49 min
Rosanne Liu — Conducting Fundamental ML Research as a Nonprofit Gradient Dissent: Conversations on AI
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- Technology
How Rosanne is working to democratize AI research and improve diversity and fairness in the field through starting a non-profit after being a founding member of Uber AI Labs, doing lots of amazing research, and publishing papers at top conferences.
Rosanne is a machine learning researcher, and co-founder of ML Collective, a nonprofit organization for open collaboration and mentorship. Before that, she was a founding member of Uber AI. She has published research at NeurIPS, ICLR, ICML, Science, and other top venues. While at school she used neural networks to help discover novel materials and to optimize fuel efficiency in hybrid vehicles.
ML Collective: http://mlcollective.org/
Controlling Text Generation with Plug and Play Language Models: https://eng.uber.com/pplm/
LCA: Loss Change Allocation for Neural Network Training: https://eng.uber.com/research/lca-loss-change-allocation-for-neural-network-training/
Topics covered
0:00 Sneak peek, Intro
1:53 The origin of ML Collective
5:31 Why a non-profit and who is MLC for?
14:30 LCA, Loss Change Allocation
18:20 Running an org, research vs admin work
20:10 Advice for people trying to get published
24:15 on reading papers and Intrinsic Dimension paper
36:25 NeurIPS - Open Collaboration
40:20 What is your reward function?
44:44 Underrated aspect of ML
47:22 How to get involved with MLC
Get our podcast on these other platforms:
Apple Podcasts: http://wandb.me/apple-podcasts
Spotify: http://wandb.me/spotify
Google: http://wandb.me/google-podcasts
YouTube: http://wandb.me/youtube
Tune in to our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research:
http://wandb.me/salon
Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning:
http://wandb.me/slack
Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices:
https://wandb.ai/gallery
How Rosanne is working to democratize AI research and improve diversity and fairness in the field through starting a non-profit after being a founding member of Uber AI Labs, doing lots of amazing research, and publishing papers at top conferences.
Rosanne is a machine learning researcher, and co-founder of ML Collective, a nonprofit organization for open collaboration and mentorship. Before that, she was a founding member of Uber AI. She has published research at NeurIPS, ICLR, ICML, Science, and other top venues. While at school she used neural networks to help discover novel materials and to optimize fuel efficiency in hybrid vehicles.
ML Collective: http://mlcollective.org/
Controlling Text Generation with Plug and Play Language Models: https://eng.uber.com/pplm/
LCA: Loss Change Allocation for Neural Network Training: https://eng.uber.com/research/lca-loss-change-allocation-for-neural-network-training/
Topics covered
0:00 Sneak peek, Intro
1:53 The origin of ML Collective
5:31 Why a non-profit and who is MLC for?
14:30 LCA, Loss Change Allocation
18:20 Running an org, research vs admin work
20:10 Advice for people trying to get published
24:15 on reading papers and Intrinsic Dimension paper
36:25 NeurIPS - Open Collaboration
40:20 What is your reward function?
44:44 Underrated aspect of ML
47:22 How to get involved with MLC
Get our podcast on these other platforms:
Apple Podcasts: http://wandb.me/apple-podcasts
Spotify: http://wandb.me/spotify
Google: http://wandb.me/google-podcasts
YouTube: http://wandb.me/youtube
Tune in to our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research:
http://wandb.me/salon
Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning:
http://wandb.me/slack
Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices:
https://wandb.ai/gallery
49 min