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Found 7 papers, 3 papers with code
Can Large Language Model Agents Simulate Human Trust Behaviors?
In addition, we probe into the biases in agent trust and the differences in agent trust towards agents and humans.
Efficient Online Decision Tree Learning with Active Feature Acquisition
Specifically, we employ a surrogate information acquisition function based on adaptive submodularity to actively query feature values with a minimal cost, while using a posterior sampling scheme to maintain a low regret for online prediction.
Towards provably efficient quantum algorithms for large-scale machine-learning models
Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process.
Understanding the Effect of Bias in Deep Anomaly Detection
We also provide an extensive empirical study on how a biased training anomaly set affects the anomaly score function and therefore the detection performance on different anomaly classes.
Understanding Bias in Anomaly Detection: A Semi-Supervised View with PAC Guarantees
Given two different anomaly score functions, we formally define their difference in performance as the relative scoring bias of the anomaly detectors.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
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Comparison of Neural Network Architectures for Spectrum Sensing
Given abundant training data and computational and memory resources, CNN, RNN, and BiRNN are shown to achieve similar performance.
A Neural Network Detector for Spectrum Sensing under Uncertainties
The result displays the neural network's potential in exploiting implicit and incomplete knowledge about the signal's structure.
