no code implementations • 6 Apr 2024 • Jing Du, Zesheng Ye, Bin Guo, Zhiwen Yu, Lina Yao
Such a failure may overlook the conditionality between two domains and how it contributes to latent factor disentanglement, leading to negative transfer when domains are weakly correlated.
no code implementations • 4 Sep 2023 • Jing Du, Zesheng Ye, Bin Guo, Zhiwen Yu, Lina Yao
Next, we aim to build distributional implicit matchings between the domain-level preferences of two domains.
no code implementations • 23 Mar 2023 • Zesheng Ye, Jing Du, Lina Yao
Conditional Neural Processes~(CNPs) formulate distributions over functions and generate function observations with exact conditional likelihoods.
no code implementations • 15 Mar 2023 • Yao Liu, Zesheng Ye, Binghao Li, Lina Yao
In this work, we propose to separately model these two factors by implicitly deriving a flexible distribution that describes complex pedestrians' movements, whereas incorporating predictive uncertainty of individuals with explicit density functions over their future locations.
no code implementations • 9 Aug 2022 • Jing Du, Zesheng Ye, Lina Yao, Bin Guo, Zhiwen Yu
In this study, we address these concerns by learning (1) multi-scale representations of short-term interests; and (2) dynamics-aware representations of long-term interests.
no code implementations • 7 Aug 2022 • Zesheng Ye, Lina Yao, Yu Zhang, Sylvia Gustin
Recent studies demonstrate the use of a two-stage supervised framework to generate images that depict human perception to visual stimuli from EEG, referring to EEG-visual reconstruction.
no code implementations • CVPR 2022 • Zesheng Ye, Lina Yao
Conditional Neural Processes~(CNPs) bridge neural networks with probabilistic inference to approximate functions of Stochastic Processes under meta-learning settings.