Java thread and process performance for parallel machine learning on multicore HPC clusters

2016 ◽  
Author(s):  
Saliya Ekanayake ◽  
Supun Kamburugamuve ◽  
Pulasthi Wickramasinghe ◽  
Geoffrey C. Fox
Keyword(s):  
Machine Learning ◽  
Parallel Machine ◽  
Process Performance

scholarly journals Replay in minds and machines

2021 ◽  
Author(s):  
Lennart Wittkuhn ◽  
Samson Chien ◽  
Sam Hall-McMaster ◽  
Nicolas W. Schuck
Keyword(s):  
Machine Learning ◽  
Brain Activity ◽  
Activity Patterns ◽  
Representation Learning ◽  
Parallel Machine ◽  
Artificial Agents ◽  
Active Behavior ◽  
Performance Improvements ◽  
Experience Replay ◽  
Data Efficiency

Experience-related brain activity patterns have been found to reactivate during sleep, wakeful rest, and brief pauses from active behavior. In parallel, machine learning research has found that experience replay can lead to substantial performance improvements in artificial agents. Together, these lines of research have significantly expanded our understanding of the potential computational benefits replay may provide to biological and artificial agents alike. We provide an overview of findings in replay research from neuroscience and machine learning and summarize the computational benefits an agent can gain from replay that cannot be achieved through direct interactions with the world itself. These benefits include faster learning and data efficiency, less forgetting, prioritizing important experiences, as well as improved planning and generalization. In addition to the benefits of replay for improving an agent's decision-making policy, we highlight the less-well studied aspect of replay in representation learning, wherein replay could provide a mechanism to learn the structure and relevant aspects of the environment. Thus, replay might help the agent to build task-appropriate state representations.

scholarly journals Mlifdect: Android Malware Detection Based on Parallel Machine Learning and Information Fusion

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Xin Wang ◽  
Dafang Zhang ◽  
Xin Su ◽  
Wenjia Li
Keyword(s):  
Machine Learning ◽  
Information Fusion ◽  
Malware Detection ◽  
Parallel Machine ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Android Malware ◽  
Detection Model ◽  
Android Apps ◽  
Android Malware Detection

In recent years, Android malware has continued to grow at an alarming rate. More recent malicious apps’ employing highly sophisticated detection avoidance techniques makes the traditional machine learning based malware detection methods far less effective. More specifically, they cannot cope with various types of Android malware and have limitation in detection by utilizing a single classification algorithm. To address this limitation, we propose a novel approach in this paper that leverages parallel machine learning and information fusion techniques for better Android malware detection, which is named Mlifdect. To implement this approach, we first extract eight types of features from static analysis on Android apps and build two kinds of feature sets after feature selection. Then, a parallel machine learning detection model is developed for speeding up the process of classification. Finally, we investigate the probability analysis based and Dempster-Shafer theory based information fusion approaches which can effectively obtain the detection results. To validate our method, other state-of-the-art detection works are selected for comparison with real-world Android apps. The experimental results demonstrate that Mlifdect is capable of achieving higher detection accuracy as well as a remarkable run-time efficiency compared to the existing malware detection solutions.

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