Volume-preserving Recurrent Neural Networks (VPRNN)
Bibliographic record
Abstract
Volume-preserving neural networks (VPNN) are neural networks comprised solely of volume-preserving transformations, with the possible exception of the output layer. In this paper, we present a new type of recurrent neural network (RNN) for sequence processing that utilizes parametrized orthogonal matrices inspired by those transformations used in VPNNs, named the volume-preserving recurrent neural network (VPRNN) after its feed-forward predecessor. Our VPRNN models show promise on the classical addition problem for RNNs, successfully processing sequences of length 10,000. We also present a theoretical result for a matrix norm-based generalization gap of VPRNN classifiers using PAC-Bayesian analysis which is sublinear in the length of sequences being processed. This generalization gap provides a theoretical improvement when compared to typical RNNs. We find that our single-cell VPRNN models improve upon previously proposed unitary and orthogonal RNN architectures (with similar parameter counts) on the sequential and permuted pixel MNIST classification tasks, and improve over gated baselines (using far fewer parameters) on the sequential IMDB classification task. Further, we provide comparisons with several unitary and orthogonal RNNs on the HAR-2 classification task, revealing the possibility of deploying VPRNNs on memory-constrained systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".