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Found 4 papers, 2 papers with code
Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts
Temporal distributional shifts, with underlying dynamics changing over time, frequently occur in real-world time series and pose a fundamental challenge for deep neural networks (DNNs).
Self-Supervised Learning with an Information Maximization Criterion
In this article, we argue that a straightforward application of information maximization among alternative latent representations of the same input naturally solves the collapse problem and achieves competitive empirical results.
Invariant Structure Learning for Better Generalization and Causal Explainability
ISL splits the data into different environments, and learns a structure that is invariant to the target across different environments by imposing a consistency constraint.
Interpretable Mixture of Experts for Structured Data
We introduce a novel framework, Interpretable Mixture of Experts (IME), that provides interpretability for structured data while preserving accuracy.
