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ml-equivariant-neural-rendering

ml-evs.github.io

Personal website for Matthew Evans.

ml-for-beginners

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

ml-git

ML-Git is a tool which provides a Distributed Version Control system to enable efficient dataset management. Like its name emphasizes, it is meant to be like git in mindset, concept and workflows. ML-Git enables the following operations. Manage a repository of different datasets, labels and models. Versioning immutable versions of models, labels and documents. Distribute these ML artifacts between members of a team or across organizations. Apply the right data governance and security models to their artifacts.

ml-gsn

ml-hypersim

Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding

ml-interspeech2022-phi_rtn

Repository accompanying the Interspeech 2022 publication titled "Space-Efficient Representation of Entity-centric Query Language Models" by Van Gysel et al.

ml-knowledge-conflicts

Entity-Based Knowledge Conflicts in Question Answering. Code repo for EMNLP2021 paper: https://aclanthology.org/2021.emnlp-main.565/

ml-lessons

Deep Learning for medical imaging

ml-metadata

For recording and retrieving metadata associated with ML developer and data scientist workflows.

ml-mkqa

We introduce MKQA, an open-domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages (260k question-answer pairs in total). The goal of this dataset is to provide a challenging benchmark for question answering quality across a wide set of languages. Please refer to our paper for details, MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering

ml-multiple-futures-prediction

ml-ncg

ml-probabilistic-attention

ml-qrecc

Open-Domain Question Answering Goes Conversational via Question Rewriting

ml-quant

Research publication code for "Least Squares Binary Quantization of Neural Networks"

ml-shuffling-amplification

ml-stuttering-events-dataset

ml-testing-accelerators

Testing framework for Deep Learning models (Tensorflow and PyTorch) on Google Cloud hardware accelerators (TPU and GPU)

ml-transcript-translation-consistency-ratings

ml-tree-dst

ml-unit-test-models

Python scripts to generate unit level simple models with Tensoflow and Pytorch

ml-uwac

mlctl

mlctl is the control plane for MLOps. It provides a CLI and a Python SDK for supporting key operations related to MLOps, such as "model training", "model hosting" etc.

mlir-www

mlops-on-gcp

mlsh

Code for the paper "Meta-Learning Shared Hierarchies"

mly

Machine Learning executor on steroid for golang

mm

The Core Flight System (cFS) Memory Manager (MM) application.

mm113spring2021