A causal transformer trained on synthetic seismograms autoregressively continues three-component waveforms past the S-wave with median NCC of 0.93 or higher across controlled geometries.
Mahoney, Andrew Gordon Wilson, Youngsuk Park, Syama Rangapuram, Danielle C
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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.
MS-FLOW uses a capacity-limited sparse routing mechanism to model only critical inter-variable dependencies in time series data, achieving state-of-the-art accuracy on 12 benchmarks with fewer but more reliable connections.
citing papers explorer
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Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures
A causal transformer trained on synthetic seismograms autoregressively continues three-component waveforms past the S-wave with median NCC of 0.93 or higher across controlled geometries.
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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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What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies
MS-FLOW uses a capacity-limited sparse routing mechanism to model only critical inter-variable dependencies in time series data, achieving state-of-the-art accuracy on 12 benchmarks with fewer but more reliable connections.