Results 111 to 120 of about 427,913 (167)

Multiclassification of the symptom severity of social anxiety disorder using digital phenotypes and feature representation learning. [PDF]

Digit Health
Choi H, Cho Y, Min C, Kim K, Kim E, Lee S, Kim JJ.   +6 more
europepmc   +1 more source

stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multimodal feature representation. [PDF]

Gigascience
Zhang D, Yu N, Yuan Z, Li W, Sun X, Zou Q, Li X, Liu Z, Zhang W, Gao R.   +9 more
europepmc   +1 more source

Identifying subgroups of eating behavior traits unrelated to obesity using functional connectivity and feature representation learning. [PDF]

Hum Brain Mapp
Choi H, Byeon K, Lee JE, Hong SJ, Park BY, Park H.   +5 more
europepmc   +1 more source

SVFR: A novel slice-to-volume feature representation framework using deep neural networks and a clustering model for the diagnosis of Alzheimer's disease. [PDF]

Heliyon
Wang R, Gao L, Zhang X, Han J, Alzheimer's Disease Neuroimaging Initiative.   +4 more
europepmc   +1 more source

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Prefrontal feature representations drive memory recall

Nature, 2022
Memory formation involves binding of contextual features into a unitary representation1-4, whereas memory recall can occur using partial combinations of these contextual features. The neural basis underlying the relationship between a contextual memory and its constituent features is not well understood; in particular, where features are represented in
Nakul Yadav, Chelsea Noble, James E. Niemeyer, Andrea Terceros, Jonathan Victor, Conor Liston, Priyamvada Rajasethupathy   +6 more
openaire   +2 more sources

Drifting neuronal representations: Bug or feature?

Biological Cybernetics, 2022
The brain displays a remarkable ability to sustain stable memories, allowing animals to execute precise behaviors or recall stimulus associations years after they were first learned. Yet, recent long-term recording experiments have revealed that single-neuron representations continuously change over time, contravening the classical assumption that ...
Paul Masset, Shanshan Qin, Jacob A. Zavatone-Veth   +2 more
openaire   +2 more sources

Adding Metalogic Features to Knowledge Representation Languages*

Fundamenta Informaticae, 2021
In this paper we present a methodology for introducing customizable metalogic features in logic-based knowledge representation and reasoning languages. The proposed approach is based on concepts of introspection and reflection previously introduced and discussed by various authors in relevant literature.
Costantini S., Formisano A.
openaire   +3 more sources

Feature Extraction and Representation

2018
This chapter is focused on some classical feature representations for image and video analysis. In particular, we will introduce the histogram-based features, texture features, and some local point features.
Shengrong Gong, Chunping Liu, Yi Ji, Baojiang Zhong, Yonggang Li, Husheng Dong   +5 more
openaire   +1 more source

Feature self-representation based hypergraph unsupervised feature selection via low-rank representation

Neurocomputing, 2017
Dimension reduction methods always catch many attentions, because it could effectively solve the curse of dimensionality problem. In this paper, we propose an unsupervised feature selection method which could efficiently select a subset of informative features from unlabeled data.
Wei He, Xiaohui Cheng, Rongyao Hu, Yonghua Zhu, Guoqiu Wen   +4 more
openaire   +1 more source