Papers › UnICORNN: A recurrent model for learning very long time dependencies
UnICORNN: A recurrent model for learning very long time dependencies
T. Konstantin Rusch, Siddhartha Mishra
The design of recurrent neural networks (RNNs) to accurately process sequential inputs with long-time dependencies is very challenging on account of the exploding and vanishing gradient problem. To overcome this, we propose a novel RNN architecture which is based on a structure preserving discretization of a Hamiltonian system of second-order ordinary differential equations that models networks of oscillators. The resulting RNN is fast, invertible (in time), memory efficient and we derive rigorous bounds on the hidden state gradients to prove the mitigation of the exploding and vanishing gradient problem. A suite of experiments are presented to demonstrate that the proposed RNN provides state of the art performance on a variety of learning tasks with (very) long-time dependencies.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sentiment Analysis | IMDb | UnICORNN | Accuracy | 88.4 | #39 of 49 | Archive leaderboard | report |
| Sequential Image Classification | Sequential MNIST | UnICORNN | Permuted Accuracy | 98.4 | #7 of 30 | Archive leaderboard | report |
| Sequential Image Classification | noise padded CIFAR-10 | UnICORNN | % Test Accuracy | 62.4 | #2 of 7 | Archive leaderboard | report |
| Time Series Classification | EigenWorms | UnICORNN | % Test Accuracy | 90.3 | #2 of 8 | Archive leaderboard | report |
| Time Series Classification | EigenWorms | coRNN | % Test Accuracy | 86.7 | #3 of 8 | Archive leaderboard | report |
| Time Series Classification | EigenWorms | IndRNN | % Test Accuracy | 49.7 | #6 of 8 | Archive leaderboard | report |
| Time Series Classification | EigenWorms | expRNN | % Test Accuracy | 40.0 | #8 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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