PAINET: A Principled Efficient Transformer for 3D Dynamics Modeling
Fuente:
arXiv
Guardado en:
| Autores principales: | Yang, Kai, Huang, Yuqi, Tao, Junheng, Wang, Wanyu, Wu, Qitian |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
SGFormer: Single-Layer Graph Transformers with Approximation-Free Linear Complexity
por: Wu, Qitian, et al.
Publicado: (2024)
por: Wu, Qitian, et al.
Publicado: (2024)
Transformers from Diffusion: A Unified Framework for Neural Message Passing
por: Wu, Qitian, et al.
Publicado: (2024)
por: Wu, Qitian, et al.
Publicado: (2024)
Supercharging Graph Transformers with Advective Diffusion
por: Wu, Qitian, et al.
Publicado: (2023)
por: Wu, Qitian, et al.
Publicado: (2023)
Learning Divergence Fields for Shift-Robust Graph Representations
por: Wu, Qitian, et al.
Publicado: (2024)
por: Wu, Qitian, et al.
Publicado: (2024)
Dynamic Model Merging Made Slim
por: Du, Guodong, et al.
Publicado: (2026)
por: Du, Guodong, et al.
Publicado: (2026)
Handling Distribution Shifts on Graphs: An Invariance Perspective
por: Wu, Qitian, et al.
Publicado: (2022)
por: Wu, Qitian, et al.
Publicado: (2022)
Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models
por: Guo, Yongxin, et al.
Publicado: (2024)
por: Guo, Yongxin, et al.
Publicado: (2024)
Principled Understanding of Generalization for Generative Transformer Models in Arithmetic Reasoning Tasks
por: Xu, Xingcheng, et al.
Publicado: (2024)
por: Xu, Xingcheng, et al.
Publicado: (2024)
Learning Spatio-Temporal Dynamics for Trajectory Recovery via Time-Aware Transformer
por: Sun, Tian, et al.
Publicado: (2025)
por: Sun, Tian, et al.
Publicado: (2025)
SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations
por: Wu, Qitian, et al.
Publicado: (2023)
por: Wu, Qitian, et al.
Publicado: (2023)
Distilling Time Series Foundation Models for Efficient Forecasting
por: Li, Yuqi, et al.
Publicado: (2026)
por: Li, Yuqi, et al.
Publicado: (2026)
The Sharpness Disparity Principle in Transformers for Accelerating Language Model Pre-Training
por: Wang, Jinbo, et al.
Publicado: (2025)
por: Wang, Jinbo, et al.
Publicado: (2025)
Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing
por: Yang, Yubo, et al.
Publicado: (2025)
por: Yang, Yubo, et al.
Publicado: (2025)
Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning
por: Wang, Ling, et al.
Publicado: (2024)
por: Wang, Ling, et al.
Publicado: (2024)
The Compositional Architecture of Regret in Large Language Models
por: Cui, Xiangxiang, et al.
Publicado: (2025)
por: Cui, Xiangxiang, et al.
Publicado: (2025)
Towards Principled Graph Transformers
por: Müller, Luis, et al.
Publicado: (2024)
por: Müller, Luis, et al.
Publicado: (2024)
Attention Dispersion in Dynamic Graph Transformers: Diagnosis and a Transferable Fix
por: Zhang, Jinhao, et al.
Publicado: (2026)
por: Zhang, Jinhao, et al.
Publicado: (2026)
ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling
por: Chen, Yuqi, et al.
Publicado: (2024)
por: Chen, Yuqi, et al.
Publicado: (2024)
The Lifecycle Principle: Stabilizing Dynamic Neural Networks with State Memory
por: Yang, Zichuan
Publicado: (2025)
por: Yang, Zichuan
Publicado: (2025)
Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models
por: Wang, Zhenzhong, et al.
Publicado: (2024)
por: Wang, Zhenzhong, et al.
Publicado: (2024)
LLM Data Selection and Utilization via Dynamic Bi-level Optimization
por: Yu, Yang, et al.
Publicado: (2025)
por: Yu, Yang, et al.
Publicado: (2025)
Design Principles for Sequence Models via Coefficient Dynamics
por: Sieber, Jerome, et al.
Publicado: (2025)
por: Sieber, Jerome, et al.
Publicado: (2025)
Bi-LoRA: Efficient Sharpness-Aware Minimization for Fine-Tuning Large-Scale Models
por: Liu, Yuhang, et al.
Publicado: (2025)
por: Liu, Yuhang, et al.
Publicado: (2025)
Global-Lens Transformers: Adaptive Token Mixing for Dynamic Link Prediction
por: Zou, Tao, et al.
Publicado: (2025)
por: Zou, Tao, et al.
Publicado: (2025)
Dynamic Layer Tying for Parameter-Efficient Transformers
por: Hay, Tamir David, et al.
Publicado: (2024)
por: Hay, Tamir David, et al.
Publicado: (2024)
From Efficient Multimodal Models to World Models: A Survey
por: Mai, Xinji, et al.
Publicado: (2024)
por: Mai, Xinji, et al.
Publicado: (2024)
Hybrid Dynamic Pruning: A Pathway to Efficient Transformer Inference
por: Jaradat, Ghadeer, et al.
Publicado: (2024)
por: Jaradat, Ghadeer, et al.
Publicado: (2024)
When to Stop Reusing: Dynamic Gradient Gating for Sample-Efficient RLVR
por: Miao, Yuchun, et al.
Publicado: (2026)
por: Miao, Yuchun, et al.
Publicado: (2026)
Faithful Interpretation for Graph Neural Networks
por: Hu, Lijie, et al.
Publicado: (2024)
por: Hu, Lijie, et al.
Publicado: (2024)
PENGUIN: Enhancing Transformer with Periodic-Nested Group Attention for Long-term Time Series Forecasting
por: Sun, Tian, et al.
Publicado: (2025)
por: Sun, Tian, et al.
Publicado: (2025)
NEAT: Neighborhood-Guided, Efficient, Autoregressive Set Transformer for 3D Molecular Generation
por: Rose, Daniel, et al.
Publicado: (2025)
por: Rose, Daniel, et al.
Publicado: (2025)
Curriculum Learning for Efficient Chain-of-Thought Distillation via Structure-Aware Masking and GRPO
por: Yu, Bowen, et al.
Publicado: (2026)
por: Yu, Bowen, et al.
Publicado: (2026)
SIG: Efficient Self-Interpretable Graph Neural Network for Continuous-time Dynamic Graphs
por: Fang, Lanting, et al.
Publicado: (2024)
por: Fang, Lanting, et al.
Publicado: (2024)
Crystalite: A Lightweight Transformer for Efficient Crystal Modeling
por: Veljković, Tin Hadži, et al.
Publicado: (2026)
por: Veljković, Tin Hadži, et al.
Publicado: (2026)
The Principles of Diffusion Models
por: Lai, Chieh-Hsin, et al.
Publicado: (2025)
por: Lai, Chieh-Hsin, et al.
Publicado: (2025)
UniTabE: A Universal Pretraining Protocol for Tabular Foundation Model in Data Science
por: Yang, Yazheng, et al.
Publicado: (2023)
por: Yang, Yazheng, et al.
Publicado: (2023)
Learning More with Less: A Dynamic Dual-Level Down-Sampling Framework for Efficient Policy Optimization
por: Wang, Chao, et al.
Publicado: (2025)
por: Wang, Chao, et al.
Publicado: (2025)
Physics-Guided Multimodal Transformers are the Necessary Foundation for the Next Generation of Meteorological Science
por: Han, Jing, et al.
Publicado: (2025)
por: Han, Jing, et al.
Publicado: (2025)
State Space Models on Temporal Graphs: A First-Principles Study
por: Li, Jintang, et al.
Publicado: (2024)
por: Li, Jintang, et al.
Publicado: (2024)
Looping Back to Move Forward: Recursive Transformers for Efficient and Flexible Large Multimodal Models
por: Xu, Ruihan, et al.
Publicado: (2026)
por: Xu, Ruihan, et al.
Publicado: (2026)
Ejemplares similares
-
SGFormer: Single-Layer Graph Transformers with Approximation-Free Linear Complexity
por: Wu, Qitian, et al.
Publicado: (2024) -
Transformers from Diffusion: A Unified Framework for Neural Message Passing
por: Wu, Qitian, et al.
Publicado: (2024) -
Supercharging Graph Transformers with Advective Diffusion
por: Wu, Qitian, et al.
Publicado: (2023) -
Learning Divergence Fields for Shift-Robust Graph Representations
por: Wu, Qitian, et al.
Publicado: (2024) -
Dynamic Model Merging Made Slim
por: Du, Guodong, et al.
Publicado: (2026)