Deep learning methods, which form the backbone of neural network architectures, have not only demonstrated exceptional capabilities in classifying data but also in reducing false predictions when handling vast dataset...
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In real-world materials research,machine learning(ML)models are usually expected to predict and discover novel exceptional materials that deviate from the known *** is thus a pressing question to provide an objective ...
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In real-world materials research,machine learning(ML)models are usually expected to predict and discover novel exceptional materials that deviate from the known *** is thus a pressing question to provide an objective evaluation ofMLmodel performances in property prediction of out-ofdistribution(OOD)materials that are different fromthe training *** performance evaluation of materials property prediction models through the random splitting of the dataset frequently results in artificially high-performance assessments due to the inherent redundancy of typical material datasets.
Automatic Speech Recognition (ASR) has been the regnant research area in the domain of Natural Language Processing for the last few decades. Past years’ advancement provides progress in this area of research. The acc...
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Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can...
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Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can potentially address these problems by allowing systems trained on labelled datasets from the source domain(including less expensive synthetic domain)to be adapted to a novel target *** conventional approach involves automatic extraction and alignment of the representations of source and target domains *** limitation of this approach is that it tends to neglect the differences between classes:representations of certain classes can be more easily extracted and aligned between the source and target domains than others,limiting the adaptation over all ***,we address:this problem by introducing a Class-Conditional Domain Adaptation(CCDA)*** incorporates a class-conditional multi-scale discriminator and class-conditional losses for both segmentation and ***,they measure the segmentation,shift the domain in a classconditional manner,and equalize the loss over *** results demonstrate that the performance of our CCDA method matches,and in some cases,surpasses that of state-of-the-art methods.
In the massive Machine-Type Communication (mMTC), the exponential growth of Internet of Things (IoT) devices over Low Power Wide Area Networks (LPWANs) presents substantial issues regarding energy efficiency and stabi...
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Human emotions are the mind's responses to external stimuli, and due to their dynamic and unpredictable nature, research in this field has become increasingly important. There is a growing trend in utilizing deep ...
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Network attacks, such as botnets stealing sensitive data, constitute a critical concern for administrators in the Internet area. Such attacks can be prevented using a network access control (NAC) scheme. However, exis...
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The U-Net architecture is the focus of this study, which optimizes biomedical picture segmentation. Improving performance in contexts with limited resources is the goal. The methodology uses GradCAM++, k-fold cross-va...
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Recently, with the growth of the required data size for emerging applications (e.g., graph processing and machine learning), the von Neumann bottleneck has become a main problem for restricting the throughput of the a...
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The hand localization problem has been a longstanding focus due to its many applications. The task involves modeling the hand as a singular point and determining its position within a defined coordinate system. Howeve...
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