Sparsely-Gated Mixture-of-Experts layer
E1339917
UNEXPLORED
The Sparsely-Gated Mixture-of-Experts layer is a neural network architecture that routes each input through a small, dynamically selected subset of many specialized expert networks to greatly increase model capacity with limited computational cost.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Sparsely-Gated Mixture-of-Experts layer canonical | 3 |
| Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T18724106 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sparsely-Gated Mixture-of-Experts layer Context triple: [Noam Shazeer, knownFor, Sparsely-Gated Mixture-of-Experts layer]
-
A.
Sparse Transformer
Sparse Transformer is a neural network architecture that uses sparse attention patterns to efficiently model long-range dependencies in sequences while reducing computational cost compared to standard Transformers.
-
B.
Reformer architecture
The Reformer architecture is a neural network model that improves Transformer efficiency by using locality-sensitive hashing attention and reversible layers to greatly reduce memory and computational costs.
-
C.
Transformer-XL
Transformer-XL is a neural network architecture for language modeling that extends the Transformer with segment-level recurrence and relative positional encodings to better capture long-range dependencies.
-
D.
One Model To Learn Them All
"One Model To Learn Them All" is a research paper that introduces a unified neural network architecture capable of handling multiple tasks and modalities within a single model.
-
E.
DeepScale
DeepScale was an AI startup focused on efficient deep learning and computer vision models for resource-constrained devices, particularly in the automotive and embedded systems space.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sparsely-Gated Mixture-of-Experts layer Target entity description: The Sparsely-Gated Mixture-of-Experts layer is a neural network architecture that routes each input through a small, dynamically selected subset of many specialized expert networks to greatly increase model capacity with limited computational cost.
-
A.
Sparse Transformer
Sparse Transformer is a neural network architecture that uses sparse attention patterns to efficiently model long-range dependencies in sequences while reducing computational cost compared to standard Transformers.
-
B.
Reformer architecture
The Reformer architecture is a neural network model that improves Transformer efficiency by using locality-sensitive hashing attention and reversible layers to greatly reduce memory and computational costs.
-
C.
Transformer-XL
Transformer-XL is a neural network architecture for language modeling that extends the Transformer with segment-level recurrence and relative positional encodings to better capture long-range dependencies.
-
D.
One Model To Learn Them All
"One Model To Learn Them All" is a research paper that introduces a unified neural network architecture capable of handling multiple tasks and modalities within a single model.
-
E.
DeepScale
DeepScale was an AI startup focused on efficient deep learning and computer vision models for resource-constrained devices, particularly in the automotive and embedded systems space.
- F. None of above. chosen
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.
Noam Shazeer
→
coAuthorOf
→
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
ⓘ
linked to: Sparsely-Gated Mixture-of-Experts layer