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In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code.
Go-explore: a new approach for hard-exploration problems
Ecoffet, A., Huizinga, J., Lehman, J., Stanley, K. O., and Clune, J · 1901
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
Earlier work this paper cites.
Multitask learning: A knowledge-based source of inductive bias
Caruana, R · 1993
Earlier work this paper cites.
Design Patterns: Elements of Reusable Object-Oriented Software
Gamma, E., Helm, R., Johnson, R., and Vlissides, J · 1994
Earlier work this paper cites.
The WEKA data mining software: an update
Hall, M. A., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H · 2009
Earlier work this paper cites.
API design for machine learning software: experiences from the scikit-learn project
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., VanderPlas, J., Joly, A., Holt, B., and Varoquaux, G · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T · 2014
Earlier work this paper cites.
Convolutional neural networks for sentence classification
Kim, Y · 2014
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2015
Earlier work this paper cites.
Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., and Zhang, Z · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M. A., Fidjeland, A., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
Cited alongside, same era.
Improved semantic representations from tree-structured long short-term memory networks
Tai, K. S., Socher, R., and Manning, C. D · 2015
Cited alongside, same era.
Chainer: a next-generation open source framework for deep learning
Tokui, S., Oono, K., Hido, S., and Clayton, J · 2015
Theano: A Python framework for fast computation of mathematical expressions
Theano Development Team · 2016
Later among the works it cites.
Allennlp: A deep semantic natural language processing platform
Gardner, M., Grus, J., Neumann, M., Tafjord, O., Dasigi, P., Liu, N. F., Peters, M., Schmitz, M., and Zettlemoyer, L. S · 2017
Later among the works it cites.
Densely connected convolutional networks
Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K. Q · 2017
Later among the works it cites.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
Later among the works it cites.
Open sourcing Sonnet - a new library for constructing neural networks
Reynolds, M., Barth-Maron, G., Besse, F., de Las Casas, D., Fidjeland, A., Green, T., Puigdomènech, A., Racanière, S., Rae, J., and Viola, F · 2017
Later among the works it cites.
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Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Neural architectures for named entity recognition
Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., and Dyer, C · 2016
Cited alongside, same era.
Mllib: Machine learning in apache spark
Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., Freeman, J., Tsai, D., Amde, M., Owen, S., et al · 2016
Cited alongside, same era.
Schaul, T., Quan, J., Antonoglou, I., and Silver, D · 2016
Cited alongside, same era.
Cntk: Microsoft’s open-source deep-learning toolkit
Seide, F. and Agarwal, A · 2016
Cited alongside, same era.
Bayesian optimization of combinatorial structures
Baptista, R. and Poloczek, M · 2018
Later among the works it cites.
Snorkel metal: Weak supervision for multi-task learning
Ratner, A., Hancock, B., Dunnmon, J., Goldman, R. E., and Ré, C · 2018
Later among the works it cites.
Training complex models with multi-task weak supervision
Ratner, A., Hancock, B., Dunnmon, J., Sala, F., Pandey, S., and Ré, C · 2019
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The role of massively multi-task and weak supervision in software 2.0
Ratner, A. J., Hancock, B., and Ré, C · 2019
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