82 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
This paper explores a novel approach to developing explainable artificial intelligence (AI) by leveraging probabilistic causal graph learning. The core premise is that truly explainable AI requires the identification and representation of genuine causal relationships within data, translated into a readily interpretable...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data without directly exchanging the data itself. However, FL systems are vulnerable to Byzantine attacks, where malicious participants introduce corrupted data or models to compromise the learning process. This pap...
Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL approaches often struggle with data heterogeneity, communication bottlenecks, and, crucially, privacy concerns. This paper proposes a novel dec...
Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL approaches often struggle with data heterogeneity, communication bottlenecks, and, crucially, privacy concerns. This paper proposes a novel dec...
Federated learning (FL) presents a promising paradigm for training machine learning models on decentralized data, preserving data privacy. However, this approach is increasingly recognized to be vulnerable to algorithmic bias. The inherent diversity and potential imbalances within the data contributed by numerous clien...
Federated learning (FL) presents a promising paradigm for training machine learning models on decentralized data, preserving data privacy. However, this approach is increasingly recognized to be vulnerable to algorithmic bias. The inherent diversity and potential imbalances within the data contributed by numerous clien...
This paper explores the application of controllable diffusion models to generative design, addressing the limitations of traditional stochastic generative systems. Current design systems often struggle with precise control, leading to unpredictable and potentially undesirable outcomes. We propose a novel framework util...
This paper investigates the temporal dynamics of information flow within social networks, focusing on the propagation of novelty. The core claim is to develop a computational framework to model the spread of information and novelty, providing insights into collective behavior and influence. We propose a system that sim...
This paper presents a novel approach to modeling non-linear self-organization within chaotic systems, utilizing a dynamic self-organizing mechanism. The core of this work is a model based on the principles of chaos theory, employing parameterization and feedback loops to simulate complex system behaviors such as molecu...
This paper presents a novel framework for analyzing diffusion fields, incorporating adaptive mixing rules that dynamically adjust the mixing process based on field characteristics. Traditional diffusion models often rely on fixed, static mixing rules, which can limit the realism and stability of the simulations. We int...
This paper explores the interplay between geometric regularity and convergence within diffusion processes, focusing on the development of a theoretical framework to analyze long-term behavior. The core claim is to create a model that quantitatively assesses the convergence of diffusion, leveraging insights from fractal...
This paper introduces Contextualized Semantic Graph Neural Networks (CSGNNs), a novel approach to graph representation learning that addresses the inherent limitations of static graph embeddings. Existing graph neural networks often struggle to adapt to evolving semantic relationships within graphs, primarily due to th...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
