Extron Electronics, known as Extron, is a manufacturer of professional audiovisual equipment. It is headquartered in Anaheim, California. Extron operates over 30 offices and regional training and demonstration facilities around the globe.
Join Duke University & the FBI Association of Intelligence Analysts for a Cybersecurity and Fintech conference on April 7 & 8.
Scheduled speakers include leading practitioners in areas including ransomware, APTs, threat intelligence best practices, and law enforcement & private sector cooperation.
Attend Virtually or In-Person
About the FBI AIA:
Learning environments at school and at home are diversifying every day.
Classrooms are no longer places to just listen to the lectures, but are becoming places for student-led lessons,
where students can discuss, communicate, and learn from each other.
Educational institutions are also being urged to put more focus on creating spaces to support student-led learning to maximize studentsʼ learning.
Panasonic supports creating an optimized environment for the ever-diversifying "learning" with visual system solutions.
The Sennheiser Teams meeting will include a review of TeamConnect Ceiling mic V2 by discussing its place in the market, its operating characteristics, settings and built in DSP.
We will also take a look at MobileConnect, our WiFi streaming hearing assist platform by looking at its scalability and customization.
We will also look at Speechline Digital Wireless along with Control Cockpit (our managed services platform) and the ease of campus wide systems monitoring.
Deep learning excels with large-scale unstructured data - common across many modern application domains - while probabilistic modeling offers the ability to encode prior knowledge and quantify uncertainty - necessary for safety-critical applications and downstream decision-making tasks. I will discuss examples from my research that bridge the gap between these two learning paradigms. The first half will show that insights from deep learning can improve the practicality of probabilistic models.
In recent years, reinforcement learning algorithms have achieved strong empirical success on a wide variety of real-world problems. However, these algorithms usually require a huge number of samples even just for solving simple tasks. It is unclear if there are fundamental statistical limits on such methods, or such sample complexity burden can be alleviated by a better algorithm. In this talk, I will give an overview of my research efforts towards bridging the gap between the theory and the practice of reinforcement learning.