Photo of Dr. Joseph Lizier
July 24, 2018 - 11:00 am
Dr. Joseph T. Lizier, The University of Sydney
Abstract: 
The space-time dynamics of interactions in neural systems are often described using terminology of information processing, or computation, in particular with reference to information being stored, transferred and modified in these systems. In this talk, we describe an information-...
Photo of Prof. Lior Wolf
July 2, 2018 - 4:00 pm
Prof. Lior Wolf, Tel Aviv University and Facebook AI Research
Abstract: Generative models are constantly improving, thanks to recent contributions in adversarial training, unsupervised learning, and autoregressive models. In this talk, I will describe new generative models in computer vision, voice synthesis, and music.

In music – I will...
Photo of Prof. Jonathan Miller, OIST
May 21, 2018 - 4:00 pm
Jonathan Miller, Associate Professor | Physics and Biology Unit, Okinawa Institute of Science and Technology...
Abstract: By a quirk of evolution, camouflaging octopus and cuttlefish report their visual perceptions by modulating their skin color and 3-d texture on time scales of seconds or minutes to match their surroundings (they are generative image modelers).  Their survival demands that predators...
John Shlens
May 4, 2018 - 2:00 pm
Jon Shlens, Google Brain
Abstract:
Recent advances in machine learning have profoundly influenced our study of computer vision. Successes in this field have demonstrated the expressive power of learning representations directly from visual imagery — both in terms of practical utility and unexpected expressive abilities. In...
April 20, 2018 - 4:00 pm
Phil Nelson, Google Research | Google Accelerated Science team
Abstract: Google Accelerated Sciences is a translational research team that brings Google's technological expertise to the scientific community.  Recent advances in machine learning have delivered incredible results in consumer applications (e.g. photo recognition, language translation), and is now...
Photo of Prof. Mikhail Belkin, Ohio State University
April 18, 2018 - 2:00 pm
Mikhail Belkin, Ohio State University
Abstract:
A striking feature of modern supervised machine learning is its pervasive over-parametrization. Deep networks contain millions of parameters, often exceeding the number of data points by orders of magnitude. These networks are trained to nearly interpolate the data by driving the training...
Photo of Dr. Christof Koch
April 13, 2018 - 4:30 pm
Christof Koch, CBMM EAC member, Allen Institute for Brain Science
Abstract:
Rapid advances in convolutional networks and other machine learning techniques, in combination with large data bases and the relentless hardware advances due to Moore’s Law, have brought us closer to the day when we will be able to have extended conversations with programmable systems,...
Photo of Prof. Marge Livingstone
April 12, 2018 - 4:30 pm
Marge Livingstone, CBMM, Harvard Medical
Host: Tomaso Poggio
This talk is open to the CBMM Community only
Photo of Dr. Ann M. Hermundstad and AMH lab logo.
February 16, 2018 - 4:00 pm
Ann M. Hermundstad, PhD, Janelia Research Campus
Abstract: The brain exploits the statistical regularities of the natural world. In the visual system, an efficient representation of light intensity begins in retina, where statistical redundancies are removed via spatiotemporal decorrelation. Much less is known, however, about the efficient...
Photo of Jeff Hawkins
December 15, 2017 - 4:30 pm
Jeff Hawkins, Co-Founder, Numenta
Please note the change in start time. This talk will be starting at 4:30pm, on Friday, Dec. 15, 2017.
Abstract:  In this talk I will describe a theory that sensory regions of the neocortex process two inputs. One input is the well-known sensory data arriving via thalamic relay cells. We propose...
Klaus-Robert Müller
November 17, 2017 - 2:00 pm
Klaus-Robert Müller, Technische Universität Berlin
Abstract: In recent years, machine learning (ML) and artificial intelligence (AI) methods have begun to play a more and more enabling role in the sciences and in industry. In particular, the advent of large and/or complex data corpora has given rise to new technological challenges and possibilities...
Ryan Cotterell
November 3, 2017 - 4:00 pm
McGovern Seminar Room (46-3189)
Ryan Cotterell
Title: Probabilistic Typology: Deep Generative Models of Vowel Inventories
Abstract: Linguistic typology studies the range of structures present in human language. The main goal of the field is to discover which sets of possible phenomena are universal, and which are merely frequent. For example,...
Dr. David Ferrucci
September 15, 2017 - 2:00 pm
Dr. David Ferrucci
Abstract: AI systems should not only propose solutions or answers but also explain why they make sense. Statistical machine learning is a powerful tool for discovering patterns in data, but, Dr. Ferrucci asks, can it produce understanding or enable humans to justify and take reasoned responsibility...
May 26, 2017 - 4:00 pm
Kevin Murphy (Google Research) Host: Josh Tenenbaum
Abstract: In this talk, I summarize some recent work in my group related to visual scene understanding and "grounded" language understanding. In particular, I discuss the following topics:
Our DeepLab system for semantic segmentation (PAMI'17, https://arxiv.org/abs/1606.00915).
Our object detection...
May 5, 2017 - 4:00 pm
Erik Brynjolfsson (MIT)
Title: What Can Machines Learn, and What Does It Mean for Occupations and Industries?
Abstract: This talk will present a preliminary framework and approach for understanding the potential effects of machine learning (ML) on tasks, occupations and industries. Digital technologies have already had a...