Tag
EnergyMamba proposes a novel spatiotemporal framework combining a graph-enhanced selective state space model and adaptive conformalized quantile regression for accurate and reliable energy consumption prediction with uncertainty estimates, achieving improvements on real-world datasets from Florida, New York, and California.
This paper proposes a sleep-like consolidation mechanism for transformer models that uses fast weights and recurrent passes to improve long-context processing while maintaining inference speed.
MVCHead is a novel method for generating 3D Gaussian head avatars from single 2D images without multi-view data, using hierarchical state space models and multi-view consistency enforcement.
This paper proposes Physics-Informed Multi-Scale Mamba (PIMSM), a state-space architecture that aligns model memory with physical timescales to improve robustness under distribution shift in scientific time series, demonstrating improvements on fMRI and weather forecasting tasks.
Researchers introduce Raven, a novel sequence model that merges state space model efficiency with a selective slot-updating mechanism inspired by sliding window attention to improve long-context retrieval. The approach offers a more principled alternative to existing linear-time models.