Past Events

In this talk, I will talk about principled ways of solving a classical reinforcement learning (RL) problem and introduce its robust variant.

We will discuss two problems that have different application spaces but share a common mathematical core. These problems combine stochastic approximation, an iterative method for finding the fixed point of a function from noisy observations, and consensus, a general averaging technique for multiple agents to cooperatively solve a distributed problem.

Submodular functions model the intuitive notion of diminishing returns. Due to their far-reaching applications, they have been rediscovered in many fields such as information theory, operations research, statistical physics, economics, and machine learning. They also enjoy computational tractability as they can be minimized exactly or maximized approximately.  The goal of this talk is simple. We see how a little bit of randomness, a little bit of greediness, and the right combination can lead to pretty good methods for offline, streaming, and distributed solutions.

Join us for the 17th Texas Wireless Summit on November 12, 2019. ​This year's Summit will highlight advances and opportunities at the intersection of human-centered computing, sensing and connectivity. Sessions and panels will focus on wearables, virtual and mixed reality, bio-interfaces, and perception. We will explore the challenges and demands of the communication infrastructure required to support and enhance devices and experiences.

I will talk about finite sample expressivity, aka memorization power of ReLU networks. Recent results showed (unsurprisingly) that arbitrary input data could be perfectly memorized using a shallow ReLU network with one hidden layer having N hidden nodes. I will describe a more careful construction that trades of width with depth to show that a ReLU network with 2 hidden layers, each with 2*sqrt(N) hidden nodes, can perfectly memorize arbitrary datasets. Moreover, we prove that width of Θ(sqrt(N)) is necessary and sufficient for having perfect memorization power.

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Many modern neural networks are trained in an over-parameterized regime where the parameters of the model exceed the size of the training dataset. Due to their over-parameterized nature these models in principle have the capacity to (over)fit any set of labels including pure noise. Despite this high fitting capacity, somewhat paradoxically, models trained via first-order methods (often with early stopping) continue to predict well on yet unseen test data.

The role of image quality assessment in tasks such as (i) pan sharpening (PS) (i.e. merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image) and (ii) super-resolution (SR) has not been researched extensively from the natural scene statistics (NSS) perspective. For instance, even though there are several well-known measures that quantify the quality of PS and SR images, there has been little work done on analyzing the statistics of PS and SR images and associated distortions.

Soft electronic devices that can acquire vital signs from the human body represent an important trend for healthcare. Combined strategies of materials design and advanced microfabrication allow the integration of a variety of components and devices on a stretchable platform, resulting in functional systems with minimal constraints on the human body. In this presentation, I will demonstrate a wearable multichannel patch that can sense a collection of signals from the human skin in a wireless mode.