2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)最新文献_第5页

IsoClustering: A Generalized Framework for Local Data Clustering 等聚类:局部数据聚类的一种通用框架

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00058

David Haley, Ehsan Kamalinejad, Jiaofei Zhong

引用次数: 0

Machine Learning Models to Identify the Risk of Modern Slavery in Brazilian Cities 识别巴西城市现代奴隶制风险的机器学习模型

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00132

Marlu da Silva Santos, M. Ladeira, G. V. Erven, Gladston Luiz da Silva

引用次数: 6

End-to-End Reinforcement Learning for Multi-agent Continuous Control 多智能体连续控制的端到端强化学习

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00100

Z. Jiao, J. Oh

{"title":"End-to-End Reinforcement Learning for Multi-agent Continuous Control","authors":"Z. Jiao, J. Oh","doi":"10.1109/ICMLA.2019.00100","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00100","url":null,"abstract":"In end-to-end reinforcement learning, an agent captures the entire mapping from its raw sensor data to actuation commands using a single neural network. End-to-end reinforcement learning is mostly studied in single-agent domains, and its scalability to multi-agent setting is under-explored. Without effective techniques, learning effective policies based on the joint observation of agents can be intractable, particularly when sensor data perceived by each agent is high-dimensional. Extending the multi-agent actor-critic method MADDPG, this paper presents Rec-MADDPG, an end-to-end reinforcement learning method for multi-agent continuous control in a cooperative environment. To ease end-to-end learning in a multi-agent setting, we proposed two embedding mechanisms, joint and independent embedding, to project agents' joint sensor observation to low-dimensional features. For training efficiency, we applied parameter sharing and the A3C-based asynchronous framework to Rec-MADDPG. Considering the challenges that can arise in real-world multi-agent control, we evaluated Rec-MADDPG in robotic navigation tasks based on realistic simulated robots and physics enable environments. Through extensive evaluation, we demonstrated that Rec-MADDPG can significantly outperform MADDPG and was able to learn individual end-to-end policies for continuous control based on raw sensor data. In addition, compared to joint embedding, independent embedding enabled Rec-MADDPG to learn even better optimal policies.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123885115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Hyperparameter Optimization of Topological Features for Machine Learning Applications 机器学习应用中拓扑特征的超参数优化

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00185

Francis C. Motta, J. Harer, Nicholas Leiby, F. Marinozzi, Scott Novotney, G. Rocklin, Jed Singer, D. Strickland, M. Vaughn, Christopher J. Tralie, R. Bedini, F. Bini, G. Bini, Hamed Eramian, Marcio Gameiro, S. Haase, Hugh K. Haddox

引用次数: 2

Physics-Guided Neural Network with Model Discrepancy Based on Upper Troposphere Wind Prediction 基于模式差异的物理导向神经网络对流层高层风预报

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00078

Ken-ichi Fukui, Junya Tanaka, T. Tomita, M. Numao

{"title":"Physics-Guided Neural Network with Model Discrepancy Based on Upper Troposphere Wind Prediction","authors":"Ken-ichi Fukui, Junya Tanaka, T. Tomita, M. Numao","doi":"10.1109/ICMLA.2019.00078","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00078","url":null,"abstract":"In this paper, we focus on a method that integrates a physical model into a neural network. This study proposes a neural network that can predict two components, namely outputs based on a physical model and its model discrepancy. To achieve such a goal, we propose a novel neural network architecture and associated loss functions designed based on a target physical model. The physical model is used as a regularizer of spatial behavior where output from the neural network is used as an intermediate variable. Then, the model discrepancy is defined as its residual to the observation value. We also propose a network architecture which has Shared and Non-Shared networks, and the neural network can be trained by alternate optimization. We constructed the proposed method with wind prediction in the upper troposphere based on thermal wind equations as an example. The experimental results demonstrate that the proposed method can achieve higher predictive accuracy than normal convolutional neural network or using thermal wind equation, also the obtained model discrepancy expresses convergence and divergence of wind vectors.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131538635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Using Bidirectional Long Short Term Memory with Attention Layer to Estimate Driver Behavior 基于双向长短期记忆和注意层的驾驶员行为估计

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00059

Shokoufeh Monjezi Kouchak, A. Gaffar

{"title":"Using Bidirectional Long Short Term Memory with Attention Layer to Estimate Driver Behavior","authors":"Shokoufeh Monjezi Kouchak, A. Gaffar","doi":"10.1109/ICMLA.2019.00059","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00059","url":null,"abstract":"Driver distraction is one of the primary causes of fatal car accidents in U.S. Analyzing driver behavior using different types of data including driving data, driver status or a combination of them is an emerging machine learning solution to detect the distraction level and notify the driver. Deep learning methods such as recurrent neural networks outperform other machine learning methods in car safety applications. In this paper, we used time-sequenced driving data that we collected in eight driving contexts to measure the driver distraction level. Our RNN is also capable of detecting the type of behavior that caused distraction. We used the driver interaction with the car infotainment system as the distracting activity. Two types of LSTM networks were used including bidirectional LSTM network and attention network. We compare the performance of these two complex networks to that of the simple LSTM in estimating driver behavior. We show that our attention network outperforms the other two, while adding bidirectional LSTM networks enhanced the training process of simple LSTM network.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127739659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

The Effect of Time on the Maintenance of a Predictive Model 时间对预测模型维持的影响

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00304

Joffrey L. Leevy, T. Khoshgoftaar, Richard A. Bauder, Naeem Seliya

{"title":"The Effect of Time on the Maintenance of a Predictive Model","authors":"Joffrey L. Leevy, T. Khoshgoftaar, Richard A. Bauder, Naeem Seliya","doi":"10.1109/ICMLA.2019.00304","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00304","url":null,"abstract":"Periodic updating of a machine learning model may become necessary because new data could have a distribution that has drifted significantly over time from the original data distribution, thus impacting the model's usefulness. The primary objective of this paper is to evaluate temporal influence on the maintenance of a predictive model. We investigate the impact of using training data from various year-groupings on a model designed to detect Medicare Part B billing fraud. Training datasets are obtained from year-groupings of 2015, 2014-2015, 2013-2015, and 2012-2015. The test dataset is represented by 2016 data. Our study utilizes five popular learners and five class ratios obtained by Random Undersampling. Using the Area Under the Receiver Operating Characteristic (ROC) Curve as the performance metric, our case study indicates that the Logistic Regression learner yields the highest overall value for the yeargrouping of 2013-2015, with a majority-to-minority ratio of 90:10. For the problem of maintaining predictive models for Medicare fraud, we conclude that a sampled dataset should be chosen over the full dataset and that the largest training dataset (i.e., 2012- 2015) does not always produce the best results. To the best of our knowledge, this is the first big data study that examines the influence of time on the maintenance of machine learning models.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127774979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Legislative Vote Prediction using Campaign Donations and Fuzzy Hierarchical Communities 基于竞选捐款和模糊等级社区的立法投票预测

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00129

Scott Wahl, John W. Sheppard, Elizabeth A. Shanahan

引用次数: 1

Generation of Pedestrian Pose Structures using Generative Adversarial Networks 使用生成对抗网络生成行人姿态结构

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00269

James Spooner, Madeline Cheah, V. Palade, S. Kanarachos, A. Daneshkhah

引用次数: 1

A Deep Structural Model for Analyzing Correlated Multivariate Time Series 一种分析相关多元时间序列的深层结构模型

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00020

Changwei Hu, Yifan Hu, Sungyong Seo

{"title":"A Deep Structural Model for Analyzing Correlated Multivariate Time Series","authors":"Changwei Hu, Yifan Hu, Sungyong Seo","doi":"10.1109/ICMLA.2019.00020","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00020","url":null,"abstract":"Multivariate time series are routinely encountered in real-world applications, and in many cases, these time series are strongly correlated. In this paper, we present a deep learning structural time series model which can (i) handle correlated multivariate time series input, and (ii) forecast the targeted temporal sequence by explicitly learning/extracting the trend, seasonality, and event components. The trend is learned via a 1D and 2D temporal CNN and LSTM hierarchical neural net. The CNN-LSTM architecture can (i) seamlessly leverage the dependency among multiple correlated time series in a natural way, (ii) extract the weighted differencing feature for better trend learning, and (iii) memorize the long-term sequential pattern. The seasonality component is approximated via a non-liner function of a set of Fourier terms, and the event components are learned by a simple linear function of regressor encoding the event dates. We compare our model with several state-of-the-art methods through a comprehensive set of experiments on a variety of time series data sets, such as forecasts of Amazon AWS Simple Storage Service (S3) and Elastic Compute Cloud (EC2) billings, and the closing prices for corporate stocks in the same category.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133714751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4