Algorithms
Developing efficient algorithms that power faster, smarter, and more reliable computing across diverse applications.
Dense Associative Memories and Deep Learning
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…
Quantum Computing for Computer Scientists
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…
Recent Results on Learning Filters and Style Transfer
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…
Exploiting Structure Information in Machine Learning
Machine learning has recently witnessed revolutionary success in a wide spectrum of domains. Most of these applications involve learning with complex inputs and/or outputs, which could be sequences and graphs in chemical and material design,…
Bridging the Gap Between Theory and Practice in Machine Learning
Machine learning has become one of the most exciting research areas in the world, with various applications. However, there exists a noticeable gap between theory and practice. On one hand, simple algorithms like stochastic gradient…
