Overparameterized neural networks have proved to be remarkably successful in many complex tasks such as image classification and deep reinforcement learning. In this talk, we will consider the role of […]
The strong lottery ticket hypothesis (LTH) postulates that any neural network can be approximated by simply pruning a sufficiently larger network of random weights. Recent work establishes that the strong LTH […]
The Department of Bioengineering at the University of Pennsylvania and Dr. D. Kacy Cullen are pleased to announce the Doctoral Dissertation Defense of Justin Burrell. Title: Implantable Micro-Tissue Engineered Nerve […]
Bone mineral density rapidly decreases upon withdrawal from intermittent parathyroid hormone (PTH) treatment despite its potent effect of promoting bone formation. To better understand this adverse phenomenon, this study first […]
Abstract: "Heterogeneous catalysis is critically important in the chemical-processing and energy-related industries. Ideally, reaction sites on heterogeneous catalysts can be regenerated, and the catalysts should be active throughout the catalytic […]
Abstract: Sintering is a severe problem with supported-metal catalysts in high-temperature applications, such as in automotive-emissions control, because it leads to a loss of catalytically active surface area. To stabilize […]
We present methods to sculpt light at the atomic and molecular scale to detect and control chemical transformations, en-route to improved planetary and personal health. First, we study plasmon-driven chemical […]
The Department of Bioengineering at the University of Pennsylvania and Dr. Ravi Radhakrishnan are pleased to announce the Doctoral Dissertation Defense of Lindsey Fernandez. The public is welcome to attend […]
Abstract: The lecture that I will cover is going to be on Hash Tables. The lecture is designed for an introductory CS course and/or a data structures course. We are […]
The Department of Bioengineering along with Drs. Walter Witschey and John Detre are pleased to announce the Doctoral Dissertation Defense of Eileen Hwuang. The public is welcome to attend via […]
Abstract: The fundamental issue underlying natural language understanding is that of semantics – there is a need to move toward understanding natural language at an appropriate level of abstraction in order to support natural language understanding and communication with computers.
Machine Learning has become ubiquitous in our attempt to induce semantic representations of natural language and support decisions that depend on it; however, while we have made significant progress over the last few years, it has focused on classification tasks for which we have large amounts of annotated data. Supporting high level decisions that depend on natural language understanding is still beyond our capabilities, partly since most of these tasks are very sparse and generating supervision signals for it does not scale.
I will discuss some of the challenges underlying reasoning – making natural language understanding decisions that depend on multiple, interdependent, models, and exemplify it using the domain of Reasoning about Time, as it is expressed in natural language.