What is recorded here?
Trained multiple nested prefixes of a shared embedding with size-specific objectives, enabling downstream classification/retrieval to use different dimensionalities without separate encoder forward passes.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Multi-prefix loss adds training obligations; the encoder forward cost is not reduced merely by truncating its output. Quality preservation, interpolated dimensions and speedups are empirical for tested setups, not every dimension/task. Nested-dropout and other adaptive-network predecessors are acknowledged; first3equal credit includes a project-lead distinction.
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People and systems
Aditya Kusupati; Gantavya Bhatt; Aniket Rege; Matthew Wallingford; Aditya Sinha; Vivek Ramanujan; William Howard-Snyder; Kaifeng Chen; Sham Kakade; Prateek Jain; Ali Farhadi
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Matryoshka representation learning for nested embedding dimensions
Trained multiple nested prefixes of a shared embedding with size-specific objectives, enabling downstream classification/retrieval to use different dimensionalities without separate encoder forward passes.
Scientific scope
Multi-prefix loss adds training obligations; the encoder forward cost is not reduced merely by truncating its output. Quality preservation, interpolated dimensions and speedups are empirical for tested setups, not every dimension/task. Nested-dropout and other adaptive-network predecessors are acknowledged; first3equal credit includes a project-lead distinction.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Aditya Kusupati; Gantavya Bhatt; Aniket Rege; Matthew Wallingford; Aditya Sinha; Vivek Ramanujan; William Howard-Snyder; Kaifeng Chen; Sham Kakade; Prateek Jain; Ali Farhadi
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.