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Found 4 papers, 2 papers with code
Class-level Structural Relation Modelling and Smoothing for Visual Representation Learning
Representation learning for images has been advanced by recent progress in more complex neural models such as the Vision Transformers and new learning theories such as the structural causal models.
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data
To address this issue, this paper presents a cross-silo prototypical calibration method (FedCSPC), which takes additional prototype information from the clients to learn a unified feature space on the server side.
Mlinear: Rethink the Linear Model for Time-series Forecasting
In this paper, we carefully examine the opposing properties of CI and CD, and raise a practical question that has not been effectively answered, e. g.,"How to effectively mix the CI and CD properties of time series to achieve better predictive performance?"
Meta-Causal Feature Learning for Out-of-Distribution Generalization
Causal inference has become a powerful tool to handle the out-of-distribution (OOD) generalization problem, which aims to extract the invariant features.
