Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Liquid Structural State-Space Models
Authors: Ramin Hasani, Mathias Lechner, Tsun-Hsuan Wang, Makram Chahine, Alexander Amini, Daniela Rus
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive empirical evaluation, we show that Liquid-S4 consistently leads to better generalization performance compared to all variants of S4, CNNs, RNNs, and Transformers across many time-series modeling tasks. In this section, we present an extensive evaluation of Liquid-S4 on sequence modeling tasks with very long-term dependencies and compare its performance to a large series of baselines... |
| Researcher Affiliation | Academia | Ramin Hasani CSAIL, MIT Mathias Lechner CSAIL, MIT Tsun-Hsuan Wang CSAIL, MIT Makram Chahine CSAIL, MIT Alexander Amini CSAIL, MIT Daniela Rus CSAIL, MIT |
| Pseudocode | Yes | Algorithm 1 LIQUID-S4 KERNEL |
| Open Source Code | Yes | Code: https://github.com/raminmh/liquid-s4. |
| Open Datasets | Yes | We first evaluate Liquid-S4 s performance on the well-studied Long Range Arena (LRA) benchmark (Tay et al., 2020b)... We then report Liquid-S4 s performance... on the BIDMC Vital Signals dataset (Pimentel et al., 2016; Goldberger et al., 2000). We also experiment with the s CIFAR dataset... Finally, we perform Raw Speech Command (SC) recognition... |
| Dataset Splits | Yes | Table 1: Performance on Long Range Arena Tasks. Numbers indicate validation accuracy (standard deviation). Table 2: Performance on BIDMC Vital Signs dataset. Numbers indicate RMSE on the test set. Table 3: Performance on s CIFAR. Numbers indicate Accuracy (standard deviation). Table 4: Performance on Raw Speech Command dataset with Full 35 Labels.Numbers indicate Accuracy on test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Py Keops package' but does not specify its version number, nor does it list other key software components with versions. |
| Experiment Setup | Yes | Table 5: Hyperparameters for obtaining best performing models. BN= Batch normalization, LN = Layer normalization, WD= Weight decay. (followed by a table with specific values for Depth, Features, H State Size, Norm, Pre-norm, Dropout, LR, Batch Size, Epochs, WD for various tasks). |