Discovering interpretable low-dimensional dynamics using maximum entropy

arXiv:2605.16724 · q-bio.QM · Submitted 2026-05-16 · Read on arXiv

q-bio.QM

Submitted: 2026-05-16

Updated: 2026-05-16

Terminology

• Latent Dynamics — 19× in this paper · explained in 2 episodes
• Accuracy — 12× in this paper · explained in 1 episode
• Latent Space — 12× in this paper · explained in 7 episodes
• Aggregation — 11× in this paper · explained in 1 episode
• Adaptation — 10× in this paper · explained in 1 episode
• Interpretability — 10× in this paper · explained in 15 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Loss Function — 7× in this paper · explained in 2 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Pruning — 5× in this paper · explained in 4 episodes
• Regularization — 5× in this paper · explained in 3 episodes
• Symbolic Regression — 5× in this paper · explained in 5 episodes
• Computational Biology — 4× in this paper · explained in 2 episodes
• Nonnegative Matrix Factorization — 4× in this paper
• Sparsity — 4× in this paper · explained in 4 episodes
• Unified Framework — 4× in this paper · explained in 5 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Abundance — 2× in this paper · explained in 1 episode
• Clustering — 2× in this paper · explained in 2 episodes
• Distribution Shift — 2× in this paper · explained in 6 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Homogeneity — 2× in this paper · explained in 2 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Ornstein–Uhlenbeck Process — 2× in this paper · explained in 3 episodes
• Parsimony — 2× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Active Learning — 1× in this paper · explained in 6 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Limit Cycles — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Time Series Forecasting — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Using Dynamic Maximum Entropy — 1× in this paper

Sources

• Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
• Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
• End-to-end symbolic regression with transformers
• Extrapolation and learning equations
• The information bottleneck method
• A Generalized Information Bottleneck Theory of Deep Learning
• On the Convergence of the SINDy Algorithm
• The Why and How of Nonnegative Matrix Factorization
• How to Train Your Energy-Based Models

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