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pro vyhledávání: '"Hard thresholding"'
For statistical modeling wherein the data regime is unfavorable in terms of dimensionality relative to the sample size, finding hidden sparsity in the ground truth can be critical in formulating an accurate statistical model. The so-called "l0 norm"
Externí odkaz:
http://arxiv.org/abs/2409.01413
Stochastic optimization algorithms are widely used for large-scale data analysis due to their low per-iteration costs, but they often suffer from slow asymptotic convergence caused by inherent variance. Variance-reduced techniques have been therefore
Externí odkaz:
http://arxiv.org/abs/2407.16968
Color Filter Arrays (CFA) are optical filters in digital cameras that capture specific color channels. Current commercial CFAs are hand-crafted patterns with different physical and application-specific considerations. This study proposes a binary CFA
Externí odkaz:
http://arxiv.org/abs/2406.14421
Autor:
Cheng, Langlang1,2,3,4 (AUTHOR) chenglanglang@ntsc.ac.cn, Zhang, Shougang1,2,3,4 (AUTHOR) szhang@ntsc.ac.cn, Qi, Zhen1,2,3,4 (AUTHOR) qizhen@ntsc.ac.cn, Wang, Xin1,3,4 (AUTHOR) wangx@ntsc.ac.cn, Chen, Yingming1,3,4 (AUTHOR) cym@ntsc.ac.cn, Feng, Ping1,2,3,4 (AUTHOR) pingfp@ntsc.ac.cn
Publikováno v:
Remote Sensing. Aug2024, Vol. 16 Issue 16, p3012. 24p.
While extensive research has been conducted on high-dimensional data and on regression with left-censored responses, simultaneously addressing these complexities remains challenging, with only a few proposed methods available. In this paper, we utili
Externí odkaz:
http://arxiv.org/abs/2405.02539
Evolution Strategies (ES) have emerged as a competitive alternative for model-free reinforcement learning, showcasing exemplary performance in tasks like Mujoco and Atari. Notably, they shine in scenarios with imperfect reward functions, making them
Externí odkaz:
http://arxiv.org/abs/2405.01615
Akademický článek
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Akademický článek
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Autor:
Matsumoto, Namiko, Mazumdar, Arya
In 1-bit compressed sensing, the aim is to estimate a $k$-sparse unit vector $x\in S^{n-1}$ within an $\epsilon$ error (in $\ell_2$) from minimal number of linear measurements that are quantized to just their signs, i.e., from measurements of the for
Externí odkaz:
http://arxiv.org/abs/2310.08019