AI Medical Compendium

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Distributed multi-timescale algorithm for nonconvex optimization problem: A control perspective.

Neural networks : the official journal of the International Neural Network Society
The distributed nonconvex constrained optimization problem with equality and inequality constraints is researched in this paper, where the objective function and the function for constraints are all nonconvex. To solve this problem from a control per...

Don't fear peculiar activation functions: EUAF and beyond.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose a new super-expressive activation function called the Parametric Elementary Universal Activation Function (PEUAF). We demonstrate the effectiveness of PEUAF through systematic and comprehensive experiments on various industr...

Spiking neural networks on FPGA: A survey of methodologies and recent advancements.

Neural networks : the official journal of the International Neural Network Society
The mimicry of the biological brain's structure in information processing enables spiking neural networks (SNNs) to exhibit significantly reduced power consumption compared to conventional systems. Consequently, these networks have garnered heightene...

Dual view graph transformer networks for multi-hop knowledge graph reasoning.

Neural networks : the official journal of the International Neural Network Society
To address the incompleteness of knowledge graphs, multi-hop reasoning aims to find the unknown information from existing data and enhance the comprehensive understanding. The presence of reasoning paths endows multi-hop reasoning with interpretabili...

Daydreaming Hopfield Networks and their surprising effectiveness on correlated data.

Neural networks : the official journal of the International Neural Network Society
To improve the storage capacity of the Hopfield model, we develop a version of the dreaming algorithm that perpetually reinforces the patterns to be stored (as in the Hebb rule), and erases the spurious memories (as in dreaming algorithms). For this ...

Memristor-based circuit design of interweaving mechanism of emotional memory in a hippocamp-brain emotion learning model.

Neural networks : the official journal of the International Neural Network Society
Endowing robots with human-like emotional and cognitive abilities has garnered widespread attention, driving deep investigations into the complexities of these processes. However, few studies have examined the intricate circuits that govern the inter...

Constraining an Unconstrained Multi-agent Policy with offline data.

Neural networks : the official journal of the International Neural Network Society
Real-world multi-agent decision-making systems often have to satisfy some constraints, such as harmfulness, economics, etc., spurring the emergence of Constrained Multi-Agent Reinforcement Learning (CMARL). Existing studies of CMARL mainly focus on t...

Heterogeneous boundary synchronization of time-delayed competitive neural networks with adaptive learning parameter in the space-time discretized frames.

Neural networks : the official journal of the International Neural Network Society
This article presents the master-slave time-delayed competitive neural networks in space-time discretized frames(STD-CNNs) with the heterogeneous structure, induced by the design of an adaptive learning parameter in the slave STD-CNNs. This article a...

Hierarchical task network-enhanced multi-agent reinforcement learning: Toward efficient cooperative strategies.

Neural networks : the official journal of the International Neural Network Society
Navigating multi-agent reinforcement learning (MARL) environments with sparse rewards is notoriously difficult, particularly in suboptimal settings where exploration can be prematurely halted. To tackle these challenges, we introduce Hierarchical Sym...

Span-aware pre-trained network with deep information bottleneck for scientific entity relation extraction.

Neural networks : the official journal of the International Neural Network Society
Scientific entity relation extraction intends to promote the performance of each subtask through exploring the contextual representations with rich scientific semantics. However, most of existing models encounter the dilemma of scientific semantic di...