AIMC Topic: Learning

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Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals.

PLoS computational biology
Humans can learn several tasks in succession with minimal mutual interference but perform more poorly when trained on multiple tasks at once. The opposite is true for standard deep neural networks. Here, we propose novel computational constraints for...

Recurrent networks endowed with structural priors explain suboptimal animal behavior.

Current biology : CB
The strategies found by animals facing a new task are determined both by individual experience and by structural priors evolved to leverage the statistics of natural environments. Rats quickly learn to capitalize on the trial sequence correlations of...

Self-attention learning network for face super-resolution.

Neural networks : the official journal of the International Neural Network Society
Existing face super-resolution methods depend on deep convolutional networks (DCN) to recover high-quality reconstructed images. They either acquire information in a single space by designing complex models for direct reconstruction, or employ additi...

Autonomous Driving Control Based on the Technique of Semantic Segmentation.

Sensors (Basel, Switzerland)
Advanced Driver Assistance Systems (ADAS) are only applied to relatively simple scenarios, such as highways. If there is an emergency while driving, the driver should take control of the car to deal properly with the situation at any time. Obviously,...

MM-StackEns: A new deep multimodal stacked generalization approach for protein-protein interaction prediction.

Computers in biology and medicine
Accurate in-silico identification of protein-protein interactions (PPIs) is a long-standing problem in biology, with important implications in protein function prediction and drug design. Current computational approaches predominantly use a single da...

Efficient neural codes naturally emerge through gradient descent learning.

Nature communications
Human sensory systems are more sensitive to common features in the environment than uncommon features. For example, small deviations from the more frequently encountered horizontal orientations can be more easily detected than small deviations from t...

Design and Implementation of Machine Tool Life Inspection System Based on Sound Sensing.

Sensors (Basel, Switzerland)
The main causes of damage to industrial machinery are aging, corrosion, and the wear of parts, which affect the accuracy of machinery and product precision. Identifying problems early and predicting the life cycle of a machine for early maintenance c...

Method for Predicting RUL of Rolling Bearings under Different Operating Conditions Based on Transfer Learning and Few Labeled Data.

Sensors (Basel, Switzerland)
As industrial development increases, electric machine systems are more widely used in industrial production. Rolling bearings play a key role in machine systems and so the prevention of faults in rolling bearings is more important than ever before. R...

Intelligent Fault Diagnosis of Industrial Bearings Using Transfer Learning and CNNs Pre-Trained for Audio Classification.

Sensors (Basel, Switzerland)
The training of Artificial Intelligence algorithms for machine diagnosis often requires a huge amount of data, which is scarcely available in industry. This work shows that convolutional networks pre-trained for audio classification already contain k...

An Underwater Human-Robot Interaction Using a Visual-Textual Model for Autonomous Underwater Vehicles.

Sensors (Basel, Switzerland)
The marine environment presents a unique set of challenges for human-robot interaction. Communicating with gestures is a common way for interacting between the diver and autonomous underwater vehicles (AUVs). However, underwater gesture recognition i...