Photo of Thomas Serre
November 5, 2019 - 4:00 pm
Singleton Auditorium
Thomas Serre, Cognitive, Linguistic & Psychological Sciences Department, Carney Institute for Brain...
Title: Feedforward and feedback processes in visual recognition
Abstract: Progress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching – and sometimes even...
Photo of Thomas Icard
October 29, 2019 - 4:00 pm
Star Seminar Room (Stata D463)
Thomas Icard, Stanford
Abstract: How might we assess the expressive capacity of different classes of probabilistic generative models? The subject of this talk is an approach that appeals to machines of increasing strength (finite-state, recursive, etc.), or equivalently, by probabilistic grammars of increasing complexity...
Photo of Mikhail Belkin
October 28, 2019 - 4:00 pm
Singleton Auditorium
Mikhail Belkin, Professor, The Ohio State University - Department of Computer Science and Engineering,...
Title: Beyond Empirical Risk Minimization: the lessons of deep learning
Abstract: "A model with zero training error is  overfit to the training data and  will typically generalize poorly"  goes statistical textbook wisdom.  Yet, in modern practice, over-parametrized deep networks with   near ...
Photo of Jack Hidary
October 2, 2019 - 11:00 am
Singleton Auditorium
Jack Hidary, Alphabet X, formerly Google X
Abstract: Jack Hidary will take us through the nascent, but promising field of quantum computing and his new book, Quantum Computing: An Applied Approach
Bio: Jack D. Hidary is a research scientist in quantum computing and in AI at Alphabet X, formerly Google X. He and his group develop and...
Photo of Maia Fraser
September 17, 2019 - 4:00 pm
MIT Building 46-3002 (Singleton Auditorium)
Maia Fraser, Assistant Professor University of Ottawa
Abstract: Hierarchical learning is found widely in biological organisms. There are several compelling arguments for advantages of this structure. Modularity (reusable components) and function approximation are two where theoretical support is readily available. Other, more statistical, arguments...
Photo of Blake Richards
April 26, 2019 - 4:00 pm
Singleton Auditorium(46-3002)
Blake Richards, Assistant Professor, Associate Fellow of the Canadian Institute for Advanced Research (CIFAR)
Abstract: 
Theoretical and empirical results in the neural networks literature demonstrate that effective learning at a real-world scale requires changes to synaptic weights that approximate the gradient of a global loss function. For neuroscientists, this means that the brain must have mechanisms...
Photo of Jon Bloom
April 2, 2019 - 4:00 pm
MIT Building 46-3002 (Singleton Auditorium)
Dr. Jon Bloom, Broad Institute
Abstract:  When trained to minimize reconstruction error, a linear autoencoder (LAE) learns the subspace spanned by the top principal directions but cannot learn the principal directions themselves. In this talk, I'll explain how this observation became the focus of a project on representation...
March 22, 2019 - 4:00 pm
Julio Martinez-Trujillo
Abstract: The brain’s memory systems are like time machines for thought: they transport sensory experiences from the past to the present, to guide our current decisions and actions. Memories have been classified into long-term, stored for time intervals of days, months, or years, and short-term,...
Photo of Dr. Demis Hassabis
March 20, 2019 - 4:00 pm
Demis Hassabis, Co-Founder & CEO, DeepMind
This talk is co-hosted by the Center for Brains, Minds, and Machines (CBMM) and MIT Quest for Intelligence.
Abstract: Demis Hassabis will discuss the capabilities and power of self-learning systems. He will illustrate this with reference to some of DeepMind's recent breakthroughs, and talk about...
Photo of Prof. Amnon Shashua
March 19, 2019 - 4:30 pm
Dr. Amnon Shashua, President and CEO Mobileye, an Intel company; Senior Vice President, Intel Corporation;...
Please note change of location - this talk will be held in MIT 10-250.
This talk is co-hosted by the Center for Brains, Minds, and Machines (CBMM) and MIT Quest for Intelligence.
Speaker Biography: Professor Amnon Shashua is senior vice president at Intel Corporation and president and chief...
Photo of Dr. Ari Rosenberg
November 30, 2018 - 4:00 pm
Singleton Auditorium(46-3002)
Ari Rosenberg, University of Wisconsin - Madison
Abstract: Our sensory systems are unable to directly sense all the aspects of the world we perceive. For example, our perception of the world as three-dimensional (3D) is compelling, but our eyes only detect two-dimensional (2D) projections of our surroundings. Creating accurate and precise 3D...
Photo of Henry Evrard
October 26, 2018 - 4:00 pm
Henry Evrard , Head of Research Group CIN Functional and Comparative Neuroanatomy, Werner Reichardt Center...
Abstract:  Interoception substantiate embodied feelings and shape cognitive processes including perceptual awareness.  My lab combines architectonics, tract-tracing, electrophysiology, direct electrical stimulation fMRI (DES-fMRI), neural event triggered fMRI (NET-fMRI) and optogenetics in the...
Photo of Prof. Daniel J. Kersten
October 19, 2018 - 4:00 pm
Prof. Daniel J. Kersten, University of Minnesota
Abstract: The existence of feedforward and feedback neural connections between areas in the primate visual cortical hierarchy is well known.  While there is a general consensus for how feedforward connections support the sequential stages of visual processing for tasks such as object recognition,...
Photo of Prof. Samory Kpotufe, Princeton University
October 12, 2018 - 4:00 pm
Prof. Samory Kpotufe, Princeton University
Abstract:  Estimating the mode or modal-sets (i.e. extrema points or surfaces) of an unknown density from sample is a basic problem in data analysis. Such estimation is relevant to other problems such as clustering, outlier detection, or can simply serve to identify low-dimensional structures in...
September 21, 2018 - 4:00 pm
Eric Shea-Brown, University of Washington
Abstract:  There is an avalanche of new data on the brain’s activity, revealing the collective dynamics of vast numbers of neurons.  In principle, these collective dynamics can be of almost arbitrarily high dimension, with many independent degrees of freedom — and this may reflect...

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