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Found 7 papers, 5 papers with code
U-Noise: Learnable Noise Masks for Interpretable Image Segmentation
Deep Neural Networks (DNNs) are widely used for decision making in a myriad of critical applications, ranging from medical to societal and even judicial.
AASAE: Augmentation-Augmented Stochastic Autoencoders
We empirically evaluate the proposed AASAE on image classification, similar to how recent contrastive and non-contrastive learning algorithms have been evaluated.
AAVAE: Augmentation-Augmented Variational Autoencoders
We empirically evaluate the proposed AAVAE on image classification, similar to how recent contrastive and non-contrastive learning algorithms have been evaluated.
Graph Contrastive Learning for Materials
By leveraging a series of material-specific transformations, we introduce CrystalCLR, a framework for constrastive learning of representations with crystal graph neural networks.
Domain Adaptation for Time Series Under Feature and Label Shifts
Additionally, the label distributions of tasks in the source and target domains can differ significantly, posing difficulties in addressing label shifts and recognizing labels unique to the target domain.
Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials
The calculation of electron density distribution using density functional theory (DFT) in materials and molecules is central to the study of their quantum and macro-scale properties, yet accurate and efficient calculation remains a long-standing challenge in the field of material science.
UniTS: Building a Unified Time Series Model
However, current foundation models apply to sequence data but not to time series, which present unique challenges due to the inherent diverse and multidomain time series datasets, diverging task specifications across forecasting, classification and other types of tasks, and the apparent need for task-specialized models.
