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Found 3 papers, 0 papers with code
Learning for Robust Combinatorial Optimization: Algorithm and Application
Learning to optimize (L2O) has recently emerged as a promising approach to solving optimization problems by exploiting the strong prediction power of neural networks and offering lower runtime complexity than conventional solvers.
Increasing Trustworthiness of Deep Neural Networks via Accuracy Monitoring
In this paper, we address trustworthiness of DNNs by using post-hoc processing to monitor the true inference accuracy on a user's dataset.
Ranked #28 on
Image Classification
on STL-10
Calibrating Deep Neural Network Classifiers on Out-of-Distribution Datasets
In this paper, we propose a new post-hoc confidence calibration method, called CCAC (Confidence Calibration with an Auxiliary Class), for DNN classifiers on OOD datasets.
