ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Обновлено 2024-11-06 23:00:04 +03:00
The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
Обновлено 2024-10-28 14:18:34 +03:00
Hummingbird compiles trained ML models into tensor computation for faster inference.
Обновлено 2024-10-24 19:53:48 +03:00
DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.
Обновлено 2024-09-04 00:17:43 +03:00
A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.
Обновлено 2024-07-31 00:16:53 +03:00
CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.
Обновлено 2023-11-16 20:25:07 +03:00
Firefox Translations is a webextension that enables client side translations for web browsers.
Обновлено 2023-09-04 12:30:18 +03:00
Running the most popular deep learning frameworks on Azure Batch AI
Обновлено 2023-06-12 22:32:13 +03:00
View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition
Обновлено 2023-06-12 21:55:25 +03:00
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
Обновлено 2022-09-23 17:06:50 +03:00
O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis
Обновлено 2022-08-30 12:45:51 +03:00
A PyTorch Graph Neural Network Library
Обновлено 2022-02-01 20:31:29 +03:00
Synthetic exterior acoustic scattering data and sample parsing code.
Обновлено 2020-02-05 21:31:15 +03:00
Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.
Обновлено 2019-07-25 06:53:28 +03:00
Tutorials on running distributed deep learning on Batch AI
Обновлено 2018-12-18 14:53:25 +03:00