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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.

Ba et al., 2016

Ba, J. L., Kiros, J. R., & Hinton, G. E. (2016). Layer normalization. arXiv preprint arXiv:1607.06450.

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.

Bay et al., 2006

Bay, H., Tuytelaars, T., & Van Gool, L. (2006). Surf: speeded up robust features. European conference on computer vision (pp. 404–417).

Bengio et al., 2003

Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. Journal of machine learning research, 3(Feb), 1137–1155.

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.

Bodla et al., 2017

Bodla, N., Singh, B., Chellappa, R., & Davis, L. S. (2017). Soft-nms–improving object detection with one line of code. Proceedings of the IEEE international conference on computer vision (pp. 5561–5569).

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.

Brown & Sandholm, 2017

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

Brown et al., 1990

Brown, P. F., Cocke, J., Della Pietra, S. A., Della Pietra, V. J., Jelinek, F., Lafferty, J., … Roossin, P. S. (1990). A statistical approach to machine translation. Computational linguistics, 16(2), 79–85.

Brown et al., 1988

Brown, P. F., Cocke, J., Della Pietra, S. A., Della Pietra, V. J., Jelinek, F., Mercer, R. L., & Roossin, P. (1988). A statistical approach to language translation. Coling Budapest 1988 Volume 1: International Conference on Computational Linguistics.

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.

Cheng et al., 2016

Cheng, J., Dong, L., & Lapata, M. (2016). Long short-term memory-networks for machine reading. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 551–561).

Cho et al., 2014a

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.

Cho et al., 2014b

Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.

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.

Dalal & Triggs, 2005

Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05) (pp. 886–893).

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.

Dosovitskiy et al., 2021

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., … others. (2021). An image is worth 16x16 words: transformers for image recognition at scale. International Conference on Learning Representations.

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.

Dumoulin & Visin, 2016

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

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).

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).

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).

Graves, 2013

Graves, A. (2013). Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850.

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., 2017

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., 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.

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.

Hochreiter et al., 2001

Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., & others (2001). Gradient flow in recurrent nets: the difficulty of learning long-term dependencies.

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., 2020

Hu, Z., Lee, R. K.-W., & Aggarwal, C. C. (2020). Text style transfer: a review and experiment evaluation. arXiv preprint arXiv:2010.12742.

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 & Szegedy, 2015

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

Jaeger, 2002

Jaeger, H. (2002). Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the” echo state network” approach. Vol. 5. GMD-Forschungszentrum Informationstechnik Bonn.

James, 2007

James, W. (2007). The principles of psychology. Vol. 1. Cosimo, Inc.

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.

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., 2017a

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.

Lin et al., 2017b

Lin, Z., Feng, M., Santos, C. N. d., Yu, M., Xiang, B., Zhou, B., & Bengio, Y. (2017). A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130.

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).

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.

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).

Mnih et al., 2014

Mnih, V., Heess, N., Graves, A., & others. (2014). Recurrent models of visual attention. Advances in neural information processing systems (pp. 2204–2212).

Nadaraya, 1964

Nadaraya, E. A. (1964). On estimating regression. Theory of Probability & Its Applications, 9(1), 141–142.

Papineni et al., 2002

Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2002). Bleu: a method for automatic evaluation of machine translation. Proceedings of the 40th annual meeting of the Association for Computational Linguistics (pp. 311–318).

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).

Paulus et al., 2017

Paulus, R., Xiong, C., & Socher, R. (2017). A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304.

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.

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.

Redmon et al., 2016

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779–788).

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).

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., 2014

Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in neural information processing systems (pp. 3104–3112).

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.

Tay et al., 2020

Tay, Y., Dehghani, M., Bahri, D., & Metzler, D. (2020). Efficient transformers: a survey. arXiv preprint arXiv:2009.06732.

Teye et al., 2018

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

Turing, 1950

Turing, A. (1950). Computing machinery and intelligence. Mind, 59(236), 433.

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.

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.

Watson, 1964

Watson, G. S. (1964). Smooth regression analysis. Sankhyā: The Indian Journal of Statistics, Series A, pp. 359–372.

Werbos, 1990

Werbos, P. J. (1990). Backpropagation through time: what it does and how to do it. Proceedings of the IEEE, 78(10), 1550–1560.

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.

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.

Zhang et al., 2021

Zhang, A., Tay, Y., Zhang, S., Chan, A., Luu, A. T., Hui, S. C., & Fu, J. (2021). Beyond fully-connected layers with quaternions: parameterization of hypercomplex multiplications with 1/n parameters. International Conference on Learning Representations.

Zhao et al., 2019

Zhao, Z.-Q., Zheng, P., Xu, S.-t., & Wu, X. (2019). Object detection with deep learning: a review. IEEE transactions on neural networks and learning systems, 30(11), 3212–3232.

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).