Fetching the paper…
Reading the bibliography…
Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
Earlier work this paper cites.
A cooperative coevolutionary approach to function optimization. In International Conference on Parallel Problem Solving from Nature
Mitchell A Potter and Kenneth A De Jong. 1994 · 1994
Earlier work this paper cites.
Hierarchical evolution of neural networks. In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on
David E Moriarty and Risto Miikkulainen. 1998 · 1998
Earlier work this paper cites.
Solving non-Markovian control tasks with neuroevolution. In IJCAI
Faustino J Gomez and Risto Miikkulainen. 1999 · 1999
Earlier work this paper cites.
Multi-agent robot learning by means of genetic programming: Solving an escape problem. In International Conference on Evolvable Systems
Kohsuke Yanai and Hitoshi Iba. 2001 · 2001
Earlier work this paper cites.
Cooperative coevolution of multi-agent systems
Chern Han Yong and Risto Miikkulainen. 2001 · 2001
Earlier work this paper cites.
Evolving Neural Networks Through Augmenting Topologies
Kenneth O. Stanley and Risto Miikkulainen. 2002 · 2002
Earlier work this paper cites.
A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th international conference on Machine learning
Ronan Collobert and Jason Weston. 2008 · 2008
Earlier work this paper cites.
Accelerated neural evolution through cooperatively coevolved synapses
Faustino Gomez, Jürgen Schmidhuber, and Risto Miikkulainen. 2008 · 2008
Earlier work this paper cites.
Recurrent neural network based language model. In Eleventh Annual Conference of the International Speech Communication Association
Tomáš Mikolov, Martin Karafiát, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur. 2010 · 2010
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. 2010 · 2010
Earlier work this paper cites.
Curse of dimensionality
Eamonn Keogh and Abdullah Mueen. 2011 · 2011
Earlier work this paper cites.
Multiobjective evolutionary algorithms: A survey of the state of the art
Aimin Zhou, Bo-Yang Qu, Hui Li, Shi-Zheng Zhao, Ponnuthurai Nagaratnam Suganthan, and Qingfu Zhang. 2011 · 2011
Earlier work this paper cites.
Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio. 2012 · 2012
Earlier work this paper cites.
Practical bayesian optimization of machine learning algorithms. In Advances in neural information processing systems
Jasper Snoek, Hugo Larochelle, and Ryan P Adams. 2012 · 2012
Earlier work this paper cites.
Speech recognition with deep recurrent neural networks. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. 2013 · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
D. P. Kingma and J. Ba. 2014 · 2014
Cited alongside, same era.
Multi-objective evolutionary algorithms
Kalyanmoy Deb. 2015 · 2015
Cited alongside, same era.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015 · 2015
Cited alongside, same era.
Beyond Short Snippets: Deep Networks for Video Classification
Joe Yue-Hei Ng, Matthew J. Hausknecht, Sudheendra Vijayanarasimhan, Oriol Vinyals, Rajat Monga, and George Toderici. 2015 · 2015
Cited alongside, same era.
Scalable Bayesian Optimization Using Deep Neural Networks.. In ICML
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md Mostofa Ali Patwary, Mr Prabhat, and Ryan P Adams. 2015 · 2015
Cited alongside, same era.
Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning
Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, et al · 2017
Later among the works it cites.
Large-scale evolution of image classifiers
E. Real, S. Moore, A. Selle, et al · 2017
Later among the works it cites.
A genetic programming approach to designing convolutional neural network architectures. In Proc. of GECCO
M. Suganuma, S. Shirakawa, and T. Nagao. 2017 · 2017
Later among the works it cites.
Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers. 2017 · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015 · 2015
Cited alongside, same era.
Enriching Word Vectors with Subword Information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016 · 2016
Cited alongside, same era.
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu. 2016 · 2016
Cited alongside, same era.
Comment Abuse Classification with Deep Learning
Theodora Chu, Kylie Jue, and Max Wang. 2016 · 2016
Cited alongside, same era.
Identity Mappings in Deep Residual Networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016b · 2016
Cited alongside, same era.
CMA-ES for Hyperparameter Optimization of Deep Neural Networks
Ilya Loshchilov and Frank Hutter. 2016 · 2016
Cited alongside, same era.
Neural Architecture Search with Reinforcement Learning
Barret Zoph and Quoc V. Le. 2016 · 2016
Cited alongside, same era.
Evolutionary Architecture Search for Deep Multitask Networks. In Proceedings of the Genetic and Evolutionary Computation Conference
Jason Liang, Elliot Meyerson, and Risto Miikkulainen. 2018 · 2018
Later among the works it cites.
NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search
Zhichao Lu, Ian Whalen, Vishnu Boddeti, Yashesh Dhebar, Kalyanmoy Deb, Erik Goodman, and Wolfgang Banzhaf. 2018 · 2018
Later among the works it cites.
Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering
E. Meyerson and R. Miikkulainen. 2018 · 2018
Later among the works it cites.
Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le. 2018 · 2018
Later among the works it cites.
Amazon Web Services (AWS) - Cloud Computing Services
2019 · 2019
Closest in time.
Google Cloud
2019 · 2019
Closest in time.
Jigsaw Toxic Comment Classification Challenge
2019 · 2019
Closest in time.
Metric Optimization Engine
2019 · 2019
Closest in time.
Microsoft Azure Cloud Computing Platform and Services
2019 · 2019
Closest in time.
Research:Detox/Data Release
2019 · 2019
Closest in time.
StudioML
2019 · 2019
Closest in time.
Using the Microsoft TLC Machine Learning Tool
2019 · 2019
Closest in time.