127 papers indexed here
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This paper explores the application of federated learning (FL) as a novel approach to privacy-preserving data analysis. Traditional data analysis methods often require centralized data collection, raising significant privacy concerns. Federated learning offers a compelling alternative by enabling collaborative model tr...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data, preserving data privacy. However, traditional differential privacy (DP) mechanisms often introduce significant noise into the global model updates, leading to substantial accuracy degradation. This paper prese...
This paper presents a novel approach to generative modeling by integrating Generative Adversarial Networks (GANs) with Federated Learning (FL). Traditional GAN training suffers from centralized data requirements and privacy concerns. This research addresses these limitations through a distributed GAN architecture speci...
This paper explores the application of geometric analysis to the study of non-linear diffusion processes. Traditional approaches often fail to fully capture the complex dynamics arising from non-linearity, hindering a deeper understanding of these phenomena. We introduce a novel technique that leverages curvature, symm...
This paper proposes a novel approach to simulating complex biological systems utilizing graph-based modeling and simulation techniques. The core claim is that traditional simulation methods often face significant computational limitations when dealing with intricate biological systems. This limitation stems from the ex...
This paper investigates the potential of quantum-enhanced diffusion processes to revolutionize generative modeling. Traditional diffusion models, while effective, suffer from computationally intensive training times and limitations in generating diverse and accurate outputs. We propose a novel approach leveraging quant...
This paper investigates the potential of quantum-enhanced diffusion processes to revolutionize generative modeling. Traditional diffusion models, while effective, suffer from computationally intensive training times and limitations in generating diverse and accurate outputs. We propose a novel approach leveraging quant...
This paper proposes a novel approach to simulating complex biological systems utilizing graph-based modeling and simulation techniques. The core claim is that traditional simulation methods often face significant computational limitations when dealing with intricate biological systems. This limitation stems from the ex...
This paper presents a novel approach to network anomaly detection that leverages topological feature learning to address the limitations of traditional static feature engineering methods. The core idea is to dynamically capture network changes by automatically learning relevant topological features from network graphs....
This paper introduces a novel framework for generating graph embeddings that explicitly accounts for the temporal dynamics of relationships within a network. Traditional graph embedding techniques often treat graphs as static structures, neglecting the evolving nature of connections and their influence over time. Our a...
Biological regulatory networks (BRNs) govern cellular processes with inherent stochasticity, reflecting the probabilistic nature of gene expression and protein interactions. Traditional Graph Neural Networks (GNNs) often treat these networks as deterministic, leading to inaccurate representations and predictions. This...
This paper investigates the integration of topology optimization algorithms with graph neural networks (GNNs) to develop a novel framework for efficient and robust optimization of complex topological structures. Traditional topology optimization methods often struggle with intricate designs, necessitating manual config...
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