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Decoding AI's Nudge: A Unified Framework to Predict Human Behavior in AI-assisted Decision Making
In this paper, we propose a computational framework that can provide an interpretable characterization of the influence of different forms of AI assistance on decision makers in AI-assisted decision making.
Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations
The collection and curation of high-quality training data is crucial for developing text classification models with superior performance, but it is often associated with significant costs and time investment.
Implicit Euler ODE Networks for Single-Image Dehazing
Deep convolutional neural networks (CNN) have been applied for image dehazing tasks, where the residual network (ResNet) is often adopted as the basic component to avoid the vanishing gradient problem.
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