J. Bergstra

Publications45
h-index26
Citations18,640
Highly Influential Citations1,452
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Random Search for Hyper-Parameter Optimization
TLDR
This paper shows empirically and theoretically that randomly chosen trials are more efficient for hyper-parameter optimization than trials on a grid, and shows that random search is a natural baseline against which to judge progress in the development of adaptive (sequential) hyper- parameter optimization algorithms.
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Algorithms for Hyper-Parameter Optimization
TLDR
This work contributes novel techniques for making response surface models P(y|x) in which many elements of hyper-parameter assignment (x) are known to be irrelevant given particular values of other elements.
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Theano: A Python framework for fast computation of mathematical expressions
TLDR
The performance of Theano is compared against Torch7 and TensorFlow on several machine learning models and recently-introduced functionalities and improvements are discussed.
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Challenges in representation learning: A report on three machine learning contests
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Theano: new features and speed improvements
TLDR
New features and efficiency improvements to Theano are presented, and benchmarks demonstrating Theano's performance relative to Torch7, a recently introduced machine learning library, and to RNNLM, a C++ library targeted at recurrent neural networks.
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An empirical evaluation of deep architectures on problems with many factors of variation
TLDR
A series of experiments indicate that these models with deep architectures show promise in solving harder learning problems that exhibit many factors of variation.
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Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures
TLDR
This work proposes a meta-modeling approach to support automated hyperparameter optimization, with the goal of providing practical tools that replace hand-tuning with a reproducible and unbiased optimization process.
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Theano: A CPU and GPU Math Compiler in Python
TLDR
This paper illustrates how to use Theano, outlines the scope of the compiler, provides benchmarks on both CPU and GPU processors, and explains its overall design.
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Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms
TLDR
An introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization.
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Hyperopt: a Python library for model selection and hyperparameter optimization
TLDR
An introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization.
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