AIMC Topic: Neural Networks, Computer

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Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics.

Neural networks : the official journal of the International Neural Network Society
An approach to the time-accurate prediction of chaotic solutions is by learning temporal patterns from data. Echo State Networks (ESNs), which are a class of Reservoir Computing, can accurately predict the chaotic dynamics well beyond the predictabil...

Anti-transfer learning for task invariance in convolutional neural networks for speech processing.

Neural networks : the official journal of the International Neural Network Society
We introduce the novel concept of anti-transfer learning for speech processing with convolutional neural networks. While transfer learning assumes that the learning process for a target task will benefit from re-using representations learned for anot...

Probabilistic robustness estimates for feed-forward neural networks.

Neural networks : the official journal of the International Neural Network Society
Robustness of deep neural networks is a critical issue in practical applications. In the general case of feed-forward neural networks (including convolutional deep neural network architectures), under random noise attacks, we propose to study the pro...

Deep learning and the Global Workspace Theory.

Trends in neurosciences
Recent advances in deep learning have allowed artificial intelligence (AI) to reach near human-level performance in many sensory, perceptual, linguistic, and cognitive tasks. There is a growing need, however, for novel, brain-inspired cognitive archi...

Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks.

Artificial intelligence in medicine
Abdominal anatomy segmentation is crucial for numerous applications from computer-assisted diagnosis to image-guided surgery. In this context, we address fully-automated multi-organ segmentation from abdominal CT and MR images using deep learning. Th...

Male pelvic multi-organ segmentation on transrectal ultrasound using anchor-free mask CNN.

Medical physics
PURPOSE: Current prostate brachytherapy uses transrectal ultrasound images for implant guidance, where contours of the prostate and organs-at-risk are necessary for treatment planning and dose evaluation. This work aims to develop a deep learning-bas...

Benchmarking Audio Signal Representation Techniques for Classification with Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Audio signal classification finds various applications in detecting and monitoring health conditions in healthcare. Convolutional neural networks (CNN) have produced state-of-the-art results in image classification and are being increasingly used in ...

A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome.

Scientific reports
DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification responsible for many biological functions. Automated and accurate computational methods can help to identify 6mA sites in long genomes saving significant time and money...

Multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet.

PloS one
Introducing a knowledge graph into a recommender system as auxiliary information can effectively solve the sparse and cold start problems existing in traditional recommender systems. In recent years, many researchers have performed related work. A re...

A self-supervised feature-standardization-block for cross-domain lung disease classification.

Methods (San Diego, Calif.)
With the advance of deep learning technology, convolutional neural network (CNN) has been wildly used and achieved the state-of-the-art performances in the area of medical image classification. However, most existing medical image classification meth...