24,836 research outputs found
Basic Enhancement Strategies When Using Bayesian Optimization for Hyperparameter Tuning of Deep Neural Networks
Compared to the traditional machine learning models, deep neural networks (DNN) are known to be highly sensitive to the choice of hyperparameters. While the required time and effort for manual tuning has been rapidly decreasing for the well developed and commonly used DNN architectures, undoubtedly DNN hyperparameter optimization will continue to be a major burden whenever a new DNN architecture needs to be designed, a new task needs to be solved, a new dataset needs to be addressed, or an existing DNN needs to be improved further. For hyperparameter optimization of general machine learning problems, numerous automated solutions have been developed where some of the most popular solutions are based on Bayesian Optimization (BO). In this work, we analyze four fundamental strategies for enhancing BO when it is used for DNN hyperparameter optimization. Specifically, diversification, early termination, parallelization, and cost function transformation are investigated. Based on the analysis, we provide a simple yet robust algorithm for DNN hyperparameter optimization - DEEP-BO (Diversified, Early-termination-Enabled, and Parallel Bayesian Optimization). When evaluated over six DNN benchmarks, DEEP-BO mostly outperformed well-known solutions including GP-Hedge, BOHB, and the speed-up variants that use Median Stopping Rule or Learning Curve Extrapolation. In fact, DEEP-BO consistently provided the top, or at least close to the top, performance over all the benchmark types that we have tested. This indicates that DEEP-BO is a robust solution compared to the existing solutions. The DEEP-BO code is publicly available at <uri>https://github.com/snu-adsl/DEEP-BO</uri>
Safe Controller Optimization for Quadrotors with Gaussian Processes
One of the most fundamental problems when designing controllers for dynamic
systems is the tuning of the controller parameters. Typically, a model of the
system is used to obtain an initial controller, but ultimately the controller
parameters must be tuned manually on the real system to achieve the best
performance. To avoid this manual tuning step, methods from machine learning,
such as Bayesian optimization, have been used. However, as these methods
evaluate different controller parameters on the real system, safety-critical
system failures may happen. In this paper, we overcome this problem by
applying, for the first time, a recently developed safe optimization algorithm,
SafeOpt, to the problem of automatic controller parameter tuning. Given an
initial, low-performance controller, SafeOpt automatically optimizes the
parameters of a control law while guaranteeing safety. It models the underlying
performance measure as a Gaussian process and only explores new controller
parameters whose performance lies above a safe performance threshold with high
probability. Experimental results on a quadrotor vehicle indicate that the
proposed method enables fast, automatic, and safe optimization of controller
parameters without human intervention.Comment: IEEE International Conference on Robotics and Automation, 2016. 6
pages, 4 figures. A video of the experiments can be found at
http://tiny.cc/icra16_video . A Python implementation of the algorithm is
available at https://github.com/befelix/SafeOp
Practical Bayesian Optimization of Machine Learning Algorithms
Machine learning algorithms frequently require careful tuning of model
hyperparameters, regularization terms, and optimization parameters.
Unfortunately, this tuning is often a "black art" that requires expert
experience, unwritten rules of thumb, or sometimes brute-force search. Much
more appealing is the idea of developing automatic approaches which can
optimize the performance of a given learning algorithm to the task at hand. In
this work, we consider the automatic tuning problem within the framework of
Bayesian optimization, in which a learning algorithm's generalization
performance is modeled as a sample from a Gaussian process (GP). The tractable
posterior distribution induced by the GP leads to efficient use of the
information gathered by previous experiments, enabling optimal choices about
what parameters to try next. Here we show how the effects of the Gaussian
process prior and the associated inference procedure can have a large impact on
the success or failure of Bayesian optimization. We show that thoughtful
choices can lead to results that exceed expert-level performance in tuning
machine learning algorithms. We also describe new algorithms that take into
account the variable cost (duration) of learning experiments and that can
leverage the presence of multiple cores for parallel experimentation. We show
that these proposed algorithms improve on previous automatic procedures and can
reach or surpass human expert-level optimization on a diverse set of
contemporary algorithms including latent Dirichlet allocation, structured SVMs
and convolutional neural networks
Automatic LQR Tuning Based on Gaussian Process Global Optimization
This paper proposes an automatic controller tuning framework based on linear
optimal control combined with Bayesian optimization. With this framework, an
initial set of controller gains is automatically improved according to a
pre-defined performance objective evaluated from experimental data. The
underlying Bayesian optimization algorithm is Entropy Search, which represents
the latent objective as a Gaussian process and constructs an explicit belief
over the location of the objective minimum. This is used to maximize the
information gain from each experimental evaluation. Thus, this framework shall
yield improved controllers with fewer evaluations compared to alternative
approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is
used as the experimental demonstrator. Results of a two- and four-dimensional
tuning problems highlight the method's potential for automatic controller
tuning on robotic platforms.Comment: 8 pages, 5 figures, to appear in IEEE 2016 International Conference
on Robotics and Automation. Video demonstration of the experiments available
at https://am.is.tuebingen.mpg.de/publications/marco_icra_201
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