Social cognition has received much attention in fields such as neuroscience, psychology, cognitive science, and philosophy. Theory-theory (TT) and simulation theory (ST) provide the dominant theoretical frameworks for research on social cognition. Ho...
Computational intelligence and neuroscience
Feb 21, 2022
Recently, long short-term memory (LSTM) networks are extensively utilized for text classification. Compared to feed-forward neural networks, it has feedback connections, and thus, it has the ability to learn long-term dependencies. However, the LSTM ...
Knowledge Distillation (KD) is one of the widely known methods for model compression. In essence, KD trains a smaller student model based on a larger teacher model and tries to retain the teacher model's level of performance as much as possible. Howe...
Collaborative reasoning for knowledge-based visual question answering is challenging but vital and efficient in understanding the features of the images and questions. While previous methods jointly fuse all kinds of features by attention mechanism o...
A teacher plays a pivotal role in grooming a society and paves way for its social and economic developments. Teaching is a dynamic role and demands continuous adaptation. A teacher adopts teaching techniques suitable for a certain discipline and a si...
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Feb 16, 2022
The human brain's neural networks are sparsely connected via tunable and probabilistic synapses, which may be essential for performing energy-efficient cognitive and intellectual functions. In this sense, the implementation of a flexible neural netwo...
Existing disambiguation strategies for partial structured output learning just cannot generalize well to solve the problem that there are some candidates that can be false positive or similar to the ground-truth label. In this article, we propose a n...
Transfer learning has received much attention recently and has been proven to be effective in a wide range of applications, whereas studies on regression problems are still scarce. In this article, we focus on the transfer learning problem for regres...
Multilabel classification (MLC) has received much attention recently. The existing MLC algorithms usually learn multiple classifiers simultaneously by exploiting the correlations among different labels. However, it is difficult and/or expensive to co...
Medical image segmentation can provide a reliable basis for further clinical analysis and disease diagnosis. With the development of convolutional neural networks (CNNs), medical image segmentation performance has advanced significantly. However, mos...
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