Compositional Explanations is a method for identifying logical formulas of concepts that approximate the neurons' behavior. However, these explanations are linked to the small spectrum of neuron activations (i.e., the highest ones) used to check the alignment, thus lacking completeness. In this...
04b Atto di convegno in volume
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Sequential Recommender Systems (SRSs) are widely employed to model user behavior over time. However, their robustness in the face of perturbations in training data remains a largely understudied yet critical issue. A fundamental challenge emerges in previous studies aimed at assessing the...
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In this ambitious paper, we present a groundbreaking paradigm for human-computer interaction that revolutionizes the traditional notion of an operating system. Within this innovative framework, user requests issued to the machine are handled by an interconnected ecosystem of generative AI models...
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We provide a customizable environment based on Deep Reinforcement Learning (DRL) strategies for handling cooperative multi-UAV (Unmanned Aerial Vehicles) scenarios when delays are involved in the decision-making process for tasks such as spotting, tracking, coverage and many others. Users can...
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The problem of non-cooperative load balancing arises in multi-agent scenarios where users/services compete for some limited resources. This study, leveraging on results from set stability and switched systems control theory, analyses the convergence properties of a class of load-balancing...
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IoT and edge devices, capable of capturing data from their surroundings, are becoming increasingly popular. However, the onboard analysis of the acquired data is usually limited by their computational capabilities. Consequently, the most recent and accurate deep learning technologies, such as...
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Air quality forecasting plays a crucial role in environmental management and public health. In this paper, we propose a novel approach that combines deep learning techniques with the Continuous Wavelet Transform (CWT) for air quality forecasting based on sensor data. The proposed methodology is...
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Given a set of points, clustering consists of finding a partition of a point set into k clusters such that the center to which a point is assigned is as close as possible. Most commonly, centers are points themselves, which leads to the famous k-median and k-means objectives. One may also choose...
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IoT and edge devices, capable of capturing data from their surroundings, are becoming increasingly popular. However, the onboard analysis of the acquired data is usually limited by their computational capabilities. Consequently, the most recent and accurate deep learning technologies, such as...
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In recent years, there has been a growing interest in employing intelligent techniques for managing manufacturing processes in smart manufacturing. These processes often involve tens of resources distributed across several different companies that make up the supply chain. The status of these...