Our contribution is Matryoshka Representation Learning ( MRL ) which encodes information at different granularities and allows a single embedding to adapt to the computational constraints of downstream tasks. MRL minimally modiﬁes existing representation learning pipelines and imposes no additional cost during inference and deployment. MRL learns coarse-to-ﬁne representations that are at least as accurate and rich as independently trained low-dimensional representations.2022: Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, S. Kakade, Prateek Jain, Ali Farhadihttps://arxiv.org/pdf/2205.13147v2.pdf
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