153 papers indexed here
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The ability to effectively model complex relationships within data is crucial for many applications in fields ranging from natural language processing to image recognition. Traditional graph neural networks (GNNs) offer a powerful framework for this, but their performance can be significantly impacted by the data's inh...
Traditional kernel methods have demonstrated remarkable success in various machine learning tasks, particularly in capturing non-linear relationships within data. However, their applicability is severely limited when dealing with non-Euclidean data, where the notion of distance and similarity becomes ambiguous. This wo...
Graph neural networks (GNNs) have demonstrated remarkable success in various tasks involving graph data, including social network analysis, drug discovery, and knowledge graph reasoning. However, a fundamental challenge remains: effectively capturing and utilizing the inherent topological order within these graphs. Thi...
Graph Neural Networks (GNNs) have achieved remarkable success in various domains, including node classification, graph classification, and link prediction. However, their large size and high computational cost pose significant challenges for deployment, particularly on resource-constrained devices. Graph Neural Network...
Graph representation learning has emerged as a powerful technique for analyzing and understanding complex data represented as graphs. Traditional Graph Neural Networks (GNNs) typically learn static representations of nodes, often neglecting the evolving nature of relationships within the graph. This work introduces a n...
This paper proposes a novel approach to learning complex functions and representations by integrating Hypergraph Neural Networks (HNNs) with Symbolic Regression (SR). The core idea is to leverage the graph representation capabilities of HNNs to extract informative features from hypergraph data, and then utilize a symbo...
Bayesian Optimization (BO) is a powerful technique for optimizing black-box functions, particularly those where gradient information is unavailable. However, its performance can degrade significantly when faced with noisy or non-stationary objective functions. This paper proposes a novel approach, Dynamic Bayesian Opti...
The selection of an appropriate kernel function remains a significant challenge in kernel methods, frequently relying on manual tuning and heuristic approaches. This research introduces a novel framework employing meta-reinforcement learning to autonomously learn and adapt kernel functions. The system utilizes a reinfo...
This paper presents a novel self-adaptive neural network architecture leveraging machine learning to dynamically adjust network structure and parameters. Traditional neural network design relies heavily on human expertise, limiting the network's adaptability. This work introduces a framework that utilizes machine learn...
This paper presents Adaptive Network Routing Based on Reinforcement Learning (ANRRL), a novel approach to network routing optimization utilizing reinforcement learning (RL). Traditional network routing protocols often rely on static configurations or shortest-path algorithms, which may not adapt effectively to dynamic...
This paper explores the application of Deep Reinforcement Learning (DRL) for optimizing quantum resource allocation in quantum computing. Traditional quantum resource allocation often relies on manual design and expert knowledge, which can be computationally expensive and limit scalability. We propose a novel framework...
This paper investigates the application of Deep Reinforcement Learning (DRL) for dynamic hardware resource allocation in complex systems. Traditional resource allocation methods often rely on static rules, which struggle to adapt to fluctuating workloads and system states. This research proposes a DRL-based approach wh...
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