$The trade-offs of model size in large recommendation models : A 10000 $\times$ compressed criteo-tb DLRM model (100 GB parameters to mere 10MB)$

The trade-offs of model size in large recommendation models : A 10000 $\times$ compressed criteo-tb DLRM model (100 GB parameters to mere 10MB)

21 July 2022

Anshumali Shrivastava

Papers citing "The trade-offs of model size in large recommendation models : A 10000 $\times$ compressed criteo-tb DLRM model (100 GB parameters to mere 10MB)"

17 / 17 papers shown

Title
$Random Offset Block Embedding Array (ROBE) for CriteoTB Benchmark MLPerf DLRM Model : 1000$\times$ Compression and 3.1$\times$ Faster Inference$ Random Offset Block Embedding Array (ROBE) for CriteoTB Benchmark MLPerf DLRM Model : 1000 $\times$ Compression and 3.1 $\times$ Faster Inference Aditya Desai Li Chou Anshumali Shrivastava AI4CE 45 6 0 04 Aug 2021
TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models Chunxing Yin Bilge Acun Xing Liu Carole-Jean Wu 73 104 0 25 Jan 2021
Learnable Embedding Sizes for Recommender Systems Siyi Liu Chen Gao Yihong Chen Depeng Jin Yong Li 77 85 0 19 Jan 2021
Differentiable Neural Input Search for Recommender Systems Weiyu Cheng Yanyan Shen Linpeng Huang 62 36 0 08 Jun 2020
Optimizing Deep Learning Recommender Systems' Training On CPU Cluster Architectures Dhiraj D. Kalamkar E. Georganas Sudarshan Srinivasan Jianping Chen Mikhail Shiryaev A. Heinecke 70 48 0 10 May 2020
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems Antonio A. Ginart Maxim Naumov Dheevatsa Mudigere Jiyan Yang James Zou 63 100 0 25 Sep 2019
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems Hao-Jun Michael Shi Dheevatsa Mudigere Maxim Naumov Jiyan Yang 40 120 0 04 Sep 2019
Neural Input Search for Large Scale Recommendation Models Manas R. Joglekar Cong Li Jay K. Adams Pranav Khaitan Quoc V. Le 49 113 0 10 Jul 2019
The Architectural Implications of Facebook's DNN-based Personalized Recommendation Udit Gupta Carole-Jean Wu Xiaodong Wang Maxim Naumov Brandon Reagen ... Andrey Malevich Dheevatsa Mudigere M. Smelyanskiy Liang Xiong Xuan Zhang GNN 73 290 0 06 Jun 2019
Deep Learning Recommendation Model for Personalization and Recommendation Systems Maxim Naumov Dheevatsa Mudigere Hao-Jun Michael Shi Jianyu Huang Narayanan Sundaraman ... Wenlin Chen Vijay Rao Bill Jia Liang Xiong M. Smelyanskiy 85 732 0 31 May 2019
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction Tongwen Huang Zhiqi Zhang Junlin Zhang 107 285 0 23 May 2019
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications Jongsoo Park Maxim Naumov Protonu Basu Summer Deng Aravind Kalaiah ... Lin Qiao Vijay Rao Nadav Rotem S. Yoo M. Smelyanskiy FedML GNN BDL 60 187 0 24 Nov 2018
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks Weiping Song Chence Shi Zhiping Xiao Zhijian Duan Yewen Xu Ming Zhang Jian Tang CML 60 854 0 29 Oct 2018
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems Jianxun Lian Xiaohuan Zhou Fuzheng Zhang Zhongxia Chen Xing Xie Guangzhong Sun BDL 54 1,048 0 14 Mar 2018
Deep & Cross Network for Ad Click Predictions Ruoxi Wang Bin Fu Gang Fu Mingliang Wang 77 1,230 0 17 Aug 2017
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li Xiuqiang He 111 2,640 0 13 Mar 2017
Compressing Neural Networks with the Hashing Trick Wenlin Chen James T. Wilson Stephen Tyree Kilian Q. Weinberger Yixin Chen 138 1,191 0 19 Apr 2015

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.