The paper investigates anomaly detection in high-energy physics by learning low-dimensional representations of collision events. Using supervised and self-supervised neural embeddings on a large simulated dataset, the authors find that supervised objectives generally outperform self-supervised ones and improve signal efficiency compared to traditional kinematic-feature autoencoders. They also propose a local diagnostic pipeline that models the Standard Model latent space with a Gaussian Mixture Model, profiles components via high-level observables, and ranks these observables by their local discriminating power, thereby linking anomaly scores to specific kinematic features.
By Donatella Genovese, Philip Ploner, Simone Scardapane, Stefano Giagu, Thea Klaeboe Aarrestad
arXiv:2608. 13652v1 Announce Type: new Abstract: Generic event-level anomaly detection for collider physics has two recurring problems: anomaly scores are hard to interpret, and they correlate strongly with energy scale and object multiplicity.
By Haoyi Jia, Sagar Addepalli, Julia Gonski
arXiv:2606. 27411v1 Announce Type: cross Abstract: We study a quantum autoencoder (QAE) for compression-driven anomaly detection in brain MRI data.
By Santanu Ganguly, Xing Liang, Dimitrios Makris
The paper introduces a Physics-informed Predictive Prior Tensor Autoencoder (PPPTAE) for anomaly detection in multi-dimensional time series. It combines reconstruction-based and prediction-based autoencoders by embedding a Bayesian fusion approach and a predictive prior that respects tensor correlations. The method incorporates physical laws via tensor low‑rank decomposition to prevent over‑generalization and is validated on real‑world datasets.
By Jianan Liu, Chunguang Li
The paper studies a class of tabular anomaly detectors that train a neural map toward a fixed target using squared‑error loss, noting that improved optimization can paradoxically worsen detection performance—a phenomenon termed convergence collapse. It demonstrates that this collapse arises because the learned map extrapolates beyond the training data, erasing residual signals on anomalies. To address this, the authors introduce Kernel Contraction Matching (KCM), a closed‑form, training‑free kernel‑based baseline, and propose the Kernel‑Anchored Regularizer (KAR), which penalizes deviations from a kernel‑weighted average of training targets, thereby mitigating collapse and improving AUROC during extended training.
By Jos\'e Lucas De Melo Costa, Fabrice Popineau, Arpad Rimmel, Bich-Li\^en Doan
While recent advancements in anomaly detection have demonstrated the efficacy of CNN- and Transformer-based approaches, these architectures face inherent limitations: CNNs struggle to capture long-range dependencies, whereas Transformers suffer from quadratic computational complexity. Consequently, Mamba-based architectures have attracted considerable attention, as they successfully combine superior long-range dependency modeling with linear computational complexity.
arXiv:2606. 02600v1 Announce Type: cross Abstract: We study autoencoder and variational-autoencoder latent spaces through the lens of spin-glass theory.
By Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz, Clinton Fookes, Olivier Salvado
arXiv:2601.11719v4 Announce Type: replace
Abstract: Self-supervised learning, in the context of foundation model training, is a powerful pre-training method for learning feature representations witho...
By Ho Fung Tsoi, Dylan Rankin
DIFFINT is a reconstruction‑based anomaly detector that uses a differentiable autoencoder with a latent bottleneck composed of soft, axis‑aligned interval memberships. Each latent unit represents a human‑readable hyper‑rectangle in feature space, allowing the model to encode how strongly an instance falls inside each interval and to compute reconstruction error as the anomaly score. The method provides a certified lower bound on reconstruction error for points outside all active intervals, a suppression mechanism for sparse abnormalities, and a closed‑form, label‑free importance ranking for each (unit, feature) pair, achieving top performance on 48 ADBench benchmarks against 22 baselines.
By Lamine Diop, Marc Plantevit
arXiv:2606. 00130v1 Announce Type: cross Abstract: We study Automatically Differentiable Nonlinear Tensor Networks (ADNTNs), a family of structured weight generators whose compact core tensors are trained end-to-end by reverse-mode automatic differentiation (AD).
By Andrzej Cichocki, Michal Wietczak
arXiv:2606. 00130v2 Announce Type: replace-cross Abstract: Large deep neural networks are costly to store and deploy because inference must move and evaluate many parameters.
By Andrzej Cichocki, Michal Wietczak
The paper presents a theoretical analysis of symmetric autoencoders, a class of deep learning architectures frequently used in machine learning tasks. It distinguishes between different symmetric designs and shows that the reconstruction error of orthonormal symmetric autoencoders can be interpreted via the Eckart‑Young‑Schmidt theorem. Building on this insight, the authors propose an EYS‑based initialization strategy using repeated SVD, and validate its effectiveness through numerical experiments comparing it to conventional deep autoencoders.
By Simone Brivio, Nicola Rares Franco