Abstract: Graph neural networks (GNNs) are successful at learning representations from most types of network data but suffer from limitations in large graphs, which do not have the Euclidean structure that time and image...
- September2212:15 - 1 pm
Abstract: Graph neural networks (GNNs) are successful at learning representations from most types of network data but suffer from limitations...
- October612:15 - 1 pm
Abstract: During this presentation, I will delve into an innovative class of optimization problems called finite-sum coupled compositional optimization (FCCO…
- November163 - 5 pm
Join us for the 2022 Machine Learning Lab Public Lecture with Alan Bovik!
- November101 - 1:45pm
Abstract: This talk will first motivate and illustrate the use of margins as a way to interpret and analyze the...
- November412:15 - 1 pm
A central problem in machine learning is as follows: How should we train models using data generated from a collection...
- October2812:15 - 1 pm
Abstract: Although Machine learning (ML) algorithms have recently made a huge impact on medical imaging, their development and deployment for...
- October2112:15 - 1 pm
Representation learning has been widely used in many applications. In this talk, I will present our work which uncovers when...
- October1412:15 - 1 pm
When Is Partially Observable Reinforcement Learning Not Scary? Abstract: Partially observability is ubiquitous in applications of Reinforcement Learning (RL),…