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- 1An Evolution of Learning Neural Implicit Representations for 3D Shapes11:00 am - 12:00 pm
Speaker:
Professor ZHANG Hao, Richard
Amazon Scholar, Professor
School of Computing Science, Simon Fraser University, CanadaAbstract:
Neural implicit representations are the immediate precursors to neural radiance fields (NeRF). In a short span of only four years, they have quickly become the representation of choice
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- 13Smart Reconfigurable Computing for GNN and Transformer using Agile High Level Synthesis9:30 am - 10:30 am
Speaker:
Dr. HAO Cong, Callie
Assistant Professor
Department of Electrical and Computer Engineering (ECE), Georgia Institute of Technology (GaTech)Abstract:
In this talk, we introduce two architectures, one for graph neural work (GNN) called FlowGNN, one for vision transformer (ViT) called Edge-MoE. In FlowGNN, a gene
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- 20The da Vinci Research Kit: System Description, Research Highlights, and Surgical Robotics Challenge4:00 pm - 5:00 pm
Speaker:
Prof. Peter Kazanzides
Research Professor
Department of Computing Science, Johns Hopkins UniversityAbstract:
The da Vinci Research Kit (dVRK) is an open research platform that couples open-source control electronics and software with the mechanical components of the da Vinci surgical robot. This presentatio
, ... - 21Towards Scalable, Secure and Privacy-Preserving Metaverse4:30 pm - 5:30 pm
Speaker:
Prof. DAI Hong-Ning
Associate Professor
Department of Computing Science, Hong Kong Baptist University (HKBU)Abstract:
The metaverse is essentially constructed by multiple technologies, including virtual reality (VR), augmented reality (AR), mixed reality (MR), artificial intelligence (AI), digital twin (DT),
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- 27Geometric Robot Learning for Generalizable Skills Acquisition10:00 am - 11:30 am
Speaker:
Prof. Xiaolong Wang
Associate Professor
Department of Electrical and Computer Engineering, University of California, San DiegoAbstract:
Robot learning has witnessed significant progress in terms of generalization in the past few years. At the heart of such a generalization, the advancement of representation l
, ...Disentangled Representation from Generative Networks2:00 pm - 3:00 pmSpeaker:
Dr. LIU SifeiAbstract:
Disentangled representation in computer vision refers to encoding visual data into distinct, independent factors. These representations are critical for enhancing interpretability, improving generalization across tasks, and enabling controlled manipulation of specific visual attributes.
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