NOFE learns continuous function-to-function embeddings via graph kernel operators, outperforming PCA, t-SNE, and UMAP in local structure preservation on function-valued datasets like ERA5 while remaining robust to sampling changes.
Springer Series in Statistics
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UNVERDICTED 5representative citing papers
HPPCA is a hierarchical extension of PPCA that uses Gaussian processes to model within-subject dynamics in longitudinal data, outperforming standard PPCA and functional PCA in imputation under missingness and misspecification.
A spectral vision transformer achieves equitable or superior performance with fewer parameters than standard ViTs, CNNs, and other models by using spectral projections for tokenization in limited-data medical imaging.
DAERT generates diverse adversarial instructions via a uniform policy in RL to drop VLA task success rates from 93.33% to 5.85% on benchmarks with models like π0 and OpenVLA.
Earth embeddings from satellite images predict neighborhood-level urban indicators with higher accuracy for built-environment outcomes than for behavior-driven ones, showing city-specific variation but year-to-year stability.
citing papers explorer
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NOFE -- Neural Operator Function Embedding
NOFE learns continuous function-to-function embeddings via graph kernel operators, outperforming PCA, t-SNE, and UMAP in local structure preservation on function-valued datasets like ERA5 while remaining robust to sampling changes.
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Hierarchical Probabilistic Principal Component Analysis of Longitudinal Data
HPPCA is a hierarchical extension of PPCA that uses Gaussian processes to model within-subject dynamics in longitudinal data, outperforming standard PPCA and functional PCA in imputation under missingness and misspecification.
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Spectral Vision Transformer for Efficient Tokenization with Limited Data
A spectral vision transformer achieves equitable or superior performance with fewer parameters than standard ViTs, CNNs, and other models by using spectral projections for tokenization in limited-data medical imaging.
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Uncovering Linguistic Fragility in Vision-Language-Action Models via Diversity-Aware Red Teaming
DAERT generates diverse adversarial instructions via a uniform policy in RL to drop VLA task success rates from 93.33% to 5.85% on benchmarks with models like π0 and OpenVLA.
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Earth Embeddings Reveal Diverse Urban Signals from Space
Earth embeddings from satellite images predict neighborhood-level urban indicators with higher accuracy for built-environment outcomes than for behavior-driven ones, showing city-specific variation but year-to-year stability.