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pro vyhledávání: '"Kim Jihun"'
Semantic Scene Completion (SSC) aims to perform geometric completion and semantic segmentation simultaneously. Despite the promising results achieved by existing studies, the inherently ill-posed nature of the task presents significant challenges in
Externí odkaz:
http://arxiv.org/abs/2410.15674
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
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
We develop new techniques in order to deal with Riccati-type equations, subject to a further algebraic constraint, on Riemannian manifolds $(M^3,g)$. We find that the obstruction to solve the aforementioned equation has order $4$ in the metric coeffi
Externí odkaz:
http://arxiv.org/abs/2407.16915
Deploying deep models in real-world scenarios entails a number of challenges, including computational efficiency and real-world (e.g., long-tailed) data distributions. We address the combined challenge of learning long-tailed distributions using high
Externí odkaz:
http://arxiv.org/abs/2404.00285
Autor:
Choi, Hyunmin, Kim, Jihun, Kim, Seungho, Park, Seonhye, Park, Jeongyong, Choi, Wonbin, Kim, Hyoungshick
Homomorphic encryption (HE) enables privacy-preserving deep learning by allowing computations on encrypted data without decryption. However, deploying convolutional neural networks (CNNs) with HE is challenging due to the need to convert input data i
Externí odkaz:
http://arxiv.org/abs/2402.03060
Single image super-resolution (SISR) has experienced significant advancements, primarily driven by deep convolutional networks. Traditional networks, however, are limited to upscaling images to a fixed scale, leading to the utilization of implicit ne
Externí odkaz:
http://arxiv.org/abs/2311.12077
Autor:
Lew, Hah Min, Kim, Jae Seong, Lee, Moon Hwan, Park, Jaegeun, Youn, Sangyeon, Kim, Hee Man, Kim, Jihun, Hwang, Jae Youn
Endoscopic ultrasound (EUS) imaging has a trade-off between resolution and penetration depth. By considering the in-vivo characteristics of human organs, it is necessary to provide clinicians with appropriate hardware specifications for precise diagn
Externí odkaz:
http://arxiv.org/abs/2309.06770