Search Results for "multi-threaded" - Page 5
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Showing 137 open source projects for "multi-threaded"
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AdaNet
Fast and flexible AutoML with learning guarantees
...At each iteration, it measures the ensemble loss for each candidate, and selects the best one to move onto the next iteration. Adaptive neural architecture search and ensemble learning in a single train call. Regression, binary and multi-class classification, and multi-head task support. A tf.estimator.Estimator API for training, evaluation, prediction, and serving models. -
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Tensor2Tensor
Library of deep learning models and datasets
Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and... -
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Neuroph is lightweight Java Neural Network Framework which can be used to develop common neural network architectures. Small number of basic classes which correspond to basic NN concepts, and GUI editor makes it easy to learn and use.
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Image Super-Resolution (ISR)
Super-scale your images and run experiments with Residual Dense
The goal of this project is to upscale and improve the quality of low-resolution images. This project contains Keras implementations of different Residual Dense Networks for Single Image Super-Resolution (ISR) as well as scripts to train these networks using content and adversarial loss components. Docker scripts and Google Colab notebooks are available to carry training and prediction. Also, we provide scripts to facilitate training on the cloud with AWS and Nvidia-docker with only a few... -
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Torchreid
Deep learning person re-identification in PyTorch
Torchreid is a library for deep-learning person re-identification, written in PyTorch and developed for our ICCV’19 project, Omni-Scale Feature Learning for Person Re-Identification. In "deep-person-reid/scripts/", we provide a unified interface to train and test a model. See "scripts/main.py" and "scripts/default_config.py" for more details. The folder "configs/" contains some predefined configs which you can use as a starting point. The code will automatically (download and) load the... -
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midipiano chung is a free open source standalone virtual midi acoustic piano synthesizer /expander with samples based sounds and originals dsps effects, connected to the choozen virtual midi input (up to 3 if any) and midiout(thru) ports of your computer.It is easily extensible by adding or modifying sound files (mp3,wav) in the /sounds/ folder.Works well as output for midi_chung player and midirec_chung recorder or an external usb midi master keyboard. It is written in compiled freebasic...
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Coach
Enables easy experimentation with state of the art algorithms
Coach is a python framework that models the interaction between an agent and an environment in a modular way. With Coach, it is possible to model an agent by combining various building blocks, and training the agent on multiple environments. The available environments allow testing the agent in different fields such as robotics, autonomous driving, games and more. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms and allows simple integration of new environments... -
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Rainbow
Rainbow: Combining Improvements in Deep Reinforcement Learning
Combining improvements in deep reinforcement learning. Results and pretrained models can be found in the releases. Data-efficient Rainbow can be run using several options (note that the "unbounded" memory is implemented here in practice by manually setting the memory capacity to be the same as the maximum number of timesteps). -
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Tensorpack
A Neural Net Training Interface on TensorFlow, with focus on speed
...On common CNNs, it runs training 1.2~5x faster than the equivalent Keras code. Your training can probably gets faster if written with Tensorpack. Scalable data-parallel multi-GPU / distributed training strategy is off-the-shelf to use. Squeeze the best data loading performance of Python with tensorpack.dataflow. Symbolic programming (e.g. tf.data) does not offer the data processing flexibility needed in research. Tensorpack squeezes the most performance out of pure Python with various auto parallelization strategies. ... -
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DeepTraffic
DeepTraffic is a deep reinforcement learning competition
DeepTraffic is a deep reinforcement learning simulation designed to teach and evaluate autonomous driving algorithms in a dense highway environment. The system presents a simulated multi-lane highway where an AI-controlled vehicle must navigate traffic while maximizing speed and avoiding collisions. Participants design neural network policies that determine the vehicle’s actions, such as accelerating, decelerating, changing lanes, or maintaining speed. The project was created as part of an educational competition associated with MIT’s deep learning courses, encouraging students and researchers to experiment with reinforcement learning techniques. ... -
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Deepvoice3_pytorch
PyTorch implementation of convolutional neural networks
An open source implementation of Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning. -
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DeepLearn
Implementation of research papers on Deep Learning+ NLP+ CV in Python
Welcome to DeepLearn. This repository contains an implementation of the following research papers on NLP, CV, ML, and deep learning. The required dependencies are mentioned in requirement.txt. I will also use dl-text modules for preparing the datasets. If you haven't use it, please do have a quick look at it. CV, transfer learning, representation learning. -
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DIGITS
Deep Learning GPU training system
...DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. DIGITS is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging. DIGITS is available as a free download to the members of the NVIDIA Developer Program. ... -
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PyTorch Book
PyTorch tutorials and fun projects including neural talk
This is the corresponding code for the book "The Deep Learning Framework PyTorch: Getting Started and Practical", but it can also be used as a standalone PyTorch Getting Started Guide and Tutorial. The current version of the code is based on pytorch 1.0.1, if you want to use an older version please git checkout v0.4or git checkout v0.3. Legacy code has better python2/python3 compatibility, CPU/GPU compatibility test. The new version of the code has not been fully tested, it has been tested... -
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Lip Reading
Cross Audio-Visual Recognition using 3D Architectures
...Audio-visual recognition (AVR) has been considered as a solution for speech recognition tasks when the audio is corrupted, as well as a visual recognition method used for speaker verification in multi-speaker scenarios. The approach of AVR systems is to leverage the extracted information from one modality to improve the recognition ability of the other modality by complementing the missing information. The essential problem is to find the correspondence between the audio and visual streams, which is the goal of this work. We proposed the utilization of a coupled 3D Convolutional Neural Network (CNN) architecture that can map both modalities into a representation space to evaluate the correspondence of audio-visual streams using the learned multimodal features. -
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Accord.NET Framework
Scientific computing, machine learning and computer vision for .NET
The Accord.NET Framework provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. The project is compatible with the .NET Framework. NET Standard, .NET Core, and Mono. -
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mctc4bmi
Matrix and Tensor Completion for Background Model Initialization
MCTC4BMI (Multimodal Compressed Sensing and Tensor Decomposition for Brain-Machine Interfaces) is a MATLAB toolbox designed to process and analyze EEG data. It applies compressed sensing and tensor decomposition techniques to improve brain-machine interface (BMI) performance. -
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PADIC
A multilingual Parallel Arabic DIalectal Corpus
PADIC (Parallel Arabic DIalectal Corpus) is a multi-dialectal corpus built in the framework of the National Research Project "TORJMAN", led by Scientific and Technical Research Center for the Development of Arabic Language and funded by the Algerian Ministry of Higher Education and Scientific Research. PADIC is composed of 6 dialects: two Algerian dialects (Algiers and Annaba cities), Palestinian, Syrian, Tunisian, Moroccan) and MSA. -
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fantail-mlkit
The fantail machine learning toolkit (Moved)
Moved to https://github.com/quansun/fantail-ml -
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nunn
This is an implementation of a machine learning library in C++17
nunn is a collection of ML algorithms and related examples written in modern C++17. -
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JCLAL
A Java Class Library for Active Learning
JCLAL is a general purpose framework developed in Java for the active learning research area. JCLAL framework is open source software and it is distributed under the GNU general public license. It is constructed with a high-level software environment, with a strong object oriented design and use of design patterns, which allow to the developers reuse, modify and extend the framework. If you want to refer to JCLAL in a publication, please cite the following JMLR paper. The full citation... -
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This site contains four packages of Mass and mass-based density estimation. 1. The first package is about the basic mass estimation (including one-dimensional mass estimation and Half-Space Tree based multi-dimensional mass estimation). This packages contains the necessary codes to run on MATLAB. 2. The second package includes source and object files of DEMass-DBSCAN to be used with the WEKA system. 3. The third package DEMassBayes includes the source and object files of a Bayesian classifier using DEMass. DEMassBayes.7z has jar file to be used with WEKA and a readme file listing parameters used. ...
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Chordalysis
Log-linear analysis (data modelling) for high-dimensional data
===== Project moved to https://github.com/fpetitjean/Chordalysis ===== Log-linear analysis is the statistical method used to capture multi-way relationships between variables. However, due to its exponential nature, previous approaches did not allow scale-up to more than a dozen variables. We present here Chordalysis, a log-linear analysis method for big data. Chordalysis exploits recent discoveries in graph theory by representing complex models as compositions of triangular structures, also known as chordal graphs. ... -
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BudgetedSVM
BudgetedSVM: A C++ Toolbox for Large-scale, Non-linear Classification
We present BudgetedSVM, a C++ toolbox containing highly optimized implementations of three recently proposed algorithms for scalable training of Support Vector Machine (SVM) approximators: Adaptive Multi-hyperplane Machines (AMM), Budgeted Stochastic Gradient Descent (BSGD), and Low-rank Linearization SVM (LLSVM). BudgetedSVM trains models with accuracy comparable to LibSVM in time comparable to LibLinear, as it allows solving highly non-linear classi fication problems with millions of high-dimensional examples within minutes on a regular personal computer. ... -
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StabLe
An algorithm for learning stable graphical models from data
...Stable random variables are motivated by the central limit theorem for densities with (potentially) unbounded variance and can be thought of as natural generalizations of the Gaussian distribution to skewed and heavy-tailed phenomenon. SG models are multi-variate stable distributions that represent Bayesian networks whose edges encode linear dependencies amongst random variables. A preprint version of the manuscript describing stable graphical models is available at http://arxiv.org/abs/1404.4351.
