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This paper explores the integration of predictive coding, a core mechanism in neuroscience, with symbolic computation to create a novel approach to artificial intelligence reasoning. The central argument is that by combining the pattern recognition capabilities of neural networks with the rule-based reasoning of symbol...
The increasing deployment of deep learning models in critical applications necessitates a means to understand and trust their decisions. However, deep learning models are often 'black boxes,' offering limited insight into the reasoning behind their predictions. This paper proposes a novel approach to explain model deci...
This paper introduces a novel approach to enhancing the interpretability and robustness of neural models by integrating symbolic reasoning. We propose a hybrid model architecture where a neural network's attention weights are constrained and guided by symbolic rules. Simultaneously, the neural network learns to refine...
This paper proposes a novel approach to explainable artificial intelligence (XAI) by leveraging causal intervention methods. The core idea is to model systems using causal graphs and then employ techniques like do-calculus for precise intervention and control. Traditional intervention methods often lack interpretabilit...
Explainable AI (XAI) is a critical area of research aimed at increasing the transparency and interpretability of complex AI models. Traditional XAI techniques often focus on post-hoc explanations, which can be misleading or lack a deep understanding of the underlying causal relationships. This paper proposes a novel me...
The increasing sophistication of cyber threats necessitates robust and reliable cybersecurity systems. However, many current AI-based threat detection systems operate as "black boxes," offering limited transparency and explainability. This paper proposes a novel approach to cybersecurity threat detection that integrate...
This paper explores the integration of causal inference with explainable artificial intelligence (XAI) to enhance decision support systems. Traditional XAI methods often struggle with providing true explanations for decisions, particularly in complex scenarios where correlation doesn't imply causation. This research pr...
This paper explores the integration of causal inference with explainable artificial intelligence (XAI) to enhance decision support systems. Traditional XAI methods often struggle with providing true explanations for decisions, particularly in complex scenarios where correlation doesn't imply causation. This research pr...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, the inherent vulnerability of FL to malicious participants, known as Byzantine attacks, poses a significant threat to the integrity and convergence of the lear...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized datasets while preserving data privacy. However, current FL security mechanisms often rely on centralized trust assumptions, posing significant vulnerabilities. This paper proposes a novel security framework leverag...
This paper presents a novel approach to enhancing data privacy in federated learning by integrating differential privacy with homomorphic encryption. Federated learning, a distributed machine learning paradigm, allows training models across multiple devices without directly sharing raw data. However, this approach stil...
This paper presents a novel approach to achieving robust differential privacy within federated learning systems, specifically designed to mitigate the risks posed by Byzantine attacks. Traditional federated learning methods frequently struggle to provide rigorous privacy guarantees when confronted with malicious partic...
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