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Neural networks : the official journal of the International Neural Network Society
Nov 4, 2024
In the realm of artificial intelligence, simultaneous multi-task learning is crucial, particularly for dense scene understanding. To address this, we introduce HirMTL, a novel hierarchical multi-task learning framework designed to enhance dense scene...
IEEE transactions on neural networks and learning systems
Oct 29, 2024
Learning from a sequence of tasks for a lifetime is essential for an agent toward artificial general intelligence. Despite the explosion of this research field in recent years, most work focuses on the well-known catastrophic forgetting issue. In con...
Neural networks : the official journal of the International Neural Network Society
Oct 28, 2024
Goal-conditioned reinforcement learning is widely used in robot control, manipulating the robot to accomplish specific tasks by maximizing accumulated rewards. However, the useful reward signal is only received when the desired goal is reached, leadi...
Radiography (London, England : 1995)
Oct 19, 2024
INTRODUCTION: Artificial intelligence has permeated all aspects of our existence, and medical imaging has shown the burgeoning use of artificial intelligence in clinical environments. However, there are limited empirical studies on radiography studen...
Respiration; international review of thoracic diseases
Oct 17, 2024
INTRODUCTION: Simulation-based training has proven effective for learning flexible bronchoscopy. However, no studies have tested the efficacy of training toward established proficiency criteria, i.e., mastery learning (ML). We wish to test the effect...
Neural networks : the official journal of the International Neural Network Society
Oct 16, 2024
Learning embeddings in hyperbolic space has gained increasing interest in the community, due to its property of negative curvature, as a way of encoding data hierarchy. Recent works investigate the improvement of the representation power of hyperboli...
Neural networks : the official journal of the International Neural Network Society
Oct 10, 2024
Graph Neural Networks (GNNs) have emerged as a powerful tool for data-driven learning on various graph domains. They are usually based on a message-passing mechanism and have gained increasing popularity for their intuitive formulation, which is clos...
Small methods
Sep 30, 2024
In recent years, the rapid development of brain-inspired neuromorphic systems has created an imperative demand for artificial photonic synapses that operate with low power consumption. In this study, a self-driven memristor synapse based on gallium o...
Trends in cognitive sciences
Sep 30, 2024
Humans and machines rarely have access to explicit external feedback or supervision, yet manage to learn. Most modern machine learning systems succeed because they benefit from unsupervised data. Humans are also expected to benefit and yet, mysteriou...
Neural networks : the official journal of the International Neural Network Society
Sep 28, 2024
Efficient sensory detection requires the capacity to ignore task-irrelevant information, for example when optic flow patterns created by egomotion need to be disentangled from object perception. To investigate how this is achieved in the visual syste...