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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
self-attention
technology37 mentionslast seen Nov 10, 2026
A mechanism integrated into the BiHDTrans architecture to capture temporal patterns.
Relationships 27
- Transformerdepends onself-attention99% confidence5 sources
- quantum self-attentionimplementsself-attention95% confidence1 source
- Quantum-inspired Transformercomposed ofself-attention93% confidence1 source
- MiDASdepends onself-attention92% confidence1 source
- SimpliHuMoNdepends onself-attention92% confidence1 source
- Signal-Context Gated Fusion Networkdepends onself-attention92% confidence1 source
- BiHDTransimplementsself-attention92% confidence1 source
- self-attention Wasserstein-GANdepends onself-attention90% confidence1 source
- self-attention in-the-loopdepends onself-attention90% confidence1 source
- Multimodality Context Attentiondepends onself-attention90% confidence1 source
- self-attentionapplies tofar-field holography90% confidence1 source
- Robots as Tokensdepends onself-attention90% confidence1 source
- AUWavedepends onself-attention90% confidence1 source
- TRAACdepends onself-attention90% confidence1 source
- fuzzy attention mechanismdepends onself-attention90% confidence1 source
- cross-modal transformerdepends onself-attention88% confidence1 source
- Jacobian matrix analysisapplies toself-attention88% confidence1 source
- multi-layer cross-attentionsupersedesself-attention85% confidence1 source
- self-attentionimplementsin-context learning85% confidence1 source
- layer normalizationcomposed ofself-attention85% confidence1 source
- diffusion modeldepends onself-attention85% confidence1 source
- Walsh-Hadamard token mixingsupersedesself-attention85% confidence1 source
- self-attentionapplies totime series forecasting85% confidence1 source
- Hyperbolic Positional Encodingdepends onself-attention80% confidence1 source
- Adaptive Anisotropic Attentionsupersedesself-attention75% confidence1 source
- event sparse linear transformersupersedesself-attention75% confidence1 source
- self-attentionsupersedesphase retrieval75% confidence1 source
Relationship
Transformerdepends onself-attention
99% confidence5 supporting mentions
Either name opens that entity’s relationships.
Sources 5
- preprintarXivNov 6, 2026Trilinear Compute-in-Memory Architecture for Energy-Efficient Transformer Acceleration
Self-attention in Transformers
- preprintarXivOct 18, 2026Patch-Level Tokenization with CNN Encoders and Attention for Improved Transformer Time-Series Forecasting
Transformer-based models have shown strong performance in time-series forecasting by leveraging self-attention
- preprintarXivOct 15, 2026WHTMix: Efficient Stereo Depth Estimation via Walsh-Hadamard Token Mixing
in transformer-based matchers the global self-attention that aggregates scene context
- preprintarXivOct 14, 2026Rational Transductors
Standard Transformers excel at semantic modeling but struggle with rigid sequential logic
- preprintarXivOct 2, 2026Permutation Robustness Is Not Enough: Action Collapse in Multi-Agent Transformer Policies
Transformer policies are attractive for multi-agent robot learning because self-attention can model interactions among agents