Search Results for "python neural"
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Showing 32 open source projects for "python neural"
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DeepXDE
A library for scientific machine learning & physics-informed learning
DeepXDE is a library for scientific machine learning and physics-informed learning. DeepXDE includes the following algorithms. Physics-informed neural network (PINN). Solving different problems. Solving forward/inverse ordinary/partial differential equations (ODEs/PDEs) [SIAM Rev.] Solving forward/inverse integro-differential equations (IDEs) [SIAM Rev.] fPINN: solving forward/inverse fractional PDEs (fPDEs) [SIAM J. Sci. Comput.] NN-arbitrary polynomial chaos (NN-aPC): solving... -
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PyTorch Geometric Temporal
Spatiotemporal Signal Processing with Neural Machine Learning Models
The library consists of various dynamic and temporal geometric deep learning, embedding, and Spatio-temporal regression methods from a variety of published research papers. Moreover, it comes with an easy-to-use dataset loader, train-test splitter and temporal snaphot iterator for dynamic and temporal graphs. The framework naturally provides GPU support. It also comes with a number of benchmark datasets from the epidemiological forecasting, sharing economy, energy production and web traffic... -
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TorchQuantum
A PyTorch-based framework for Quantum Classical Simulation
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers. Researchers on quantum algorithm design, parameterized quantum circuit training, quantum optimal control, quantum machine learning, and quantum neural networks. Dynamic computation graph, automatic gradient computation, fast GPU support, batch model terrorized processing. -
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FairChem
FAIR Chemistry's library of machine learning methods for chemistry
FAIRChem is a unified library for machine learning in chemistry and materials, consolidating data, pretrained models, demos, and application code into a single, versioned toolkit. Version 2 modernizes the stack with a cleaner core package and breaking changes relative to V1, focusing on simpler installs and a stable API surface for production and research. The centerpiece models (e.g., UMA variants) plug directly into the ASE ecosystem via a FAIRChem calculator, so users can run relaxations,... -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
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 an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of... -
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Conscious Artificial Intelligence
It's possible for machines to become self-aware.
This project is a quest for conscious artificial intelligence. A number of prototypes will be developed as the project progresses. This project has 2 subprojects: Object Pascal based CAI NEURAL API - https://github.com/joaopauloschuler/neural-api Python based K-CAI NEURAL API - https://github.com/joaopauloschuler/k-neural-api A video from the first prototype has been made: http://www.youtube.com/watch?v=qH-IQgYy9zg Above video shows a popperian agent collecting mining ore from 3 mining sites and bringing to the base. ... -
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GNNePCSAFT
Smart Thermodynamic Modeling with Graph Neural Networks
Our project harnesses the power of Graph Neural Network (GNN) to estimate pure-component parameters of the state-of-the-art Equation of State, PC-SAFT. We aim to empower users to leverage this robust equation without the need for prior experimental data, revolutionizing the calculation of thermodynamic properties and enhancing process simulations. FeOS is used for the PC-SAFT calculations. The estimated parameters can be used in DWSIM and Aspen HYSYS process simulators. -
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Paddle Quantum
Paddle Quantum
Paddle Quantum (量桨) is the world's first cloud-integrated quantum machine learning platform based on Baidu PaddlePaddle. It supports the building and training of quantum neural networks, making PaddlePaddle the first deep-learning framework in China. Paddle Quantum is feature-rich and easy to use. It provides comprehensive API documentation and tutorials help users get started right away. Paddle Quantum aims at establishing a bridge between artificial intelligence (AI) and quantum computing... -
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MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
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Argos Translate
Open-source offline translation library written in Python
Argos Translate uses OpenNMT for translations and can be used as either a Python library, command-line, or GUI application. Argos Translate supports installing language model packages which are zip archives with a ".argosmodel" extension containing the data needed for translation. LibreTranslate is an API and web-app built on top of Argos Translate. Argos Translate also manages automatically pivoting through intermediate languages to translate between languages that don't have a direct... -
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Neural Networks Collection
Neural Networks Collection
This project implements in C++ a bunch of known Neural Networks. So far the project implements: LVQ in several variants, SOM in several variants, Hopfield network and Perceptron. Other neural network types are planned, but not implemented yet. The project can run in two modes: command line tool and Python 7.2 extension. Currently, Python version appears more functional, as it allows easy interaction with algorithms developed by other people. -
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UnsupervisedMT
Phrase-Based & Neural Unsupervised Machine Translation
Unsupervised Machine Translation is a research repository that implements both phrase-based SMT and neural MT approaches for translation without parallel corpora. The neural component supports multiple architectures—seq2seq, biLSTM with attention, and Transformer—and allows extensive parameter sharing across languages to improve data efficiency. Training relies on denoising auto-encoding and back-translation, with on-the-fly, multithreaded generation of synthetic parallel data to continually... -
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CRP - Chemical Reaction Prediction
Predicting Organic Reactions using Neural Networks.
The intend is to solve the forward-reaction prediction problem, where the reactants are known and the interest is in generating the reaction products using Deep learning. This Graphical User Interface takes simplified molecular-input line-entry system (SMILES) as an input and generates the product SMILE & molecule. Beam search is used in Version 2, to generate top 5 predictions. Maximum input length for the model is 15 (excluding spaces). -
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Moose
Multiscale Neuroscience and Systems Biology Simulator
Moose is the core of a modern software platform for the simulation of neural systems ranging from subcellular components and biochemical reactions to complex models of single neurons, large networks, and systems-level processes. We have moved Github.com. This should be your source for the latest version of the code. -
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LWPR
Locally Weighted Projection Regression (LWPR)
Locally Weighted Projection Regression (LWPR) is a fully incremental, online algorithm for non-linear function approximation in high dimensional spaces, capable of handling redundant and irrelevant input dimensions. At its core, it uses locally linear models, spanned by a small number of univariate regressions in selected directions in input space. A locally weighted variant of Partial Least Squares (PLS) is employed for doing the dimensionality reduction. Please cite: [1] Sethu... -
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mullpy
Multilabel-learning library built on python
Mullpy is a machine-learning library that mainly aim to solve multi-label problems. It is classifier independent, has many ensemble capabilities (diversity methods like bagging, random subspaces, etc.) and automated results presentation (Excel, images as ROC or class-separated info, etc.). It is fully configurable. At the moment supports Neural Networks and classifiers defined in files. It is working on python3.3. -
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nevesim
NEVESIM is an event-driven neural simulation tool.
NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events... -
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Topographica
Simulation software for brain modelling
Topographica is a neural modeling package developed in a Human Brain Project grant from NIH. Topographica helps neuroscientists and computational scientists simulate and understand how topographic maps contribute to brain function. Topographica is now primarily maintained at github.com; see https://github.com/ioam/topographica for recent updates and releases. -
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UPDATE: The latest version of NNScore1 can be found here: http://git.durrantlab.com/jdurrant/nnscore1 The latest version of NNScore2 can be found here: http://git.durrantlab.com/jdurrant/nnscore2 ================ NNScore is a scoring function for characterizing the potency of receptor-ligand complexes. It is based on neural networks, computational models that simulate the microscopic organization of the brain.
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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.
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This is a c-library that provides tools for advanced analysis of electrophysiological data. It features denoising, unsupervised classification, time-frequency analysis, phase-space analysis, neural networks, time-warping and more.
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PCSIM is a tool for distributed simulation of heterogeneous networks composed of different model neurons and synapses. The development of PCSIM was supported by the FACETS EU project.
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ptsa (pronounced pizza) stands for Python Time Series Analysis. A python module specifically designed with neural data in mind (EEG, MEG, fMRI, etc...), but applicable to almost any type of time series.
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A three-step approach towards experimental brain-computer-interfaces, based on the OCZ nia device for EEG-data acquisition and artificial neural networks for signal-interpretation.
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Brainlab is a Python toolkit to aid in the design, simulation, and analysis of spiking neural networks with the NeoCortical Simulator (NCS).
