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pro vyhledávání: '"Lai, Kevin"'
Autor:
Luo, Chester, Lai, Kevin
In recent years, there has been a significant increase in the utilization of deep learning methods, particularly convolutional neural networks (CNNs), which have emerged as the dominant approach in various domains that involve structured grid data, s
Externí odkaz:
http://arxiv.org/abs/2402.07710
Autor:
Lai, Kevin
This thesis is a collaboration with Stockholm Globe Arena Fastigher AB (SGAF) and focuses on a case study involving the multi-functional arenas Avicii Arena and Annexet in Stockholm, Sweden. The objective of this study is to investigate Key Performan
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-339372
Autor:
Lai, Kevin1, Dilger, Katharine1, Cunningham, Rachael1, Lam, Kathy T.1, Boquiren, Rhea1, Truong, Khiet1, Louie, Maggie C.1, Rava, Richard1, Abdueva, Diana1 diana.abdueva@aqtual.com
Publikováno v:
Communications Biology. 9/4/2024, Vol. 7 Issue 1, p1-10. 10p.
Autor:
Lai, Kevin, Sundman, Lina
Integrationen av hållbarhet blir allt viktigare för produktutvecklingsföretag. Men hållbarhet handlar inte bara om att hitta klimatsmarta lösningar. För att företag ska bli konkurrenskraftiga på marknaden samt vara attraktiva arbetsgivare så
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297970
Autor:
Mitra, Tatum Priyambada *, Coulter-Nile, Sarah, Jegathees, Thuvarahan, Luong, Jason, Shetty, Amith *, Lai, Kevin
Publikováno v:
In Journal of Emergency Medicine February 2024 66(2):57-63
Autor:
Bullins, Brian, Lai, Kevin A.
We provide improved convergence rates for constrained convex-concave min-max problems and monotone variational inequalities with higher-order smoothness. In min-max settings where the $p^{th}$-order derivatives are Lipschitz continuous, we give an al
Externí odkaz:
http://arxiv.org/abs/2007.04528
Fictitious Play (FP) is a simple and natural dynamic for repeated play in zero-sum games. Proposed by Brown in 1949, FP was shown to converge to a Nash Equilibrium by Robinson in 1951, albeit at a slow rate that may depend on the dimension of the pro
Externí odkaz:
http://arxiv.org/abs/1911.08418
Akademický článek
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Publikováno v:
In Safety Science October 2023 166
While classic work in convex-concave min-max optimization relies on average-iterate convergence results, the emergence of nonconvex applications such as training Generative Adversarial Networks has led to renewed interest in last-iterate convergence
Externí odkaz:
http://arxiv.org/abs/1906.02027