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Task-Induced Riemannian Metrics for Vision Transformer Feature Spaces
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arXiv Machine Learning
Sep 24
Task-Induced Riemannian Metrics for Vision Transformer Feature Spaces
arXiv:2609.27988v1 Announce Type: cross Abstract: Methods operating on Vision Transformer (ViT) feature spaces typically rely on Euclidean distance or cosine similarity. This assumes that every direc...
arXiv AI
Jun 3
Representational Capacity: Geometric Limits on Feature Representation in Transformer Language Models
arXiv:2606. 02765v1 Announce Type: cross Abstract: Model dimension ($d_{model}$) is a fundamental hyperparameter in transformer language models, yet its role in setting the geometric limits of feature representation remains under-explored.
arXiv Machine Learning
Aug 4
Riemannian Attention Mechanisms for Transformers: A Theoretical Framework and Architecture Design
arXiv:2608. 01283v1 Announce Type: new Abstract: All Transformer-based large language models compute attention via the Euclidean inner product, an architectural choice that Dong et al.
arXiv Computer Vision
Aug 31
Cut-ViT: Task-Specific Model Pruning via Gram Anchoring Subspace Consistency
Cut‑ViT introduces a task‑specific pruning pipeline for visual foundation models that uses gram anchoring matrices and subspace decomposition to align feature representations between native and pruned DINOv3 models. The method incorporates basis‑agnostic and residual constraints to preserve robustness across spatial and channel dimensions, and employs spectral entropy adaptation to tailor the pruning objective to downstream tasks. Experiments demonstrate that Cut‑ViT achieves state‑of‑the‑art performance on six tasks across nine datasets while reducing pruning time to about one minute on a single A100 GPU, using only 20.9% of the time and 45.5% of the GPU memory compared to prior methods.
arXiv Machine Learning
1d ago
Predictive Geometry of Hidden Trajectories in Transformers
arXiv:2609.37717v1 Announce Type: new Abstract: Decoder-only transformers are trained only through a terminal next-token prediction loss, yet this loss constrains every intermediate hidden state thro...
arXiv Machine Learning
Jul 3
Transformer Geometry Observatory TGO-II: Representational Similarity Observatory
arXiv:2607. 02386v1 Announce Type: cross Abstract: While Vision Transformers have achieved remarkable success across computer vision and language applications, the geometric evolution of their internal representations throughout training remains insufficiently understood.