AIMC Topic: Generalization, Psychological

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Deep learning assisted sparse array ultrasound imaging.

PloS one
This study aims to restore grating lobe artifacts and improve the image resolution of sparse array ultrasonography via a deep learning predictive model. A deep learning assisted sparse array was developed using only 64 or 16 channels out of the 128 c...

Walking and falling: Using robot simulations to model the role of errors in infant walking.

Developmental science
What is the optimal penalty for errors in infant skill learning? Behavioral analyses indicate that errors are frequent but trivial as infants acquire foundational skills. In learning to walk, for example, falling is commonplace but appears to incur o...

DeepMethylation: a deep learning based framework with GloVe and Transformer encoder for DNA methylation prediction.

PeerJ
DNA methylation is a crucial topic in bioinformatics research. Traditional wet experiments are usually time-consuming and expensive. In contrast, machine learning offers an efficient and novel approach. In this study, we propose DeepMethylation, a no...

A biologically inspired architecture with switching units can learn to generalize across backgrounds.

Neural networks : the official journal of the International Neural Network Society
Humans and other animals navigate different environments effortlessly, their brains rapidly and accurately generalizing across contexts. Despite recent progress in deep learning, this flexibility remains a challenge for many artificial systems. Here,...

A synergistic future for AI and ecology.

Proceedings of the National Academy of Sciences of the United States of America
Research in both ecology and AI strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of independent, asynchronous advances in comp...

A Self-Supervised Deep Learning Method for Seismic Data Deblending Using a Blind-Trace Network.

IEEE transactions on neural networks and learning systems
The simultaneous-source technology for high-density seismic acquisition is a key solution to efficient seismic surveying. It is a cost-effective method when blended subsurface responses are recorded within a short time interval using multiple seismic...

Multi-Constraint Latent Representation Learning for Prognosis Analysis Using Multi-Modal Data.

IEEE transactions on neural networks and learning systems
The Cox proportional hazard model has been widely applied to cancer prognosis prediction. Nowadays, multi-modal data, such as histopathological images and gene data, have advanced this field by providing histologic phenotype and genotype information....

Strengthening transferability of adversarial examples by adaptive inertia and amplitude spectrum dropout.

Neural networks : the official journal of the International Neural Network Society
Deep neural networks are sensitive to adversarial examples and would produce wrong results with high confidence. However, most existing attack methods exhibit weak transferability, especially for adversarially trained models and defense models. In th...

A continuation method for image registration based on dynamic adaptive kernel.

Neural networks : the official journal of the International Neural Network Society
Image registration is a fundamental problem in computer vision and robotics. Recently, learning-based image registration methods have made great progress. However, these methods are sensitive to abnormal transformation and have insufficient robustnes...

Improving the Accuracy of Spiking Neural Networks for Radar Gesture Recognition Through Preprocessing.

IEEE transactions on neural networks and learning systems
Event-based neural networks are currently being explored as efficient solutions for performing AI tasks at the extreme edge. To fully exploit their potential, event-based neural networks coupled to adequate preprocessing must be investigated. Within ...