Seminars

Upcoming Seminars

No upcoming events found.

Previous Seminars

The One World Seminar on the Mathematics of Machine Learning

Wed, Apr 7, 2021, 12:00 pm
In this talk we study the problem of signal recovery for group models. More precisely for a given set of groups, each containing a small subset of indices, and for given linear sketches of the true signal vector which is known to be group-sparse in the sense that its support is contained in the union of a small number of these groups, we study...
Speaker(s):

Leveraging Dataset Symmetries in Neural Network Prediction

Mon, Mar 22, 2021, 12:30 pm

Scientists and engineers are increasingly applying deep neural networks (DNNs) to modelling and design of complex systems. While the flexibility of DNNs makes them an attractive tool, it also makes their solutions difficult to interpret and their predictive capability difficult to quantify.

Speaker(s):

Function Approximation via Sparse Random Fourier Features

Wed, Mar 17, 2021, 12:00 pm
Random feature methods have been successful in various machine learning tasks, are easy to compute, and come with theoretical accuracy bounds. They serve as an alternative approach to standard neural networks since they can represent similar function spaces without a costly training phase. However, for accuracy, random feature methods require more...

AI Meets Large-scale Sensing: preserving and exploiting structure of the real world to enhance machine perception

Thu, Mar 11, 2021, 3:00 pm
Machine capability has reached an inflection point, achieving human-level performance in tasks traditionally associated with cognition (vision, speech, strategic gameplay).  However, efforts to move such capability pervasively into the real world, have in many cases fallen far short of the relatively constrained and isolated demonstrations of...
Speaker(s):

Finite Width, Large Depth Neural Networks as Perturbatively Solvable Models

Wed, Mar 10, 2021, 12:00 pm

Abstract: Deep neural networks are often considered to be complicated "black boxes," for which a systematic analysis is not only out of reach but potentially impossible. In this talk, which is based on ongoing joint work with Dan Roberts and Sho Yaida, I will make the opposite claim. Namely, that deep neural networks at initialization are...

Speaker(s):

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...
Speaker(s):

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...
Speaker(s):

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).
Speaker(s):

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.

Speaker(s):

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...

Speaker(s):

Pages