MLC: Lab

MLC: Lab is the part of MLC that's most similar to a traditional academic or industrial research lab. Members pursue research directions individually or in small teams, and we hold regular group meetings for people to present research updates. We all hang out, stare at plots, complain about axes and baselines, and ideate on next steps. Due to COVID and our distributed nature, our meetings are held over Zoom. This also means that everyone has to bring their own group meeting snacks.

To keep research focused and active, we ask that those interested in joining do so with a specific research project in mind and with a committment to presenting progress updates on it. If this sounds like you, please send us an email (hello at mlcollective dot org) for specifics!

If you're interested in being part of MLC but aren't yet committed to a concrete direction, we invite you to join the reading group and the Discord, both of which are open to all and useful for talking over papers and ideas and converging on research directions. You may also wish to check out our Open Collab community events which sometimes include "Request for Plot" events where people pitch their project ideas and recruit collaborators! Finally, if you're interested in working on topics related to the work of any of the researchers listed below, feel free to contact them to see if they're up for collaboration.

Projects

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Supermasks in Superposition

Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, Ali Farhadi

Published at NeurIPS 2020

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Estimating Q(s,s’) with Deep Deterministic Dynamics Gradients

Ashley D. Edwards, Himanshu Sahni, Rosanne Liu, Jane Hung, Ankit Jain, Rui Wang, Adrien Ecoffet, Thomas Miconi, Charles Isbell, Jason Yosinski

Published at ICML 2020

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Plug and Play Language Models: a Simple Approach to Controlled Text Generation

Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, Rosanne Liu

Published at ICLR 2020

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LCA: Loss Change Allocation for Neural Network Training

Janice Lan, Rosanne Liu, Hattie Zhou, Jason Yosinski

Published at NeurIPS 2019

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Hamiltonian Neural Networks

Sam Greydanus, Misko Dzamba, and Jason Yosinski

Published at NeurIPS 2019

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Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask

Hattie Zhou, Janice Lan, Rosanne Liu, Jason Yosinski

Published at NeurIPS 2019

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Metropolis-Hastings Generative Adversarial Networks

Ryan Turner, Jane Hung, Eric Frank, Yunus Saatci, and Jason Yosinski

Published at ICML 2019

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Faster Neural Networks Straight from JPEG

Lionel Gueguen, Alex Sergeev, Ben Kadlec, Rosanne Liu, Jason Yosinski

Published at NeurIPS 2018

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An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, Jason Yosinski

Published at NeurIPS 2018

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Measuring the intrinsic dimension of objective landscapes

Chunyuan Li, Heerad Farkhoor, Rosanne Liu, Jason Yosinski

Published at ICLR 2018

Researchers

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Rosanne Liu

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Josh Roy

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Jane Hung

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Gregory Clark

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Brian Cheung

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Zach Nussbaum

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Daniel D'souza

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Yaroslav Bulatov

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Marcos Pereira

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Chirag Agarwal

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Chloe Hsu

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Jonathan Frankle

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Sebastian Ruder

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Stephanie Sher

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Sara Hooker

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Ankit Jain

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Andrea Madotto

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Thomas Miconi

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Eric Frank

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Yariv Sadan

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Sam Greydanus

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Rui Wang

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Janice Lan

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Hattie Zhou

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Xinyu Hu

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Piero Molino

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Niel Teng Hu

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Mitchell Wortsman

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Jason Yosinski