Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search
The performance of deep neural networks is highly sensitive to the choice of the hyperparameters that define the structure of the network and the learning process. When facing a new application, tuning a deep neural netw…
Hyperparameter OptimizationSingle-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter Optimization
Can we reduce the search cost of Neural Architecture Search (NAS) from days down to only few hours? NAS methods automate the design of Convolutional Networks (ConvNets) under hardware constraints and they have emerged as…
AutoMLGPUHyperparameter Optimizationimage-classification+2Hyp-RL : Hyperparameter Optimization by Reinforcement Learning
Hyperparameter tuning is an omnipresent problem in machine learning as it is an integral aspect of obtaining the state-of-the-art performance for any model. Most often, hyperparameters are optimized just by training a mo…
Bayesian OptimizationHyperparameter Optimizationreinforcement-learningReinforcement Learning+1PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Design
The ever increasing computational cost of Deep Neural Networks (DNN) and the demand for energy efficient hardware for DNN acceleration has made accuracy and hardware cost co-optimization for DNNs tremendously important, …
Bayesian OptimizationHyperparameter OptimizationMultivariate, Multistep Forecasting, Reconstruction and Feature Selection of Ocean Waves via Recurrent and Sequence-to-Sequence Networks
This article explores the concepts of ocean wave multivariate multistep forecasting, reconstruction and feature selection. We introduce recurrent neural network frameworks, integrated with Bayesian hyperparameter optimiz…
feature selectionHyperparameter OptimizationMeta-Surrogate Benchmarking for Hyperparameter Optimization
Despite the recent progress in hyperparameter optimization (HPO), available benchmarks that resemble real-world scenarios consist of a few and very large problem instances that are expensive to solve. This blocks researc…
BenchmarkingHyperparameter OptimizationLambdaOpt: Learn to Regularize Recommender Models in Finer Levels
Recommendation models mainly deal with categorical variables, such as user/item ID and attributes. Besides the high-cardinality issue, the interactions among such categorical variables are usually long-tailed, with the h…
Hyperparameter OptimizationRecommendation SystemsDataset2Vec: Learning Dataset Meta-Features
Meta-learning, or learning to learn, is a machine learning approach that utilizes prior learning experiences to expedite the learning process on unseen tasks. As a data-driven approach, meta-learning requires meta-featur…
Auxiliary LearningFew-Shot LearningHyperparameter OptimizationMeta-LearningSequential Gaussian Processes for Online Learning of Nonstationary Functions
Many machine learning problems can be framed in the context of estimating functions, and often these are time-dependent functions that are estimated in real-time as observations arrive. Gaussian processes (GPs) are an at…
Gaussian ProcessesHyperparameter OptimizationMixture-of-ExpertsTime Series+2DEEP-BO for Hyperparameter Optimization of Deep Networks
The performance of deep neural networks (DNN) is very sensitive to the particular choice of hyper-parameters. To make it worse, the shape of the learning curve can be significantly affected when a technique like batchnor…
Bayesian OptimizationHyperparameter OptimizationEvolving Rewards to Automate Reinforcement Learning
Many continuous control tasks have easily formulated objectives, yet using them directly as a reward in reinforcement learning (RL) leads to suboptimal policies. Therefore, many classical control tasks guide RL training …
continuous-controlContinuous ControlHyperparameter OptimizationMuJoCo+3Software Engineering for Fairness: A Case Study with Hyperparameter Optimization
We assert that it is the ethical duty of software engineers to strive to reduce software discrimination. This paper discusses how that might be done. This is an important topic since machine learning software is increasi…
BIG-bench Machine LearningFairnessHyperparameter OptimizationTabular Benchmarks for Joint Architecture and Hyperparameter Optimization
Due to the high computational demands executing a rigorous comparison between hyperparameter optimization (HPO) methods is often cumbersome. The goal of this paper is to facilitate a better empirical evaluation of HPO me…
Hyperparameter OptimizationExploring the Hyperparameter Landscape of Adversarial Robustness
Adversarial training shows promise as an approach for training models that are robust towards adversarial perturbation. In this paper, we explore some of the practical challenges of adversarial training. We present a sen…
Adversarial RobustnessHyperparameter OptimizationNavigateOptimizing for Generalization in Machine Learning with Cross-Validation Gradients
Cross-validation is the workhorse of modern applied statistics and machine learning, as it provides a principled framework for selecting the model that maximizes generalization performance. In this paper, we show that th…
BIG-bench Machine LearningHyperparameter OptimizationregressionReducing The Search Space For Hyperparameter Optimization Using Group Sparsity
We propose a new algorithm for hyperparameter selection in machine learning algorithms. The algorithm is a novel modification of Harmonica, a spectral hyperparameter selection approach using sparse recovery methods. In p…
BIG-bench Machine LearningHyperparameter OptimizationAdaptive Bayesian Linear Regression for Automated Machine Learning
To solve a machine learning problem, one typically needs to perform data preprocessing, modeling, and hyperparameter tuning, which is known as model selection and hyperparameter optimization.The goal of automated machine…
AutoMLBayesian OptimizationBIG-bench Machine LearningHyperparameter Optimization+3Hyperparameter Optimization in Black-box Image Processing using Differentiable Proxies
Nearly every commodity imaging system we directly interact with, or indirectly rely on, leverages power efficient, application-adjustable black-box hardware image signal processing (ISPs) units, running either in dedicat…
Hyperparameter OptimizationImage DenoisingImage Quality AssessmentObject DetectionsharpDARTS: Faster and More Accurate Differentiable Architecture Search
Neural Architecture Search (NAS) has been a source of dramatic improvements in neural network design, with recent results meeting or exceeding the performance of hand-tuned architectures. However, our understanding of ho…
Hyperparameter OptimizationImage ClassificationNeural Architecture SearchSelf-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions
Hyperparameter optimization can be formulated as a bilevel optimization problem, where the optimal parameters on the training set depend on the hyperparameters. We aim to adapt regularization hyperparameters for neural n…
Bilevel OptimizationData AugmentationHyperparameter Optimization