Applied Mathematics Colloquium - John Bush

Sept. 10, 2021

John Bush, Department of Mathematics, Massachusetts Institute of Technology (MIT) Hydrodynamic quantum analogs In 2005, Yves Couder and Emmanuel Fort discovered that droplets walking on a vibrating fluid bath exhibit several features previously thought to be exclusive to the microscopic, quantum realm. These walking droplets propel themselves by virtue of...

Applied Mathematics Colloquium - Doug Nychka and Florian Gerber

Sept. 3, 2021

Doug Nychka, Department of Applied Mathematics and Statistics, Colorado School of Mines Florian Gerber, Department of Biostatistics, University of Zurich Climate models, large spatial datasets, and harnessing deep learning for a statistical computation Numerical simulations of the motion and state of the Earth's atmosphere and ocean yield large and complex...

Applied Mathematics Colloquium - Mason Porter

Aug. 27, 2021

Mason Porter; Department of Mathematics; University of California, Los Angeles (UCLA) Opinion Dynamics on Networks From the spreading of diseases and memes to the development ofopinions and social influence, dynamical processes are influenced heavilyby the networks on which they occur. In this talk, I'll discuss the socialinfluence and opinion models...

APPM Department Colloquium - Matthew Peters

April 30, 2021

Matthew Peters, Senior Research Scientist, Allen Institute for Artificial Intelligence A guided tour of contextual word representations for language understanding The last 3-4 years have seen a tremendous increase in the abilities of natural language understanding systems to perform tasks such as text generation, question answering, and information extraction. These...

APPM Department Colloquium - Abdelrahman Mohamed

April 26, 2021

Abdelrahman Mohamed, Research Scientist, Facebook AI Research Recent advances in speech representation learning Self-supervised representation learning methods recently achieved great successes in NLP and computer vision domains, reaching new performance levels while reducing required labels for many downstream scenarios. Speech representation learning is experiencing similar progress, with work primarily focused...

APPM Department Colloquium - Pieter Abbeel

April 23, 2021

Pieter Abbeel; Professor of Electrical Engineering and Computer Science; University of California, Berkeley Unsupervised Reinforcement Learning Deep reinforcement learning (Deep RL) has seen many successes, including learning to play Atari games, the classical game of Go, robotic locomotion and manipulation. However, past successes are ultimately in fairly narrow problem domains...

APPM Department Colloquium - Mihaela van der Schaar

April 16, 2021

Mihaela van der Schaar, John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine, University of Cambridge; Turing Fellow, The Alan Turing Institute in London; Chancellor’s Professor, UCLA Why medicine is creating exciting new frontiers for machine learning Medicine stands apart from other areas where machine learning can be...

APPM Department Colloquium - Abdelrahman Mohamed

April 16, 2021

Abdelrahman Mohamed, Research Scientist, Facebook AI Research Recent advances in speech representation learning Self-supervised representation learning methods recently achieved great successes in NLP and computer vision domains, reaching new performance levels while reducing required labels for many downstream scenarios. Speech representation learning is experiencing similar progress, with work primarily focused...

APPM Department Colloquium - Oriol Vinyals

April 14, 2021

Oriol Vinyals, Research Scientist, Google DeepMind AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning Games have been used for decades as an important way to test and evaluate the performance of artificial intelligence systems. As capabilities have increased, the research community has sought games with increasing complexity that...

APPM Department Colloquium - Yanping Huang

April 9, 2021

Yanping Huang, Staff Software Engineer, Google Brain GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding Neural network scaling has been critical for improving the model quality in many real-world machine learning applications with vast amounts of training data and computation. Although this trend of scaling is affirmed to...

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