arXiv subjects

Onur Efe

Publications and source records attributed to Onur Efe.

3 recordsLinked to original sources

arXiv2026

Blind Recovery of Latent Domains via Unsupervised Symmetry Discovery

Primary motivation in blind inverse problems is to recover signals of interest from corrupted observations without knowing the obfuscating mechanism. Blind deconvolution is a prominent approach when the corruption is convolutional, but it is not applicable when general linear transformations obfuscate the domain structure. In this work, we propose an unsupervised framework for recovering latent domains and signals by discovering symmetries of the data distribution. Our framework models observations as linear measurements of signals sampled from a latent random field, and optimizes a shallow group-convolutional network by imposing stationarity and locality regularization at the model output. The model learns a latent symmetry action and an appropriate filter, thereby mapping unstructured observations to a symmetry-based representation that reveals latent signals. Experiments on stochastic processes, Ising models, shuffled and bit-scrambled images, and neural recordings show that the method recovers latent domains and signals from unstructured observations, suggesting symmetry discovery as a new direction for unsupervised structure learning and blind inverse problems.

cs.LG↗

arXiv2024

SymmetryLens: Unsupervised Symmetry Learning via Locality and Density Preservation

We develop a new unsupervised symmetry learning method that starts with raw data and provides the minimal generator of an underlying Lie group of symmetries, together with a symmetry-equivariant representation of the data, which turns the hidden symmetry into an explicit one. The method is able to learn the pixel translation operator from a dataset with only an approximate translation symmetry and can learn quite different types of symmetries that are not apparent to the naked eye. The method is based on the formulation of an information-theoretic loss function that measures both the degree of symmetry of a dataset under a candidate symmetry generator and a proposed notion of locality of the samples, which is coupled to symmetry. We demonstrate that this coupling between symmetry and locality, together with an optimization technique developed for entropy estimation, results in a stable system that provides reproducible results.

cs.LG↗

arXiv2024

RECOVAR: Representation Covariances on Deep Latent Spaces for Seismic Event Detection

While modern deep learning methods have shown great promise in the problem of earthquake detection, the most successful methods so far have been based on supervised learning, which requires large datasets with ground-truth labels. The curation of such datasets is both time consuming and prone to systematic biases, which result in difficulties with cross-dataset generalization, hindering general applicability. In this paper, we develop an unsupervised method for earthquake detection that learns to detect earthquakes from raw waveforms, without access to ground truth labels. The performance is comparable to, and in some cases better than, some state-of-the-art supervised methods. Moreover, the method has strong \emph{cross-dataset generalization} performance. The algorithm utilizes deep autoencoders that learn to reproduce the waveforms after a data-compressive bottleneck and uses a simple, cross-covariance-based triggering algorithm at the bottleneck for labeling. The approach has the potential to be useful for time series datasets from other domains.

cs.LG↗