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Entities25 of 207,987
- large language modeltechnology1,043 mentions
- robotic manipulationmarket824 mentions
- artificial intelligencetechnology739 mentions
- reinforcement learningtechnology491 mentions
- density-functional theorytechnology386 mentions
- Transformertechnology371 mentions
- agentic AItechnology353 mentions
- vision-language-action modeltechnology350 mentions
- U.S. Food and Drug Administrationorganization320 mentions
- Vision-Language Modeltechnology318 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 communicationtechnology175 mentions
- Amazon Web Services, Inc.organization174 mentions
- OpenAIorganization172 mentions
- diffusion modeltechnology169 mentions
- IBMorganization163 mentions
Entity
recurrent CNN-LSTM
technology7 mentionslast seen Nov 10, 2026
A baseline model combining convolutional and recurrent neural networks that the proposed method outperforms.
Relationships 11
- recurrent CNN-LSTMapplies todepression detection92% confidence1 source
- processing-in-memory acceleratorimplementsrecurrent CNN-LSTM92% confidence1 source
- 65 nm SoC for closed-loop in-ear sleep modulationimplementsrecurrent CNN-LSTM92% confidence1 source
- mixed-precision CNN-LSTM acceleratorimplementsrecurrent CNN-LSTM91% confidence1 source
- recurrent CNN-LSTMcomposed ofLong Short-Term Memory90% confidence1 source
- recurrent CNN-LSTMcomposed ofconvolutional neural network90% confidence1 source
- Time2Vec Transformersupersedesrecurrent CNN-LSTM85% confidence1 source
- recurrent CNN-LSTMdepends onpermutation-invariant SI-SINR85% confidence1 source
- recurrent CNN-LSTMapplies tostorm surge prediction80% confidence1 source
- recurrent CNN-LSTMapplies togesture recognition80% confidence1 source
- recurrent CNN-LSTMapplies toobject tracking in cluttered environments75% confidence1 source
Relationship
recurrent CNN-LSTMdepends onpermutation-invariant SI-SINR
85% confidence1 supporting mentions
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Sources 1