FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3081255579> ?p ?o ?g. }

• W3081255579 endingPage "9579" @default.
• W3081255579 startingPage "9568" @default.
• W3081255579 abstract "The Internet of Things (IoT) has been widely adopted in a range of verticals, e.g., automation, health, energy, and manufacturing. Many of the applications in these sectors, such as self-driving cars and remote surgery, are critical and high stakes applications, calling for advanced machine learning (ML) models for data analytics. Essentially, the training and testing data that are collected by massive IoT devices may contain noise (e.g., abnormal data, incorrect labels, and incomplete information) and adversarial examples. This requires high robustness of ML models to make reliable decisions for IoT applications. The research of robust ML has received tremendous attention from both academia and industry in recent years. This article will investigate the state of the art and representative works of robust ML models that can enable high resilience and reliability of IoT intelligence. Two aspects of robustness will be focused on, i.e., when the training data of ML models contain noises and adversarial examples, which may typically happen in many real-world IoT scenarios. In addition, the reliability of both neural networks and reinforcement learning framework will be investigated. Both of these two ML paradigms have been widely used in handling data in IoT scenarios. The potential research challenges and open issues will be discussed to provide future research directions." @default.
• W3081255579 created "2020-09-01" @default.
• W3081255579 creator A5004458037 @default.
• W3081255579 date "2021-06-15" @default.
• W3081255579 modified "2023-10-16" @default.
• W3081255579 title "Robust Learning-Enabled Intelligence for the Internet of Things: A Survey From the Perspectives of Noisy Data and Adversarial Examples" @default.
• W3081255579 cites W2008164459 @default.
• W3081255579 cites W2083059234 @default.
• W3081255579 cites W2159756685 @default.
• W3081255579 cites W2243397390 @default.
• W3081255579 cites W2583350981 @default.
• W3081255579 cites W2613020807 @default.
• W3081255579 cites W2770547749 @default.
• W3081255579 cites W2785065914 @default.
• W3081255579 cites W2786070938 @default.
• W3081255579 cites W2790149281 @default.
• W3081255579 cites W2797226653 @default.
• W3081255579 cites W2806064096 @default.
• W3081255579 cites W2810482148 @default.
• W3081255579 cites W2833256931 @default.
• W3081255579 cites W2888878287 @default.
• W3081255579 cites W2891931318 @default.
• W3081255579 cites W2896320160 @default.
• W3081255579 cites W2897290534 @default.
• W3081255579 cites W2899015031 @default.
• W3081255579 cites W2900147861 @default.
• W3081255579 cites W2901779167 @default.
• W3081255579 cites W2902554997 @default.
• W3081255579 cites W2902981848 @default.
• W3081255579 cites W2905146905 @default.
• W3081255579 cites W2907745422 @default.
• W3081255579 cites W2908021422 @default.
• W3081255579 cites W2908219578 @default.
• W3081255579 cites W2911301616 @default.
• W3081255579 cites W2912977751 @default.
• W3081255579 cites W2914773061 @default.
• W3081255579 cites W2916933545 @default.
• W3081255579 cites W2932176981 @default.
• W3081255579 cites W2933424288 @default.
• W3081255579 cites W2933751056 @default.
• W3081255579 cites W2941258581 @default.
• W3081255579 cites W2943622802 @default.
• W3081255579 cites W2945929948 @default.
• W3081255579 cites W2946073848 @default.
• W3081255579 cites W2946933691 @default.
• W3081255579 cites W2947710575 @default.
• W3081255579 cites W2950590641 @default.
• W3081255579 cites W2954005681 @default.
• W3081255579 cites W2955097190 @default.
• W3081255579 cites W2955701401 @default.
• W3081255579 cites W2959587146 @default.
• W3081255579 cites W2961816263 @default.
• W3081255579 cites W2962804345 @default.
• W3081255579 cites W2963327228 @default.
• W3081255579 cites W2963405596 @default.
• W3081255579 cites W2965060429 @default.
• W3081255579 cites W2965189679 @default.
• W3081255579 cites W2965481567 @default.
• W3081255579 cites W2965483395 @default.
• W3081255579 cites W2967521687 @default.
• W3081255579 cites W2973068117 @default.
• W3081255579 cites W2973076431 @default.
• W3081255579 cites W2978008000 @default.
• W3081255579 cites W2980126319 @default.
• W3081255579 cites W2980822420 @default.
• W3081255579 cites W2982162079 @default.
• W3081255579 cites W2984138548 @default.
• W3081255579 cites W2987748564 @default.
• W3081255579 cites W2991178537 @default.
• W3081255579 cites W2992245431 @default.
• W3081255579 cites W2994053796 @default.
• W3081255579 cites W2995942567 @default.
• W3081255579 cites W2998908795 @default.
• W3081255579 cites W2999972761 @default.
• W3081255579 cites W3001494301 @default.
• W3081255579 cites W3003303706 @default.
• W3081255579 cites W3006670753 @default.
• W3081255579 cites W3007741690 @default.
• W3081255579 cites W3008021034 @default.
• W3081255579 cites W3008177173 @default.
• W3081255579 cites W3008293028 @default.
• W3081255579 cites W3008450544 @default.
• W3081255579 cites W3009360213 @default.
• W3081255579 cites W3010157558 @default.
• W3081255579 cites W3016224608 @default.
• W3081255579 cites W3023974636 @default.
• W3081255579 cites W3027921152 @default.
• W3081255579 cites W3034588296 @default.
• W3081255579 cites W3035012142 @default.
• W3081255579 cites W3035529916 @default.
• W3081255579 cites W3037583881 @default.
• W3081255579 cites W3037930875 @default.
• W3081255579 cites W3039623901 @default.
• W3081255579 cites W3043706633 @default.
• W3081255579 cites W3046465350 @default.
• W3081255579 cites W3102834148 @default.
• W3081255579 cites W3105199275 @default.
• W3081255579 doi "https://doi.org/10.1109/jiot.2020.3018691" @default.

• next