mlsanityData to 7 Oct 2026

Spectral Inference Networks: Unifying Deep and Spectral Learning

arXiv 1806.02215 ↗First posted 6 Jun 2018cs.LG cs.AI stat.MLcited by 34

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Abstract (arXiv)

We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, they can be a powerful tool for unsupervised representation learning from video or graph-structured data. We cast training Spectral Inference Networks as a bilevel optimization problem, which allows for online learning of multiple eigenfunctions. We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators and can discover interpretable representations from video in a fully unsupervised manner.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jun 2018.

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Citing papers per year: 2021: 4; 2022: 7; 2023: 9; 2024: 3; 2025: 6; 2026: 5.

34 papers.

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  1. 2023Flow Matching on General Geometriescited by 260
  2. 2021Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyondcited by 151
  3. 2022Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methodscited by 146
  4. 2023Proto-Value Networks: Scaling Representation Learning with Auxiliary Taskscited by 41
  5. 2022Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functionscited by 32
  6. 2023Deep Laplacian-based Options for Temporally-Extended Explorationcited by 28
  7. 2022NeuralEF: Deconstructing Kernels by Deep Neural Networkscited by 26
  8. 2021Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawingcited by 22
  9. 2022Neural Eigenfunctions Are Structured Representation Learnerscited by 16
  10. 2023The Edge of Orthogonality: A Simple View of What Makes BYOL Tickcited by 15
  11. 2022Spectral Representation Learning for Conditional Moment Modelscited by 12
  12. 2024Operator SVD with Neural Networks via Nested Low-Rank Approximationcited by 12
22 more
  1. 2023Proper Laplacian Representation Learningcited by 9
  2. 2024Occam's Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations?cited by 8
  3. 2022A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspacescited by 7
  4. 2023Harnessing small projectors and multiple views for efficient vision pretrainingcited by 5
  5. 2023Learning Neural Eigenfunctions for Unsupervised Semantic Segmentationcited by 4
  6. 2021Network Representation Learning: From Traditional Feature Learning to Deep Learningcited by 3
  7. 2023Unsupervised Learning on a DIET: Datum IndEx as Target Free of Self-Supervision, Reconstruction, Projector Headcited by 3
  8. 2023Spectal Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learningcited by 2
  9. 2025STNet: Spectral Transformation Network for Solving Operator Eigenvalue Problemcited by 2
  10. 2025Generalizable Spectral Embedding with an Application to UMAPcited by 1
  11. 2025Deep Clustering via Probabilistic Ratio-Cut Optimizationcited by 1
  12. 2021Multilayer Graph Clustering with Optimized Node Embeddingcited by 0
  13. 2022The Symmetric Generalized Eigenvalue Problem as a Nash Equilibriumcited by 0
  14. 2024Nash Equilibria via Stochastic Eigendecompositioncited by 0
  15. 2025Revisiting Orbital Minimization Method for Neural Operator Decompositioncited by 0
  16. 2025Eigenfunction Extraction for Ordered Representation Learningcited by 0
  17. 2025Learning-Guided Integration Contours Construction for Fast Large-Scale Generalized Eigensolverscited by 0
  18. 2026NeuraLSP: A Neural Spectral Preconditioner for Accelerating PDE Solverscited by 0
  19. 2026Minimax Rates and Spectral Distillation for Tree Ensemblescited by 0
  20. 2026Learning Orthonormal Bases for Function Spacescited by 0
  21. 2026Score the Algebra, Not the Span: Dimension Reduction for Transfer Operator Models of Dynamical Systemscited by 0
  22. 2026Revisiting Spectral Representations in Generative Diffusion Modelscited by 0