Fetching the paper…
Reading the bibliography…
Predicting user responses, such as click-through rate and conversion rate, are critical in many web applications including web search, personalised recommendation, and online advertising.
Fukushima, K.: Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological cybernetics 36(4), 193–202 (1980)
1980
Earlier work this paper cites.
Breiman, L.: Bagging predictors. Machine learning 24(2), 123–140 (1996)
1996
Earlier work this paper cites.
Elizondo, D., Fiesler, E.: A survey of partially connected neural networks. International journal of neural systems 8(05n06), 535–558 (1997)
1997
Earlier work this paper cites.
Kittler, J., Hatef, M., Duin, R.P., Matas, J.: On combining classifiers. PAMI 20(3), 226–239 (1998)
1998
Earlier work this paper cites.
Prechelt, L.: Automatic early stopping using cross validation: quantifying the criteria. Neural Networks 11(4), 761–767 (1998)
1998
Earlier work this paper cites.
Beck, J.E., Woolf, B.P.: High-level student modeling with machine learning. In: Intelligent tutoring systems. pp. 584–593. Springer (2000)
2000
Earlier work this paper cites.
Hand, D.J., Yu, K.: Idiot’s bayes¡ªnot so stupid after all? International statistical review 69(3), 385–398 (2001)
2001
Earlier work this paper cites.
Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural computation 14(8), 1771–1800 (2002)
2002
Earlier work this paper cites.
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
2006
Earlier work this paper cites.
Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H., et al.: Greedy layer-wise training of deep networks. NIPS 19, 153 (2007)
2007
Earlier work this paper cites.
Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click-through rate for new ads. In: WWW. pp. 521–530. ACM (2007)
2007
Earlier work this paper cites.
Broder, A.Z.: Computational advertising. In: SODA. vol. 8, pp. 992–992 (2008)
2008
Earlier work this paper cites.
Bengio, Y.: Learning deep architectures for ai. Foundations and trends® in Machine Learning 2(1), 1–127 (2009)
2009
Earlier work this paper cites.
Larochelle, H., Bengio, Y., Louradour, J., Lamblin, P.: Exploring strategies for training deep neural networks. JMLR 10, 1–40 (2009)
2009
Earlier work this paper cites.
Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P., Bengio, S.: Why does unsupervised pre-training help deep learning? JMLR 11 (2010)
2010
Cited alongside, same era.
Graepel, T., Candela, J.Q., Borchert, T., Herbrich, R.: Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft’s bing search engine. In: ICML. pp. 13–20 (2010)
2010
Cited alongside, same era.
Hinton, G.: A practical guide to training restricted boltzmann machines. Momentum 9(1), 926 (2010)
2010
Cited alongside, same era.
Wang, X., Li, W., Cui, Y., Zhang, R., Mao, J.: Click-through rate estimation for rare events in online advertising. Online Multimedia Advertising: Techniques and Technologies pp. 1–12 (2010)
2010
Cited alongside, same era.
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. JMLR 12, 2493–2537 (2011)
Graves, A., Mohamed, A.r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: ICASSP. pp. 6645–6649. IEEE (2013)
2013
Later among the works it cites.
Huang, P.S., He, X., Gao, J., Deng, L., Acero, A., Heck, L.: Learning deep structured semantic models for web search using clickthrough data. In: CIKM. pp. 2333–2338 (2013)
2013
Later among the works it cites.
McMahan, H.B., Holt, G., Sculley, D., Young, M., Ebner, D., Grady, J., Nie, L., Phillips, T., Davydov, E., Golovin, D., et al.: Ad click prediction: a view from the trenches. In: KDD. pp. 1222–1230. ACM (2013)
2013
Later among the works it cites.
Sutskever, I., Martens, J., Dahl, G., Hinton, G.: On the importance of initialization and momentum in deep learning. In: ICML. pp. 1139–1147 (2013)
2013
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2011
Cited alongside, same era.
Juan, Y.C., Zhuang, Y., Chin, W.S.: 3 idiots¡¯ approach for display advertising challenge. In: Internet and Network Economics, pp. 254–265. Springer (2011)
2011
Cited alongside, same era.
Zeiler, M.D., Taylor, G.W., Fergus, R.: Adaptive deconvolutional networks for mid and high level feature learning. In: ICCV. pp. 2018–2025. IEEE (2011)
2011
Cited alongside, same era.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
2012
Cited alongside, same era.
Lee, K.c., Orten, B., Dasdan, A., Li, W.: Estimating conversion rate in display advertising from past performance data. In: KDD. pp. 768–776. ACM (2012)
2012
Cited alongside, same era.
Rendle, S.: Factorization machines with libfm. ACM TIST 3(3), 57 (2012)
2012
Cited alongside, same era.
Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: NIPS. pp. 2951–2959 (2012)
2012
Cited alongside, same era.
Trofimov, I., Kornetova, A., Topinskiy, V.: Using boosted trees for click-through rate prediction for sponsored search. In: WINE. p. 2. ACM (2012)
2012
Cited alongside, same era.
2014
Later among the works it cites.
Kurashima, T., Iwata, T., Takaya, N., Sawada, H.: Probabilistic latent network visualization: inferring and embedding diffusion networks. In: KDD. pp. 1236–1245. ACM (2014)
2014
Later among the works it cites.
Liao, H., Peng, L., Liu, Z., Shen, X.: ipinyou global rtb bidding algorithm competition dataset. In: ADKDD. pp. 1–6. ACM (2014)
2014
Later among the works it cites.
Oentaryo, R.J., Lim, E.P., Low, D.J.W., Lo, D., Finegold, M.: Predicting response in mobile advertising with hierarchical importance-aware factorization machine. In: WSDM (2014)
2014
Later among the works it cites.
Shen, Y., He, X., Gao, J., Deng, L., Mesnil, G.: A latent semantic model with convolutional-pooling structure for information retrieval. In: CIKM (2014)
2014
Later among the works it cites.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting. JMLR 15(1), 1929–1958 (2014)
2014
Later among the works it cites.
Zhang, W., Yuan, S., Wang, J.: Optimal real-time bidding for display advertising. In: KDD. pp. 1077–1086. ACM (2014)
2014
Later among the works it cites.
Zou, Y., Jin, X., Li, Y., Guo, Z., Wang, E., Xiao, B.: Mariana: Tencent deep learning platform and its applications. VLDB 7(13), 1772–1777 (2014)
2014
Later among the works it cites.
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553) (2015)
2015
Later among the works it cites.
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Large-scale information network embedding. In: WWW. pp. 1067–1077 (2015)
2015
Later among the works it cites.