Home ›
Entities
› academia
› Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia
Research article (NeuroImage, 2015) · cited 367× · AI/ML
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia
Summary
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia is a scholarly article[1].
Key Facts
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. Retrieved May 24, 2026, from https://4ort.xyz/entity/deep-neural-network-with-weight-sparsity-control-and-pre-training-extracts-hierarchical-features-and-enhances-classifica
MLA“Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/deep-neural-network-with-weight-sparsity-control-and-pre-training-extracts-hierarchical-features-and-enhances-classifica.
BibTeX@misc{4ortxyz_deep-neural-network-with-weight-sparsity-control-and-pre-training-extracts-hierarchical-features-and-enhances-classifica_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia}}, year = {2026}, url = {https://4ort.xyz/entity/deep-neural-network-with-weight-sparsity-control-and-pre-training-extracts-hierarchical-features-and-enhances-classifica}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia — https://4ort.xyz/entity/deep-neural-network-with-weight-sparsity-control-and-pre-training-extracts-hierarchical-features-and-enhances-classifica (retrieved 2026-05-24)