Author

Yuki Markus Asano

University of Amsterdam

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Jul 2026

AVQ-Attention: Adaptive Vector-Quantized Attention

This work proposes Adaptive Vector-Quantized (AVQ) Attention, which adaptively allocates codebook capacity based on attention importance and develops an implementation using custom Triton kernels that enables the full adaptive refinement process, including importance scoring, child codeword insertion, and parent contribution replacement, to be carried out within the tiled computation paradigm of Flash Attention with minimal overhead.

W.V.S.O. van den Dool, Patrick Forré, A. Habibian et al. · arXiv.org · 0 citations

UvA-DARE (Digital Academic Repository) Elastic ViTs from Pretrained Models without Retraining

SnapViT: single-shot network approximation for pruned Vision Transformers is introduced, a new post-pretraining structured pruning method that enables elastic inference across a continuum of compute budgets, and a self-supervised importance scoring mechanism that maintains strong performance without requiring retraining or labels.

Walter Simoncini, Michael Dorkenwald, Tijmen Blankevoort et al. · 0 citations