AIMC Topic: Attention
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Deep learning-based automated speech detection as a marker of social functioning in late-life depression.
Psychological medicine
Jan 16, 2020
BACKGROUND: Late-life depression (LLD) is associated with poor social functioning. However, previous research uses bias-prone self-report scales to measure social functioning and a more objective measure is lacking. We tested a novel wearable device ...
Neuronal population correlates of target selection and distractor filtering.
NeuroImage
Jan 8, 2020
Frontal Eye Field (FEF) neurons discriminate between relevant and irrelevant visual stimuli and their response magnitude predicts conscious perception. How this is reflected in the spatial representation of a visual stimulus at the neuronal populatio...
Forecasting stock prices with long-short term memory neural network based on attention mechanism.
PloS one
Jan 3, 2020
The stock market is known for its extreme complexity and volatility, and people are always looking for an accurate and effective way to guide stock trading. Long short-term memory (LSTM) neural networks are developed by recurrent neural networks (RNN...
Quasi-compositional mapping from form to meaning: a neural network-based approach to capturing neural responses during human language comprehension.
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Dec 16, 2019
We argue that natural language can be usefully described as quasi-compositional and we suggest that deep learning-based neural language models bear long-term promise to capture how language conveys meaning. We also note that a successful account of h...
Channel-spatial attention network for fewshot classification.
PloS one
Dec 12, 2019
Learning a powerful representation for a class with few labeled samples is a challenging problem. Although some state-of-the-art few-shot learning algorithms perform well based on meta-learning, they only focus on novel network architecture and fail ...
An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records.
BMC medical informatics and decision making
Dec 5, 2019
BACKGROUND: Clinical named entity recognition (CNER) is important for medical information mining and establishment of high-quality knowledge map. Due to the different text features from natural language and a large number of professional and uncommon...
Simultaneously learning affinity matrix and data representations for machine fault diagnosis.
Neural networks : the official journal of the International Neural Network Society
Nov 22, 2019
Recently, preserving geometry information of data while learning representations have attracted increasing attention in intelligent machine fault diagnosis. Existing geometry preserving methods require to predefine the similarities between data point...
The Effects of Population Tuning and Trial-by-Trial Variability on Information Encoding and Behavior.
The Journal of neuroscience : the official journal of the Society for Neuroscience
Nov 21, 2019
Identifying the features of population responses that are relevant to the amount of information encoded by neuronal populations is a crucial step toward understanding population coding. Statistical features, such as tuning properties, individual and ...
Being watched over by a conversation robot may enhance safety in simulated driving.
Journal of safety research
Nov 21, 2019
INTRODUCTION: In an aging society that is more and more information-oriented, being able to replace human passengers' protective effects on vehicle drivers with those of social robots is both essential and promising. However, the effects of a social ...
Computational Approaches to Comics Analysis.
Topics in cognitive science
Nov 8, 2019
Comics are complex documents whose reception engages cognitive processes such as scene perception, language processing, and narrative understanding. Possibly because of their complexity, they have rarely been studied in cognitive science. Modeling th...
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