The standard Kalman filter cannot handle inequality constraints imposed on the state variables, as state truncation induces a nonlinear and non-Gaussian model. We propose a Rao-Blackwellized particle ...

In this talk, we will discuss the nuts and bolts of the novel continuous-time neural network models: Liquid Time-Constant Networks (LTCs). Liquid neural networks are nonlinear state-space models with ...

For a while now, we’ve been talking about transformers, frontier neural network logic models, as a transformative technology, no pun intended. But now, these attention mechanisms have other competing ...

Recently, we talked to Dan Fu and Tri Dao – authors of “Hungry Hungry Hippos” (aka “H3”) – on our Deep Papers podcast. H3 is a proposed language modeling architecture that performs comparably to ...

Estimating time varying state correlations in state space models using indirect inference. In this paper, a multivariate state space model is proposed where the correlations between the innovations of ...

Researchers have introduced a new approach to sequence modeling called linear oscillatory state-space (LinOSS) models, designed for efficient learning on long sequences. Drawing inspiration from ...

SHENZHEN, China, May 12, 2025 (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the "Company"), a technology service provider, announced the development of a groundbreaking ...

Bayesian methods for dynamic models in marketing have so far been parametric. For instance, it is invariably assumed that model errors emerge from normal distributions. Yet using arbitrary ...