Tài liệu tham khảo

[Ahmed et al., 2012]

Ahmed, A., Aly, M., Gonzalez, J., Narayanamurthy, S., & Smola, A. J. (2012). Scalable inference in latent variable models. Proceedings of the fifth ACM international conference on Web search and data mining (pp. 123–132).

[Aji & McEliece, 2000]

Aji, S. M., & McEliece, R. J. (2000). The generalized distributive law. IEEE transactions on Information Theory, 46(2), 325–343.

[Bahdanau et al., 2014]

Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.

[Bishop, 1995]

Bishop, C. M. (1995). Training with noise is equivalent to tikhonov regularization. Neural computation, 7(1), 108–116.

[Bishop, 2006]

Bishop, C. M. (2006). Pattern recognition and machine learning. springer.

[Bojanowski et al., 2017]

Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5, 135–146.

[Bollobas, 1999]

Bollobás, B. (1999). Linear analysis. Cambridge University Press, Cambridge.

[Bowman et al., 2015]

Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015). A large annotated corpus for learning natural language inference. arXiv preprint arXiv:1508.05326.

[Boyd & Vandenberghe, 2004]

Boyd, S., & Vandenberghe, L. (2004). Convex Optimization. Cambridge, England: Cambridge University Press.

[Brown & Sandholm, 2017]

Brown, N., & Sandholm, T. (2017). Libratus: the superhuman ai for no-limit poker. IJCAI (pp. 5226–5228).

[Campbell et al., 2002]

Campbell, M., Hoane Jr, A. J., & Hsu, F.-h. (2002). Deep blue. Artificial intelligence, 134(1-2), 57–83.

[Cer et al., 2017]

Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I., & Specia, L. (2017). Semeval-2017 task 1: semantic textual similarity multilingual and crosslingual focused evaluation. Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017) (pp. 1–14).

[Cho et al., 2014]

Cho, K., Van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014). On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259.

[Chung et al., 2014]

Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.

[Csiszar, 2008]

Csiszár, I. (2008). Axiomatic characterizations of information measures. Entropy, 10(3), 261–273.

[DeCock, 2011]

De Cock, D. (2011). Ames, iowa: alternative to the boston housing data as an end of semester regression project. Journal of Statistics Education, 19(3).

[DeCandia et al., 2007]

DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., … Vogels, W. (2007). Dynamo: amazon’s highly available key-value store. ACM SIGOPS operating systems review (pp. 205–220).

[Devlin et al., 2018]

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

[Doucet et al., 2001]

Doucet, A., De Freitas, N., & Gordon, N. (2001). An introduction to sequential monte carlo methods. Sequential Monte Carlo methods in practice (pp. 3–14). Springer.

[Duchi et al., 2011]

Duchi, J., Hazan, E., & Singer, Y. (2011). Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12(Jul), 2121–2159.

[Dumoulin & Visin, 2016]

Dumoulin, V., & Visin, F. (2016). A guide to convolution arithmetic for deep learning. arXiv preprint arXiv:1603.07285.

[Edelman et al., 2007]

Edelman, B., Ostrovsky, M., & Schwarz, M. (2007). Internet advertising and the generalized second-price auction: selling billions of dollars worth of keywords. American economic review, 97(1), 242–259.

[Flammarion & Bach, 2015]

Flammarion, N., & Bach, F. (2015). From averaging to acceleration, there is only a step-size. Conference on Learning Theory (pp. 658–695).

[Gatys et al., 2016]

Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image style transfer using convolutional neural networks. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2414–2423).

[Ginibre, 1965]

Ginibre, J. (1965). Statistical ensembles of complex, quaternion, and real matrices. Journal of Mathematical Physics, 6(3), 440–449.

[Girshick, 2015]

Girshick, R. (2015). Fast r-cnn. Proceedings of the IEEE international conference on computer vision (pp. 1440–1448).

[Girshick et al., 2014]

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580–587).

[Glorot & Bengio, 2010]

Glorot, X., & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the thirteenth international conference on artificial intelligence and statistics (pp. 249–256).

[Goh, 2017]

Goh, G. (2017). Why momentum really works. Distill. URL: http://distill.pub/2017/momentum, doi:10.23915/distill.00006

[Goldberg et al., 1992]

Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61–71.

[Goodfellow et al., 2016]

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. http://www.deeplearningbook.org.

[Goodfellow et al., 2014]

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems (pp. 2672–2680).

[Gotmare et al., 2018]

Gotmare, A., Keskar, N. S., Xiong, C., & Socher, R. (2018). A closer look at deep learning heuristics: learning rate restarts, warmup and distillation. arXiv preprint arXiv:1810.13243.

[Graves & Schmidhuber, 2005]

Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks, 18(5-6), 602–610.

[Gunawardana & Shani, 2015]

Gunawardana, A., & Shani, G. (2015). Evaluating recommender systems. Recommender systems handbook (pp. 265–308). Springer.

[Guo et al., 2017]

Guo, H., Tang, R., Ye, Y., Li, Z., & He, X. (2017). Deepfm: a factorization-machine based neural network for ctr prediction. Proceedings of the 26th International Joint Conference on Artificial Intelligence (pp. 1725–1731).

[He et al., 2017a]

He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. Proceedings of the IEEE international conference on computer vision (pp. 2961–2969).

[He et al., 2016a]

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).

[He et al., 2016b]

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Identity mappings in deep residual networks. European conference on computer vision (pp. 630–645).

[He & Chua, 2017]

He, X., & Chua, T.-S. (2017). Neural factorization machines for sparse predictive analytics. Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval (pp. 355–364).

[He et al., 2017b]

He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.-S. (2017). Neural collaborative filtering. Proceedings of the 26th international conference on world wide web (pp. 173–182).

[Hebb & Hebb, 1949]

Hebb, D. O., & Hebb, D. (1949). The organization of behavior. Vol. 65. Wiley New York.

[Hendrycks & Gimpel, 2016]

Hendrycks, D., & Gimpel, K. (2016). Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415.

[Hennessy & Patterson, 2011]

Hennessy, J. L., & Patterson, D. A. (2011). Computer architecture: a quantitative approach. Elsevier.

[Herlocker et al., 1999]

Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1999 (pp. 230–237).

[Hidasi et al., 2015]

Hidasi, B., Karatzoglou, A., Baltrunas, L., & Tikk, D. (2015). Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939.

[Hochreiter & Schmidhuber, 1997]

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.

[Hoyer et al., 2009]

Hoyer, P. O., Janzing, D., Mooij, J. M., Peters, J., & Schölkopf, B. (2009). Nonlinear causal discovery with additive noise models. Advances in neural information processing systems (pp. 689–696).

[Hu et al., 2018]

Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132–7141).

[Hu et al., 2008]

Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. 2008 Eighth IEEE International Conference on Data Mining (pp. 263–272).

[Huang et al., 2017]

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700–4708).

[Ioffe, 2017]

Ioffe, S. (2017). Batch renormalization: towards reducing minibatch dependence in batch-normalized models. Advances in neural information processing systems (pp. 1945–1953).

[Ioffe & Szegedy, 2015]

Ioffe, S., & Szegedy, C. (2015). Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167.

[Izmailov et al., 2018]

Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., & Wilson, A. G. (2018). Averaging weights leads to wider optima and better generalization. arXiv preprint arXiv:1803.05407.

[Jia et al., 2018]

Jia, X., Song, S., He, W., Wang, Y., Rong, H., Zhou, F., … others. (2018). Highly scalable deep learning training system with mixed-precision: training imagenet in four minutes. arXiv preprint arXiv:1807.11205.

[Jouppi et al., 2017]

Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., … others. (2017). In-datacenter performance analysis of a tensor processing unit. 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA) (pp. 1–12).

[Kim, 2014]

Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.

[Kingma & Ba, 2014]

Kingma, D. P., & Ba, J. (2014). Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980.

[Koller & Friedman, 2009]

Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press.

[Kolter, 2008]

Kolter, Z. (2008). Linear algebra review and reference. Available online: http.

[Koren, 2009]

Koren, Y. (2009). Collaborative filtering with temporal dynamics. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 447–456).

[Koren et al., 2009]

Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, pp. 30–37.

[Krizhevsky et al., 2012]

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems (pp. 1097–1105).

[Kung, 1988]

Kung, S. Y. (1988). Vlsi array processors. Englewood Cliffs, NJ, Prentice Hall, 1988, 685 p. Research supported by the Semiconductor Research Corp., SDIO, NSF, and US Navy.

[LeCun et al., 1998]

LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., & others. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.

[Li, 2017]

Li, M. (2017). Scaling Distributed Machine Learning with System and Algorithm Co-design (Doctoral dissertation). PhD Thesis, CMU.

[Li et al., 2014]

Li, M., Andersen, D. G., Park, J. W., Smola, A. J., Ahmed, A., Josifovski, V., … Su, B.-Y. (2014). Scaling distributed machine learning with the parameter server. 11th $\$USENIX$\$ Symposium on Operating Systems Design and Implementation ($\$OSDI$\$ 14) (pp. 583–598).

[Lin et al., 2013]

Lin, M., Chen, Q., & Yan, S. (2013). Network in network. arXiv preprint arXiv:1312.4400.

[Lin et al., 2017]

Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. Proceedings of the IEEE international conference on computer vision (pp. 2980–2988).

[Lin et al., 2010]

Lin, Y., Lv, F., Zhu, S., Yang, M., Cour, T., Yu, K., … others. (2010). Imagenet classification: fast descriptor coding and large-scale svm training. Large scale visual recognition challenge.

[Lipton & Steinhardt, 2018]

Lipton, Z. C., & Steinhardt, J. (2018). Troubling trends in machine learning scholarship. arXiv preprint arXiv:1807.03341.

[Liu et al., 2016]

Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016). Ssd: single shot multibox detector. European conference on computer vision (pp. 21–37).

[Liu et al., 2019]

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., … Stoyanov, V. (2019). Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.

[Long et al., 2015]

Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431–3440).

[Loshchilov & Hutter, 2016]

Loshchilov, I., & Hutter, F. (2016). Sgdr: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983.

[Luo et al., 2018]

Luo, P., Wang, X., Shao, W., & Peng, Z. (2018). Towards understanding regularization in batch normalization. arXiv preprint.

[Maas et al., 2011]

Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011). Learning word vectors for sentiment analysis. Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies-volume 1 (pp. 142–150).

[McCann et al., 2017]

McCann, B., Bradbury, J., Xiong, C., & Socher, R. (2017). Learned in translation: contextualized word vectors. Advances in Neural Information Processing Systems (pp. 6294–6305).

[McCulloch & Pitts, 1943]

McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115–133.

[McMahan et al., 2013]

McMahan, H. B., Holt, G., Sculley, D., Young, M., Ebner, D., Grady, J., … others. (2013). Ad click prediction: a view from the trenches. Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1222–1230).

[Merity et al., 2016]

Merity, S., Xiong, C., Bradbury, J., & Socher, R. (2016). Pointer sentinel mixture models. arXiv preprint arXiv:1609.07843.

[Mikolov et al., 2013a]

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

[Mikolov et al., 2013b]

Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems (pp. 3111–3119).

[Mirhoseini et al., 2017]

Mirhoseini, A., Pham, H., Le, Q. V., Steiner, B., Larsen, R., Zhou, Y., … Dean, J. (2017). Device placement optimization with reinforcement learning. Proceedings of the 34th International Conference on Machine Learning-Volume 70 (pp. 2430–2439).

[Morey et al., 2016]

Morey, R. D., Hoekstra, R., Rouder, J. N., Lee, M. D., & Wagenmakers, E.-J. (2016). The fallacy of placing confidence in confidence intervals. Psychonomic bulletin & review, 23(1), 103–123.

[Nesterov & Vial, 2000]

Nesterov, Y., & Vial, J.-P. (2000). Confidence level solutions for stochastic programming, Stochastic Programming E-Print Series.

[Nesterov, 2018]

Nesterov, Y. (2018). Lectures on convex optimization. Vol. 137. Springer.

[Neyman, 1937]

Neyman, J. (1937). Outline of a theory of statistical estimation based on the classical theory of probability. Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 236(767), 333–380.

[Parikh et al., 2016]

Parikh, A. P., Täckström, O., Das, D., & Uszkoreit, J. (2016). A decomposable attention model for natural language inference. arXiv preprint arXiv:1606.01933.

[Pennington et al., 2017]

Pennington, J., Schoenholz, S., & Ganguli, S. (2017). Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice. Advances in neural information processing systems (pp. 4785–4795).

[Pennington et al., 2014]

Pennington, J., Socher, R., & Manning, C. (2014). Glove: global vectors for word representation. Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532–1543).

[Peters et al., 2017a]

Peters, J., Janzing, D., & Schölkopf, B. (2017). Elements of causal inference: foundations and learning algorithms. MIT press.

[Peters et al., 2017b]

Peters, M., Ammar, W., Bhagavatula, C., & Power, R. (2017). Semi-supervised sequence tagging with bidirectional language models. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1756–1765).

[Peters et al., 2018]

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (pp. 2227–2237).

[Petersen et al., 2008]

Petersen, K. B., Pedersen, M. S., & others. (2008). The matrix cookbook. Technical University of Denmark, 7(15), 510.

[Polyak, 1964]

Polyak, B. T. (1964). Some methods of speeding up the convergence of iteration methods. USSR Computational Mathematics and Mathematical Physics, 4(5), 1–17.

[Quadrana et al., 2018]

Quadrana, M., Cremonesi, P., & Jannach, D. (2018). Sequence-aware recommender systems. ACM Computing Surveys (CSUR), 51(4), 66.

[Radford et al., 2015]

Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.

[Radford et al., 2018]

Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. OpenAI.

[Radford et al., 2019]

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9.

[Rajpurkar et al., 2016]

Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016). Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250.

[Reddi et al., 2019]

Reddi, S. J., Kale, S., & Kumar, S. (2019). On the convergence of adam and beyond. arXiv preprint arXiv:1904.09237.

[Reed & DeFreitas, 2015]

Reed, S., & De Freitas, N. (2015). Neural programmer-interpreters. arXiv preprint arXiv:1511.06279.

[Ren et al., 2015]

Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: towards real-time object detection with region proposal networks. Advances in neural information processing systems (pp. 91–99).

[Rendle, 2010]

Rendle, S. (2010). Factorization machines. 2010 IEEE International Conference on Data Mining (pp. 995–1000).

[Rendle et al., 2009]

Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2009). Bpr: bayesian personalized ranking from implicit feedback. Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence (pp. 452–461).

[Rumelhart et al., 1988]

Rumelhart, D. E., Hinton, G. E., Williams, R. J., & others. (1988). Learning representations by back-propagating errors. Cognitive modeling, 5(3), 1.

[Russell & Norvig, 2016]

Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,.

[Santurkar et al., 2018]

Santurkar, S., Tsipras, D., Ilyas, A., & Madry, A. (2018). How does batch normalization help optimization? Advances in Neural Information Processing Systems (pp. 2483–2493).

[Sarwar et al., 2001]

Sarwar, B. M., Karypis, G., Konstan, J. A., Riedl, J., & others. (2001). Item-based collaborative filtering recommendation algorithms. Www, 1, 285–295.

[Schein et al., 2002]

Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002). Methods and metrics for cold-start recommendations. Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 253–260).

[Schuster & Paliwal, 1997]

Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681.

[Sedhain et al., 2015]

Sedhain, S., Menon, A. K., Sanner, S., & Xie, L. (2015). Autorec: autoencoders meet collaborative filtering. Proceedings of the 24th International Conference on World Wide Web (pp. 111–112).

[Sennrich et al., 2015]

Sennrich, R., Haddow, B., & Birch, A. (2015). Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909.

[Sergeev & DelBalso, 2018]

Sergeev, A., & Del Balso, M. (2018). Horovod: fast and easy distributed deep learning in tensorflow. arXiv preprint arXiv:1802.05799.

[Shannon, 1948]

Shannon, C. E. (1948 , 7). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423.

[Silver et al., 2016]

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., … others. (2016). Mastering the game of go with deep neural networks and tree search. nature, 529(7587), 484.

[Simonyan & Zisserman, 2014]

Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

[Smola & Narayanamurthy, 2010]

Smola, A., & Narayanamurthy, S. (2010). An architecture for parallel topic models. Proceedings of the VLDB Endowment, 3(1-2), 703–710.

[Srivastava et al., 2014]

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929–1958.

[Strang, 1993]

Strang, G. (1993). Introduction to linear algebra. Vol. 3. Wellesley-Cambridge Press Wellesley, MA.

[Su & Khoshgoftaar, 2009]

Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009.

[Sukhbaatar et al., 2015]

Sukhbaatar, S., Weston, J., Fergus, R., & others. (2015). End-to-end memory networks. Advances in neural information processing systems (pp. 2440–2448).

[Sutskever et al., 2013]

Sutskever, I., Martens, J., Dahl, G., & Hinton, G. (2013). On the importance of initialization and momentum in deep learning. International conference on machine learning (pp. 1139–1147).

[Szegedy et al., 2017]

Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. Thirty-First AAAI Conference on Artificial Intelligence.

[Szegedy et al., 2015]

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., … Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1–9).

[Szegedy et al., 2016]

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818–2826).

[Tallec & Ollivier, 2017]

Tallec, C., & Ollivier, Y. (2017). Unbiasing truncated backpropagation through time. arXiv preprint arXiv:1705.08209.

[Tang & Wang, 2018]

Tang, J., & Wang, K. (2018). Personalized top-n sequential recommendation via convolutional sequence embedding. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (pp. 565–573).

[Teye et al., 2018]

Teye, M., Azizpour, H., & Smith, K. (2018). Bayesian uncertainty estimation for batch normalized deep networks. arXiv preprint arXiv:1802.06455.

[Tieleman & Hinton, 2012]

Tieleman, T., & Hinton, G. (2012). Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning, 4(2), 26–31.

[Treisman & Gelade, 1980]

Treisman, A. M., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive psychology, 12(1), 97–136.

[Toscher et al., 2009]

Töscher, A., Jahrer, M., & Bell, R. M. (2009). The bigchaos solution to the netflix grand prize. Netflix prize documentation, pp. 1–52.

[Uijlings et al., 2013]

Uijlings, J. R., Van De Sande, K. E., Gevers, T., & Smeulders, A. W. (2013). Selective search for object recognition. International journal of computer vision, 104(2), 154–171.

[VanLoan & Golub, 1983]

Van Loan, C. F., & Golub, G. H. (1983). Matrix computations. Johns Hopkins University Press.

[Vaswani et al., 2017]

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems (pp. 5998–6008).

[Wang et al., 2018]

Wang, L., Li, M., Liberty, E., & Smola, A. J. (2018). Optimal message scheduling for aggregation. NETWORKS, 2(3), 2–3.

[Wang et al., 2016]

Wang, Y., Davidson, A., Pan, Y., Wu, Y., Riffel, A., & Owens, J. D. (2016). Gunrock: a high-performance graph processing library on the gpu. ACM SIGPLAN Notices (p. 11).

[Warstadt et al., 2019]

Warstadt, A., Singh, A., & Bowman, S. R. (2019). Neural network acceptability judgments. Transactions of the Association for Computational Linguistics, 7, 625–641.

[Wasserman, 2013]

Wasserman, L. (2013). All of statistics: a concise course in statistical inference. Springer Science & Business Media.

[Watkins & Dayan, 1992]

Watkins, C. J., & Dayan, P. (1992). Q-learning. Machine learning, 8(3-4), 279–292.

[Welling & Teh, 2011]

Welling, M., & Teh, Y. W. (2011). Bayesian learning via stochastic gradient langevin dynamics. Proceedings of the 28th international conference on machine learning (ICML-11) (pp. 681–688).

[Wigner, 1958]

Wigner, E. P. (1958). On the distribution of the roots of certain symmetric matrices. Ann. Math (pp. 325–327).

[Wood et al., 2011]

Wood, F., Gasthaus, J., Archambeau, C., James, L., & Teh, Y. W. (2011). The sequence memoizer. Communications of the ACM, 54(2), 91–98.

[Wu et al., 2017]

Wu, C.-Y., Ahmed, A., Beutel, A., Smola, A. J., & Jing, H. (2017). Recurrent recommender networks. Proceedings of the tenth ACM international conference on web search and data mining (pp. 495–503).

[Wu et al., 2016]

Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., … others. (2016). Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.

[Xiao et al., 2017]

Xiao, H., Rasul, K., & Vollgraf, R. (2017). Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747.

[Xiong et al., 2018]

Xiong, W., Wu, L., Alleva, F., Droppo, J., Huang, X., & Stolcke, A. (2018). The microsoft 2017 conversational speech recognition system. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5934–5938).

[Ye et al., 2011]

Ye, M., Yin, P., Lee, W.-C., & Lee, D.-L. (2011). Exploiting geographical influence for collaborative point-of-interest recommendation. Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (pp. 325–334).

[You et al., 2017]

You, Y., Gitman, I., & Ginsburg, B. (2017). Large batch training of convolutional networks. arXiv preprint arXiv:1708.03888.

[Zaheer et al., 2018]

Zaheer, M., Reddi, S., Sachan, D., Kale, S., & Kumar, S. (2018). Adaptive methods for nonconvex optimization. Advances in Neural Information Processing Systems (pp. 9793–9803).

[Zeiler, 2012]

Zeiler, M. D. (2012). Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701.

[Zhang et al., 2019]

Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: a survey and new perspectives. ACM Computing Surveys (CSUR), 52(1), 5.

[Zhu et al., 2017]

Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE international conference on computer vision (pp. 2223–2232).

[Zhu et al., 2015]

Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., & Fidler, S. (2015). Aligning books and movies: towards story-like visual explanations by watching movies and reading books. Proceedings of the IEEE international conference on computer vision (pp. 19–27).