Fetching the paper…
Reading the bibliography…
State space models (SSMs) have shown remarkable empirical performance on many long sequence modeling tasks, but a theoretical understanding of these models is still lacking.
Neural networks and principal component analysis: Learning from examples without local minima
Pierre Baldi and Kurt Hornik · 1989
Earlier work this paper cites.
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M. Saxe, James L. McClelland, and Surya Ganguli · 2014
Earlier work this paper cites.
Legendre memory units: Continuous-time representation in recurrent neural networks
Aaron Voelker, Ivana Kajić, and Chris Eliasmith · 2019
Earlier work this paper cites.
A mathematical theory of semantic development in deep neural networks
Andrew M. Saxe, James L. McClelland, and Surya Ganguli · 2019
Earlier work this paper cites.
Combining recurrent, convolutional, and continuous-time models with linear state-space layers
Albert Gu, Karan Goel, and Christopher Ré · 2021
Earlier work this paper cites.
Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Ré · 2022
Earlier work this paper cites.
Diagonal state spaces are as effective as structured state spaces
Ankit Gupta, Albert Gu, and Jonathan Berant · 2022
Earlier work this paper cites.
S4nd: Modeling images and videos as multidimensional signals with state spaces
Eric Nguyen, Karan Goel, Albert Gu, Gordon Downs, Preey Shah, Tri Dao, Stephen Baccus, and Christopher Ré · 2022
Cited alongside, same era.
It’s raw! audio generation with state-space models
Karan Goel, Albert Gu, Chris Donahue, and Christopher Ré · 2022
Cited alongside, same era.
Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
Cited alongside, same era.
Simplified state space layers for sequence modeling
Jimmy T. H. Smith, Andrew Warrington, and Scott Linderman · 2023
Cited alongside, same era.
Liquid structural state-space models
Ramin Hasani, Mathias Lechner, Tsun-Hsuan Wang, Makram Chahine, Alexander Amini, and Daniela Rus · 2023
Cited alongside, same era.
Hungry hungry hippos: Towards language modeling with state space models
Convolutional state space models for long-range spatiotemporal modeling
Jimmy T. H. Smith, Shalini De Mello, Jan Kautz, Scott Linderman, and Wonmin Byeon · 2024
Closest in time.
State-free inference of state-space models: The transfer function approach
Rom Parnichkun, Stefano Massaroli, Alessandro Moro, Jimmy T. H. Smith, Ramin Hasani, Mathias Lechner, Qi An, Christopher Ré, Hajime Asama, Stefano Ermon, et al · 2024
Closest in time.
Simba: Simplified mamba-based architecture for vision and multivariate time series
Badri N. Patro and Vijay S. Agneeswaran · 2024
Closest in time.
Repeat after me: Transformers are better than state space models at copying
Samy Jelassi, David Brandfonbrener, Sham M. Kakade, and Eran Malach · 2024
Closest in time.
Stablessm: Alleviating the curse of memory in state-space models through stable reparameterization
Shida Wang and Qianxiao Li · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Daniel Y Fu, Tri Dao, Khaled Kamal Saab, Armin W Thomas, Atri Rudra, and Christopher Ré · 2023
Cited alongside, same era.
Resurrecting recurrent neural networks for long sequences
Antonio Orvieto, Samuel L. Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre, Razvan Pascanu, and Soham De · 2023
Cited alongside, same era.
Universality of linear recurrences followed by non-linear projections: Finite-width guarantees and benefits of complex eigenvalues
Antonio Orvieto, Soham De, Caglar Gulcehre, Razvan Pascanu, and Samuel L Smith · 2024
Closest in time.