Research
Discover an index of datasets, SDKs, APIs and open-source tools developed by Microsoft researchers and shared with the global academic community below. These experimental technologies—available through Azure AI Foundry Labs (opens in new tab)—offer a glimpse into the future of AI innovation.
As part of this release, Navana Tech and Microsoft Research India are open-sourcing 1648 hours of validated Odia speech dataset and a baseline model for Odia speech recognition. The speech dataset consists of recordings in…
We present a new method LiST for efficient fine-tuning of large pre-trained language models (PLMs) in few-shot learning settings. LiST significantly improves over recent methods that adopt prompt fine-tuning using two key techniques. The first…
LITMUS Predictor provides support for simulating performance in ~100 languages given training observations of the desired task-model. Each training observation specifies the finetuning-datasize + test-performance in different languages. Further, the tool provides support for constructing…
DistIR is an intermediate representation (IR) and associated set of tools for optimizing distributed machine learning computations (both training and inference). An IR is a format for representing programs used by compilers and software analysis…
Modulo allows optimal selection of vehicles for effective drive-by sensing.
A number of PyTorch modules (neural network components) that are broadly (re)usable. The goal of this release is to provide a library to machine learning researchers to use and advance the state-of-the-art in the area.
This PyTorch package implements Taming Sparsely Activated Transformer with Stochastic Experts.
This PyTorch package implements Very Deep Transformers for Neural Machine Translation, to stabilize the large scale language model and neural machine translation training, as described in: Very deep transformers for neural machine translation
This repository contains necessary data associated with the Analyzing the Effects of Reasoning Types on Cross-Lingual Transfer Performance (opens in new tab), published in EMNLP 2021 Multilingual Representation Learning workshop (opens in new tab). This data is…
This is the implementation of the paper Meta Self-training for Few-shot Neural Sequence Labeling. MetaST is short for meta-learning for self-training.