2 papers
stat.ML2024
Enhancing Sufficient Dimension Reduction via Hellinger Correlation
Seungbeom Hong, Ilmun Kim, Jun Song
In this work, we develop a new theory and method for sufficient dimension reduction (SDR) in single-index models, where SDR is a sub-field of supervised dimension reduction based o…
cs.IR2024
Fine-Grained Embedding Dimension Optimization During Training for Recommender Systems
Qinyi Luo, Penghan Wang, Wei Zhang +8
Huge embedding tables in modern deep learning recommender models (DLRM) require prohibitively large memory during training and inference. This paper proposes FIITED, a system to au…