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Ameet Talwalkar is an assistant professor in the Machine Learning Department at Carnegie Mellon University, and also co-founder and chief scientist at Determined AI. He led the initial development of the MLlib project in Apache Spark, is a co-author of the textbook Foundations of Machine Learning (MIT Press), and created an award-winning edX MOOC on distributed machine learning.

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Episode 19 of the Stanford MLSys Seminar Series!

Automating Architecture Transfer on Diverse Tasks
Speaker: Ameet Talwalkar

Abstract:
Hand-crafted neural architecture design has played a major role in accelerating progress in computer vision, resulting in effective backbones like ResNet. Unfortunately, these convolutional backbones are not as effective in other domains. Successfully transferring existing architectures to applications such as sequence modeling, learning on graphs, or solving partial differential equations has required the manual design of task-specific neural operations to replace convolutions. In this talk, we will first motivate the problem of 'automating architecture transfer' to enable users to find the right operations given data from their specific domain. We will next present our ongoing work on this problem, by introducing a family of neural operations called 'XD-Operations' that mimic the inductive bias of multichannel convolutions while being much more expressive, provably containing numerous well-known operations. We then demonstrate the effectiveness of XD-operations on a diverse set of applications---in some cases outperforming the latest neural operation designs.

Speaker bio:
Ameet Talwalkar is an assistant professor in the Machine Learning Department at CMU, and also co-founder and Chief Scientist at Determined AI. His interests are in the field of statistical machine learning. His current work is motivated by the goal of democratizing machine learning, with a focus on topics related to automation, fairness, interpretability, and federated learning. He led the initial development of the MLlib project in Apache Spark, is a co-author of the textbook 'Foundations of Machine Learning' (MIT Press), and created an award-winning edX MOOC on distributed machine learning. He also helped to create the MLSys conference, serving as the inaugural Program Chair in 2018, General Chair in 2019, and currently as President of the MLSys Board.

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The Stanford MLSys Seminar is hosted by Dan Fu, Karan Goel, Fiodar Kazhamiaka, and Piero Molino, Chris Ré, and Matei Zaharia.

Twitter:
https://twitter.com/realDanFu​
https://twitter.com/krandiash​
https://twitter.com/w4nderlus7

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Check out our website for the schedule: http://mlsys.stanford.edu
Join our mailing list to get weekly updates: https://groups.google.com/forum/#!forum/stanford-mlsys-seminars/join

#machinelearning #ai #artificialintelligence #systems #mlsys #computerscience #stanford #cmu #determinedai #apachespark

Automating Architecture Transfer feat. Ameet Talwalkar | Stanford MLSys Seminar Episode 19

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