AIMC Topic: Neural Networks, Computer

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FDCN-C: A deep learning model based on frequency enhancement, deformable convolution network, and crop module for electroencephalography motor imagery classification.

PloS one
Motor imagery (MI)-electroencephalography (EEG) decoding plays an important role in brain-computer interface (BCI), which enables motor-disabled patients to communicate with external world via manipulating smart equipment. Currently, deep learning (D...

MERIT: Accurate Prediction of Multi Ligand-binding Residues with Hybrid Deep Transformer Network, Evolutionary Couplings and Transfer Learning.

Journal of molecular biology
Multi-ligand binding residues (MLBRs) are amino acids in protein sequences that interact with multiple different ligands that include proteins, peptides, nucleic acids, and a variety of small molecules. MLBRs are implicated in a number of cellular fu...

Developing a method for predicting DNA nucleosomal sequences using deep learning.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundDeep learning excels at processing raw data because it automatically extracts and classifies high-level features. Despite biology's low popularity in data analysis, incorporating computer technology can improve biological research.Objective...

Continual learning in the presence of repetition.

Neural networks : the official journal of the International Neural Network Society
Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often con...

Quantum-inspired neural network with hierarchical entanglement embedding for matching.

Neural networks : the official journal of the International Neural Network Society
Quantum-inspired neural networks (QNNs) have shown potential in capturing various non-classical phenomena in language understanding, e.g., the emgerent meaning of concept combinations, and represent a leap beyond conventional models in cognitive scie...

Approximation of functionals on Korobov spaces with Fourier Functional Networks.

Neural networks : the official journal of the International Neural Network Society
Learning from functional data with deep neural networks has become increasingly useful, and numerous neural network architectures have been developed to tackle high-dimensional problems raised in practical domains. Despite the impressive practical ac...

Learning extreme expected shortfall and conditional tail moments with neural networks. Application to cryptocurrency data.

Neural networks : the official journal of the International Neural Network Society
We propose a neural networks method to estimate extreme Expected Shortfall, and even more generally, extreme conditional tail moments as functions of confidence levels, in heavy-tailed settings. The convergence rate of the uniform error between the l...

Deep temporal representation learning for language identification.

Neural networks : the official journal of the International Neural Network Society
Language identification (LID) is a key component in downstream tasks. Recently, the self-supervised speech representation learned by Wav2Vec 2.0 (W2V2) has been demonstrated to be very effective for various speech-related tasks. In LID, it is commonl...

VC dimension of Graph Neural Networks with Pfaffian activation functions.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) have emerged in recent years as a powerful tool to learn tasks across a wide range of graph domains in a data-driven fashion. Based on a message passing mechanism, GNNs have gained increasing popularity due to their intui...