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pro vyhledávání: '"Casey, Michael P."'
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
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Akademický článek
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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