Jan 9, 2024 · Aiming to reduce the memory footprint of training, this paper proposes FIne-grained In-Training Embedding Dimension optimization (FIITED).
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Specifically, they demonstrate that FIITED can reduce embedding size during training by more than 65% while maintaining the quality of the trained model.
Jan 9, 2024 · This paper proposes FIITED, a FIne-grained In-Training Embedding Dimension optimization method, where the dimension of each embedding vector is adjusted during ...
Jan 9, 2024 · Huge embedding tables in modern deep learning recommender models (DLRM) require prohibitively large memory during training and inference.
Aiming to reduce the memory footprint of training, this paper proposes FIne-grained In-Training Embedding Dimension optimization (FIITED). Given the observation ...
Jan 12, 2024 · This paper from Meta proposes FIITED, a method to optimize the embedding dimensions in deep learning recommendation models during training to ...
[PDF] Embedding Compression in Recommender Systems: A Survey
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This survey provides a comprehensive review of embedding compression approaches in recommender systems and systematically organize existing approaches into ...
Fine-Grained Embedding Dimension Optimization During Training for Recommender Systems ... Huge embedding tables in modern Deep Learning Recommender Models (DLRM) ...
Oct 10, 2024 · In this paper, we adapt iterative examples for deep recommender systems. Specifically, we propose a Deep Recommender with Iteration Directional ...
A Single-Shot Embedding Dimension Search method, called SSEDS, which can efficiently assign dimensions for each feature field via a single-shot embedding ...