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Found 7 papers, 2 papers with code
Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
Transformer-based architectures achieved breakthrough performance in natural language processing and computer vision, yet they remain inferior to simpler linear baselines in multivariate long-term forecasting.
Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift
Our key idea is that the model should be adjusted with a higher magnitude of gradients when it does not generalize to the test dataset with a distribution shift.
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias
Self-training is a well-known approach for semi-supervised learning.
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption
As an application, we derive a hyperparameter selection policy that finds the best balance between the supervised and the unsupervised terms of our learning criterion.
Self-Training: A Survey
Semi-supervised algorithms aim to learn prediction functions from a small set of labeled observations and a large set of unlabeled observations.
Multi-class Probabilistic Bounds for Self-learning
First, we derive a transductive bound over the risk of the multi-class majority vote classifier.
Semi-supervised Wrapper Feature Selection by Modeling Imperfect Labels
In this paper, we propose a new wrapper feature selection approach with partially labeled training examples where unlabeled observations are pseudo-labeled using the predictions of an initial classifier trained on the labeled training set.
