Chronicle is the first model jointly pretrained from scratch on text and time series in a unified transformer that matches a comparable language model on NLU tasks and sets new bars for time series classification and multimodal forecasting.
and Ermon, Stefano and Rudra, Atri and R
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PhysEDA folds separable Manhattan-distance exponential decay into linear attention and potential-based rewards, cutting complexity to linear while improving zero-shot transfer and sparse-reward performance on decoupling-cap placement, macro placement, and IR-drop prediction.
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Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding
Chronicle is the first model jointly pretrained from scratch on text and time series in a unified transformer that matches a comparable language model on NLU tasks and sets new bars for time series classification and multimodal forecasting.
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PhysEDA: Physics-Aware Learning Framework for Efficient EDA With Manhattan Distance Decay
PhysEDA folds separable Manhattan-distance exponential decay into linear attention and potential-based rewards, cutting complexity to linear while improving zero-shot transfer and sparse-reward performance on decoupling-cap placement, macro placement, and IR-drop prediction.