties, generating new graphs with GANs, clustering nodes, predicting links, and any other task where data is described by graphs. Spektral implements some of the most popular layers for graph...

... Networks in detail to work with this. Brain.js performs computations using GPU and gracefully fallback to pure JavaScript when GPU is not available. Brain.js provides multiple neural network implementations as different neural nets can be trained to do different things well. Easily export and import trained models using JSON format or as a function. Host pre-trained models on your website easily.

... sensory activity, etc. NeuraNEP handles microthread scheduling, synchronization, distribution and communication. This project is a fork of SpikeOS (sourceforge.net/projects/spikeos) and represents a major update to that code base, including a scripting interface and low-level rewrite of several components. SpikeOS was oriented towards computational modeling. NeuraNEP is oriented toward neural network research.

tlearn is a backpropagation neural network simulator, written by Jeff Elman. xtlearn is a version of tlearn for the X Window System. OSXtlearn is xtlearn wrapped in a MacOSX application bundle that runs ons MacOSX 10.5 or higher and that requires XQuartz. OSXtlearn is created by Harm Brouwer.

AHANNS enables the user to create and train artificial neural networks. Training is carried out by an implementation of back propagation learning algorithm. See the documentation for details. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope

The DV-Hop, Amorphous, Centroid and APIT source code for NS2. Please reference this work as follows: A. Pineda-Briseño, R. Menchaca-Mendez, E. Chavez, G. Guzman, R. Menchaca-Mendez, R. Quintero, M. Torres, M. Moreno, and J. L. Diaz-De-Leon. "A Probabilistic Approach to Location Estimation in MANETs." Ad Hoc & Sensor Wireless Networks, vol. 28, no. 1-2, pp. 97-114, 2015.

NeMo is a high-performance spiking neural network simulator which simulates networks of Izhikevich neurons on CUDA-enabled GPUs. NeMo is a C++ class library, with additional interfaces for pure C, Python, and Matlab.

Geeks Artificial Neural Network (G.A.N.N) is an open source project that started with the philosophy of being a new more advanced A.N.N that works as a platform for other applications. In other words, G.A.N.N should be considered as a "Black Box".

Cervelletto is a neural network simulator. It uses a new neural model based on biological, neurological and psychological studies. [it's not yet completed... just give me some weeks! sorry!]

OpenDiscreteDynamicProgrammingTemplate : founds optimal constrainted parameters of a discrete controls with second order optimization template replacing Hessian with directional derivatives and backpropagation for digital filter(as neural network)

This is an implementation of the Granular Neural Network architecture defined by S. Dick, A. Tappenden, C. Badke, O. Olarewaju. It is provided for the use of the public, and the convenience of researchers who may wish to develop or use this new system.

‘intelliworm’ is a prototype simulation of a Intelligent Species of a common earth worm,which inherits human like decision making capabilities through Neural Network intelligence representation,all developed by integrating .NET with Lisp and Yacc.

iSNS is an interactive neural network simulator written in Java/Java3D. The program is intended to be used in lessons of Neural Networks. The program was developed by students as the software project at Charles University in Prague.

SNNSraster is a utility for quick ANN analysis of raster GIS maps with the use of Stuttgart Neural Network Simulator trained network files. It was developed to read and write binary raster files. SNNSraster is a project of the Geography Laboratory of the University of Siena. The code was developed by Giancarlo Macchi Jánica between 2006 and 2007. SNNSraster's fundamental objective is to improve the ability to integrate the use of artificial neural networks in GIS environments.

ROOTSNNS is a set of C++ classes which allows one to use the Stuttgart Neural Network Simulator kernel (ansi-C) within the ROOT, a data analysis package. Multiple ANNs can be built, trained, and tested, while results and ANN performance can be saved.

Pure Java full-featured multi-player gaming system Pluggable, configurable game engine Distributable, load-balancing, "world engine". flocking, Genetic Algorithms and neural networks 3D clients, ZMUD(tm) clients, regular TELNET access (TCP/UDP

GNNS - GNNS Neural Network Simulator, is both a set of libraries and an interface for creating and learning neural networks. It is aimed to support as many network typs and learning algorithms as possible. GNNS is meant to support different platforms.

Cluster Networks are a new style of neural simulation / neural network modeling, that models networks of neural populations ("clusters") that transform and transmit information using precisely-timed, graded bursts ("pulses" or "volleys") of firing.

Zeus is a small, lightweight, very fast object oriented framework for developing neural networks with an integrated shell interface.

RooCARDS is a set of C++ classes written for the ROOT analysis framework which interface ROOT to the Stuttgart Neural Network Simulator (SNNS). This interface is based on a concept originally developed by Professor Yibin Pan at the UW-Madison.

Neural Network calculation & learning library

Morgana is (will be) a free implementation of a neural network simulator.