no code implementations • 28 Dec 2022 • Chih-Jung Tracy Chang, Yuan Gao, Beicheng Lou
In this paper, we introduce a novel variation of model-agnostic meta-learning, where an extra multiplicative parameter is introduced in the inner-loop adaptation.
1 code implementation • 15 Dec 2022 • Oliver Johnson, Beicheng Lou, Janet Zhong, Andrey Kurenkov
Often clickbait articles have a title that is phrased as a question or vague teaser that entices the user to click on the link and read the article to find the explanation.
no code implementations • 18 Sep 2020 • Nathan Zhao, Beicheng Lou
In analogy to compressed sensing, which allows sample-efficient signal reconstruction given prior knowledge of its sparsity in frequency domain, we propose to utilize policy simplicity (Occam's Razor) as a prior to enable sample-efficient imitation learning.
1 code implementation • 1 Mar 2020 • Momchil Minkov, Ian A. D. Williamson, Lucio C. Andreani, Dario Gerace, Beicheng Lou, Alex Y. Song, Tyler W. Hughes, Shanhui Fan
Here, we overcome this through the use of automatic differentiation, which is a generalization of the adjoint variable method to arbitrary computational graphs.
Optics Computational Engineering, Finance, and Science Applied Physics Computational Physics
no code implementations • 16 Aug 2019 • Weiquan Mao, Beicheng Lou, Jiyao Yuan
In this paper, we introduce a tunable generative adversary network (TunaGAN) that uses an auxiliary network on top of existing generator networks (Style-GAN) to modify high-resolution face images according to user's high-level instructions, with good qualitative and quantitative performance.