Zobrazeno 1 - 10
of 2 214
pro vyhledávání: '"Yang, Jerry A."'
This paper presents a framework for deep transfer learning, which aims to leverage information from multi-domain upstream data with a large number of samples $n$ to a single-domain downstream task with a considerably smaller number of samples $m$, wh
Externí odkaz:
http://arxiv.org/abs/2410.09383
Learning a data representation for downstream supervised learning tasks under unlabeled scenario is both critical and challenging. In this paper, we propose a novel unsupervised transfer learning approach using adversarial contrastive training (ACT).
Externí odkaz:
http://arxiv.org/abs/2408.08533
Autor:
Krayev, Andrey, Isotta, Eleonora, Hoang, Lauren, Yang, Jerry A., Neilson, Kathryn, Wang, Minyuan, Haughn, Noah, Pop, Eric, Mannix, Andrew, Balogun, Oluwaseyi, Wang, Chih-Feng
We present a systematic study of the dependence of gap mode tip-enhanced Raman scattering (TERS) of mono- and bi-layer WS$_2$ and MoS$_2$ as a function of excitation laser energy. We collected consecutive TERS maps of mono-and bi-layer regions with 6
Externí odkaz:
http://arxiv.org/abs/2407.13932
In this work, we address a foundational question in the theoretical analysis of the Deep Ritz Method (DRM) under the over-parameteriztion regime: Given a target precision level, how can one determine the appropriate number of training samples, the ke
Externí odkaz:
http://arxiv.org/abs/2407.09032
In this paper, we effectively solve the inverse source problem of the fractional Poisson equation using MC-fPINNs. We construct two neural networks $ u_{NN}(x;\theta )$ and $f_{NN}(x;\psi)$ to approximate the solution $u^{*}(x)$ and the forcing term
Externí odkaz:
http://arxiv.org/abs/2407.03801
Autor:
Jaikissoon, Marc, Köroğlu, Çağıl, Yang, Jerry A., Neilson, Kathryn M., Saraswat, Krishna C., Pop, Eric
Strain engineering has played a key role in modern silicon electronics, having been introduced as a mobility booster in the 1990s and commercialized in the early 2000s. Achieving similar advances with two-dimensional (2D) semiconductors in a CMOS (co
Externí odkaz:
http://arxiv.org/abs/2405.09792
We propose the characteristic generator, a novel one-step generative model that combines the efficiency of sampling in Generative Adversarial Networks (GANs) with the stable performance of flow-based models. Our model is driven by characteristics, al
Externí odkaz:
http://arxiv.org/abs/2405.05512
In this article, we propose a novel Stabilized Physics Informed Neural Networks method (SPINNs) for solving wave equations. In general, this method not only demonstrates theoretical convergence but also exhibits higher efficiency compared to the orig
Externí odkaz:
http://arxiv.org/abs/2403.19090
We introduce an ordinary differential equation (ODE) based deep generative method for learning conditional distributions, named Conditional F\"ollmer Flow. Starting from a standard Gaussian distribution, the proposed flow could approximate the target
Externí odkaz:
http://arxiv.org/abs/2402.01460
We propose SDORE, a semi-supervised deep Sobolev regressor, for the nonparametric estimation of the underlying regression function and its gradient. SDORE employs deep neural networks to minimize empirical risk with gradient norm regularization, allo
Externí odkaz:
http://arxiv.org/abs/2401.04535