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pro vyhledávání: '"A. Moon"'
Abstract. The advancement of deep learning has coincided with the proliferation of both models and available data. The surge in dataset sizes and the subsequent surge in computational requirements have led to the development of the Dataset Condensati
Externí odkaz:
http://arxiv.org/abs/2409.14538
Autor:
Bai, Yuehao, Huang, Shunzhuang, Moon, Sarah, Santos, Andres, Shaikh, Azeem M., Vytlacil, Edward J.
In a setting with a multi-valued outcome, treatment and instrument, this paper considers the problem of inference for a general class of treatment effect parameters. The class of parameters considered are those that can be expressed as the expectatio
Externí odkaz:
http://arxiv.org/abs/2411.05220
Autor:
Khan, Muhammad Tayyab, Chen, Lequn, Ng, Ye Han, Feng, Wenhe, Tan, Nicholas Yew Jin, Moon, Seung Ki
Geometric Dimensioning and Tolerancing (GD&T) plays a critical role in manufacturing by defining acceptable variations in part features to ensure component quality and functionality. However, extracting GD&T information from 2D engineering drawings i
Externí odkaz:
http://arxiv.org/abs/2411.03707
The stability of an irreversible singularity, such as a Riemann shock solution to the full Euler system, in the absence of any technical conditions for perturbations, remains a major open problem even within a mono-dimensional framework. A natural ap
Externí odkaz:
http://arxiv.org/abs/2411.03613
Autor:
Lee, Seunghun, Moon, Eun-Gook
Mixed-state phases of matter under local decoherence have recently garnered significant attention due to the ubiquitous presence of noise in current quantum processors. One of the key issues is understanding how topological quantum memory is affected
Externí odkaz:
http://arxiv.org/abs/2411.03441
Due to the lack of state dimension optimization methods, deep state space models (SSMs) have sacrificed model capacity, training search space, or stability to alleviate computational costs caused by high state dimensions. In this work, we provide a s
Externí odkaz:
http://arxiv.org/abs/2411.02824
Autor:
Khan, Muhammad Tayyab, Chen, Lequn, Ng, Ye Han, Feng, Wenhe, Tan, Nicholas Yew Jin, Moon, Seung Ki
Automatic feature recognition (AFR) is essential for transforming design knowledge into actionable manufacturing information. Traditional AFR methods, which rely on predefined geometric rules and large datasets, are often time-consuming and lack gene
Externí odkaz:
http://arxiv.org/abs/2411.02810
We introduce an industrial Head Blending pipeline for the task of seamlessly integrating an actor's head onto a target body in digital content creation. The key challenge stems from discrepancies in head shape and hair structure, which lead to unnatu
Externí odkaz:
http://arxiv.org/abs/2411.00652
Excited-state molecular dynamics (ESMD) simulations near conical intersections (CIs) pose significant challenges when using machine learning potentials (MLPs). Although MLPs have gained recognition for their integration into mixed quantum-classical (
Externí odkaz:
http://arxiv.org/abs/2410.22801
To effectively address potential harms from AI systems, it is essential to identify and mitigate system-level hazards. Current analysis approaches focus on individual components of an AI system, like training data or models, in isolation, overlooking
Externí odkaz:
http://arxiv.org/abs/2410.22526