A conspicuous ability of the brain is to seamlessly assimilate and process spatial and
temporal features of sensory stimuli. This ability is indispensable for the recognition of …

The degree of correspondence between parallel distributed processing (PDP) models which
learn mappings of this sort and children's acquisition of inflectional morphology has recently …

Time series prediction with neural networks has been the focus of much research in the past
few decades. Given the recent deep learning revolution, there has been much attention in …

The COVID-19 pandemic continues to have major impact to health and medical
infrastructure, economy, and agriculture. Prominent computational and mathematical models …

Social scientists and psychologists take interest in understanding how people express
emotions and sentiments when dealing with catastrophic events such as natural disasters …

In recent years, machine-learning techniques, particularly deep learning, have outperformed
traditional time-series forecasting approaches in many contexts, including univariate and …

Scientists working with large volumes of high-dimensional data, such as global climate
patterns, stellar spectra, or human gene distributions, regularly confront the problem of …

Time underlies many interesting human behaviors. Thus, the question of how to represent
time in connectionist models is very important. One approach is to represent time implicitly …

Artificial neural net models have been studied for many years in the hope of achieving
human-like performance in the fields of speech and image recognition. These models are …

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