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Entities25 of 207,987
- large language modeltechnology1,048 mentions
- robotic manipulationmarket834 mentions
- artificial intelligencetechnology739 mentions
- reinforcement learningtechnology492 mentions
- density-functional theorytechnology386 mentions
- Transformertechnology372 mentions
- vision-language-action modeltechnology355 mentions
- agentic AItechnology353 mentions
- Vision-Language Modeltechnology321 mentions
- U.S. Food and Drug Administrationorganization320 mentions
- Microsoftorganization291 mentions
- Googleorganization276 mentions
- NVIDIAorganization264 mentions
- Model Context Protocoltechnology232 mentions
- machine learningtechnology204 mentions
- MarketsandMarketsorganization199 mentions
- Claudeproduct192 mentions
- AI agenttechnology188 mentions
- ChatGPTproduct178 mentions
- Anthropicorganization177 mentions
- integrated sensing and communicationtechnology176 mentions
- Amazon Web Services, Inc.organization174 mentions
- OpenAIorganization172 mentions
- diffusion modeltechnology170 mentions
- IBMorganization163 mentions
Entity
attention-enhanced dual-branch network
technology1 mentionslast seen Sep 22, 2026
A neural network that jointly learns time-frequency and temporal representations from EEG segments for seizure detection.
Relationships 7
- attention-enhanced dual-branch networkapplies toEEG seizure detection93% confidence1 source
- attention-enhanced dual-branch networkdepends onConvNeXt-Tiny93% confidence1 source
- attention-enhanced dual-branch networkdepends onbidirectional long short-term memory network92% confidence1 source
- attention-enhanced dual-branch networkdepends onmultilayer perceptron92% confidence1 source
- attention-enhanced dual-branch networkdepends onwavelet transform92% confidence1 source
- attention-enhanced dual-branch networkdepends onsqueeze-and-excitation attention92% confidence1 source
- attention-enhanced dual-branch networkdepends onmulti-head self-attention90% confidence1 source
Relationship
attention-enhanced dual-branch networkdepends onsqueeze-and-excitation attention
92% confidence1 supporting mentions
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Sources 1