SSDA uses spectral magnitude alignment and structural-guided low-rank adaptation to close frequency and adjacency gaps when large vision models process time series rendered as images.
Informer: Beyond efficient transformer for long sequence time-series forecasting
3 Pith papers cite this work. Polarity classification is still indexing.
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FactoryBench reveals that frontier LLMs achieve under 50% on structured causal questions and under 18% on decision-making in industrial robotic telemetry.
Mask2Cause recovers causal graphs directly during time-series forecasting via adjacency-constrained masked attention and achieves state-of-the-art discovery performance with over 70% reduction in forecasting parameters on average.
citing papers explorer
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SSDA: Bridging Spectral and Structural Gaps via Dual Adaptation for Vision-Based Time Series Forecasting
SSDA uses spectral magnitude alignment and structural-guided low-rank adaptation to close frequency and adjacency gaps when large vision models process time series rendered as images.
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FactoryBench: Evaluating Industrial Machine Understanding
FactoryBench reveals that frontier LLMs achieve under 50% on structured causal questions and under 18% on decision-making in industrial robotic telemetry.
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Mask2Cause: Causal Discovery via Adjacency Constrained Causal Attention
Mask2Cause recovers causal graphs directly during time-series forecasting via adjacency-constrained masked attention and achieves state-of-the-art discovery performance with over 70% reduction in forecasting parameters on average.