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pro vyhledávání: '"Kirby, Michael"'
In this paper a multi-domain multi-task algorithm for feature selection in bulk RNAseq data is proposed. Two datasets are investigated arising from mouse host immune response to Salmonella infection. Data is collected from several strains of collabor
Externí odkaz:
http://arxiv.org/abs/2405.02534
Autor:
McCaughan, Eilís, Flannagan, Carrie, Parahoo, Kader, Connaghan, John, Maguire, Roma, Steele, Mary, Thompson, Samantha, Jain, Suneil, Kirby, Michael, Brady, Nuala, O'Connor, Seán R
Publikováno v:
JMIR Cancer, Vol 6, Iss 2, p e20137 (2020)
BackgroundLong-term side-effects associated with different prostate cancer treatment approaches are common. Sexual challenges are the most frequently occurring issues and can result in increased psychological morbidity. It is recognized that barriers
Externí odkaz:
https://doaj.org/article/8fa8b06f33ef4dc2949985773a1d0d35
Autor:
Kirby, Michael
We summarize the status of Deep Underground Neutrino Experiment (DUNE) Offline Software and Computing program. We describe plans for the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE exp
Externí odkaz:
http://arxiv.org/abs/2312.11239
A ReLU neural network leads to a finite polyhedral decomposition of input space and a corresponding finite dual graph. We show that while this dual graph is a coarse quantization of input space, it is sufficiently robust that it can be combined with
Externí odkaz:
http://arxiv.org/abs/2306.17418
We present a novel feature selection technique, Sparse Linear Centroid-Encoder (SLCE). The algorithm uses a linear transformation to reconstruct a point as its class centroid and, at the same time, uses the $\ell_1$-norm penalty to filter out unneces
Externí odkaz:
http://arxiv.org/abs/2306.04824
Autor:
Ghosh, Tomojit, Kirby, Michael
We introduce a novel nonlinear model, Sparse Adaptive Bottleneck Centroid-Encoder (SABCE), for determining the features that discriminate between two or more classes. The algorithm aims to extract discriminatory features in groups while reconstructin
Externí odkaz:
http://arxiv.org/abs/2306.04795
Autor:
Ghosh, Tomojit, Kirby, Michael
We propose a new supervised dimensionality reduction technique called Supervised Linear Centroid-Encoder (SLCE), a linear counterpart of the nonlinear Centroid-Encoder (CE) \citep{ghosh2022supervised}. SLCE works by mapping the samples of a class to
Externí odkaz:
http://arxiv.org/abs/2306.04622
Autor:
Jamil, Huma, Liu, Yajing, Caglar, Turgay, Cole, Christina M., Blanchard, Nathaniel, Peterson, Christopher, Kirby, Michael
Researchers typically investigate neural network representations by examining activation outputs for one or more layers of a network. Here, we investigate the potential for ReLU activation patterns (encoded as bit vectors) to aid in understanding and
Externí odkaz:
http://arxiv.org/abs/2305.01808
Autor:
Jamil, Huma, Liu, Yajing, Cole, Christina M., Blanchard, Nathaniel, King, Emily J., Kirby, Michael, Peterson, Christopher
Previous work has shown that a neural network with the rectified linear unit (ReLU) activation function leads to a convex polyhedral decomposition of the input space. These decompositions can be represented by a dual graph with vertices corresponding
Externí odkaz:
http://arxiv.org/abs/2211.13305