Quaternion Recurrent Neural Networks
Bibliographic record
Abstract
Recurrent neural networks (RNNs) are powerful architectures to model\nsequential data, due to their capability to learn short and long-term\ndependencies between the basic elements of a sequence. Nonetheless, popular\ntasks such as speech or images recognition, involve multi-dimensional input\nfeatures that are characterized by strong internal dependencies between the\ndimensions of the input vector. We propose a novel quaternion recurrent neural\nnetwork (QRNN), alongside with a quaternion long-short term memory neural\nnetwork (QLSTM), that take into account both the external relations and these\ninternal structural dependencies with the quaternion algebra. Similarly to\ncapsules, quaternions allow the QRNN to code internal dependencies by composing\nand processing multidimensional features as single entities, while the\nrecurrent operation reveals correlations between the elements composing the\nsequence. We show that both QRNN and QLSTM achieve better performances than RNN\nand LSTM in a realistic application of automatic speech recognition. Finally,\nwe show that QRNN and QLSTM reduce by a maximum factor of 3.3x the number of\nfree parameters needed, compared to real-valued RNNs and LSTMs to reach better\nresults, leading to a more compact representation of the relevant information.\n
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".