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Random Offset Block Embedding Array (ROBE) for CriteoTB Benchmark MLPerf DLRM Model : 1000
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Compression and 3.1
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Faster Inference
4 August 2021
Aditya Desai
Li Chou
Anshumali Shrivastava
AI4CE
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Papers citing
"Random Offset Block Embedding Array (ROBE) for CriteoTB Benchmark MLPerf DLRM Model : 1000$\times$ Compression and 3.1$\times$ Faster Inference"
4 / 4 papers shown
The trade-offs of model size in large recommendation models : A 10000
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compressed criteo-tb DLRM model (100 GB parameters to mere 10MB)
Aditya Desai
Anshumali Shrivastava
AI4CE
295
4
0
21 Jul 2022
Efficient model compression with Random Operation Access Specific Tile (ROAST) hashing
Aditya Desai
K. Zhou
Anshumali Shrivastava
375
2
0
21 Jul 2022
Efficient Mixed Dimension Embeddings for Matrix Factorization
D. Beloborodov
Andrei Zimovnov
Petr Molodyk
Dmitrii Kirillov
175
2
0
18 May 2022
Differentiable Neural Input Search for Recommender Systems
Weiyu Cheng
Yanyan Shen
Linpeng Huang
289
39
0
08 Jun 2020
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