Seminars

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Previous Seminars

Computational Optics for Control and Readout of Neural Activity

Wed, Feb 17, 2021, 4:30 pm
Nearly all aspects of cognition and behavior require the coordinated action of multiple brain regions that are spread out over a large 3D volume. To understand the long-distance communication between these brain regions, we need optical techniques that can simultaneously monitor and control tens of thousands of individual neurons at cellular...
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Optimization Inspired Deep Architectures for Multiview 3D

Thu, Feb 11, 2021, 3:00 pm
Multiview 3D has traditionally been approached as continuous optimization: the solution is produced by an algorithm that solves an optimization problem over continuous variables (camera pose, 3D points, motion) to maximize the satisfaction of known constraints from multiview geometry. In contrast, deep learning offers an alternative strategy where...
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Deep Networks from First Principles

Fri, Jan 15, 2021, 4:30 pm
In this talk, we offer an entirely “white box’’ interpretation of deep (convolutional) networks from the perspective of data compression. In particular, we show how modern deep architectures, linear (convolution) operators and nonlinear activations, and parameters of each layer can be derived from the principle of rate reduction (and invariance).
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Breaking the Sample Size Barrier in Statistical Inference and Reinforcement Learning

Tue, Dec 8, 2020, 11:00 am

A proliferation of emerging data science applications require efficient extraction of information from complex data. The unprecedented scale of relevant features, however, often overwhelms the volume of available samples, which dramatically complicates statistical inference and decision making.

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HEE Seminar- Taylor Faucett-UCI-Physics Learning from Machines Learning

Tue, Dec 1, 2020, 2:00 pm

Machine Learning methods are extremely powerful but often function as black-box problem solvers, providing improved performance at the expense of clarity. Our work describes a new machine learning approach which translates the strategy of a deep neural network into simple functions that are meaningful and intelligible to the physicist, without...

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Deep Learning: It’s Not All About Recognizing Cats and Dogs

Thu, Nov 12, 2020, 12:30 pm
In this seminar, we will examine the underinvested deep learning personalization and recommendation systems in the overall research community. The training of state-of-the-art industry-scale personalized and recommendation models consumes the highest number of compute cycles among all deep learning use cases. For AI inference, personalization and...
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Analysis of Stochastic Gradient Descent in Continuous Time

Wed, Nov 4, 2020, 12:00 pm

Stochastic gradient descent is an optimisation method that combines classical gradient descent with random subsampling within the target functional. In this work, we introduce the stochastic gradient process as a continuous-time representation of stochastic gradient descent.

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Consistency of Cheeger cuts: Total Variation, Isoperimetry, and Clustering

Wed, Oct 21, 2020, 12:00 pm

Clustering unlabeled point clouds is a fundamental problem in machine learning. One classical method for constructing clusters on graph-based data is to solve for Cheeger cuts, which balance between finding clusters that require cutting few graph edges and finding clusters which are similar in size.

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CITP Seminar: Tal Zarsky – When Small Change Makes a Big Difference: Algorithmic Equity Among Similarly Situated Individuals

Tue, Oct 20, 2020, 12:30 pm

Please join the webinar here.

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Online Optimization & Energy

Thu, Oct 15, 2020, 12:30 pm
Online optimization is a powerful framework in machine learning that has seen numerous applications to problems in energy and sustainability. In my group at Caltech, we began by applying online optimization to ‘right-size’ capacity in data centers nearly a decade ago; and by now tools from online optimization have been applied to develop...
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