Videos

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A rigorous and robust quantum speed-up in supervised machine learning Thumbnail

A rigorous and robust quantum speed-up in supervised machine learning

Presenter
  • Kristan Temme
February 9, 2021
IPAM
Dealing with COVID-19 in Theory and Practice: Session II: Data Science I Thumbnail

Dealing with COVID-19 in Theory and Practice: Session II: Data Science I

Presenters
  • Mihaela van der Schaar
  • Michael Jordan
October 29, 2020
IMSI
Secure Learning in Adversarial Environments Thumbnail

Secure Learning in Adversarial Environments

Presenter
  • Bo Li
October 6, 2020
IPAM
Learning with Few Labeled Data Thumbnail

Learning with Few Labeled Data

Presenter
  • Pratik Chaudhari
October 6, 2020
IPAM
Meta-Learning: Why It’s Hard and What We Can Do Thumbnail

Meta-Learning: Why It’s Hard and What We Can Do

Presenter
  • Ke Li
April 9, 2020
IAS

Predictive data science for physical systems- From model reduction to scientific machine learning

Presenter
  • Karen Willcox
February 20, 2020
ICERM
Connectome-based modeling of real world clinical outcomes in addictions Thumbnail

Connectome-based modeling of real world clinical outcomes in addictions

Presenter
  • Sarah Yip
February 19, 2020
IPAM
Algorithm and Hardness for Kernel Matrices in Numerical Linear Algebra and Machine Learning Thumbnail

Algorithm and Hardness for Kernel Matrices in Numerical Linear Algebra and Machine Learning

Presenter
  • Zhao Song
February 4, 2020
IAS
Improving PDE solvers and PDE-constrained optimization with deep learning and differentiable programming Thumbnail

Improving PDE solvers and PDE-constrained optimization with deep learning and differentiable programming

Presenter
  • Stephan Hoyer
October 18, 2019
IPAM
Learning structured models of physics Thumbnail

Learning structured models of physics

Presenter
  • Peter Battaglia
October 16, 2019
IPAM
PCS workflow, interpretable machine learning, and DeepTune Thumbnail

PCS workflow, interpretable machine learning, and DeepTune

Presenter
  • Bin Yu
May 2, 2019
IPAM
Background reduction in searches for gravitational-wave signals from supernovae: A machine learning approach Thumbnail

Background reduction in searches for gravitational-wave signals from supernovae: A machine learning approach

Presenter
  • Marco Cavaglia
February 1, 2019
IPAM