Unsupervised Dictionary Learning

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1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Iterative Atom Refinement: A Monotonicity Principle for Dictionary Learning
Sep 20, 2026Alexander Christie, Miguel Moscoso, Alexei Novikov +2Unsupervised Dictionary Learning Iterative Refinement
Department of Mathematics, Stanford University, Stanford, CA 94305, USA · Department of Mathematics, Universidad Carlos III de Madrid, Leganés, Madrid 28911, Spain · Department of Mathematics, Pennsylvania State University, University Park, PA 16802, USA +1
SignSeek: Learning Transferable Representations for Sign Dictionary Retrieval
Sep 3, 2026Sobhan Asasi, Ozge Mercanoglu Sincan, Richard BowdenSign Language Translation Unsupervised Dictionary Learning
CVSSP, University of Surrey
SignMatch: Matching Dictionary Signs to Continuous Sign Language Video
Sep 1, 2026Ryan Wong, Youngjoon Jang, Liliane Momeni +2Sign Language Translation Sign
Visual Geometry Group Department of Engineering Science University of Oxford UK · LIGM, École des Ponts, IP Paris, Univ Gustave Eiffel, CNRS France
Large Language Models for Low-Resource Languages: A Conceptual Framework for an Electronic Explanatory Dictionary of the Tajik Language
Aug 4, 2026Mullosharaf K. Arabov, Saidali M. Pirzoda, Behruz A. SultonovLow-Resource Languages Unsupervised Dictionary Learning
Kazan Federal University, Institute of Computational Mathematics and Information Technologies, Kazan, Russia · Tajik National University, Department of Information and Communication Technologies, Faculty of Mechanics and Mathematics, Dushanbe, Tajikistan
Causal dictionary learning reveals and validates transcription-factor binding features in genomic language models
Jul 21, 2026Sarwan AliUnsupervised Dictionary Learning Exploratory Factor Analysis
Columbia University Irving Medical Center, New York, USA
Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models
Jul 7, 2026Franz Motzkus, Sebastian BernhardCausality-Aware End-To-End Autonomous Driving Interpretability
AUMOVIO, Germany · University of Bamberg, Germany
Early Language Learning via Spreading Activation and Category Exploration in Complex Networks
Jul 7, 2026Salvatore CitraroLanguage Acquisition Unsupervised Dictionary Learning
Institute of Information Science and Technologies “A. Faedo” (ISTI) National Research Council (CNR) Via G. Moruzzi, 1, 56124 Pisa, Italy · National Research Council (CNR) Via G. Moruzzi, 1, 56124 Pisa, Italy
Residual-Guided Dictionary Learning for Spectrally Accurate Koopman Approximation
Jun 27, 2026George Coote, Matthew J. ColbrookKoopman Operator Unsupervised Dictionary Learning
Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, CB3 0WA, United Kingdom
Towards Structuring an Arabic-English Machine-Readable Dictionary Using Parsing Expression Grammars
Jun 23, 2026Diaa M. Fayed, Aly A. Fahmy, Mohsen A. Rashwan +1Arabic Context-Free Grammars
Department of Computer Science, Faculty of Computers and Information, Cairo University · Department of EECE, Faculty of Engineering, Cairo University · Department of Arabic Language and Literatures, Faculty of Arts, Cairo University.
On the Sparsity-Storage-Accuracy Tradeoff in Parsimoniously Activated Dictionary Learning
Jun 21, 2026Zihui Zhao, Yuanbo Tang, Yang LiUnsupervised Dictionary Learning Sparsity
Tsinghua University, Institute of Data and Information Shenzhen Key Laboratory of Ubiquitous Data Enabling
MUDIDI: A Two-Stage Framework for Multilingual Dictionary Digitization with Language Models
Jun 8, 2026David Setiawan, Temuulen Khishigsuren, Milind Agarwal +3Unsupervised Dictionary Learning Recent Vision-Language Models
λSchool of Computing and Information Systems, The University of Melbourne · ΨMelbourne School of Psychological Sciences, The University of Melbourne · ΦLILT
Interactions Between Crosscoder Features: A Compact Proofs Perspective
Jun 8, 2026Dmitry Manning-Coe, Thomas Read, Anna Soligo +4Unsupervised Dictionary Learning Cross-Encoder
1Anthony J. Leggett Institute for Condensed Matter Theory · 2MATS · 3UK AI Security Institute (AISI) +4
How Optimality Structures Sparse Dictionaries: A Theory for Understanding SAE Representations
Jun 1, 2026William DorrellUnsupervised Dictionary Learning Theory
Kempner Institute · Harvard University
Ablating Archetypes: The Stability of Archetypal SAEs is an Artifact of Initialization and Metric Design
Jun 1, 2026Michał Brzozowski, Neo Christopher ChungImproving Sparse Autoencoders Unsupervised Dictionary Learning
Samsung AI Center, Warsaw, Poland · University of Warsaw, Poland
Toward Identifiable Sparse Autoencoders
May 29, 2026Walter Nelson, Theofanis Karaletsos, Francesco LocatelloImproving Sparse Autoencoders Sparse Autoencoder Features
Institute of Science and Technology Austria · 2Pyramidal Inc. · and Achira Inc., USA.
Learning from Semantic Dictionaries: Discriminative Codebook Contrastive Learning for Unified Visual Representation and Generation
May 24, 2026Imanol G. Estepa, Jesús M Rodríguez-de-Vera, Bhalaji Nagarajan +1Contrastive Loss Contrastive Learning
1Universitat de Barcelona, Barcelona, Spain · 2Barcelona Supercomputing Center (BSC), Barcelona, Spain
High-Dimensional Latents Should Be Diagnosed Through Phase Structure
May 23, 2026Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz +2Conditional Variational Autoencoder Latent Space
School of Electrical Engineering and Robotics, Faculty of Engineering, Queensland University of Technology, Brisbane, Queensland, Australia
Sparse Compositional Flow Matching by geometric assembly from motion primitives
May 22, 2026Yan Tang, Yuanbo Tang, Tingyu Cao +2Contrastive Flow Matching Motion Primitives
Tsinghua Shenzhen Graduate School, Tsinghua University, Shenzhen, China · School of AI, Chinese University of Hong Kong (Shenzhen)
Learning fMRI activations dictionaries across individual geometries via optimal transport
May 20, 2026Sonia Mazelet, Rémi Flamary, Bertrand ThirionFunctional Magnetic Resonance Imaging Unsupervised Dictionary Learning
CMAP, Ecole Polytechnique · Palaiseau, France · Mind, Inria-Saclay
DeFed-GMM-DaDiL: A Decentralized Federated Framework for Domain Adaptation
May 5, 2026Rebecca Clain, Eduardo Fernandes Montesuma, Fred Ngole MboulaDomain Adaptation Unsupervised Dictionary Learning
Universit´e Paris-Saclay, CEA, List, F-91120 Palaiseau, France · Sigma Nova
Diverse Dictionary Learning
Apr 19, 2026Yujia Zheng, Zijian Li, Shunxing Fan +2Unsupervised Dictionary Learning Identifiability
CMU · MBZUAI · NYU
Data-Driven Variational Basis Learning Beyond Neural Networks: A Non-Neural Framework for Adaptive Basis Discovery
Apr 17, 2026Andrew KirulutaBasis Functions Unsupervised Dictionary Learning
UC Berkeley, CA
Multi-Dictionary Learning for Low Rank Sparse Coding
Sep 12, 2025Boya Ma, Abram Magner, Maxwell McNeil +1Unsupervised Dictionary Learning Codebooks
University at Albany – SUNY, Computer Science, Albany, NY, USA
Boosting Adversarial Robustness and Generalization with Dictionary Structure
Feb 2, 2025Zhichao Hou, Weizhi Gao, Hamid Krim +2Unsupervised Dictionary Learning Residual Networks
Department of Computer Science, North Carolina State University, Raleigh, USA.

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Iterative Atom Refinement: A Monotonicity Principle for Dictionary Learning

Alexander Christie, Miguel Moscoso, Alexei Novikov, George Papanicolaou+1

Department of Mathematics, Stanford University, Stanford, CA 94305, USA · Department of Mathematics, Universidad Carlos III de Madrid, Leganés, Madrid 28911, Spain · Department of Mathematics, Pennsylvania State University, University Park, PA 16802, USA+1

Unsupervised Dictionary Learning Iterative Refinement Atoms Monotonicity

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Dictionary learning seeks to recover an unknown dictionary

A

from observations

{\bf y}_i = A{\bf x}_i

with sparse coefficient vectors

{\bf x}_i

. We introduce the \emph{Iterative Atom Refinement} (IAR) algorithm, a simple procedure for recovering individual dictionary atoms. Starting from a random direction, IAR repeatedly selects the observations most strongly correlated with the current iterate and updates the direction by averaging the selected data. Our main contribution is a rigorous convergence theory of IAR. Using high-dimensional probabilistic estimates and a novel monotonicity principle for atom-selection probabilities, we show that a small initial advantage of one atom is amplified until that atom is isolated. Under our model assumptions, IAR identifies a generating atom after only three refinement steps. Numerical experiments support the theory and show that the resulting dynamics accurately capture the behavior observed in dictionary refinement.

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