2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第3页

Empirical analysis of the convergence of Double DQN in relation to reward sparsity 双DQN收敛性与奖励稀疏性的实证分析

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00102

Samuel Blad, Martin Längkvist, Franziska Klügl-Frohnmeyer, A. Loutfi

引用次数: 0

Imitation from Observation using RL and Graph-based Representation of Demonstrations 基于RL的观察模仿和基于图的演示表示

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00202

Y. Manyari, P. Callet, Laurent Dollé

引用次数: 0

DeepWafer: A Generative Wafermap Model with Deep Adversarial Networks DeepWafer:一个具有深度对抗网络的生成晶圆图模型

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00025

H. Mahyar, Peter Tulala, E. Ghalebi, R. Grosu

{"title":"DeepWafer: A Generative Wafermap Model with Deep Adversarial Networks","authors":"H. Mahyar, Peter Tulala, E. Ghalebi, R. Grosu","doi":"10.1109/ICMLA55696.2022.00025","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00025","url":null,"abstract":"A certain amount of process deviations characterizes semiconductor manufacturing processes. Automated detection of these production issues followed by an automated root cause analysis has the potential to increase the effectiveness of semiconductor production. Manufacturing defects exhibit typical patterns in measured wafer test data, e.g., rings, spots, repetitive textures, or scratches. Recognizing these patterns is an essential step for finding the root cause of production issues. This paper demonstrates that combining Information Maximizing Generative Adversarial Network (InfoGAN) and Wasserstein GAN (WGAN) with a new loss function is suitable for extracting the most characteristic features from extensive real-world sensory wafer test data, which in various aspects outperforms traditional unsupervised techniques. These features are then used in subsequent clustering tasks to group wafers into clusters according to their exhibit patterns. The primary outcome of this work is a statistical generative model for recognizing spatial wafermaps patterns using deep adversarial neural networks. We experimentally evaluate the performance of the proposed approach over a real dataset.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130621903","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: 0

Multi-stream Deep Residual Network for Cloud Imputation Using Multi-resolution Remote Sensing Imagery 基于多分辨率遥感影像的云计算多流深度残差网络

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00021

Yifan Zhao, Xian Yang, Ranga Raju Vatsavai

{"title":"Multi-stream Deep Residual Network for Cloud Imputation Using Multi-resolution Remote Sensing Imagery","authors":"Yifan Zhao, Xian Yang, Ranga Raju Vatsavai","doi":"10.1109/ICMLA55696.2022.00021","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00021","url":null,"abstract":"For more than five decades, remote sensing imagery has been providing critical information for many applications such as crop monitoring, disaster assessment, and urban planning. Unfortunately, more than 50% of optical remote sensing images are contaminated by clouds severely affecting the object identification. However, thanks to recent advances in remote sensing instruments and increase in number of operational satellites, we now have petabytes of multi-sensor observations covering the globe. Historically cloud imputation techniques were designed for single sensor images, thus existing benchmarks were mostly limited to single sensor images, which precludes design and validation of cloud imputation techniques on multi-sensor data. In this paper, we introduce a new benchmark data set consisting of images from two widely used and publicly available satellite images, Landsat-8 and Sentinel-2, and a new multi-stream deep residual network (MDRN). This newly introduced benchmark dataset fills an important gap in the existing benchmark datasets, which allows exploitation of multi-resolution spectral information from the cloud-free regions of temporally nearby images, and the MDRN algorithm addresses imputation using the multi-resolution data. Both quantitative and qualitative experiments show that the utility of our benchmark dataset and as well as efficacy of our MDRN architecture in cloud imputation. The MDRN outperforms the closest competing method by 14.1%.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131002729","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: 2

BlinkNet: Software-Defined Deep Learning Analytics with Bounded Resources BlinkNet:有限资源下的软件定义深度学习分析

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00037

Brian Koga, Theresa VanderWeide, Xinghui Zhao, Xuechen Zhang

引用次数: 0

Attention-based Partial Decoupling of Policy and Value for Generalization in Reinforcement Learning 基于注意力的强化学习泛化策略与值的部分解耦

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00011

N. Nafi, Creighton Glasscock, W. Hsu

{"title":"Attention-based Partial Decoupling of Policy and Value for Generalization in Reinforcement Learning","authors":"N. Nafi, Creighton Glasscock, W. Hsu","doi":"10.1109/ICMLA55696.2022.00011","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00011","url":null,"abstract":"In this work, we introduce Attention-based Partially Decoupled Actor-Critic (APDAC), an actor-critic architecture for generalization in reinforcement learning, which partially separates the policy and the value functions. To learn directly from images, traditional actor-critic architectures use a shared network to represent the policy and value functions. While a shared representation allows parameter and feature sharing, it can also lead to overfitting that catastrophically damages generalization performance. On the other hand, two separate networks for policy and value can help to avoid overfitting and reduce the generalization gap, but at the cost of added complexity both in terms of architecture design and computation time. APDAC is a hybrid architecture that builds upon the combined strengths of both architectures by sharing initial layer blocks of the network and separating the later ones for policy and value. APDAC incorporates an attention mechanism to enable robust representation learning. We present meaningful visualization of the policy and value that explains the perception of the trained agent. Our empirical analysis, including an ablation study, shows that APDAC significantly outperforms the standard PPO baseline on the challenging RL generalization benchmark Procgen and achieves performance that is competitive with the recent state-of-the-art method (IDAAC) while using fewer convolutional layers and requiring less computational time. Our code is available at https://github.com/nasiknafi/apdac.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123390348","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

Automatic Key Information Extraction from Visually Rich Documents 从视觉丰富的文档中自动提取关键信息

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00020

Charles De Trogoff, Rim Hantach, Gisela Lechuga, P. Calvez

引用次数: 0

Transfer Learning model for Social Emotion Prediction using Writers Emotions in Comments 基于评论作者情绪的社会情绪预测迁移学习模型

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00063

Abdullah Alsaedi, S. Thomason, F. Grasso, Phillip Brooker

引用次数: 0

PerMTL: A Multi-Task Learning Framework for Skilled Human Performance Assessment PerMTL:一个用于技术人员绩效评估的多任务学习框架

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00177

Indrajeet Ghosh, Avijoy Chakma, S. R. Ramamurthy, Nirmalya Roy, Nicholas R. Waytowich

{"title":"PerMTL: A Multi-Task Learning Framework for Skilled Human Performance Assessment","authors":"Indrajeet Ghosh, Avijoy Chakma, S. R. Ramamurthy, Nirmalya Roy, Nicholas R. Waytowich","doi":"10.1109/ICMLA55696.2022.00177","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00177","url":null,"abstract":"Intelligent and complex human motion analysis can help design the next generation IoT and AR/VR systems for automated human performance assessment. Such an automated system can help advocate the interpretability and translatability of complex human motions, intelligent motion feedback, and fine-grained motion skill assessment to design next-generation interactive human-machine teaming systems. Motivated by this, we design a wearable sensing framework for assessing the players’ performance and consider a live badminton game as our use case. Generally, the players on the field try to improve their performance by focusing on fast and synchronous coordination of their limbs’ reflex actions to have the ideal body postures to perform the desired shot. Learning the minute dissimilarities and distinctive traits from each limb of the players simultaneously can help assess the players’ performance and specific skillsets during a game. This paper proposes a multi-task learning framework, PerMTL to learn the shared features from each player’s limb. The PerMTL comprises a task-specific regressor output layer that helps to determine the dissimilarities and distinctive traits between the player’s limbs for collective inference in a body sensor network (BSN) environment. We evaluate the PerMTL framework using publicly available Badminton Activity Recognition (BAR) and Daily and Sports Activities (DSA) datasets. Empirical results indicate that PerMTL achieves R2 Score of ≈ 82% in predicting the players’ performance.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115284275","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: 0

The Performance-Actionability Trade-Off in Retention Prediction at Middle School 中学保留预测的绩效-行动权衡

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00087

Susana Lavado, Miguel Mateus, Leid Zejnilovic

{"title":"The Performance-Actionability Trade-Off in Retention Prediction at Middle School","authors":"Susana Lavado, Miguel Mateus, Leid Zejnilovic","doi":"10.1109/ICMLA55696.2022.00087","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00087","url":null,"abstract":"Predicting students’ retention risk is one of the major trends in machine learning applications in education. While early identification of at-risk students allows timely planning and implementation of measures to prevent adverse outcomes, there is a trade-off between the predictive model’s performance and the prediction window size, or model performance and its actionability. In this study, we used a dataset of 83,596 unique Portuguese students in grades 5th to 9th to predict retention at or before the end of 9th grade. We explored how different prediction window sizes impact the predictive model’s performance, the feature importance, and the models’ bias. The models with the shorter prediction window performed better in terms of precision, but the model with the largest prediction window showed a higher lift over the existing rule-based model. Prediction window size impacted the importance of demographic features and model’s fairness. Our results contribute to the extant discussion on predicting retention, by adding empirical evidence about the models’ added value in performance versus the existing practice, suggesting types of data to collect and use, and discussing education-specific challenges of responsible data science.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125228784","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: 0