AIMC Topic: Neural Networks, Computer

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Structured sequence processing and combinatorial binding: neurobiologically and computationally informed hypotheses.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Understanding how the brain forms representations of structured information distributed in time is a challenging endeavour for the neuroscientific community, requiring computationally and neurobiologically informed approaches. The neural mechanisms f...

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
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...

Training neural networks to encode symbols enables combinatorial generalization.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Combinatorial generalization-the ability to understand and produce novel combinations of already familiar elements-is considered to be a core capacity of the human mind and a major challenge to neural network models. A significant body of research su...

Linguistic generalization and compositionality in modern artificial neural networks.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
In the last decade, deep artificial neural networks have achieved astounding performance in many natural language-processing tasks. Given the high productivity of language, these models must possess effective generalization abilities. It is widely as...

Terminologies augmented recurrent neural network model for clinical named entity recognition.

Journal of biomedical informatics
OBJECTIVE: We aimed to enhance the performance of a supervised model for clinical named-entity recognition (NER) using medical terminologies. In order to evaluate our system in French, we built a corpus for 5 types of clinical entities.

Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques.

Computational intelligence and neuroscience
This study develops tomato disease detection methods based on deep convolutional neural networks and object detection models. Two different models, Faster R-CNN and Mask R-CNN, are used in these methods, where Faster R-CNN is used to identify the typ...

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks.

Journal of biophotonics
The importance of T cells in immunotherapy has motivated developing technologies to improve therapeutic efficacy. One objective is assessing antigen-induced T cell activation because only functionally active T cells are capable of killing the desired...

Multiple Compounds Recognition from The Tandem Mass Spectral Data Using Convolutional Neural Network.

Molecules (Basel, Switzerland)
Mixtures analysis can provide more information than individual components. It is important to detect the different compounds in the real complex samples. However, mixtures are often disturbed by impurities and noise to influence the accuracy. Purific...

Distinguishing Emotional Responses to Photographs and Artwork Using a Deep Learning-Based Approach.

Sensors (Basel, Switzerland)
Visual stimuli from photographs and artworks raise corresponding emotional responses. It is a long process to prove whether the emotions that arise from photographs and artworks are different or not. We answer this question by employing electroenceph...