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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