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Feature selection has been an essential step in developing industry-scale deep Click-Through Rate (CTR) prediction systems.
Neural Input Search for Large Scale Recommendation Models. CoRR abs/1907.04471 (2019)
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Discrete model compression with resource constraint for deep neural networks
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Mixed dimension embeddings with application to memory-efficient recommendation systems
Ginart, A.; Naumov, M.; Mudigere, D.; Yang, J.; and Zou, J. 2019 · 1909
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Differentiable neural input search for recommender systems
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Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data
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Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
Bengio, Y.; Léonard, N.; and Courville, A. C. 2013 · 2013
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Xbox movies recommendations: Variational Bayes matrix factorization with embedded feature selection
Koenigstein, N.; and Paquet, U. 2013 · 2013
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Selecting content-based features for collaborative filtering recommenders
Ronen, R.; Koenigstein, N.; Ziklik, E.; and Nice, N. 2013 · 2013
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A survey on feature selection methods
Chandrashekar, G.; and Sahin, F. 2014 · 2014
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Learning both weights and connections for efficient neural networks
Han, S.; Pool, J.; Tran, J.; and Dally, W. J. 2015 · 2015
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Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. L. 2015 · 2015
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Binarized neural networks
Hubara, I.; Courbariaux, M.; Soudry, D.; El-Yaniv, R.; and Bengio, Y. 2016 · 2016
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Categorical Reparameterization with Gumbel-Softmax
Jang, E.; Gu, S.; and Poole, B. 2016 · 2016
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B.; Pritzel, A.; and Blundell, C. 2016 · 2016
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The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J.; Mnih, A.; and Teh, Y. W. 2016 · 2016
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Deep exploration via bootstrapped DQN
Osband, I.; Blundell, C.; Pritzel, A.; and Van Roy, B. 2016 · 2016
Cited alongside, same era.
DeepFM: a factorization-machine based neural network for CTR prediction
Learning binary residual representations for domain-specific video streaming
Tsai, Y.-H.; Liu, M.-Y.; Sun, D.; Yang, M.-H.; and Kautz, J. 2018 · 2018
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SNAS: stochastic neural architecture search
Xie, S.; Zheng, H.; Liu, C.; and Lin, L. 2018 · 2018
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ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
Han, C.; Ligeng, Z.; and Song, H. 2019 · 2019
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Self-supervised exploration via disagreement
Pathak, D.; Gandhi, D.; and Gupta, A. 2019 · 2019
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Autoint: Automatic feature interaction learning via self-attentive neural networks
Song, W.; Shi, C.; Xiao, Z.; Duan, Z.; Xu, Y.; Zhang, M.; and Tang, J. 2019 · 2019
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Guo, H.; Tang, R.; Ye, Y.; Li, Z.; and He, X. 2017 · 2017
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Training sparse neural networks
Srinivas, S.; Subramanya, A.; and Venkatesh Babu, R. 2017 · 2017
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Deep & cross network for ad click predictions
Wang, R.; Fu, B.; Fu, G.; and Wang, M. 2017 · 2017
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Frankle, J.; and Carbin, M. 2018 · 2018
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Morphnet: Fast & simple resource-constrained structure learning of deep networks
Gordon, A.; Eban, E.; Nachum, O.; Chen, B.; Wu, H.; Yang, T.-J.; and Choi, E. 2018 · 2018
Cited alongside, same era.
Darts: Differentiable architecture search
Liu, H.; Simonyan, K.; and Yang, Y. 2018 · 2018
Cited alongside, same era.
Xia, X.; Zigeng, W.; and Sanguthevar, R. 2019 · 2019
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Differentiable Feature Selection by Discrete Relaxation
Sheth, R.; and Fusi, N. 2020 · 2020
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Feature selection using stochastic gates
Yamada, Y.; Lindenbaum, O.; Negahban, S.; and Kluger, Y. 2020 · 2020
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LEARNABLE EMBEDDING SIZES FOR RECOMMENDER SYSTEMS
Liu, S.; Gao, C.; Chen, Y.; Jin, D.; and Li, Y. 2021 · 2021
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Automatic recommendation of feature selection algorithms based on dataset characteristics
Parmezan, A. R. S.; Lee, H. D.; Spolaôr, N.; and Wu, F. C. 2021 · 2021
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AutoDim: Field-aware Embedding Dimension Searchin Recommender Systems
Zhao, X.; Liu, H.; Liu, H.; Tang, J.; Guo, W.; Shi, J.; Wang, S.; Gao, H.; and Long, B. 2021 · 2021
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