References

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.

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.

Canny, 1987

Canny, J. (1987). A computational approach to edge detection. Readings in computer vision (pp. 184–203). Elsevier.

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.

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).

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.

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).

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

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.

He et al., 2015

He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: surpassing human-level performance on imagenet classification. Proceedings of the IEEE international conference on computer vision (pp. 1026–1034).

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).

Hebb & Hebb, 1949

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

Hennessy & Patterson, 2011

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

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).

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).

Karras et al., 2017

Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017). Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196.

Kingma & Ba, 2014

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

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).

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., 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., 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.

Loshchilov & Hutter, 2016

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

Lowe, 2004

Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91–110.

Luo et al., 2018

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

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.

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).

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.

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.

Park et al., 2019

Park, T., Liu, M.-Y., Wang, T.-C., & Zhu, J.-Y. (2019). Semantic image synthesis with spatially-adaptive normalization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2337–2346).

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., 2017

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

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.

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.

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.

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).

Schuster & Paliwal, 1997

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

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.

Shao et al., 2020

Shao, H., Yao, S., Sun, D., Zhang, A., Liu, S., Liu, D., … Abdelzaher, T. (2020). Controlvae: controllable variational autoencoder. Proceedings of the 37th International Conference on Machine Learning.

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.

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.

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.

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).

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).

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.

Xiao et al., 2018

Xiao, L., Bahri, Y., Sohl-Dickstein, J., Schoenholz, S., & Pennington, J. (2018). Dynamical isometry and a mean field theory of cnns: how to train 10,000-layer vanilla convolutional neural networks. International Conference on Machine Learning (pp. 5393–5402).

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).

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.

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).