Zobrazeno 1 - 10
of 214
pro vyhledávání: '"Chen, Yanzhen"'
This paper proposes a generative model to detect change points in time series of graphs. The proposed framework consists of learnable prior distributions for low-dimensional graph representations and of a decoder that can generate graphs from the lat
Externí odkaz:
http://arxiv.org/abs/2404.04719
Implicit neural representation has opened up new possibilities for inverse rendering. However, existing implicit neural inverse rendering methods struggle to handle strongly illuminated scenes with significant shadows and indirect illumination. The e
Externí odkaz:
http://arxiv.org/abs/2310.13030
This paper studies change point detection in time series of networks, with the Separable Temporal Exponential-family Random Graph Model (STERGM). Dynamic network patterns can be inherently complex due to dyadic and temporal dependence. Detection of t
Externí odkaz:
http://arxiv.org/abs/2303.17642
The Exponential-family Random Graph Model (ERGM) is a powerful model to fit networks with complex structures. However, for dynamic valued networks whose observations are matrices of counts that evolve over time, the development of the ERGM framework
Externí odkaz:
http://arxiv.org/abs/2205.13651
Publikováno v:
In Chemical Engineering Journal 1 October 2024 497
Publikováno v:
In Journal of Energy Chemistry September 2024 96:153-163
Statisticians show growing interest in estimating and analyzing heterogeneity in causal effects in observational studies. However, there usually exists a trade-off between accuracy and interpretability for developing a desirable estimator for treatme
Externí odkaz:
http://arxiv.org/abs/2110.02401
We propose a novel method for estimating heterogeneous treatment effects based on the fused lasso. By first ordering samples based on the propensity or prognostic score, we match units from the treatment and control groups. We then run the fused lass
Externí odkaz:
http://arxiv.org/abs/2110.00901
Visible light promoted synthesis of 2-amino sugar analogues from glycals and N-aminopyridinium salts
Publikováno v:
In Tetrahedron 21 August 2024 163
Publikováno v:
In Neurochemistry International July 2024 177