Research
Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c)…
The learnability of different neural architectures can be characterized directly by computable measures of data complexity. In this talk, we reframe the problem of architecture selection as understanding how data determines the most expressive and…
Episode 14, February 28, 2018 – Today, Dr. Crawford talks about both the promises and the problems of AI; why— when it comes to data – bigger isn’t necessarily better; and how – even in…
Dense Associative Memories are generalizations of Hopfield nets to higher order (higher than quadratic) interactions between the spins/neurons. I will describe a relationship between these models and neural networks commonly used in deep learning. From…
This talk discards hand-wavy pop-science metaphors and answers a simple question: from a computer science perspective, how can a quantum computer outperform a classical computer? Attendees will learn the following: Representing computation with basic linear…
In the first part of this talk, I will present recent results on learning image filters for low-level vision. We formulate numerous low-level vision problems (e.g., edge-preserving filtering and denoising) as recursive image filtering via…
