Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Suh, Namjoon"'
Autor:
Suh, Namjoon, Yang, Yuning, Hsieh, Din-Yin, Luan, Qitong, Xu, Shirong, Zhu, Shixiang, Cheng, Guang
In this paper, we leverage the power of latent diffusion models to generate synthetic time series tabular data. Along with the temporal and feature correlations, the heterogeneous nature of the feature in the table has been one of the main obstacles
Externí odkaz:
http://arxiv.org/abs/2406.16028
With the proliferation of generative AI and the increasing volume of generative data (also called as synthetic data), assessing the fidelity of generative data has become a critical concern. In this paper, we propose a discriminative approach to esti
Externí odkaz:
http://arxiv.org/abs/2405.15337
Motivated by the abundance of functional data such as time series and images, there has been a growing interest in integrating such data into neural networks and learning maps from function spaces to R (i.e., functionals). In this paper, we study the
Externí odkaz:
http://arxiv.org/abs/2403.12187
Large language models (LLMs) are powerful models that can learn concepts at the inference stage via in-context learning (ICL). While theoretical studies, e.g., \cite{zhang2023trained}, attempt to explain the mechanism of ICL, they assume the input $x
Externí odkaz:
http://arxiv.org/abs/2402.00743
Autor:
Suh, Namjoon, Cheng, Guang
In this article, we review the literature on statistical theories of neural networks from three perspectives: approximation, training dynamics and generative models. In the first part, results on excess risks for neural networks are reviewed in the n
Externí odkaz:
http://arxiv.org/abs/2401.07187
Diffusion model has become a main paradigm for synthetic data generation in many subfields of modern machine learning, including computer vision, language model, or speech synthesis. In this paper, we leverage the power of diffusion model for generat
Externí odkaz:
http://arxiv.org/abs/2310.15479
The recent success of neural networks in pattern recognition and classification problems suggests that neural networks possess qualities distinct from other more classical classifiers such as SVMs or boosting classifiers. This paper studies the perfo
Externí odkaz:
http://arxiv.org/abs/2309.15075
We prove the support recovery for a general class of linear and nonlinear evolutionary partial differential equation (PDE) identification from a single noisy trajectory using $\ell_1$ regularized Pseudo-Least Squares model~($\ell_1$-PsLS). In any ass
Externí odkaz:
http://arxiv.org/abs/2103.07045
We propose a combined model, which integrates the latent factor model and the logistic regression model, for the citation network. It is noticed that neither a latent factor model nor a logistic regression model alone is sufficient to capture the str
Externí odkaz:
http://arxiv.org/abs/1912.00524
Publikováno v:
Journal of Computational & Graphical Statistics. Oct-Dec2024, Vol. 33 Issue 4, p1264-1275. 12p.
