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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
- Googleorganization277 mentions
- NVIDIAorganization265 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
system identification
technology12 mentionslast seen Nov 10, 2026
A methodology for determining dynamic parameters of underwater vehicles through optimal excitation trajectories.
Relationships 10
- end-to-end reinforcement learning controldepends onsystem identification88% confidence1 source
- system identificationdepends onTransformer85% confidence1 source
- sparse equation discoveryapplies tosystem identification85% confidence1 source
- least squaresimplementssystem identification85% confidence1 source
- FedSysIDdepends onsystem identification85% confidence1 source
- Bezier curveimplementssystem identification85% confidence1 source
- system identificationcomposed ofmulti-step simulation82% confidence1 source
- system identificationcomposed ofone-step-ahead prediction82% confidence1 source
- uncertainty quantificationdepends onsystem identification80% confidence1 source
- autonomous racingcomposed ofsystem identification70% confidence1 source
Relationship
end-to-end reinforcement learning controldepends onsystem identification
88% confidence1 supporting mentions
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Sources 1