Events

Faculty Candidate Talk Regularization in Deep Neural Networks
Export Event Save iCal Send to Google Calendar Send to Yahoo Calendar Send to Outlook Live Send to Microsoft Outlook Save iCal Preserve formatting in description (only supported in some calendar applications) Close Monday, March 10, 2025, 10:30am - 12:00pm

Speaker: Dr. Bernhard Firner

Bio

Dr. Bernhard Firner's career has focused on two main interests: deep learning and low-power embedded systems. His recent work has focused on the intersection of those topics: real-time control of embedded systems with onboard neural networks.

After completing his PhD at Rutgers in 2014, Bernhard joined a newly created group in NVIDIA to work on end-to-end learning for autonomous vehicles. His work resulted in numerous patents, popular academic papers, and notable technology demonstrations. Within five years, his team's autonomous vehicles could achieve average distances of 500km between failures. These results were on public U.S. highways in all lighting and weather conditions, including snow.

Bernhard's experience includes time with startup companies. In one case developing low-power wireless sensors that ran for 20 years on coin cell batteries and in the other creating neural networks for real-time control of autonomous drones. Earlier in his career, he developed software for real-time embedded avionics platforms at a well-established avionics company.

Location : CoRE 301

Committee: 

Event Type: Faculty Candidate Talk

Abstract: You may have heard that overfitting is a problem in machine learning. You may have even heard that regularization fixes the problem. But what is regularization?Regularization techniques pre-date modern machine learning, including deep neural networks. Although deep neural networks are surprisingly robust to overfitting, regularization is still an essential part of neural network training. In this talk, we will look at overfitting and regularization in neural networks. How do they resist overfitting? What techniques can we use to regularize neural networks? And what are some of the problems that regularization solves?

Organization: 

Contact  Professor Richard Martin

Join Zoom Meeting
https://rutgers.zoom.us/j/2014444359?pwd=WW9ybFNCNVFrUWlycHowSHdNZjhzUT09

Meeting ID: 201 444 4359
Password: 550978