As socially assistive robots (SARs) enter more diverse care settings, including users’ homes, it is critical to identify the shifting privacy risks and dimensions of privacy this technology and its data collection ca...
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Blockchain introduction has made a revolutionary change in the crypto currency around the world but it has not delivered on its promises of free and faster transaction confirmation. Serguei Popov's proposal of usi...
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We propose a new method for autonomous navigation in uneven terrains by utilizing a sparse Gaussian Process (SGP) based local perception model. The SGP local perception model is trained on local ranging observation (p...
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Gaussian Process (GP) models are widely used for Robotic Information Gathering (RIG) in exploring unknown environments due to their ability to model complex phenomena with non-parametric flexibility and accurately qua...
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The most significant zone of study for Natural Language Processing (NLP) is text compression. Since the number of characters in Unicode are many, 16 bits are needed to process the entire code. This study suggests a us...
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Bounded rational agents often make decisions by evaluating a finite selection of choices, typically derived from a reference point termed the ‘default policy,’ based on previous experience. However, the inherent rig...
Unsupervised domain adaptive semantic segmentation (UDA-SS) aims to train a model on the source domain data (e.g., synthetic) and adapt the model to predict target domain data (e.g. real-world) without accessing targe...
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An Earth station system serves as a key hub in the ever-changing environment of satellite communication technology, allowing uninterrupted data interchange between the Earth and orbiting satellites. These systems are ...
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Autonomous navigation in unknown environments is challenging and demands the consideration of both geometric and semantic information in order to parse the navigability of the environment. In this work, we propose a n...
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Machine learning algorithms play a pivotal role in a wide range of Artificial Intelligence (AI) applications. Explaining the results and behavior of a machine learning model, however, remains a challenge. In this pape...
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ISBN:
(数字)9798350362480
ISBN:
(纸本)9798350362497
Machine learning algorithms play a pivotal role in a wide range of Artificial Intelligence (AI) applications. Explaining the results and behavior of a machine learning model, however, remains a challenge. In this paper, we present a new approach to the explanation of machine learning models using a large language model (LLM). In this work, we seek natural language descriptions of the behavioral patterns of a machine learning model by a combination of prompting and model sampling. A subspace sampling technique is developed to generate ML model outputs using partial features in a user defined space. A projective visualization method is employed to guide the sampling process, including user-directed interactive sampling and feature-based sampling, so that an optimal amount of information can be provided to the LLM to ensure accurate and concise natural language explanations. Two public datasets, a student performance dataset and a weather dataset, were used to test our approach under various conditions.
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