liquid_time_constant_networks and Liquid-Time-stochasticity-networks
LTCs provide a foundational neural network architecture with continuous-time dynamics, while LTSs extend this framework by introducing stochastic elements to the liquid time-constant mechanism, making them evolutionary variants rather than direct competitors or complements.
About liquid_time_constant_networks
raminmh/liquid_time_constant_networks
Code Repository for Liquid Time-Constant Networks (LTCs)
Implements multiple continuous-time recurrent architectures—LTCs, Neural ODEs, CT-RNNs, and continuous-time GRUs—trainable via backpropagation through time using TensorFlow 1.14. Includes benchmarking scripts across five temporal sequence datasets (gesture recognition, occupancy detection, activity recognition, traffic forecasting, ozone prediction) with standardized evaluation pipelines that output detailed metrics including best-epoch checkpoints and test performance comparisons.
About Liquid-Time-stochasticity-networks
Ammar-Raneez/Liquid-Time-stochasticity-networks
Code repository for Liquid Time-stochasticity networks (LTSs)
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