Image de couverture de livre

Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	de Haan, Pim, Cohen, Taco, Brehmer, Johann
Format:	Preprint
Publié:	2023
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Does equivariance matter at scale?
par: Brehmer, Johann, et autres
Publié: (2024)

Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics
par: Spinner, Jonas, et autres
Publié: (2024)

Deconfounding Imitation Learning with Variational Inference
par: Vuorio, Risto, et autres
Publié: (2022)

A Lorentz-Equivariant Transformer for All of the LHC
par: Brehmer, Johann, et autres
Publié: (2024)

A Deep Dive into Scaling RL for Code Generation with Synthetic Data and Curricula
par: Sancaktar, Cansu, et autres
Publié: (2026)

Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebras
par: Lin, Tzu-Yuan, et autres
Publié: (2023)

Geometric Representation Condition Improves Equivariant Molecule Generation
par: Li, Zian, et autres
Publié: (2024)

Information-driven Affordance Discovery for Efficient Robotic Manipulation
par: Mazzaglia, Pietro, et autres
Publié: (2024)

GeoAda: Efficiently Finetune Geometric Diffusion Models with Equivariant Adapters
par: Zhao, Wanjia, et autres
Publié: (2025)

Equivariant Spherical Transformer for Efficient Molecular Modeling
par: An, Junyi, et autres
Publié: (2025)

Conformal Transformations for Symmetric Power Transformers
par: Kumar, Saurabh, et autres
Publié: (2025)

Soft Policy Optimization: Online Off-Policy RL for Sequence Models
par: Cohen, Taco, et autres
Publié: (2025)

GoQuant: Geometric Orthogonal Residual Projection for Multiplier-Free Power-of-Two Transformer Quantization
par: Xiang, Maoyang, et autres
Publié: (2026)

A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
par: Duval, Alexandre, et autres
Publié: (2023)

Rigidity-Aware Geometric Pretraining for Protein Design and Conformational Ensembles
par: Ni, Zhanghan, et autres
Publié: (2026)

Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation
par: Park, Jiwoong, et autres
Publié: (2024)

MViewRouter: Internalizing Geometric Equivariance via Multi-view Alternating Attention for Combinatorial Routing
par: Liu, Shiyan, et autres
Publié: (2026)

Geometric Flow Matching for Molecular Conformation Generation via Manifold Decomposition
par: Liu, Yunqing, et autres
Publié: (2026)

Choosing a Classical Planner with Graph Neural Networks
par: Vatter, Jana, et autres
Publié: (2024)

Toward a Functional Geometric Algebra for Natural Language Semantics
par: Pustejovsky, James
Publié: (2026)

Spacetime $E(n)$-Transformer: Equivariant Attention for Spatio-temporal Graphs
par: Charles, Sergio G.
Publié: (2024)

AdS-GNN -- a Conformally Equivariant Graph Neural Network
par: Zhdanov, Maksim, et autres
Publié: (2025)

Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation
par: An, Junyi, et autres
Publié: (2026)

Transformers trained on proteins can learn to attend to Euclidean distance
par: Ellmen, Isaac, et autres
Publié: (2025)

Geometric and Dynamic Scaling in Deep Transformers
par: Su, Haoran, et autres
Publié: (2026)

Learning from Streaming Data when Users Choose
par: Su, Jinyan, et autres
Publié: (2024)

Multiple Invertible and Partial-Equivariant Function for Latent Vector Transformation to Enhance Disentanglement in VAEs
par: Jung, Hee-Jun, et autres
Publié: (2025)

Position: Beyond Euclidean -- Foundation Models Should Embrace Non-Euclidean Geometries
par: He, Neil, et autres
Publié: (2025)

Geometric Neural Operators (GNPs) for Data-Driven Deep Learning of Non-Euclidean Operators
par: Quackenbush, Blaine, et autres
Publié: (2024)

Choosing How to Remember: Adaptive Memory Structures for LLM Agents
par: Lu, Mingfei, et autres
Publié: (2026)

Extrapolative Weight Averaging Reveals Correctness-Efficiency Frontiers in Code RL
par: Zheng, Kunhao, et autres
Publié: (2026)

CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay
par: Butt, Natasha, et autres
Publié: (2024)

Equivariant Symmetry Breaking Sets
par: Xie, YuQing, et autres
Publié: (2024)

Controlled Generation with Equivariant Variational Flow Matching
par: Eijkelboom, Floor, et autres
Publié: (2025)

Magnitude-Modulated Equivariant Adapter for Parameter-Efficient Fine-Tuning of Equivariant Graph Neural Networks
par: Jin, Dian, et autres
Publié: (2025)

Metric Learning for Clifford Group Equivariant Neural Networks
par: Ali, Riccardo, et autres
Publié: (2024)

Non-Euclidean Spatial Graph Neural Network
par: Zhang, Zheng, et autres
Publié: (2023)

Separation Power of Equivariant Neural Networks
par: Pacini, Marco, et autres
Publié: (2024)

Symbol-Equivariant Recurrent Reasoning Models
par: Freinschlag, Richard, et autres
Publié: (2026)

Algebraic Priors for Approximately Equivariant Networks
par: Ali, Riccardo, et autres
Publié: (2025)