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pro vyhledávání: '"Casey, Michael"'
Autor:
Casey, Michael P.
For a set $X$ of $N$ points in $\mathbb{R}^D$, the Johnson-Lindenstrauss lemma provides random linear maps that approximately preserve all pairwise distances in $X$ -- up to multiplicative error $(1\pm \epsilon)$ with high probability -- using a targ
Externí odkaz:
http://arxiv.org/abs/2307.07704
Autor:
Paulsen, Sean, Casey, Michael
We present a sequential transfer learning framework for transformers on functional Magnetic Resonance Imaging (fMRI) data and demonstrate its significant benefits for decoding musical timbre. In the first of two phases, we pre-train our stacked-encod
Externí odkaz:
http://arxiv.org/abs/2305.13226
Autor:
Paulsen, Sean, Casey, Michael
In this work we introduce a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general unde
Externí odkaz:
http://arxiv.org/abs/2305.09057
Stimulus decoding of functional Magnetic Resonance Imaging (fMRI) data with machine learning models has provided new insights about neural representational spaces and task-related dynamics. However, the scarcity of labelled (task-related) fMRI data i
Externí odkaz:
http://arxiv.org/abs/2305.08987
Autor:
Guo, Xueqi, Zhou, Bo, Pigg, David, Spottiswoode, Bruce, Casey, Michael E., Liu, Chi, Dvornek, Nicha C.
Subject motion in whole-body dynamic PET introduces inter-frame mismatch and seriously impacts parametric imaging. Traditional non-rigid registration methods are generally computationally intense and time-consuming. Deep learning approaches are promi
Externí odkaz:
http://arxiv.org/abs/2206.06341
Autor:
Mulas, Raffaella, Casey, Michael J.
Publikováno v:
Mathematical Biosciences (2021)
Networks of genetic expression can be modelled by hypergraphs with the additional structure that real coefficients are given to each vertex-edge incidence. The spectra, i.e. the multiset of the eigenvalues, of such hypergraphs, are known to encode st
Externí odkaz:
http://arxiv.org/abs/2106.03663
Autor:
Casey, Michael P.
For any finite point set in $D$-dimensional space equipped with the 1-norm, we present random linear embeddings to $k$-dimensional space, with a new metric, having the following properties. For any pair of points from the point set that are not too c
Externí odkaz:
http://arxiv.org/abs/1906.03536
In this paper we generalize the estimation-control duality that exists in the linear-quadratic-Gaussian setting. We extend this duality to maximum a posteriori estimation of the system's state, where the measurement and dynamical system noise are ind
Externí odkaz:
http://arxiv.org/abs/1607.02522
Complex-valued neural networks (CVNNs) are an emerging field of research in neural networks due to their potential representational properties for audio, image, and physiological signals. It is common in signal processing to transform sequences of re
Externí odkaz:
http://arxiv.org/abs/1511.06351
Autor:
Gladders, Michael D., Clarke, Tracy E., Burns, Christopher R., Attard, Allen, Casey, Michael P., Hamilton, Devon, Mallen-Ornelas, Gabriela, Karr, Jennifer L., Poirier, Sara M., Sawicki, Marcin, Barrientos, Felipe, Barkhouse, Wayne, Brodwin, Mark, Clark, Jason, McNaughton, Rosemary, Ruetalo-Pacheco, Marcelo, Mochnacki, Stefan W.
We present the first set of distance limits from the David Dunlap Observatory Intermediate Velocity Cloud (DDO IVC) distance project. Such distance measures are crucial to understanding the origins and dynamics of IVCs, as the distances set most of t
Externí odkaz:
http://arxiv.org/abs/astro-ph/9811291