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Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller
  Embedding Dimensions

Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions

17 July 2024
Chang Jo Kim
Raj Sinha
Sercan O. Arik
Tomas Pfister
ArXiv (abs)PDFHTML

Papers citing "Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions"

5 / 5 papers shown
CoRECT: A Framework for Evaluating Embedding Compression Techniques at Scale
CoRECT: A Framework for Evaluating Embedding Compression Techniques at Scale
L. Caspari
M. Dinzinger
K. Ghosh Dastidar
C. Fellicious
J. Mitrović
M. Granitzer
155
0
0
22 Oct 2025
SMEC: Rethinking Matryoshka Representation Learning for Retrieval Embedding Compression
SMEC: Rethinking Matryoshka Representation Learning for Retrieval Embedding Compression
Biao Zhang
Lixin Chen
Tong Liu
Bo Zheng
131
1
0
14 Oct 2025
Compressed Concatenation of Small Embedding Models
Compressed Concatenation of Small Embedding Models
M. Ayoub Ben Ayad
Michael Dinzinger
Kanishka Ghosh Dastidar
Jelena Mitrović
Michael Granitzer
112
0
0
06 Oct 2025
DocPruner: A Storage-Efficient Framework for Multi-Vector Visual Document Retrieval via Adaptive Patch-Level Embedding Pruning
DocPruner: A Storage-Efficient Framework for Multi-Vector Visual Document Retrieval via Adaptive Patch-Level Embedding Pruning
Yibo Yan
Guangwei Xu
Xin Zou
Shuliang Liu
James Kwok
Xuming Hu
190
5
0
28 Sep 2025
ConceptCarve: Dynamic Realization of Evidence
ConceptCarve: Dynamic Realization of EvidenceAnnual Meeting of the Association for Computational Linguistics (ACL), 2025
Eylon Caplan
Dan Goldwasser
399
0
0
09 Apr 2025
1