Perspectives on Latent Factor Indeterminacy and its Implications for Data Representation

arXiv:2606.28854 · stat.ML, cs.AI, cs.LG, math.ST, stat.CO · Submitted 2026-06-27 · Read on arXiv

Carel F. W. Peeters

stat.ML, cs.AI, cs.LG, math.ST, stat.CO

Submitted: 2026-06-27

Comments: 86 pages: 32 pages Main Text followed by 54 pages of Supplementary Material

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Main Text — 19× in this paper
Latent Space — 18× in this paper · explained in 7 episodes
Generative Model — 10× in this paper · explained in 2 episodes
Representation Learning — 7× in this paper · explained in 3 episodes
Artificial Intelligence — 6× in this paper
Latent Representation — 6× in this paper · explained in 2 episodes
Regularization — 6× in this paper · explained in 3 episodes
Sufficiency — 6× in this paper · explained in 2 episodes
Composition — 5× in this paper · explained in 2 episodes
Core — 5× in this paper · explained in 2 episodes
Hilbert Space — 5× in this paper · explained in 2 episodes
Latent Variables — 5× in this paper · explained in 2 episodes
Supplementary Material — 5× in this paper
Clarity — 4× in this paper · explained in 3 episodes
Conditional Independence — 4× in this paper · explained in 2 episodes
Faithfulness — 4× in this paper · explained in 2 episodes
Generative Models — 4× in this paper · explained in 3 episodes
Intrinsic Dimension — 4× in this paper · explained in 3 episodes
Scale-Invariance — 4× in this paper · explained in 2 episodes
Equivariance — 3× in this paper · explained in 3 episodes
Generative Modeling — 3× in this paper · explained in 3 episodes
Inverse Problem — 3× in this paper · explained in 2 episodes
Papers — 3× in this paper · explained in 3 episodes
Predictive Coding — 3× in this paper · explained in 3 episodes
Trace — 3× in this paper · explained in 2 episodes
Decoder — 2× in this paper · explained in 2 episodes
EMMA — 2× in this paper · explained in 2 episodes
Implicitly Restarted Lanczos Bidiagonalization Method — 2× in this paper
In International Conference on Learning Representations — 2× in this paper
Interpretability — 2× in this paper · explained in 15 episodes
Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
Augmentation — 1× in this paper · explained in 2 episodes
Bayes Factor — 1× in this paper · explained in 2 episodes
Bayesian Inference — 1× in this paper · explained in 6 episodes
Benchmark — 1× in this paper · explained in 2 episodes
Causal Inference — 1× in this paper · explained in 5 episodes
Coherence — 1× in this paper · explained in 2 episodes
Comprehensive R Archive Network — 1× in this paper
Consistency — 1× in this paper · explained in 3 episodes
Decoherence — 1× in this paper · explained in 2 episodes
Fidelity — 1× in this paper · explained in 2 episodes
Gaussian Maximum Likelihood — 1× in this paper
Generalization — 1× in this paper · explained in 14 episodes
Hierarchical Structure — 1× in this paper · explained in 3 episodes
Hybrid Architecture — 1× in this paper · explained in 2 episodes
Latent Representations — 1× in this paper · explained in 3 episodes
Localization — 1× in this paper · explained in 2 episodes
Orthogonal Projection — 1× in this paper · explained in 2 episodes
Robustness — 1× in this paper · explained in 6 episodes
Sparsity — 1× in this paper · explained in 5 episodes
Superradiance — 1× in this paper · explained in 5 episodes
Transfer Learning — 1× in this paper · explained in 16 episodes
Uncertainty Quantification — 1× in this paper · explained in 9 episodes
Unsupervised Learning — 1× in this paper · explained in 2 episodes
Variance Reduction — 1× in this paper · explained in 2 episodes
Variational Bayes — 1× in this paper · explained in 2 episodes

Terminology

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