7 papers
DarkVGGT: Seeing Through Darkness Using Thermal Geometry without Daylight Tax
Minseong Kweon, Wenyuan Zhao, Nuo Chen +6
Recent feed-forward 3D reconstruction methods have demonstrated strong performance and flexibility in efficient end-to-end scene geometry estimation from image streams. However, th…
SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning
Wenyuan Zhao, Rui Tuo, Chao Tian
Gaussian processes (GPs) provide a principled Bayesian framework for uncertainty estimation, but their computational complexity severely limits scalability to large datasets. We pr…
Trust It or Not: Evidential Uncertainty for Feed-Forward 3D Reconstruction with Trust3R
Zihao Zhu, Wenyuan Zhao, Nuo Chen +2
Geometric foundation models hold promise for unconstrained dense geometry prediction from uncalibrated images. However, in current feed-forward designs, their predicted confidence…
Partial Information Decomposition via Normalizing Flows in Latent Gaussian Distributions
Wenyuan Zhao, Adithya Balachandran, Chao Tian +1
The study of multimodality has garnered significant interest in fields where the analysis of interactions among multiple information sources can enhance predictive modeling, data f…
Weakly Private Information Retrieval from Heterogeneously Trusted Servers
Wenyuan Zhao, Yu-Shin Huang, Ruida Zhou +1
We study the problem of weakly private information retrieval (PIR) when there is heterogeneity in servers' trustworthiness under the maximal leakage (Max-L) metric and mutual infor…
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation
Wenyuan Zhao, Haoyuan Chen, Tie Liu +2
With the strengths of both deep learning and kernel methods like Gaussian Processes (GPs), Deep Kernel Learning (DKL) has gained considerable attention in recent years. From the co…