Zobrazeno 1 - 10
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pro vyhledávání: '"To-Nguyen Lam"'
In time-series analysis, many recent works seek to provide a unified view and representation for time-series across multiple domains, leading to the development of foundation models for time-series data. Despite diverse modeling techniques, existing
Externí odkaz:
http://arxiv.org/abs/2411.01006
Publikováno v:
38th Conference on Neural Information Processing Systems (NeurIPS 2024
This paper aims at developing novel shuffling gradient-based methods for tackling two classes of minimax problems: nonconvex-linear and nonconvex-strongly concave settings. The first algorithm addresses the nonconvex-linear minimax model and achieves
Externí odkaz:
http://arxiv.org/abs/2410.22297
Physics-informed neural networks (PINNs) incorporate physical laws into their training to efficiently solve partial differential equations (PDEs) with minimal data. However, PINNs fail to guarantee adherence to conservation laws, which are also impor
Externí odkaz:
http://arxiv.org/abs/2410.17445
We greatly expand the application of multiphoton microscopy to geological investigations by using a tightly focused femtosecond laser beam to excite fluorescent emissions among minimally prepared rock and mineral samples. This new finding provides a
Externí odkaz:
http://arxiv.org/abs/2408.16006
Foundational models (FMs), pretrained on extensive datasets using self-supervised techniques, are capable of learning generalized patterns from large amounts of data. This reduces the need for extensive labeled datasets for each new task, saving both
Externí odkaz:
http://arxiv.org/abs/2406.09837
The Stochastic Gradient Descent method (SGD) and its stochastic variants have become methods of choice for solving finite-sum optimization problems arising from machine learning and data science thanks to their ability to handle large-scale applicati
Externí odkaz:
http://arxiv.org/abs/2403.03180
We develop and analyze stochastic variants of ISTA and a full backtracking FISTA algorithms [Beck and Teboulle, 2009, Scheinberg et al., 2014] for composite optimization without the assumption that stochastic gradient is an unbiased estimator. This w
Externí odkaz:
http://arxiv.org/abs/2402.15646
Autor:
Yu, Guangsheng, Wang, Qin, Sun, Caijun, Nguyen, Lam Duc, Bandara, H. M. N. Dilum, Chen, Shiping
In this paper, we study how to optimize existing Non-Fungible Token (NFT) incentives. Upon exploring a large number of NFT-related standards and real-world projects, we come across an unexpected finding. That is, the current NFT incentive mechanisms,
Externí odkaz:
http://arxiv.org/abs/2402.06459
This paper addresses the task of counting human actions of interest using sensor data from wearable devices. We propose a novel exemplar-based framework, allowing users to provide exemplars of the actions they want to count by vocalizing predefined s
Externí odkaz:
http://arxiv.org/abs/2312.17330
Autor:
Nguyen, Anh Duc, Nguyen, Tuan Dung, Nguyen, Quang Minh, Nguyen, Hoang H., Nguyen, Lam M., Toh, Kim-Chuan
This paper studies the Partial Optimal Transport (POT) problem between two unbalanced measures with at most $n$ supports and its applications in various AI tasks such as color transfer or domain adaptation. There is hence the need for fast approximat
Externí odkaz:
http://arxiv.org/abs/2312.13970