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pro vyhledávání: '"McDermott, Luke"'
Autor:
McDermott, Luke
Real world re-identfication (ReID) algorithms aim to map new observations of an object to previously recorded instances. These systems are often constrained by quantity and size of the stored embeddings. To combat this scaling problem, we attempt to
Externí odkaz:
http://arxiv.org/abs/2405.14730
We develop an automated pipeline to streamline neural architecture codesign for fast, real-time Bragg peak analysis in high-energy diffraction microscopy. Traditional approaches, notably pseudo-Voigt fitting, demand significant computational resource
Externí odkaz:
http://arxiv.org/abs/2312.05978
Multimodal Re-Identification (ReID) is a popular retrieval task that aims to re-identify objects across diverse data streams, prompting many researchers to integrate multiple modalities into a unified representation. While such fusion promises a holi
Externí odkaz:
http://arxiv.org/abs/2310.18812
Autor:
McDermott, Luke, Cummings, Daniel
With the rise in interest of sparse neural networks, we study how neural network pruning with synthetic data leads to sparse networks with unique training properties. We find that distilled data, a synthetic summarization of the real data, paired wit
Externí odkaz:
http://arxiv.org/abs/2310.18769
Object Re-Identification (ReID) is pivotal in computer vision, witnessing an escalating demand for adept multimodal representation learning. Current models, although promising, reveal scalability limitations with increasing modalities as they rely he
Externí odkaz:
http://arxiv.org/abs/2310.16856
Deep learning harnesses massive parallel floating-point processing to train and evaluate large neural networks. Trends indicate that deeper and larger neural networks with an increasing number of parameters achieve higher accuracy than smaller neural
Externí odkaz:
http://arxiv.org/abs/2308.14605
Autor:
McDermott, Luke, Cummings, Daniel
This work introduces a novel approach to pruning deep learning models by using distilled data. Unlike conventional strategies which primarily focus on architectural or algorithmic optimization, our method reconsiders the role of data in these scenari
Externí odkaz:
http://arxiv.org/abs/2307.03364
Autor:
Mcdermott, Luke
In this thesis, I investigate the linguistic resources and strategies used to describe spatial concepts such as location orientation and motion in the variety of Chiapas Zoque (CZ; Mixe-Zoquean) spoken in the southern Mexican town of Ocotepec, Chiapa
Externí odkaz:
http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740334
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