Sagun, Levent

ConViT: improving vision transformers with soft convolutional inductive biases

d’Ascoli, S., Touvron, H., Leavitt, M. L., Morcos, A. S., Biroli, G., & Sagun, L. (2022) · Journal of Statistical Mechanics: Theory and Experiment

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 782

SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine

Dunn, M., Sagun, L., Higgins, M., Guney, V. U., Cirik, V., & Cho, K. (2017) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 359

Jamming transition as a paradigm to understand the loss landscape of deep neural networks

Geiger, M., Spigler, S., d'Ascoli, S., Sagun, L., Baity-Jesi, M., Biroli, G., & Wyart, M. (2019) · Physical Review E

Neural Networks and ApplicationsStochastic Gradient Optimization Techniques

Cited by 143

Fairness indicators for systematic assessments of visual feature extractors

Goyal, P., Soriano, A. R., Hazirbas, C., Sagun, L., & Usunier, N. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 24

LLM Knowledge is Brittle: Truthfulness Representations Rely on Superficial Resemblance

Haller, P., Ibrahim, M., Kirichenko, P., Sagun, L., & Bell, S. J. (2025) · arXiv

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 15

Learning the Wrong Lessons: Syntactic-Domain Spurious Correlations in Language Models

Shaib, C., Suriyakumar, V. M., Sagun, L., Wallace, B. C., & Ghassemi, M. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0