Zobrazeno 1 - 10
of 6 184
pro vyhledávání: '"An, Jihun"'
Autor:
Jang, Doohyuk, Park, Sihwan, Yang, June Yong, Jung, Yeonsung, Yun, Jihun, Kundu, Souvik, Kim, Sung-Yub, Yang, Eunho
Auto-Regressive (AR) models have recently gained prominence in image generation, often matching or even surpassing the performance of diffusion models. However, one major limitation of AR models is their sequential nature, which processes tokens one
Externí odkaz:
http://arxiv.org/abs/2410.03355
Autor:
Kim, Jihun, Lavaei, Javad
This paper is concerned with the online bandit nonlinear control, which aims to learn the best stabilizing controller from a pool of stabilizing and destabilizing controllers of unknown types for a given nonlinear dynamical system. We develop an algo
Externí odkaz:
http://arxiv.org/abs/2410.03230
Autor:
Kim, Jihun, Lavaei, Javad
This paper studies the linear system identification problem in the general case where the disturbance is sub-Gaussian, correlated, and possibly adversarial. First, we consider the case with noncentral (nonzero-mean) disturbances for which the ordinar
Externí odkaz:
http://arxiv.org/abs/2410.03218
A Riemannian manifold $(M,g)$ is called \emph{weakly Einstein} if the tensor $R_{iabc}R_{j}^{~~abc}$ is a scalar multiple of the metric tensor $g_{ij}$. We give a complete classification of weakly Einstein hypersurfaces in the spaces of nonzero const
Externí odkaz:
http://arxiv.org/abs/2409.12766
Prior research works have evaluated quantized LLMs using limited metrics such as perplexity or a few basic knowledge tasks and old datasets. Additionally, recent large-scale models such as Llama 3.1 with up to 405B have not been thoroughly examined.
Externí odkaz:
http://arxiv.org/abs/2409.11055
Current AI-assisted skin image diagnosis has achieved dermatologist-level performance in classifying skin cancer, driven by rapid advancements in deep learning architectures. However, unlike traditional vision tasks, skin images in general present un
Externí odkaz:
http://arxiv.org/abs/2409.09520
Autor:
Jeong, Halyun, Han, Jihun
Fourier embedding has shown great promise in removing spectral bias during neural network training. However, it can still suffer from high generalization errors, especially when the labels or measurements are noisy. We demonstrate that introducing a
Externí odkaz:
http://arxiv.org/abs/2409.02052
Optimal control problems can be solved via a one-shot (single) optimization or a sequence of optimization using dynamic programming (DP). However, the computation of their global optima often faces NP-hardness, and thus only locally optimal solutions
Externí odkaz:
http://arxiv.org/abs/2409.00655
The rapid evolution of artificial intelligence, especially in large language models (LLMs), has significantly impacted various domains, including healthcare. In chest X-ray (CXR) analysis, previous studies have employed LLMs, but with limitations: ei
Externí odkaz:
http://arxiv.org/abs/2408.16213
We present Text-driven Object-Centric Style Transfer (TEXTOC), a novel method that guides style transfer at an object-centric level using textual inputs. The core of TEXTOC is our Patch-wise Co-Directional (PCD) loss, meticulously designed for precis
Externí odkaz:
http://arxiv.org/abs/2408.08461