CIS Seminar: “What Society Must Require from AI”
Abstract: Artificial intelligence (AI) algorithms, especially machine learning (ML) programs, are now being employed or proposed for use in: a) scanning résumés to weed out job applicants; b) evaluating risks […]
PRiML Seminar: “Building Algorithms by Playing Games”
A very popular trick for solving certain types of optimization problems is this: write your objective as the solution of a two-player zero-sum game, endow both players with an appropriate learning […]
MEAM Seminar: “Machine Learning for Robotics: Achieving Safety, Performance and Reliability by Combining Models and Data in a Closed-Loop System Architecture”
The ultimate promise of robotics is to design devices that can physically interact with the world. To date, robots have been primarily deployed in highly structured and predictable environments. However, […]
Penn Engineering Undergraduate Commencement Ceremony
Doors open for guest seating at 1:30 p.m. Access livestream here.
Penn Engineering Master’s Commencement Ceremony
Doors open for guest seating at 3:00 p.m. Access livestream here.
CIS Seminar: “Towards a New Synthesis of Reasoning and Learning”
This talk discusses the role of logical reasoning in statistical machine learning. While their unification has been a long-standing and crucial open problem, automated reasoning and machine learning are still […]
CIS Seminar: ” Deep Learning for Network Biomedicine”
Abstract: Large datasets are being generated that can transform biology and medicine. New machine learning methods are necessary to unlock these data and open doors for scientific discoveries. In this […]
CIS Seminar: “Making Parallelism Pervasive with the Swarm Architecture”
Abstract: Parallelism is critical to achieve high performance in modern computer systems. Unfortunately, most programs scale poorly beyond a few cores, and those that scale well often require heroic implementation […]
CIS Seminar: “Towards Human-Level Recognition via Contextual, Dynamic, and Predictive Representations”
Abstract: Existing state-of-the-art computer vision models usually specialize in single domains or tasks, while human-level recognition can be contextual for diverse scales and tasks. This specialization isolates different vision tasks […]