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A neural network trained for prediction mimics diverse features of biological neurons and perception [video]

Recorded:

May 1, 2020

Uploaded:

May 19, 2020

Part of

All Captioned Videos, Publication Releases

CBMM Speaker(s):

Bill Lotter

Lead author, Bill Lotter, discusses their recent work published in Nature Machine Intelligence that demonstrates that the PredNet, a recurrent predictive neural network, can reproduce various phenomena observed in the brain. A neural network trained...

Sobolev Independence Criterion: Non-Linear Feature Selection with False Discovery Control.

Recorded:

Apr 28, 2020

Uploaded:

May 5, 2020

Part of

All Captioned Videos, CBMM Research

Speaker(s):

Youssef Mroueh, IBM Research and MIT-IBM Watson AI lab

Youssef Mroueh, IBM Research and MIT-IBM Watson AI lab Abstract: In this talk I will show how learning gradients help us designing new non-linear algorithms for feature selection, black box sampling and also, in understanding neural style transfer....

Spatial Perception for Robots and Autonomous Vehicles: Certifiable Algorithms and Human-level Understanding

Recorded:

Apr 21, 2020

Uploaded:

April 23, 2020

Part of

All Captioned Videos, Brains, Minds and Machines Seminar Series

Speaker(s):

Luca Carlone, MIT

Abstract:
Spatial perception has witnessed an unprecedented progress in the last decade. Robots are now able to detect objects and create large-scale maps of an unknown environment, which are crucial capabilities for navigation and...

Stability of overparametrized learning models

Recorded:

Apr 15, 2020

Uploaded:

April 15, 2020

Part of

All Captioned Videos, CBMM Research

CBMM Speaker(s):

Tomaso Poggio, Lorenzo Rosasco

Speaker(s):

Mikhail Belkin, Constantinos Daskalakis, Gil Strang

A panel discussion featuring Tomaso Poggio (CBMM), Mikhail Belkin (Ohio State University), Constantinos Daskalakis (CSAIL), Gil Strang (Mathematics) and Lorenzo Rosasco (University of Genova). Abstract: Developing theoretical foundations for...

Predictive Maps in the Brain

Recorded:

Apr 7, 2020

Uploaded:

April 7, 2020

Part of

All Captioned Videos, CBMM Research

CBMM Speaker(s):

Samuel Gershman

Sam Gershman, Harvard University Abstract: In this talk, I will present a theory of reinforcement learning that falls in between "model-based" and "model-free" approaches. The key idea is to represent a "predictive map" of the environment, which can...

Can we Contain Covid-19 without Locking-down the Economy?

Recorded:

Apr 1, 2020

Uploaded:

April 1, 2020

Part of

All Captioned Videos, CBMM Special Seminars

CBMM Speaker(s):

Amnon Shashua

Speaker(s):

Shai Shalev-Shwartz

Abstract:
We present an analysis of a risk-based selective quarantine model where the population is divided into low and high-risk groups. The high-risk group is quarantined until the low-risk group achieves herd-immunity. We tackle the...

A conversation with Michael R. Douglas

Recorded:

Feb 25, 2020

Uploaded:

March 9, 2020

Part of

Scientific Interviews

CBMM Speaker(s):

Andrzej Banburski

Speaker(s):

Michael R. Douglas, Simons Center for Geometry and Physics at Stony Brook University

On February 25, 2020, CBMM Postdoctoral Fellow Andrzej Banburski took the opportunity to sit down and chat briefly with Michael R. Douglas of the Simons Center for Geometry and Physics at Stony Brook University.