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Found 7 papers, 0 papers with code
Joint Identifiability of Cross-Domain Recommendation via Hierarchical Subspace Disentanglement
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.
Distributional Domain-Invariant Preference Matching for Cross-Domain Recommendation
Next, we aim to build distributional implicit matchings between the domain-level preferences of two domains.
Adversarially Contrastive Estimation of Conditional Neural Processes
Conditional Neural Processes~(CNPs) formulate distributions over functions and generate function observations with exact conditional likelihoods.
Uncertainty-Aware Pedestrian Trajectory Prediction via Distributional Diffusion
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.
IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation
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.
See What You See: Self-supervised Cross-modal Retrieval of Visual Stimuli from Brain Activity
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.
Contrastive Conditional Neural Processes
Conditional Neural Processes~(CNPs) bridge neural networks with probabilistic inference to approximate functions of Stochastic Processes under meta-learning settings.
