The paper introduces DSReg, a method that provably recovers individual world latent variables without relying on reconstruction, decoders, or labels. It leverages a Structural Diversity condition—distinct dependency footprints of different latents—to achieve full identifiability up to signed permutation. DSReg can be applied post hoc to any linearly identified representation, preserving dense prediction while improving latent recovery across synthetic and real-world experiments.
By Yujia Zheng, David Klindt, Randall Balestriero, Bernhard Sch\"olkopf
The paper argues that the linear representation hypothesis—where neural activations encode concepts as linear mixtures—breaks down under superposition, causing linear decision boundaries to lose linearity after projection. Classical sparse coding with per‑sample inference can recover latent factors, but sparse autoencoders (SAEs) amortise this inference into a fixed encoder, creating a systematic gap that persists across training sizes, latent dimensions, and sparsity levels. Experiments show that the failure of SAEs under out‑of‑distribution compositional shifts stems from poor dictionary learning rather than inference, and that a good dictionary can solve the problem at all tested scales, highlighting scalable dictionary learning as the key open challenge.
By Vit\'oria Barin-Pacela, Shruti Joshi, Isabela Camacho, Simon Lacoste-Julien, David Klindt
arXiv:2606. 28854v1 Announce Type: cross Abstract: The common factor analytic model is related to Helmholtz and Boltzmann machines, can be conceived as a linear autoencoder, or can be thought of as a single-hidden-layer generative neural network.
By Carel F. W. Peeters
The paper introduces a lightweight, observation-conditioned latent energy prior to improve inference for frozen implicit neural representation (INR) decoders when only sparse off‑grid signed distance function (SDF) samples are available. By standardizing latent codes based on a permutation‑invariant encoding of the sparse observations and combining this energy with a validation‑selected L2 prior, the method consistently outperforms baseline L2 and a six‑component Gaussian mixture model prior on both a controlled cell‑nucleus SDF dataset and a MedShapeNet‑derived SDF completion dataset, especially in the sparsest regimes. Ablation studies confirm that the energy term’s contribution is specific to the observed context rather than generic.
whyItMatters":"The approach demonstrates that pretrained INR decoders can become more observation‑aware without retraining, improving shape completion accuracy in data‑sparse scenarios."
By Paul B\"uschl, Ezequiel de la Rosa, Julia Wolleb, Julian McGinnis, C\'esar Nombela-Arrieta, Bjoern Menze
arXiv:2503. 21796v2 Announce Type: replace-cross Abstract: Self-supervised learning has become an increasingly important paradigm in the domain of machine intelligence.
By Alexander Ororbia, Karl Friston, Rajesh P. N. Rao
arXiv:2607. 16295v1 Announce Type: cross Abstract: Mechanistic interpretability has made significant strides in understanding neural network representations, with sparse dictionary learning (SDL) methods, most prominently sparse autoencoders, as a central paradigm.
By Yiming Tang, Qinglin Qi, Zhaoqian Yao, Harshvardhan Saini, Dianbo Liu