Learning dynamical systems in noise using convolutional neural networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The problem of distinguishing deterministic chaos from non-chaotic dynamics has been an area of active research in time series analysis. Since noise contamination is unavoidable, it renders deterministic chaotic dynamics corrupted by noise to appear in close resemblance to stochastic dynamics. As a result, the problem of distinguishing noise-corrupted chaotic dynamics from randomness based on observations without access to the measurements of the state variables is difficult. We propose a new angle to tackle this problem by formulating it as a multi-class classification task. The task of classification involves allocating the observations/measurements to the unknown state variables in order to find the nature of these unobserved internal state variables. We employ signal and image processing based methods to characterize the different system dynamics. A deep learning technique using a state-of-the-art image classifier known as the Convolutional Neural Network (CNN) is designed to learn the dynamics. The time series are transformed into textured images of spectrogram and unthresholded recurrence plot (UTRP) for learning stochastic and deterministic chaotic dynamical systems in noise. We have designed a CNN that learns the dynamics of systems from the joint representation of the textured patterns from these images, thereby solving the problem as a pattern recognition task. The robustness and scalability of our approach is evaluated at different noise levels. Our approach demonstrates the advantage of applying the dynamical properties of chaotic systems in the form of joint representation of UTRP images along with spectrogram to improve learning dynamical systems in colored noise.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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 it