Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems. - Jimmy T. H. Smith; Scott W. Linderman; David Sussillo (NeurIPS, 2021)

Simplified State Space Layers for Sequence Modeling. - Jimmy T. H. Smith; Andrew Warrington; Scott W. Linderman (ICLR, 2023)

Convolutional State Space Models for Long-Range Spatiotemporal Modeling. - Jimmy T. H. Smith; Shalini De Mello; Jan Kautz; Scott W. Linderman; Wonmin Byeon (NeurIPS, 2023)

Birdie: Advancing State Space Language Modeling with Dynamic Mixtures of Training Objectives. - Sam Blouir; Jimmy T. H. Smith; Antonios Anastasopoulos; Amarda Shehu (EMNLP, 2024)

State-Free Inference of State-Space Models: The *Transfer Function* Approach. - Rom N. Parnichkun; Stefano Massaroli; Alessandro Moro; Jimmy T. H. Smith; Ramin M. Hasani; Mathias Lechner; Qi An; Christopher Ré; Hajime Asama; Stefano Ermon; Taiji Suzuki; Michael Poli; et al. (ICML, 2024)

Towards Scalable and Stable Parallelization of Nonlinear RNNs. - Xavier Gonzalez; Andrew Warrington; Jimmy T. H. Smith; Scott W. Linderman (NeurIPS, 2024)

Towards a theory of learning dynamics in deep state space models. - Jakub Smékal; Jimmy T. H. Smith; Michael Kleinman; Dan Biderman; Scott W. Linderman (CoRR, 2024)

Birdie: Advancing State Space Models with Reward-Driven Objectives and Curricula. - Sam Blouir; Jimmy T. H. Smith; Antonios Anastasopoulos; Amarda Shehu (CoRR, 2024)