Seminar

Abstract: As the intelligence of everyday smart devices continues to evolve, they can already monitor basic health behaviors such as physical activities and heart rates. The vision of an intelligent behavior change intervention pipeline for health -- combining behavior modeling & interaction design -- seems to be within reach. How do we get there? In […]

Abstract: "Modern database systems aim to support a large class of different use cases while simultaneously achieving high performance. However, as a result of their generality, databases often achieve adequate performance for the average use case but do not achieve the best performance for any individual use case. In this talk, I will describe my […]

HDSI! Come and join MathWorks Engineers for a technical seminar on AI (and lunch!) on Wednesday, April 3! Come learn why data scientists should learn MATLAB – we will highlight the tools that will be serve your role as data scientists and data science students. You can also learn about our engineer’s journey, roles available […]

Abstract: "In biomedical data analysis, an emerging trend focuses on contextualizing observations within biological and real-world processes. This approach facilitates high-resolution, context-specific insights by integrating information across datasets, but it is difficult to design systems which both share information and dynamically adapt to context. Toward this aim, this presentation will examine “contextualized learning”, a meta-learning […]

Abstract: Images are everywhere, especially images of the real world, and visual computing is important both for reconstructing useful models from these images and for providing us humans with interactive tools for visualization and analysis. These methods aid real world sensing and measurement, scientific and medical imaging, and media and the arts. The past three […]

Transformers learn in-context by (functional) gradient descent Xiang Cheng, TILOS Postdoctoral Scholar at MIT HDSI 123 and Zoom: https://ucsd.zoom.us/j/99334315002 Abstract: Motivated by the in-context learning phenomenon, we investigate how the Transformer neural network can implement learning algorithms in its forward pass. We show that a linear Transformer naturally learns to implement gradient descent, which enables it to […]