Fetching the paper…
Reading the bibliography…
Modern learning models are characterized by large hyperparameter spaces and long training times.
Asymptotic minimax character of the sample distribution function and of the classical multinomial estimator
Dvoretzky, A., Kiefer, J., and Wolfowitz, J · 1956
Earlier work this paper cites.
Building a large annotated corpus of english: The penn treebank
Marcus, M., Marcinkiewicz, M., and Santorini, B · 1993
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
Kriging is well-suited to parallelize optimization
Ginsbourger, D., Le Riche, R., and Carraro, L · 2010
Earlier work this paper cites.
Gaussian process optimization in the bandit setting: No regret and experimental design
Srinivas, N., Krause, A., Kakade, S., and Seeger, M · 2010
Earlier work this paper cites.
Oracle inequalities for computationally budgeted model selection
Agarwal, A., Duchi, J., Bartlett, P. L., and Levrard, C · 2011
Earlier work this paper cites.
Algorithms for hyper-parameter optimization
Bergstra, J., Bardenet, R., Bengio, Y., and Kegl., B · 2011
Earlier work this paper cites.
Efficient multi-start strategies for local search algorithms
György, A. and Kocsis, L · 2011
Earlier work this paper cites.
Sequential model-based optimization for general algorithm configuration
Hutter, F., Hoos, H., and Leyton-Brown., K · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
Earlier work this paper cites.
Convolutional neural networks applied to house numbers digit classification
Sermanet, P., Chintala, S., and LeCun, Y · 2012
Earlier work this paper cites.
Practical bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R · 2012
Earlier work this paper cites.
Almost optimal exploration in multi-armed bandits
Karnin, Z., Koren, T., and Somekh, O · 2013
Earlier work this paper cites.
Multi-task bayesian optimization
Swersky, K., Snoek, J., and Adams, R · 2013
Earlier work this paper cites.
One weird trick for parallelizing convolutional neural networks
Krizhevsky, A · 2014
Cited alongside, same era.
Freeze-thaw bayesian optimization
Swersky, K., Snoek, J., and Adams, R. P · 2014
Cited alongside, same era.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S. E., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2014
Cited alongside, same era.
Recurrent neural network regularization
Zaremba, W., Sutskever, I., and Vinyals, O · 2014
Cited alongside, same era.
Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
Domhan, T., Springenberg, J. T., and Hutter, F · 2015
Auto-weka 2.0: Automatic model selection and hyperparameter optimization in weka
Kotthoff, L., Thornton, C., Hoos, H. H., Hutter, F., and Leyton-Brown, K · 2017
Later among the works it cites.
Hyperband: Bandit-based configuration evaluation for hyperparameter optimization
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A · 2017
Later among the works it cites.
Paleo: A performance model for deep neural networks
Qi, H., Sparks, E. R., and Talwalkar, A · 2017
Later among the works it cites.
Scaling SGD batch size to 32k for imagenet training
You, Y., Gitman, I., and Ginsburg, B · 2017
Later among the works it cites.
100-epoch ImageNet Training with AlexNet in 24 Minutes
You, Y., Zhang, Z., Hsieh, C.-J., Demmel, J., and Keutzer, K · 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…
Cited alongside, same era.
Efficient and robust automated machine learning
Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., and Hutter, F · 2015
Cited alongside, same era.
Non-stochastic best arm identification and hyperparameter optimization
Jamieson, K. and Talwalkar, A · 2015
Cited alongside, same era.
Fast cross-validation via sequential testing
Krueger, T., Panknin, D., and Braun, M · 2015
Cited alongside, same era.
Announcing tensorflow 0.8 now with distributed computing support!, 2016
et al., D. M · 2016
Cited alongside, same era.
Batch bayesian optimization via local penalization
González, J., Zhenwen, D., Hennig, P., and Lawrence, N · 2016
Cited alongside, same era.
Selecting near-optimal learners via incremental data allocation
Sabharwal, A., Samulowitz, H., and Tesauro, G · 2016
Cited alongside, same era.
Google vizier: A service for black-box optimization
Golovin, D., Sonik, B., Moitra, S., Kochanski, G., Karro, J., and D.Sculley · 2017
Cited alongside, same era.
Bohb: Robust and efficient hyperparameter optimization at scale
Falkner, S., Klein, A., and Hutter, F · 2018
Closest in time.
CHOPT : Automated hyperparameter optimization framework for cloud-based machine learning platforms
Kim, J., Kim, M., Park, H., Kusdavletov, E., Lee, D., Kim, A., Kim, J., Ha, J., and Sung, N · 2018
Closest in time.
Hyperband: A novel bandit-based approach to hyperparameter optimization
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A · 2018
Closest in time.
Tune: A research platform for distributed model selection and training
Liaw, R., Liang, E., Nishihara, R., Moritz, P., Gonzalez, J. E., and Stoica, I · 2018
Closest in time.
Regularizing and optimizing LSTM language models
Merity, S., Keskar, N., and Socher, R · 2018
Closest in time.
Gandiva: Introspective cluster scheduling for deep learning
Xiao, W., Bhardwaj, R., Ramjee, R., Sivathanu, M., Kwatra, N., Han, Z., Patel, P., Peng, X., Zhao, H., Zhang, Q., et al · 2018
Closest in time.
A generalized framework for population based training
Li, A., Spyra, O., Perel, S., Dalibard, V., Jaderberg, M., Gu, C., Budden, D., Harley, T., and Gupta, P · 2019
Closest in time.
Random search and reproducibility for neural architecture search
Li, L. and Talwalkar, A · 2019
Closest in time.
DARTS: Differentiable architecture search
Liu, H., Simonyan, K., and Yang, Y · 2019
Closest in time.