57 papers indexed here
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This paper introduces an Adaptive Graph Neural Network (AGNN) designed to enhance Bayesian inference, particularly within complex, high-dimensional datasets. Traditional graph neural networks struggle with data complexities, often requiring extensive hyperparameter optimization. We propose an innovative AGNN architectu...
Quantum state tomography (QST) is a fundamental technique for characterizing the state of a quantum system. However, traditional QST protocols often require a large number of measurements, leading to significant experimental overhead and uncertainties in the reconstructed state. This paper proposes a novel approach to...
This paper proposes a novel approach to reinforcement learning (RL) that combines the strengths of hierarchical reinforcement learning (HRL) and memory networks. Traditional RL methods often struggle with complex tasks due to the curse of dimensionality and the difficulty of learning long-term dependencies. Hierarchica...
This paper introduces a novel reinforcement learning framework for robot control, leveraging adaptive reinforcement learning (ARL) to dynamically adjust control strategies based on both environmental feedback and the robot's internal state. Traditional reinforcement learning models often suffer from static policies, li...
Dynamic Geometry for Adaptive Machine Learning addresses the limitations of static machine learning models by introducing a mechanism for dynamically adjusting geometric properties during training. This novel approach leverages quantum annealing to optimize for robustness and efficiency, ultimately enhancing generaliza...
This paper presents a novel approach to parameter optimization utilizing adaptive neural networks, specifically designed to model complex function structures. The core innovation lies in integrating function structure modeling directly into the optimization process, enabling automated learning and optimization of the c...
Relational Reinforcement Learning (RRL) aims to address the challenges posed by environments where actions influence the state, creating complex dependencies between states. However, traditional RRL methods often struggle to scale effectively, particularly in environments with intricate relational structures. This pape...
This paper proposes a novel approach to designing distributed control systems utilizing Multi-Agent Reinforcement Learning (MARL). Traditional distributed control systems often rely on manually engineered control strategies, leading to limitations in adaptability, robustness, and scalability. This work introduces a fra...
This paper introduces Adaptive Neuron Growth Networks (ANGNs), a novel neural network architecture designed for dynamic adaptation to varying input complexities and network performance. ANGNs utilize a reinforcement learning-based mechanism where individual neurons dynamically adjust their parameters – both synaptic we...
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