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pro vyhledávání: '"Hsieh YA"'
Many modern machine learning applications - from online principal component analysis to covariance matrix identification and dictionary learning - can be formulated as minimization problems on Riemannian manifolds, and are typically solved with a Rie
Externí odkaz:
http://arxiv.org/abs/2311.02374
Autor:
Yadav, Shyam Narayan Singh, Chen, Po-Liang, Yao, Yu-Chi, Wang, Yen-Yu, Lien, Der-Hsien, Lu, Yu-Jung, Hsieh, Ya-Ping, Liu, Chang-Hua, Yen, Ta-Jen
Owing to its atomically thin thickness, layer-dependent tunable band gap, flexibility, and CMOS compatibility, MoS$_2$ is a promising candidate for photodetection. However, mono-layer MoS2-based photodetectors typically show poor optoelectronic perfo
Externí odkaz:
http://arxiv.org/abs/2308.14750
Schr\"odinger bridges (SBs) provide an elegant framework for modeling the temporal evolution of populations in physical, chemical, or biological systems. Such natural processes are commonly subject to changes in population size over time due to the e
Externí odkaz:
http://arxiv.org/abs/2306.09099
Autor:
Somnath, Vignesh Ram, Pariset, Matteo, Hsieh, Ya-Ping, Martinez, Maria Rodriguez, Krause, Andreas, Bunne, Charlotte
Diffusion Schr\"odinger bridges (DSB) have recently emerged as a powerful framework for recovering stochastic dynamics via their marginal observations at different time points. Despite numerous successful applications, existing algorithms for solving
Externí odkaz:
http://arxiv.org/abs/2302.11419
Non-convex sampling is a key challenge in machine learning, central to non-convex optimization in deep learning as well as to approximate probabilistic inference. Despite its significance, theoretically there remain many important challenges: Existin
Externí odkaz:
http://arxiv.org/abs/2210.13867
Algorithms that solve zero-sum games, multi-objective agent objectives, or, more generally, variational inequality (VI) problems are notoriously unstable on general problems. Owing to the increasing need for solving such problems in machine learning,
Externí odkaz:
http://arxiv.org/abs/2207.07105
We examine a wide class of stochastic approximation algorithms for solving (stochastic) nonlinear problems on Riemannian manifolds. Such algorithms arise naturally in the study of Riemannian optimization, game theory and optimal transport, but their
Externí odkaz:
http://arxiv.org/abs/2206.06795
We develop a flexible stochastic approximation framework for analyzing the long-run behavior of learning in games (both continuous and finite). The proposed analysis template incorporates a wide array of popular learning algorithms, including gradien
Externí odkaz:
http://arxiv.org/abs/2206.03922
The static optimal transport $(\mathrm{OT})$ problem between Gaussians seeks to recover an optimal map, or more generally a coupling, to morph a Gaussian into another. It has been well studied and applied to a wide variety of tasks. Here we focus on
Externí odkaz:
http://arxiv.org/abs/2202.05722
Autor:
Chand, Pradyumna Kumar, Raman, Radha, Yen, Zhi-Long, Santos, Ian Daniell, Liao, Wei-Ssu, Hsieh, Ya-Ping, Hofmann, Mario
Publikováno v:
In Journal of Science: Advanced Materials and Devices September 2024 9(3)
