Training pipelines for Firefox Translations neural machine translation models
Обновлено 2024-12-03 12:46:31 +03:00
TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data
Обновлено 2024-11-30 15:17:22 +03:00
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
nlp
multimodal
beit
beit-3
deepnet
document-ai
foundation-models
kosmos
kosmos-1
layoutlm
layoutxlm
llm
minilm
mllm
pre-trained-model
textdiffuser
trocr
unilm
xlm-e
Обновлено 2024-11-28 01:33:05 +03:00
Example models using DeepSpeed
Обновлено 2024-11-28 01:16:09 +03:00
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
machine-learning
data-science
causal-inference
causality
treatment-effects
bayesian-networks
causal-machine-learning
causal-models
do-calculus
graphical-models
python3
Обновлено 2024-11-24 23:25:55 +03:00
Hummingbird compiles trained ML models into tensor computation for faster inference.
Обновлено 2024-11-16 00:52:33 +03:00
Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.
machine-learning
deep-learning
python
platform
research
finance
algorithmic-trading
auto-quant
fintech
investment
paper
quant
quant-dataset
quant-models
quantitative-finance
quantitative-trading
research-paper
stock-data
Обновлено 2024-11-13 06:41:06 +03:00
Subseasonal forecasting models
Обновлено 2024-11-07 02:35:52 +03:00
Sharing Updatable Models (SUM) on Blockchain
machine-learning
python
react
ai
ml
artificial-intelligence
node
economics
blockchain
ethereum
prediction-mar
prediction-market
smart-contracts
truffle
Обновлено 2024-10-31 16:38:58 +03:00
Azure Machine Learning for Visual Studio Code, previously called Visual Studio Code Tools for AI, is an extension to easily build, train, and deploy machine learning models to the cloud or the edge with Azure Machine Learning service.
Обновлено 2024-10-30 10:28:02 +03:00
Ongoing research training transformer language models at scale, including: BERT & GPT-2
Обновлено 2024-10-18 13:31:05 +03:00
Обновлено 2024-07-31 17:58:35 +03:00
This repo contains the scripts, models, and required files for the Deep Noise Suppression (DNS) Challenge.
Обновлено 2024-07-25 13:19:02 +03:00
VideoX: a collection of video cross-modal models
Обновлено 2024-06-03 05:11:25 +03:00
Code to reproduce experiments in the paper "Constrained Language Models Yield Few-Shot Semantic Parsers" (EMNLP 2021).
Обновлено 2024-05-31 20:48:22 +03:00
Fit Sparse Synthetic Control Models in Python
Обновлено 2024-03-26 21:53:01 +03:00
Enables inference and deployment of InnerEye-DeepLearning (https://github.com/microsoft/InnerEye-deeplearning) models as an async REST API on Azure
Обновлено 2024-03-21 12:48:29 +03:00
Medical Imaging Deep Learning library to train and deploy 3D segmentation models on Azure Machine Learning
Обновлено 2024-03-21 12:43:17 +03:00
The object detection solution accelerator provides a pre-packaged solution to train, deploy and monitor custom object detection models using the TensorFlow object detection API within Azure ML.
Обновлено 2024-01-24 22:16:23 +03:00
Federated Learning Utilities and Tools for Experimentation
machine-learning
pytorch
simulation
gloo
nccl
personalization
privacy-tools
transformers-models
distributed-learning
federated-learning
Обновлено 2024-01-11 22:20:09 +03:00
Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
Обновлено 2024-01-09 18:03:18 +03:00
XtremeDistil framework for distilling/compressing massive multilingual neural network models to tiny and efficient models for AI at scale
Обновлено 2023-12-20 23:40:01 +03:00
Translation quality evaluation for Firefox Translations models
Обновлено 2023-10-24 00:14:07 +03:00
Translation quality evaluation for Firefox Translations models
Обновлено 2023-10-24 00:14:07 +03:00
a CLI that provides a generic automation layer for assessing the security of ML models
Обновлено 2023-10-04 03:30:41 +03:00
This project provides an official implementation of our recent work on real-time multi-object tracking in videos. The previous works conduct object detection and tracking with two separate models so they are very slow. In contrast, we propose a one-stage solution which does detection and tracking with a single network by elegantly solving the alignment problem. The resulting approach achieves groundbreaking results in terms of both accuracy and speed: (1) it ranks first among all the trackers on the MOT challenges; (2) it is significantly faster than the previous state-of-the-arts. In addition, it scales gracefully to handle a large number of objects.
Обновлено 2023-10-04 00:42:58 +03:00
Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.
machine-learning
artificial-intelligence
causality
domain-generalization
privacy-preserving-machine-learning
Обновлено 2023-10-03 07:31:52 +03:00
A flexible framework for running experiments with PyTorch models in a simulated Federated Learning (FL) environment.
Обновлено 2023-08-11 23:02:52 +03:00
A library & tools to evaluate predictive language models.
nlp
language-model
evaluation
evaluation-toolkit
language-model-evaluation
lm-challenge
next-word-prediction
ngram-model
prediction-model
research-tool
Обновлено 2023-08-09 18:22:29 +03:00
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
azure
microsoft
machine-learning
deep-learning
devops
azureml
mlops
azuremachinelearning
mlops-environment
mlops-template
mlops-workflow
Обновлено 2023-07-28 08:11:02 +03:00