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Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax
TL;DR: It is found that Ax, BoTorch and GPyTorch together provide a simple-to-use but powerful framework for Bayesian hyperparameter optimization, using Ax's high-level API that constructs and runs a full optimization loop and returns the besthyperparameter configuration.
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Abstract: Deep learning models are full of hyperparameters, which are set manually before the learning process can start. To find the best configuration for these hyperparameters in such a high dimensional space, with time-consuming and expensive model training / validation, is not a trivial challenge. Bayesian optimization is a powerful tool for the joint optimization of hyperparameters, efficiently trading off exploration and exploitation of the hyperparameter space. In this paper, we discuss Bayesian hyperparameter optimization, including hyperparameter optimization, Bayesian optimization, and Gaussian processes. We also review BoTorch, GPyTorch and Ax, the new open-source frameworks that we use for Bayesian optimization, Gaussian process inference and adaptive experimentation, respectively. For experimentation, we apply Bayesian hyperparameter optimization, for optimizing group weights, to weighted group pooling, which couples unsupervised tiered graph autoencoders learning and supervised graph prediction learning for molecular graphs. We find that Ax, BoTorch and GPyTorch together provide a simple-to-use but powerful framework for Bayesian hyperparameter optimization, using Ax's high-level API that constructs and runs a full optimization loop and returns the best hyperparameter configuration.
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Citations
Bayesian optimization algorithm based support vector regression analysis for estimation of shear capacity of FRP reinforced concrete members
TL;DR: A hybrid of the Bayesian optimization algorithm (BOA) and support vector regression (SVR) as a novel modeling tool for the prediction of the shear capacity of FRP-reinforced members with no stirrups is presented.
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A Novel CNN-LSTM Hybrid Model for Prediction of Electro-Mechanical Impedance Signal Based Bond Strength Monitoring
Lukesh Parida,Sumedha Moharana,Victor M. Ferreira,Sourav Kumar Giri,G. Ascensão +4 more
TL;DR: In this article , the performance of EMI techniques using a piezo sensor to monitor the development of bond strength in reinforced concrete through a pull-out test was determined using a CNN-LSTM-based hybrid model.
Dynamic Configuration Tuning of Working Database Management Systems
Yoshiteru Ishihara,Masahito Shiba +1 more
- 01 Mar 2020
TL;DR: This paper describes a system that tunes the configuration of active DBMSs, calculates the optimal values of the configuration knobs, tests them using the same SQL queries that the DBMS receives, and applies the optimal knob values to theDBMS.
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Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Models.
TL;DR: Probabilistic deep learning as mentioned in this paper is a generalization of deep learning that accounts for uncertainty, both model uncertainty and data uncertainty, using probabilistic models and deep neural networks.
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Towards Reliable Parameter Extraction in MEMS Final Module Testing Using Bayesian Inference
TL;DR: A new machine learning approach in MEMS testing based on Bayesian inference to determine whether the estimation is trustworthy is presented, and BayesFlow consistently outperformed the other methods in terms of the predictive performance.
References
•Proceedings Article
Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek,Hugo Larochelle,Ryan P. Adams +2 more
- 03 Dec 2012
TL;DR: This work describes new algorithms that take into account the variable cost of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation and shows that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms.
Taking the Human Out of the Loop: A Review of Bayesian Optimization
Bobak Shahriari,Kevin Swersky,Ziyu Wang,Ryan P. Adams,Nando de Freitas +4 more
- 01 Jan 2016
TL;DR: This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.
•Posted Content
A Tutorial on Bayesian Optimization
TL;DR: This tutorial describes how Bayesian optimization works, including Gaussian process regression and three common acquisition functions: expected improvement, entropy search, and knowledge gradient, and provides a generalization of expected improvement to noisy evaluations beyond the noise-free setting where it is more commonly applied.
1.3K
•Posted Content
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration.
TL;DR: This work presents an efficient and general approach to GP inference based on Blackbox Matrix-Matrix multiplication (BBMM), a modified batched version of the conjugate gradients algorithm to derive all terms for training and inference in a single call.
891
When Gaussian Process Meets Big Data: A Review of Scalable GPs
TL;DR: In this article, a review of state-of-the-art scalable Gaussian process regression (GPR) models is presented, focusing on global and local approximations for subspace learning.