AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

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EMBANet: A flexible efficient multi-branch attention network.

Neural networks : the official journal of the International Neural Network Society
Recent advances in the design of convolutional neural networks have shown that performance can be enhanced by improving the ability to represent multi-scale features. However, most existing methods either focus on designing more sophisticated attenti...

Neural-network-based accelerated safe Q-learning for optimal control of discrete-time nonlinear systems with state constraints.

Neural networks : the official journal of the International Neural Network Society
For unknown nonlinear systems with state constraints, it is difficult to achieve the safe optimal control by using Q-learning methods based on traditional quadratic utility functions. To solve this problem, this article proposes an accelerated safe Q...

Intervening on few-shot object detection based on the front-door criterion.

Neural networks : the official journal of the International Neural Network Society
Most few-shot object detection methods aim to utilize the learned generalizable knowledge from base categories to identify instances of novel categories. The fundamental assumption of these approaches is that the model can acquire sufficient transfer...

Enhancing spatial perception and contextual understanding for 3D dense captioning.

Neural networks : the official journal of the International Neural Network Society
3D dense captioning (3D-DC) transcends traditional 2D image captioning by requiring detailed spatial understanding and object localization, aiming to generate high-quality descriptions for objects within 3D environments. Current approaches struggle w...

PrediRep: Modeling hierarchical predictive coding with an unsupervised deep learning network.

Neural networks : the official journal of the International Neural Network Society
Hierarchical predictive coding (hPC) provides a compelling framework for understanding how the cortex predicts future sensory inputs by minimizing prediction errors through an internal generative model of the external world. Existing deep learning mo...

Augmenting interaction effects in convolutional networks with taylor polynomial gated units.

Neural networks : the official journal of the International Neural Network Society
Transformer-based vision models are often assumed to have an advantage over traditional convolutional neural networks (CNNs) due to their ability to model long-range dependencies and interactions between inputs. However, the remarkable success of pur...

SH: Long-tailed classification via spatial constraint sampling, scalable network, and hybrid task.

Neural networks : the official journal of the International Neural Network Society
Long-tailed classification is a significant yet challenging vision task that aims to making the clearest decision boundaries via integrating semantic consistency and texture characteristics. Unlike prior methods, we design spatial constraint sampling...

Incremental model-based reinforcement learning with model constraint.

Neural networks : the official journal of the International Neural Network Society
In model-based reinforcement learning (RL) approaches, the estimated model of a real environment is learned with limited data and then utilized for policy optimization. As a result, the policy optimization process in model-based RL is influenced by b...

Robust deep learning from weakly dependent data.

Neural networks : the official journal of the International Neural Network Society
Recent developments on deep learning established some theoretical properties of deep neural networks estimators. However, most of the existing works on this topic are restricted to bounded loss functions or (sub)-Gaussian or bounded variables. This p...

Bio-inspired two-stage network for efficient RGB-D salient object detection.

Neural networks : the official journal of the International Neural Network Society
Recently, with the development of the Convolutional Neural Network and Vision Transformer, the detection accuracy of the RGB-D salient object detection (SOD) model has been greatly improved. However, most of the existing methods cannot balance comput...