9月26日
4:30pm - 5:50pm
研討會, 演講, 講座
The analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community. For the first part of the talk, I will introduce Lanczos network (LanczosNet), which uses the Lanczos algorithm to construct low rank approximations of the graph Laplacian for graph convolution. Relying on the tridiagonal decomposition of the Lanczos algorithm, we efficiently exploit multi-scale information via fast approximated computation of matrix power, and design learnable spectral filters. Being fully differentiable, LanczosNet facilitates both graph kernel learning as well as learning node embeddings. I will show the application of LanczosNet to citation networks and QM8 quantum chemistry dataset.
For the second part of the talk, I will introduce a novel multi-representation learning paradigm for manifolds naturally equipped with a group action. Utilizing a representation theoretic mechanism, multiple associated vector bundles can be constructed over the orbit space, providing multiple views for learning the geometry of the underlying manifold. The consistency across these associated vector bundles form a common base for unsupervised manifold learning, through the redundancy inherent to the algebraic relations across irreducible representations of the transformation group. I will demonstrate the efficacy of the proposed algorithmic paradigm through dramatically improved robust nearest neighbor search in cryo-electron microscopy image analysis.
For the second part of the talk, I will introduce a novel multi-representation learning paradigm for manifolds naturally equipped with a group action. Utilizing a representation theoretic mechanism, multiple associated vector bundles can be constructed over the orbit space, providing multiple views for learning the geometry of the underlying manifold. The consistency across these associated vector bundles form a common base for unsupervised manifold learning, through the redundancy inherent to the algebraic relations across irreducible representations of the transformation group. I will demonstrate the efficacy of the proposed algorithmic paradigm through dramatically improved robust nearest neighbor search in cryo-electron microscopy image analysis.
9月26日
4:30pm - 5:50pm
地點
Room 2405, Academic Building (near Lifts 17-18), HKUST
講者/表演者
Prof. Zhizhen ZHAO
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
主辦單位
Department of Mathematics
聯絡方法
mathseminar@ust.hk
付款詳情
對象
Alumni, Faculty and Staff, PG Students, UG Students
語言
英語
其他活動
6月21日
研討會, 演講, 講座
IAS / School of Science Joint Lecture - Alzheimer’s Disease is Likely a Lipid-disorder Complication: an Example of Functional Lipidomics for Biomedical and Biological Research
Abstract
Functional lipidomics is a frontier in lipidomics research, which identifies changes of cellular lipidomes in disease by lipidomics, uncovers the molecular mechanism(s) leading to the chan...
5月24日
研討會, 演講, 講座
IAS / School of Science Joint Lecture - Confinement Controlled Electrochemistry: Nanopore beyond Sequencing
Abstract
Nanopore electrochemistry refers to the promising measurement science based on elaborate pore structures, which offers a well-defined geometric confined space to adopt and characterize sin...