pith. sign in

arXiv preprint arXiv:2302.06015 , year=

3 Pith papers cite this work. Polarity classification is still indexing.

3 Pith papers citing it

citation-role summary

background 1

citation-polarity summary

years

2026 3

verdicts

UNVERDICTED 3

roles

background 1

polarities

unclear 1

representative citing papers

Benign Overfitting in Adversarial Training for Vision Transformers

cs.LG · 2026-04-21 · unverdicted · novelty 7.0

Adversarial training on simplified Vision Transformers achieves benign overfitting with near-zero robust loss and generalization error when signal-to-noise ratio and perturbation budget meet specific conditions.

Radiomics-Guided Vision Transformers for Survival Analysis

physics.med-ph · 2026-04-22 · unverdicted · novelty 5.0

A radiomics-guided hybrid Vision Transformer integrates pixel embeddings with interpretable radiomic features in a multimodal Cox model for survival analysis, yielding competitive discrimination and clinically meaningful attention maps on COVID-19 chest X-ray data.

citing papers explorer

Showing 3 of 3 citing papers.

  • Benign Overfitting in Adversarial Training for Vision Transformers cs.LG · 2026-04-21 · unverdicted · none · ref 67

    Adversarial training on simplified Vision Transformers achieves benign overfitting with near-zero robust loss and generalization error when signal-to-noise ratio and perturbation budget meet specific conditions.

  • Visual prompting reimagined: The power of the Activation Prompts cs.CV · 2026-04-07 · unverdicted · none · ref 62

    Activation prompts on intermediate layers outperform input-level visual prompting and parameter-efficient fine-tuning in accuracy and efficiency across 29 datasets.

  • Radiomics-Guided Vision Transformers for Survival Analysis physics.med-ph · 2026-04-22 · unverdicted · none · ref 21

    A radiomics-guided hybrid Vision Transformer integrates pixel embeddings with interpretable radiomic features in a multimodal Cox model for survival analysis, yielding competitive discrimination and clinically meaningful attention maps on COVID-19 chest X-ray data.