# Liquid Structural State-Space Models

@article{Hasani2022LiquidSS, title={Liquid Structural State-Space Models}, author={Ramin M. Hasani and Mathias Lechner and Tsun-Hsuan Wang and Makram Chahine and Alexander Amini and Daniela Rus}, journal={ArXiv}, year={2022}, volume={abs/2209.12951} }

A proper parametrization of state transition matrices of linear state-space models (SSMs) followed by standard nonlinearities enables them to efﬁciently learn representations from sequential data, establishing the state-of-the-art on a large series of long-range sequence modeling benchmarks. In this paper, we show that we can improve further when the structural SSM such as S4 is given by a linear liquid time-constant (LTC) state-space model. LTC neural networks are causal continuous-time neural…

## 6 Citations

### Simplified State Space Layers for Sequence Modeling

- Computer ScienceArXiv
- 2022

A state space layer that can leverage e-cient and widely implemented parallel scans, allowing S5 to match the computational e-ciency of S4, while also achieving state-of-the-art performance on several long-range sequence modeling tasks.

### Gated Recurrent Neural Networks with Weighted Time-Delay Feedback

- Computer ScienceArXiv
- 2022

Results show that τ -GRU can converge faster and generalize better than state-of-the-art recurrent units and gated recurrent architectures on a range of tasks, including time-series classiﬁcation, human activity recognition, and speech recognition.

### Closed-form continuous-time neural networks

- Computer ScienceNature Machine Intelligence
- 2022

It is shown that it is possible to closely approximate the interaction between neurons and synapses—the building blocks of natural and artificial neural networks—constructed by liquid time-constant networks efficiently in closed form and obtain models that are between one and five orders of magnitude faster in training and inference compared with differential equation-based counterparts.

### Are All Vision Models Created Equal? A Study of the Open-Loop to Closed-Loop Causality Gap

- Computer ScienceArXiv
- 2022

The results imply that the causality gap can be solved in situation one with the proposed training guideline with any modern network architecture, whereas achieving out-of-distribution generalization requires further investigations, for instance, on data diversity rather than the model architecture.

### On the Forward Invariance of Neural ODEs

- Computer Science, MathematicsArXiv
- 2022

The invariance propagation is demonstrated on a comprehensive series of representation learning tasks, including spiral curve regression, autoregressive modeling of joint physical dynamics, convexity portrait of a function, and safe neural control of collision avoidance for autonomous vehicles.

### Interpreting Neural Policies with Disentangled Tree Representations

- Computer ScienceArXiv
- 2022

A new algorithm is designed that programmatically extracts tree representations from compact neural policies, in the form of a set of logic programs grounded by world state, that allows for interpretability metrics that measure disentanglement of learned neural dynamics from a concentration of decisions, mutual information and modularity perspectives.

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- Computer ScienceArXiv
- 2022

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- Computer ScienceArXiv
- 2022

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- Computer ScienceArXiv
- 2022

A state space layer that can leverage e-cient and widely implemented parallel scans, allowing S5 to match the computational e-ciency of S4, while also achieving state-of-the-art performance on several long-range sequence modeling tasks.

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