Fetching the paper…
Reading the bibliography…
Cancer is a complex disease, the understanding and treatment of which are being aided through increases in the volume of collected data and in the scale of deployed computing power.
Policy gradient methods for reinforcement learning with function approximation. In Advances in neural information processing systems . 1057–1063
Richard S. Sutton, David A. McAllester, Satinder P. Singh, and Yishay Mansour. 2000 · 2000
Earlier work this paper cites.
Modeling systems with internal state using Evolino. In Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation . ACM, 1795–1802
Daan Wierstra, Faustino J Gomez, and Jürgen Schmidhuber. 2005 · 2005
Earlier work this paper cites.
Neuroevolution: From architectures to learning
Dario Floreano, Peter Dürr, and Claudio Mattiussi. 2008 · 2008
Earlier work this paper cites.
A hypercube-based encoding for evolving large-scale neural networks
Kenneth O Stanley, David B D’Ambrosio, and Jason Gauci. 2009 · 2009
Earlier work this paper cites.
Scikit-learn: Machine Learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 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.
A survey of actor-critic reinforcement learning: SStandard and natural policy gradients
Ivo Grondman, Lucian Busoniu, Gabriel AD Lopes, and Robert Babuska. 2012 · 2012
Earlier work this paper cites.
Practical Bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems . 2951–2959
Jasper Snoek, Hugo Larochelle, and Ryan P Adams. 2012 · 2012
Earlier work this paper cites.
Making a science of model search: Hperparameter optimization in hundreds of dimensions for vision architectures
James Bergstra, Daniel Yamins, and David Daniel Cox. 2013b · 2013
Earlier work this paper cites.
Do we need hundreds of classifiers to solve real world classification problems?
Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, and Dinani Amorim. 2014 · 2014
Earlier work this paper cites.
Keras (2015)
François Chollet et al · 2015
Earlier work this paper cites.
Extremely high genetic diversity in a single tumor points to prevalence of non-Darwinian cell evolution
Shaoping Ling, Zheng Hu, Zuyu Yang, Fang Yang, Yawei Li, Pei Lin, Ke Chen, Lili Dong, Lihua Cao, Yong Tao, et al · 2015
Earlier work this paper cites.
Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015 · 2015
Earlier work this paper cites.
Scalable Bayesian optimization using deep neural networks. In International Conference on Machine Learning . 2171–2180
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, and Ryan Adams. 2015 · 2015
Earlier work this paper cites.
TensorFlow: A system for large-scale machine learning. In OSDI , Vol. 16. 265–283
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Earlier work this paper cites.
Designing neural network architectures using reinforcement learning
Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar. 2016 · 2016
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016 · 2016
Earlier work this paper cites.
Learning curve prediction with Bayesian neural networks
Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, and Frank Hutter. 2016 · 2016
Earlier work this paper cites.
Hyperband: Bandit-based configuration evaluation for hyperparameter optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar. 2016 · 2016
Earlier work this paper cites.
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science. In Proceedings of the Genetic and Evolutionary Computation Conference 2016 (GECCO ’16) . ACM, New York, NY, USA, 485–492
Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, and Jason H. Moore. 2016 · 2016
Earlier work this paper cites.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le. 2016 · 2016
Earlier work this paper cites.
N2n learning: Network to network compression via policy gradient reinforcement learning
Anubhav Ashok, Nicholas Rhinehart, Fares Beainy, and Kris M Kitani. 2017 · 2017
Earlier work this paper cites.
Neural optimizer search with reinforcement learning. In Proceedings of the 34th International Conference on Machine Learning , Vol. 70. JMLR. org, 459–468
Irwan Bello, Barret Zoph, Vijay Vasudevan, and Quoc V Le. 2017 · 2017
Earlier work this paper cites.
Geometric deep learning: Going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. 2017 · 2017
Earlier work this paper cites.
A downsampled variant of ImageNet as an alternative to the CIFAR datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter. 2017 · 2017
Earlier work this paper cites.
OpenAI baselines
Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, Yuhuai Wu, and Peter Zhokhov. 2017 · 2017
Cited alongside, same era.
Population Based Training of Neural Networks
Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, et al · 2017
Cited alongside, same era.
RoBO: A flexible and robust Bayesian Optimization framework in Python. In NeurIPS 2017 Bayesian Optimization Workshop
A. Klein, S. Falkner, N. Mansur, and F. Hutter. 2017 · 2017
Cited alongside, same era.
Progressive neural architecture search
Chenxi Liu, Barret Zoph, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy. 2017 · 2017
Cited alongside, same era.
Hyper-parameter selection in deep neural networks using parallel particle swarm optimization. In Proceedings of the Genetic and Evolutionary Computation Conference Companion . ACM, 1864–1871
Pablo Ribalta Lorenzo, Jakub Nalepa, Luciano Sanchez Ramos, and José Ranilla Pastor. 2017 · 2017
Efficient Neural Architecture Search via Parameter Sharing
Hieu Pham, Melody Y Guan, Barret Zoph, Quoc V Le, and Jeff Dean. 2018 · 2018
Later among the works it cites.
From nodes to networks: Evolving recurrent neural networks
Aditya Rawal and Risto 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.
A landscape of metabolic variation across tumor types
Ed Reznik, Augustin Luna, Bülent Arman Aksoy, Eric Minwei Liu, Konnor La, Irina Ostrovnaya, Chad J Creighton, A Ari Hakimi, and Chris Sander. 2018 · 2018
Later among the works it cites.
Constructing Deep Neural Networks by Bayesian Network Structure Learning. In Advances in Neural Information Processing Systems . 3051–3062
Raanan Y Rohekar, Shami Nisimov, Yaniv Gurwicz, Guy Koren, and Gal Novik. 2018 · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Evolving deep neural networks
Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, Olivier Francon, Bala Raju, Arshak Navruzyan, Nigel Duffy, and Babak Hodjat. 2017 · 2017
Cited alongside, same era.
Deeparchitect: Automatically designing and training deep architectures
Renato Negrinho and Geoff Gordon. 2017 · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
Cited alongside, same era.
A genetic programming approach to designing convolutional neural network architectures. In Proceedings of the Genetic and Evolutionary Computation Conference . ACM, 497–504
Masanori Suganuma, Shinichi Shirakawa, and Tomoharu Nagao. 2017 · 2017
Cited alongside, same era.
Attention is all you need. In Advances in Neural Information Processing Systems . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Bayesian Optimization Combined with Incremental Evaluation for Neural Network Architecture Optimization
Martin Wistuba. 2017 · 2017
Cited alongside, same era.
Evolving deep networks using HPC. In Proceedings of the Machine Learning on HPC Environments . ACM
Steven R Young, Derek C Rose, Travis Johnston, William T Heller, Thomas P Karnowski, Thomas E Potok, Robert M Patton, Gabriel Perdue, and Jonathan Miller. 2017 · 2017
Cited alongside, same era.
Balsam: Automated Scheduling and Execution of Dynamic, Data-Intensive Workflows. In PyHPC 2018: Proceedings of the 8th Workshop on Python for High-Performance and Scientific Computing
Michael A. Salim, Thomas D. Uram, Taylor Childers, Prasanna Balaprakash, Venkatram Vishwanath, and Michael E. Papka. 2018 · 2018
Later among the works it cites.
Oncogenic signaling pathways in the cancer genome atlas
Francisco Sanchez-Vega, Marco Mina, Joshua Armenia, Walid K Chatila, Augustin Luna, Konnor C La, Sofia Dimitriadoy, David L Liu, Havish S Kantheti, Sadegh Saghafinia, et al · 2018
Later among the works it cites.
Exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search
Masanori Suganuma, Mete Ozay, and Takayuki Okatani. 2018 · 2018
Later among the works it cites.
Reinforcement learning: An introduction
Richard S. Sutton and Andrew G. Barto. 2018 · 2018
Later among the works it cites.
Evolutionary Neural Architecture Search for Image Restoration
Gerard Jacques van Wyk and Anna Sergeevna Bosman. 2018 · 2018
Later among the works it cites.
Combination of hyperband and Bayesian optimization for hyperparameter optimization in deep learning
Jiazhuo Wang, Jason Xu, and Xuejun Wang. 2018 · 2018
Later among the works it cites.
CANDLE/Supervisor: A workflow framework for machine learning applied to cancer research
Justin M. Wozniak, Rajeev Jain, Prasanna Balaprakash, Jonathan Ozik, Nicholson T. Collier, John Bauer, Fangfang Xia, Thomas S. Brettin, Rick Stevens, Jamaludin Mohd-Yusof, Cristina Garcia-Cardona, Brian Van Essen, and Matthew Baughman. 2018 · 2018
Later among the works it cites.
Predicting tumor cell line response to drug pairs with deep learning
Fangfang Xia, Maulik Shukla, Thomas Brettin, Cristina Garcia-Cardona, Judith Cohn, Jonathan E Allen, Sergei Maslov, Susan L Holbeck, James H Doroshow, Yvonne A Evrard, et al · 2018
Later among the works it cites.
SNAS: Stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin. 2018 · 2018
Later among the works it cites.
Towards automated deep learning: Efficient joint neural architecture and hyperparameter search
Arber Zela, Aaron Klein, Stefan Falkner, and Frank Hutter. 2018 · 2018
Later among the works it cites.
Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 8697–8710
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le. 2018 · 2018
Later among the works it cites.
An End-to-End AutoML Solution for Tabular Data at KaggleDays
[n. d.]b · 2019
Closest in time.
Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search
Xiangxiang Chu, Bo Zhang, Hailong Ma, Ruijun Xu, Jixiang Li, and Qingyuan Li. 2019 · 2019
Closest in time.
Bayesian Learning of Neural Network Architectures
Georgi Dikov, Patrick van der Smagt, and Justin Bayer. 2019 · 2019
Closest in time.
Automated Machine Learning: Methods, Systems, Challenges
F. Hutter, L. Kotthoff, and J. Vanschoren (Eds.). 2019 · 2019
Closest in time.
Random Search and Reproducibility for Neural Architecture Search
Liam Li and Ameet Talwalkar. 2019 · 2019
Closest in time.
Evolutionary Neural AutoML for Deep Learning
Jason Liang, Elliot Meyerson, Babak Hodjat, Dan Fink, Karl Mutch, and Risto Miikkulainen. 2019 · 2019
Closest in time.
Evaluating the Search Phase of Neural Architecture Search
Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann. 2019 · 2019
Closest in time.
Designing neural networks through neuroevolution
Kenneth O. Stanley, Jeff Clune, Joel Lehman, and Risto Miikkulainen. 2019 · 2019
Closest in time.
Neural architecture search with Bayesian optimisation and optimal transport. In Advances in Neural Information Processing Systems . 2020–2029
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, and Eric P Xing. 2018 · 2029
Closest in time.