Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Hao-Jun Michael Shi"'
Publikováno v:
Optimization Methods and Software. 38:289-311
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9517d3edc8a1a12ffe290f61c968efb7
https://doi.org/10.1080/10556788.2022.2121832
https://doi.org/10.1080/10556788.2022.2121832
A common approach for minimizing a smooth nonlinear function is to employ finite-difference approximations to the gradient. While this can be easily performed when no error is present within the function evaluations, when the function is noisy, the o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3af9c33d3a9ffb24f3b21143241a76ed
Publikováno v:
SIAM Journal on Scientific Computing; 2022, Vol. 44 Issue 4, pA2302-A2321, 20p
Publikováno v:
IEEE Signal Processing Letters. 25:45-49
This letter is focused on quantized Compressed Sensing, assuming that Lasso is used for signal estimation. Leveraging recent work, we provide a framework to optimize the quantization function and show that the recovered signal converges to the actual
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f165e3adbd4e651382e4d6d185d61a02
https://doi.org/10.1109/lsp.2017.2770018
https://doi.org/10.1109/lsp.2017.2770018
Publikováno v:
KDD
Modern deep learning-based recommendation systems exploit hundreds to thousands of different categorical features, each with millions of different categories ranging from clicks to posts. To respect the natural diversity within the categorical data,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f5f653a5c6b3e8363a7d85adf26ba8c1
We propose two practical non-convex approaches for learning near-isometric, linear embeddings of finite sets of data points. Given a set of training points $\mathcal{X}$, we consider the secant set $S(\mathcal{X})$ that consists of all pairwise diffe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13f6e16b56c96456a9d6e6476578968b
Publikováno v:
ITA
In this paper, we compare and catalog the performance of various greedy quantized compressed sensing algorithms that reconstruct sparse signals from quantized compressed measurements. We also introduce two new greedy approaches for reconstruction: Qu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6606ace657528032532755c9b519d5cf
http://arxiv.org/abs/1512.09184
http://arxiv.org/abs/1512.09184
Publikováno v:
IEEE Signal Processing Letters; Jan2018, Vol. 25 Issue 1, p45-49, 5p