Copertina

LieAugmenter: Equivariant Learning by Discovering Symmetries with Learnable Augmentations

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Santos-Escriche, Eduardo, Lin, Ya-Wei Eileen, Jegelka, Stefanie
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Any-Subgroup Equivariant Networks via Symmetry Breaking
di: Goel, Abhinav, et al.
Pubblicazione: (2026)

Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters
di: Lin, Ya-Wei Eileen, et al.
Pubblicazione: (2024)

A Canonicalization Perspective on Invariant and Equivariant Learning
di: Ma, George, et al.
Pubblicazione: (2024)

Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models
di: Putterman, Theo, et al.
Pubblicazione: (2024)

LAC: Graph Contrastive Learning with Learnable Augmentation in Continuous Space
di: Lin, Zhenyu, et al.
Pubblicazione: (2024)

Equivariant Neural Networks for General Linear Symmetries on Lie Algebras
di: Kim, Chankyo, et al.
Pubblicazione: (2025)

Higher-Order Graphon Neural Networks: Approximation and Cut Distance
di: Herbst, Daniel, et al.
Pubblicazione: (2025)

Sample Complexity Bounds for Estimating Probability Divergences under Invariances
di: Tahmasebi, Behrooz, et al.
Pubblicazione: (2023)

The Exact Sample Complexity Gain from Invariances for Kernel Regression
di: Tahmasebi, Behrooz, et al.
Pubblicazione: (2023)

Understanding the Role of Equivariance in Self-supervised Learning
di: Wang, Yifei, et al.
Pubblicazione: (2024)

In-Context Symmetries: Self-Supervised Learning through Contextual World Models
di: Gupta, Sharut, et al.
Pubblicazione: (2024)

To Augment or Not to Augment? Diagnosing Distributional Symmetry Breaking
di: Lawrence, Hannah, et al.
Pubblicazione: (2025)

SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning
di: Peng, Tianhao, et al.
Pubblicazione: (2025)

The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof
di: Lim, Derek, et al.
Pubblicazione: (2024)

Model Debiasing by Learnable Data Augmentation
di: Morerio, Pietro, et al.
Pubblicazione: (2024)

Leap: Inductive Link Prediction via Learnable TopologyAugmentation
di: Samy, Ahmed E., et al.
Pubblicazione: (2025)

GraphLearner: Graph Node Clustering with Fully Learnable Augmentation
di: Yang, Xihong, et al.
Pubblicazione: (2022)

Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks
di: Lin, Ya-Wei Eileen, et al.
Pubblicazione: (2025)

Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs
di: Rauchwerger, Levi, et al.
Pubblicazione: (2024)

Neural Networks With Dense Weights Are Not Universal Approximators
di: Rauchwerger, Levi, et al.
Pubblicazione: (2026)

Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results
di: Tahmasebi, Behrooz, et al.
Pubblicazione: (2020)

A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs
di: Le, Thien, et al.
Pubblicazione: (2023)

SE(3) Equivariant Augmented Coupling Flows
di: Midgley, Laurence I., et al.
Pubblicazione: (2023)

Learning with Exact Invariances in Polynomial Time
di: Soleymani, Ashkan, et al.
Pubblicazione: (2025)

Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebras
di: Lin, Tzu-Yuan, et al.
Pubblicazione: (2023)

Canonicalizing Multimodal Contrastive Representation Learning
di: Gupta, Sharut, et al.
Pubblicazione: (2026)

Augmented Equivariant Mesh Networks for Anatomical Segmentation
di: Saragih, Daniel
Pubblicazione: (2026)

Data Augmentation and Regularization for Learning Group Equivariance
di: Nordenfors, Oskar, et al.
Pubblicazione: (2025)

Symmetry-Guided Memory Augmentation for Efficient Locomotion Learning
di: Bao, Kaixi, et al.
Pubblicazione: (2025)

Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policies
di: Das, Sourajit, et al.
Pubblicazione: (2025)

EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost
di: Heo, Jaeseung, et al.
Pubblicazione: (2023)

Mitigating Degree Bias in Graph Representation Learning with Learnable Structural Augmentation and Structural Self-Attention
di: Hoang, Van Thuy, et al.
Pubblicazione: (2025)

Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables
di: Nagar, Eden, et al.
Pubblicazione: (2026)

Learning-Augmented Online Packet Scheduling with Deadlines
di: Liang, Ya-Chun, et al.
Pubblicazione: (2023)

On the Emergence of Position Bias in Transformers
di: Wu, Xinyi, et al.
Pubblicazione: (2025)

Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency
di: Lin, Yuchao, et al.
Pubblicazione: (2024)

Partially Equivariant Reinforcement Learning in Symmetry-Breaking Environments
di: Chang, Junwoo, et al.
Pubblicazione: (2025)

Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning
di: Xie, Fengze, et al.
Pubblicazione: (2024)

Joint Hierarchical Representation Learning of Samples and Features via Informed Tree-Wasserstein Distance
di: Lin, Ya-Wei Eileen, et al.
Pubblicazione: (2025)

Symmetry Breaking and Equivariant Neural Networks
di: Kaba, Sékou-Oumar, et al.
Pubblicazione: (2023)