Fetching the paper…
Reading the bibliography…
Production recommendation systems rely on embedding methods to represent various features.
Building a Large Annotated Corpus of English: The Penn Treebank
M. P. Marcus, M. A. Marcinkiewicz, and B. Santorini · 1993
Earlier work this paper cites.
Pattern recognition and machine learning
C. M. Bishop · 2006
Earlier work this paper cites.
K-means++: The Advantages of Careful Seeding
D. Arthur and S. Vassilvitskii · 2007
Earlier work this paper cites.
Translating Embeddings for Modeling Multi-relational Data
A. Bordes, N. Usunier, A. Garcia-Durán, J. Weston, and O. Yakhnenko · 2013
Earlier work this paper cites.
Predicting Parameters in Deep Learning
M. Denil, B. Shakibi, L. Dinh, M. Ranzato, and N. de Freitas · 2013
Earlier work this paper cites.
Learning Deep Structured Semantic Models for Web Search Using Clickthrough Data
P.-S. Huang, X. He, J. Gao, L. Deng, A. Acero, and L. Heck · 2013
Earlier work this paper cites.
Provable Bounds for Learning Some Deep Representations
S. Arora, A. Bhaskara, R. Ge, and T. Ma · 2014
Earlier work this paper cites.
Distributed Representations of Sentences and Documents
Q. Le and T. Mikolov · 2014
Earlier work this paper cites.
Compressing Neural Networks with the Hashing Trick
W. Chen, J. Wilson, S. Tyree, K. Weinberger, and Y. Chen · 2015
Cited alongside, same era.
Learning Both Weights and Connections for Efficient Neural Networks
S. Han, J. Pool, J. Tran, and W. J. Dally · 2015
Cited alongside, same era.
Distilling the Knowledge in a Neural Network
G. Hinton, O. Vinyals, and J. Dean · 2015
Cited alongside, same era.
Recommending Items to More Than a Billion People
M. Kabiljo and A. Ilic · 2015
Cited alongside, same era.
Entity Embedding-based Anomaly Detection for Heterogeneous Categorical Events
T. Chen, L.-A. Tang, Y. Sun, Z. Chen, and K. Zhang · 2016
Cited alongside, same era.
Deep Neural Networks for YouTube Recommendations
P. Covington, J. Adams, and E. Sargin · 2016
DRAM Supply to Remain Tight With Its Annual Bit Growth for 2018 Forecast at Just 19.6%, According to TrendForce
A. Wu · 2017
Later among the works it cites.
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
T. Chen, M. R. Min, and Y. Sun · 2018
Later among the works it cites.
Bandana: Using Non-volatile Memory for Storing Deep Learning Models
A. Eisenman, M. Naumov, D. Gardner, M. Smelyanskiy, S. Pupyrev, K. Hazelwood, A. Cidon, and S. Katti · 2018
Later among the works it cites.
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
Y. He, J. Lin, Z. Liu, H. Wang, L.-J. Li, and S. Han · 2018
Later among the works it cites.
Compressing Word Embeddings via Deep Compositional Code Learning
R. Shu and H. Nakayama · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
S. Han, H. Mao, and W. J. Dally · 2016
Cited alongside, same era.
Learning Structured Sparsity in Deep Neural Networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
Cited alongside, same era.
Deep k k -Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions
J. Wu, Y. Wang, Z. Wu, Z. Wang, A. Veeraraghavan, and Y. Lin · 2018
Later among the works it cites.
https://memory.net/memory-prices/ , Feb 2019
memory.net · 2019
Closest in time.