raminmh/liquid_time_constant_networks
Code Repository for Liquid Time-Constant Networks (LTCs) observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2296
- days_rel: n/a
- days_push: 821
- n_releases_24m: 0
Adoption not part of the score
1857 stars · 335 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Official code repository for Liquid Time-Constant Networks (LTCs), a class of continuous-time recurrent neural networks with varying time-constants, from the AAAI-21 paper. It provides TensorFlow implementations of LTCs alongside Neural ODEs, CT-RNNs, and continuous-time GRUs, with training scripts for several time-series datasets.
Use cases
- train liquid time-constant networks on time-series data
- compare LTCs against LSTMs and CT-RNNs for sequence modeling
- reproduce results from the LTC research paper
- experiment with continuous-time recurrent models like neural ODEs
- benchmark RNN variants on activity recognition and forecasting datasets
When to choose
- you want to experiment with or reproduce research on liquid neural networks and continuous-time RNNs
- you need reference TensorFlow 1.x implementations of LTC, Neural ODE, CT-RNN, and CT-GRU models
- you are doing academic work on time-series sequence modeling
When to avoid
- you need a production-ready or maintained library - use the sister repository mlech26l/ncps with PyTorch support instead
- you are on modern TensorFlow 2.x or PyTorch and cannot use TensorFlow 1.14
- you need general-purpose deep learning tooling rather than research code
Facets
library · maturity maintenance
machine-learning deep-learning machine-learning deep-learning data-science time-series python liquid-neural-networks recurrent-neural-networks neural-odes time-series research-code tensorflow state-space-models linux
6 sources
- readme: https://github.com/raminmh/liquid_time_constant_networks · fetched 2026-08-28 · d8a4ab7c6877
- homepage: https://arxiv.org/abs/2006.04439 · fetched 2026-08-29 · f1d30d2acd39
- site_page: https://info.arxiv.org/about/donate.html · fetched 2026-08-29 · cca9c3a11c56
- site_page: https://info.arxiv.org/about/ourmembers.html · fetched 2026-08-29 · 47cbc55ff1de
- site_page: https://info.arxiv.org/about · fetched 2026-08-29 · a1f16f915a9a
- site_page: https://info.arxiv.org/labs/index.html · fetched 2026-08-29 · b14a8d05a0ec
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| raminmh/liquid_time_constant_networks | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="raminmh/liquid_time_constant_networks")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem