4月29日
研讨会, 演讲, 讲座
Department of Mathematics - PhD Student Seminar - Normalizing Flows with Variational Latent Representation
Normalizing flow (NF) has gained popularity over traditional maximum likelihood based methods due to its strong capability to model complex data distributions.
4月29日
研讨会, 演讲, 讲座
Department of Mathematics - PhD Student Seminar - Connecting spatial transcriptomics data and single-cell RNA sequencing data using the deep generative model
Spatial transcriptomics (ST) is a groundbreaking method that allows scientists to measure gene activity in a tissue sample and retain spatial information. However, most spatial ST technologies are limited by their resolution.
4月29日
研讨会, 演讲, 讲座
Department of Mathematics - Seminar on Applied Mathematics - Riemannian Proximal Gradient Methods
In the Euclidean setting, the proximal gradient method and its accelerated variants are a class of efficient algorithms for optimization problems with decomposable objective.
4月29日
研讨会, 演讲, 讲座
OCES Departmental Seminar: Local to Global Drivers of Past and Future Sea-Level and Coastal Environmental Change
Geological proxies provide valuable archives of the sea-level response to past climate variability over periods of more extreme global mean surface temperatures than the brief instrumental period.
4月29日
研讨会, 演讲, 讲座
Department of Mathematics - Seminar on PDE - Anisotropic Dynamical Horizons Arising in Gravitational Collapse
Black holes are predicted by Einstein's theory of general relativity, and now we have ample observational evidence for their existence.
4月29日
研讨会, 演讲, 讲座
Department of Mathematics - Seminar on Statistics - A Learning System in Pandemic Prevention — from macro predictive modeling to small probability estimation
Reproduction number (R
4月27日
研讨会, 演讲, 讲座
Physics Department - Condensed Matter Seminar: Correlations and Topology in a Transition Metal Dichalcogenide Compound
4月27日
研讨会, 演讲, 讲座
Department of Mathematics - Seminar on Applied Mathematics - Riemannian optimization with three metrics for Hermitian PSD fixed rank constraints
Hermitian PSD fixed rank constraint is used in many applications, e.g., it is also used for approximating the Hermitian PSD constraint. We study and compare three methodologies for minimizing f(X) with X being a Hermitian PSD fixed rank matrix.