Block-Recurrent Dynamics in Vision Transformers
Fuente:
arXiv
Guardado en:
| Autores principales: | Jacobs, Mozes, Fel, Thomas, Hakim, Richard, Brondetta, Alessandra, Ba, Demba, Keller, T. Andy |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Traveling Waves Integrate Spatial Information Through Time
por: Jacobs, Mozes, et al.
Publicado: (2025)
por: Jacobs, Mozes, et al.
Publicado: (2025)
A Geometric Unification of Concept Learning with Concept Cones
por: Rocchi--Henry, Alexandre, et al.
Publicado: (2025)
por: Rocchi--Henry, Alexandre, et al.
Publicado: (2025)
Sparks of Explainability: Recent Advancements in Explaining Large Vision Models
por: Fel, Thomas
Publicado: (2025)
por: Fel, Thomas
Publicado: (2025)
Origins of Creativity in Attention-Based Diffusion Models
por: Finn, Emma, et al.
Publicado: (2025)
por: Finn, Emma, et al.
Publicado: (2025)
Saliency strikes back: How filtering out high frequencies improves white-box explanations
por: Muzellec, Sabine, et al.
Publicado: (2023)
por: Muzellec, Sabine, et al.
Publicado: (2023)
Bi-Orthogonal Factor Decomposition for Vision Transformers
por: Doshi, Fenil R., et al.
Publicado: (2026)
por: Doshi, Fenil R., et al.
Publicado: (2026)
SparseSwin: Swin Transformer with Sparse Transformer Block
por: Pinasthika, Krisna, et al.
Publicado: (2023)
por: Pinasthika, Krisna, et al.
Publicado: (2023)
Evaluation Framework for Feedback Generation Methods in Skeletal Movement Assessment
por: Hakim, Tal
Publicado: (2024)
por: Hakim, Tal
Publicado: (2024)
Transformers vs. Recurrent Models for Estimating Forest Gross Primary Production
por: Montero, David, et al.
Publicado: (2025)
por: Montero, David, et al.
Publicado: (2025)
Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments
por: Lillemark, Hansen Jin, et al.
Publicado: (2026)
por: Lillemark, Hansen Jin, et al.
Publicado: (2026)
On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective
por: Serrurier, Mathieu, et al.
Publicado: (2022)
por: Serrurier, Mathieu, et al.
Publicado: (2022)
Into the Rabbit Hull: From Task-Relevant Concepts in DINO to Minkowski Geometry
por: Fel, Thomas, et al.
Publicado: (2025)
por: Fel, Thomas, et al.
Publicado: (2025)
Steering Sparse Autoencoder Latents to Control Dynamic Head Pruning in Vision Transformers (Student Abstract)
por: Lee, Yousung, et al.
Publicado: (2026)
por: Lee, Yousung, et al.
Publicado: (2026)
GalaxyDiT: Efficient Video Generation with Guidance Alignment and Adaptive Proxy in Diffusion Transformers
por: Song, Zhiye, et al.
Publicado: (2025)
por: Song, Zhiye, et al.
Publicado: (2025)
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models
por: Zhou, Andy, et al.
Publicado: (2023)
por: Zhou, Andy, et al.
Publicado: (2023)
Unearthing Skill-Level Insights for Understanding Trade-Offs of Foundation Models
por: Moayeri, Mazda, et al.
Publicado: (2024)
por: Moayeri, Mazda, et al.
Publicado: (2024)
Improving Interpretation Faithfulness for Vision Transformers
por: Hu, Lijie, et al.
Publicado: (2023)
por: Hu, Lijie, et al.
Publicado: (2023)
bViT: Investigating Single-Block Recurrence in Vision Transformers for Image Recognition
por: Byra, Michal, et al.
Publicado: (2026)
por: Byra, Michal, et al.
Publicado: (2026)
HalluRNN: Mitigating Hallucinations via Recurrent Cross-Layer Reasoning in Large Vision-Language Models
por: Yu, Le, et al.
Publicado: (2025)
por: Yu, Le, et al.
Publicado: (2025)
Empowering Urban Traffic Management: Elevated 3D LiDAR for Data Collection and Advanced Object Detection Analysis
por: Guefrachi, Nawfal, et al.
Publicado: (2024)
por: Guefrachi, Nawfal, et al.
Publicado: (2024)
Continual Adaptation of Vision Transformers for Federated Learning
por: Halbe, Shaunak, et al.
Publicado: (2023)
por: Halbe, Shaunak, et al.
Publicado: (2023)
Mechanisms of Non-Monotonic Scaling in Vision Transformers
por: Kumar, Anantha Padmanaban Krishna
Publicado: (2025)
por: Kumar, Anantha Padmanaban Krishna
Publicado: (2025)
Discovering Influential Neuron Path in Vision Transformers
por: Wang, Yifan, et al.
Publicado: (2025)
por: Wang, Yifan, et al.
Publicado: (2025)
DiffiT: Diffusion Vision Transformers for Image Generation
por: Hatamizadeh, Ali, et al.
Publicado: (2023)
por: Hatamizadeh, Ali, et al.
Publicado: (2023)
ADAPT to Robustify Prompt Tuning Vision Transformers
por: Eskandar, Masih, et al.
Publicado: (2024)
por: Eskandar, Masih, et al.
Publicado: (2024)
Accelerating Vision Transformers with Adaptive Patch Sizes
por: Choudhury, Rohan, et al.
Publicado: (2025)
por: Choudhury, Rohan, et al.
Publicado: (2025)
Class-Discriminative Attention Maps for Vision Transformers
por: Brocki, Lennart, et al.
Publicado: (2023)
por: Brocki, Lennart, et al.
Publicado: (2023)
Intriguing Equivalence Structures of the Embedding Space of Vision Transformers
por: Salman, Shaeke, et al.
Publicado: (2024)
por: Salman, Shaeke, et al.
Publicado: (2024)
Oscillation-Reduced MXFP4 Training for Vision Transformers
por: Chen, Yuxiang, et al.
Publicado: (2025)
por: Chen, Yuxiang, et al.
Publicado: (2025)
Enhancing Vision Transformer Explainability Using Artificial Astrocytes
por: Echevarrieta-Catalan, Nicolas, et al.
Publicado: (2025)
por: Echevarrieta-Catalan, Nicolas, et al.
Publicado: (2025)
FasterViT: Fast Vision Transformers with Hierarchical Attention
por: Hatamizadeh, Ali, et al.
Publicado: (2023)
por: Hatamizadeh, Ali, et al.
Publicado: (2023)
From $\mathcal{O}(n^{2})$ to $\mathcal{O}(n)$ Parameters: Quantum Self-Attention in Vision Transformers for Biomedical Image Classification
por: Boucher, Thomas, et al.
Publicado: (2025)
por: Boucher, Thomas, et al.
Publicado: (2025)
VLSM-Adapter: Finetuning Vision-Language Segmentation Efficiently with Lightweight Blocks
por: Dhakal, Manish, et al.
Publicado: (2024)
por: Dhakal, Manish, et al.
Publicado: (2024)
VariViT: A Vision Transformer for Variable Image Sizes
por: Varma, Aswathi, et al.
Publicado: (2026)
por: Varma, Aswathi, et al.
Publicado: (2026)
A Survey of the Self Supervised Learning Mechanisms for Vision Transformers
por: Khan, Asifullah, et al.
Publicado: (2024)
por: Khan, Asifullah, et al.
Publicado: (2024)
VisTabNet: Adapting Vision Transformers for Tabular Data
por: Wydmański, Witold, et al.
Publicado: (2024)
por: Wydmański, Witold, et al.
Publicado: (2024)
Beyond Scalars: Concept-Based Alignment Analysis in Vision Transformers
por: Vielhaben, Johanna, et al.
Publicado: (2024)
por: Vielhaben, Johanna, et al.
Publicado: (2024)
ScaleKD: Strong Vision Transformers Could Be Excellent Teachers
por: Fan, Jiawei, et al.
Publicado: (2024)
por: Fan, Jiawei, et al.
Publicado: (2024)
ScriptViT: Vision Transformer-Based Personalized Handwriting Generation
por: Acharya, Sajjan, et al.
Publicado: (2025)
por: Acharya, Sajjan, et al.
Publicado: (2025)
Pre-training Vision Transformers with Formula-driven Supervised Learning
por: Kataoka, Hirokatsu, et al.
Publicado: (2022)
por: Kataoka, Hirokatsu, et al.
Publicado: (2022)
Ejemplares similares
-
Traveling Waves Integrate Spatial Information Through Time
por: Jacobs, Mozes, et al.
Publicado: (2025) -
A Geometric Unification of Concept Learning with Concept Cones
por: Rocchi--Henry, Alexandre, et al.
Publicado: (2025) -
Sparks of Explainability: Recent Advancements in Explaining Large Vision Models
por: Fel, Thomas
Publicado: (2025) -
Origins of Creativity in Attention-Based Diffusion Models
por: Finn, Emma, et al.
Publicado: (2025) -
Saliency strikes back: How filtering out high frequencies improves white-box explanations
por: Muzellec, Sabine, et al.
Publicado: (2023)