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检索条件"主题词=hyper-parameter optimization"

共 190 条记录，以下是131-140 订阅

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Knowledge Sharing for Population Based Neural Network Training 41st

Knowledge Sharing for Population Based Neural Network Traini...

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41st German Conference on Artificial Intelligence (AI)

作者： Oehmcke, Stefan Kramer, Oliver Carl von Ossietzky Univ Oldenburg Dept Comp Sci Computat Intelligence Grp Oldenburg Germany

ISBN: (纸本)9783030001117;9783030001100

Finding good hyper-parameter settings to train neural networks is challenging, as the optimal settings can change during the training phase and also depend on random factors such as weight initialization or random batch sampling. Most state-of-the-art methods for the adaptation of these settings are either static (e.g. learning rate scheduler) or dynamic (e.g ADAM optimizer), but only change some of the hyper-parameters and do not deal with the initialization problem. In this paper, we extend the asynchronous evolutionary algorithm, population based training, which modifies all given hyper-parameters during training and inherits weights. We introduce a novel knowledge distilling scheme. Only the best individuals of the population are allowed to share part of their knowledge about the training data with the whole population. This embraces the idea of randomness between the models, rather than avoiding it, because the resulting diversity of models is important for the population's evolution. Our experiments on MNIST, fashionMNIST, and EMNIST (MNIST split) with two classic model architectures show significant improvements to convergence and model accuracy compared to the original algorithm. In addition, we conduct experiments on EMNIST (balanced split) employing a ResNet and a WideResNet architecture to include complex architectures and data as well.

关键词： Asynchronous evolutionary algorithms hyper-parameter optimization Population based training

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Modelling of pollutants and particulate matter in air using auto-tuned deep recurrent networks 6

Modelling of pollutants and particulate matter in air using ...

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6th Conference on Advances in Control and optimization of Dynamical Systems (ACODS)

作者： Inapakurthi, Ravi Kiran Miriyala, Srinivas Soumitri Mitra, Kishalay Indian Inst Technol Dept Chem Engn Hyderabad 502285 Telangana India

Atmospheric pollutants and Particulate Matter of size less than 10 mu m (PM10) are becoming dominant in the atmosphere due to human activities and natural calamities. To address their associated problems on human health, the interactions between pollutants and PM10 have to be envisaged. Machine learning techniques like Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) were successfully employed in establishing the interactions between various factors at play. However, these techniques are denounced for following a heuristic approach for determining network hyper-parameters. We propose a novel evolutionary multi objective optimization algorithm which can optimally determine the hyper-parameters in deep recurrent neural networks. We test the algorithm to build optimal RNNs and LSTMs for modelling and forecasting the pollutants and PM10 data generated in northern Taiwan region during the year 2015. A state-of-the-art network training algorithm, Truncated Back Propagation Through Time was used in our study and single variable regression was done for CO, NOx, SO2, and PM10. Except for SO2 with RNN, model developed with the proposed algorithm gave high R2 values. LSTM was found to be superior than RNN in all the cases with R2 going as high as 0.9584 for PM10, while that attained by RNN is 0.93. (C) 2020, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Particulate matter Machine learning hyper-parameter optimization Pareto Air quality monitoring Auto tuning of deep neural networks

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Energy and Entropy based Intelligence Metric for Performance Estimation in DNNs 5

Energy and Entropy based Intelligence Metric for Performance...

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5th International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Kartal, Burcu Ustundag, Burak Berk Istanbul Tech Univ Dept Comp Engn Istanbul Turkiye

ISBN: (纸本)9781665456456

Model selection and hyper-parameter tuning are highly dependent on problem-specific data characterization in machine learning. Conventional performance evaluation in machine learning methods widely refers to metrics such as accuracy, sensitivity, and error rates. Estimation of the appropriate hyper-parameter set for a machine learning application on initially uncharacterized data sets requires iterative hyperparameter updates besides the training and test cycles. In this paper, an intelligence factor is proposed for rapid evaluation of model consistency and update of the hyper-parameters. The proposed metric can be used as an alternative to or used together with the traditional post-training performance-oriented success measurement methods. An additional artificial neural network is used for the prediction of the performance of the first network in the proposed method. The second neural network uses variations of the intelligence factor acquired from the first or the main neural network. Living organisms tend to conform themselves for minimum energy dissipation in time and space. By having the inspiration of energy consumption and entropy variation of the organisms and the systems in nature, we have defined an intelligence factor depending on energy consumption and the change of entropy in artificial neural networks. Subsampled data is applied to the first neural network and variation of intelligence factor is used as input of the second neural network. The second neural network is trained with the accuracy of the first neural network for different input data set, model, and hyper-parameter combinations. The proposed intelligence factor-based method enables model and hyper-parameter compliance verification of the first neural network by using less than 10% of any new input data set with the accuracy higher than 80%.

关键词： entropy intelligence predictability performance hyper-parameter optimization

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Downscaling Temperature and Precipitation using Support Vector Regression with Evolutionary Strategy

Downscaling Temperature and Precipitation using Support Vect...

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International Joint Conference on Neural Networks (IJCNN)

作者： Lima, Aranildo R. Cannon, Alex J. Hsieh, William W. Univ British Columbia Dept Earth & Ocean Sci Vancouver BC V6T 1Z4 Canada

ISBN: (纸本)9781467314909

In this work, we propose a hybrid algorithm combining support vector regression with evolutionary strategy (SVR-ES) in order to build successful predictive models for downscaling problems. SVR-ES uses uncorrelated mutation with p step sizes to find the optimal SVR hyper-parameters. Two downscaling forecast problems used in the WCCI-2006 contest - surface air temperature and precipitation - were tested. We used multiple linear regression (MLR) as benchmark and a variety of machine learning techniques including bootstrap-aggregated ensemble artificial neural network (ANN), SVR with hyper-parameters given by the Cherkassky-Ma estimate and random forest (RF). We also tested all techniques with using stepwise linear regression (SLR) first to screen out irrelevant predictors. We concluded that SVR-ES is an attractive approach because it tends to outperform the other techniques and can also be implemented in an almost automatic way. The Cherkassky-Ma estimate is a useful approach to minimizing the MAE error and also saves computational time related to the hyper-parameter search. The ANN and RF are also good options to outperform multiple linear regression (MLR). Finally, the use of SLR for predictor selection can dramatically reduce computational time and often help to enhance accuracy.

关键词： Support Vector Regression Evolutionary Strategy hyper-parameter optimization Downscaling Forecasting

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ROBUST MAML: PRIORITIZATION TASK BUFFER WITH ADAPTIVE LEARNING PROCESS FOR MODEL-AGNOSTIC META-LEARNING

ROBUST MAML: PRIORITIZATION TASK BUFFER WITH ADAPTIVE LEARNI...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Thanh Nguyen Tung Luu Trung Pham Rakhimkul, Sanzhar Yoo, Chang D. Korea Adv Inst Sci & Technol KAIST Daejeon South Korea

ISBN: (纸本)9781728176055

Model agnostic meta-learning (MAML) is a popular state-of-the-art meta-learning algorithm that provides good weight initialization of a model given a variety of learning tasks. The model initialized by provided weight can be fine-tuned to an unseen task despite only using a small amount of samples and within a few adaptation steps. MAML is simple and versatile but requires costly learning rate tuning and careful design of the task distribution which affects its scalability and generalization. This paper proposes a more robust MAML based on an adaptive learning scheme and a prioritization task buffer (PTB) referred to as Robust MAML (RMAML) for improving scalability of training process and alleviating the problem of distribution mismatch. RMAML uses gradient-based hyper-parameter optimization to automatically find the optimal learning rate and uses the PTB to gradually adjust training task distribution toward testing task distribution over the course of training. Experimental results on meta reinforcement learning environments demonstrate a substantial performance gain as well as being less sensitive to hyper-parameter choice and robust to distribution mismatch.

关键词： meta-learning reinforcement learning hyper-parameter optimization learning to learn

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Development of an Explainable Heart Failure Patients Survival Status Prediction Model Using Machine Learning Algorithms 2nd

Development of an Explainable Heart Failure Patients Surviva...

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2nd Pan-African Conference on Artificial Intelligence (PanAfriCon AI)

作者： Demis, Betimihirt Getnet Tsehay Yibre, Abdulkerim M. Bahir Dar Univ Dept Informat Technol Fac Comp Bahir Dar Inst Technol Bahir Dar Ethiopia

ISBN: (纸本)9783031576232;9783031576249

Heart failure is a condition where insufficient blood circulation occurs throughout the body, causing the heart to become weak or stiff and not function properly. This can lead to congestive heart failure. The life expectancy of a congestive heart failure patients can vary depending on factors such as disease severity, age, genetics, and other variables. The Centers for Disease Control and Prevention (CDC) has reported that almost half of the patients with congestive heart failure survive for more than five years. Previous studies have attempted to predict the survival rate of heart failure patients, but most of them have been limited by unbalanced data sets and a small number of examples. The main goal of this research is to develop a machine learning model that can accurately predict the survival status of heart failure patients while providing an insight into its decision-making process. Machine learning algorithms are a part of artificial intelligence systems and can be used to predict output values based on input data. However, Many machine learning models lack an explanation of how they arrive at their decisions. Therefore, explainable machine learning algorithms are now being used to understand and explain the decision-making process of machine learning models. In this research, we not only use the latest machine learning algorithms for prediction, but we also incorporate explainability features to understand the results. hyper-parameter optimization techniques are employed to find the best parameter values for better predictive accuracy. Our data set consists of 1334 heart failure patients' data collected from Felege Hiwot referral hospital and Injibara general hospital. The prediction models used include Decision Tree, Linear Regression, K-Nearest Neighbor, Deep Neural Network, and XGBoost. Prior to conducting the actual experiment, the data set is balanced using the Synthetic Minority Oversampling Technique (SMOTE). Experimental findings indicate that XGBo

关键词： Heart Failure Survival Event Follow-up Period Synthetic Minority Oversampling Technique hyper-parameter optimization Model Explainability

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Automated machine vision enabled detection of movement disorders from hand drawn spirals 8

Automated machine vision enabled detection of movement disor...

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8th IEEE International Conference on Healthcare Informatics (ICHI)

作者： Seedat, Nabeel Aharonson, Vered Schlesinger, Ilana Univ Witwatersrand Johannesburg South Africa Shutterstock New York NY 10118 USA Univ Witwatersrand Elect & Informat Engn Johannesburg South Africa Rambam Hlth Care Campus Dept Neurol Haifa Israel

ISBN: (纸本)9781728153827

A widely used test for the diagnosis of Parkinson's disease (PD) and Essential tremor (ET) is hand-drawn shapes, where the analysis is observationally performed by the examining neurologist. This method is subjective and is prone to bias amongst different physicians. Due to the similarities in the symptoms of the two diseases, they are often misdiagnosed. Studies which attempt to automate the process typically use digitized input, where the tablet or specialized equipment are not affordable in many clinical settings. This study uses a dataset of scanned pen and paper drawings and a convolutional neural network (CNN) to perform classification between PD, ET and control subjects. The discrimination accuracy of PD from controls was 98.2%. The discrimination accuracy of PD from ET and from controls was 92%. An ablation study was conducted and indicated that correct hyper-parameter optimization can increases the accuracy up to 4.33%. Finally, the study indicates the viability of using a CNN-enabled machine vision system to provide robust and accurate detection of movement disorders from hand drawn spirals.

关键词： convolutional neural network deep learning Essential Tremor hyper-parameter optimization movement disorder Parkinson's disease

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Lessons learned from hyper-parameter tuning for microservice candidate identification 36

Lessons learned from hyper-parameter tuning for microservice...

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36th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Yedida, Rahul Krishna, Rahul Kalia, Anup Menzies, Tim Xiao, Jin Vukovic, Maja Dept Comp Sci Raleigh NC 27695 USA IBM Res Yorktown Hts NY USA

ISBN: (纸本)9781665403375

When optimizing software for the cloud, monolithic applications need to be partitioned into many smaller microservices. While many tools have been proposed for this task, we warn that the evaluation of those approaches has been incomplete;e.g. minimal prior exploration of hyperparameter optimization. Using a set of open source Java EE applications, we show here that (a) such optimization can significantly improve microservice partitioning;and that (b) an open issue for future work is how to find which optimizer works best for different problems. To facilitate that future work, see https://***/yrahu13910/ase-tuned-mono2micro for a reproduction package for this research.

关键词： microservices hyper-parameter optimization

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Clustering and Ranking Based Methods for Selecting Tuned Search Heuristic parameters

Clustering and Ranking Based Methods for Selecting Tuned Sea...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Waibel, Christoph Evins, Ralph Carmeliet, Jan Swiss Fed Inst Technol Zurich Switzerland Empa Dubendorf Switzerland Univ Victoria Victoria BC Canada

ISBN: (纸本)9781728121536

This paper presents two alternative methods for selecting tuned hyper-parameters of search heuristics for computationally expensive simulation-based optimization problems. The set of available hyper-parameters are obtained by conducting a meta-optimization to a number of mathematical test functions at different problem dimensions. Since a large number of tuned hyper-parameters is obtained from the meta-optimization, we develop methods to select tuned hyper-parameters for our actual simulation-based problems. The first selection method is based on clustering of the tuned hyper-parameters, where the medoids of the largest clusters are chosen. The second method constructs a ranking matrix that cross-checks the expected cost value for each tuned hyper-parameter configuration when solving the other training functions in the set, and that configuration is selected which has the minimal sum of ranks in the matrix. We apply our methods to custom particle swarm optimization and evolutionary algorithms. The feasibility of our approach is demonstrated by benchmarking the tuned search heuristics to a number of other search heuristics from open-source libraries on continuous building energy simulation problems. Our results show that our tuning and selection methods are successful in identifying hyper-parameters that result in competitive performance to other popular or recent algorihtms such as CMA-ES or RBFOpt. Future work should apply the methods developed to more recent search heuristics and make use of synthetic training functions in the meta-optimization that better resemble building simulation problems, instead of the mathematical test functions currently employed.

关键词： hyper-parameter optimization simulation-based selection clustering ranking

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Structure-Inferred Bi-level Model for Underwater Image Enhancement 22

Structure-Inferred Bi-level Model for Underwater Image Enhan...

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30th ACM International Conference on Multimedia (MM)

作者： Mu, Pan Qian, Haotian Bai, Cong Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9781450392037

Very recently, with the development of underwater robots, underwater image enhancement arising growing interests in the computer vision community. However, owing to light being scattered and absorbed while it traveling in water, underwater captured images often suffer from color cast and low visibility. Existing methods depend on specific prior knowledge and training data to enhance underwater images in the absence of structure information, which results in poor and unnatural performance. To this end, we propose a Structural-Inferred Bi-level Model (SIBM) that incorporates different modalities of knowledge (i.e., semantic domain, gradient-domain, and pixel domain) hierarchically enhancing underwater images. In particular, by introducing a semantic mask, we individually optimize the forehand branch that avoids unnecessary interference arising from the background region. We design a gradient-based high-frequency branch to exploit gradient-space guidance for preserving texture structures. Moreover, we construct a pixel-based branch by feeding semantic and gradient information to enhance underwater images. To exploit different modalities, we introduce a hyper-parameter optimization scheme to fuse the above domain information. Experimental results illustrate that the developed method not only outperforms the previous methods in quantitative scores but also generalizes well on real-world underwater datasets. Source code is available at https://***/IntegralCoCo/SIBM.

关键词： Structure-Inferred Model Deep Learning Underwater Image Enhancement hyper-parameter optimization

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