Levent Sagun

Orcid: 0000-0001-5403-4124

According to our database1, Levent Sagun authored at least 30 papers between 2015 and 2023.

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Bibliography

2023
Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction.
CoRR, 2023

Weisfeiler and Lehman Go Measurement Modeling: Probing the Validity of the WL Test.
CoRR, 2023

Confusing Large Models by Confusing Small Models.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Simplicity Bias Leads to Amplified Performance Disparities.
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

2022
Measuring and signing fairness as performance under multiple stakeholder distributions.
CoRR, 2022

Understanding out-of-distribution accuracies through quantifying difficulty of test samples.
CoRR, 2022

Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision.
CoRR, 2022

Fairness Indicators for Systematic Assessments of Visual Feature Extractors.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

2021
Transformed CNNs: recasting pre-trained convolutional layers with self-attention.
CoRR, 2021

More data or more parameters? Investigating the effect of data structure on generalization.
CoRR, 2021

On the interplay between data structure and loss function in classification problems.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Post-Workshop Report on Science meets Engineering in Deep Learning, NeurIPS 2019, Vancouver.
CoRR, 2020

Triple descent and the two kinds of overfitting: where & why do they appear?
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks.
CoRR, 2019

Scaling description of generalization with number of parameters in deep learning.
CoRR, 2019

Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
A jamming transition from under- to over-parametrization affects loss landscape and generalization.
CoRR, 2018

The jamming transition as a paradigm to understand the loss landscape of deep neural networks.
CoRR, 2018

Comparing Dynamics: Deep Neural Networks versus Glassy Systems.
Proceedings of the 35th International Conference on Machine Learning, 2018

Empirical Analysis of the Hessian of Over-Parametrized Neural Networks.
Proceedings of the 6th International Conference on Learning Representations, 2018

Easing non-convex optimization with neural networks.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine.
CoRR, 2017

Perspective: Energy Landscapes for Machine Learning.
CoRR, 2017

Entropy-SGD: Biasing Gradient Descent Into Wide Valleys.
Proceedings of the 5th International Conference on Learning Representations, 2017

Early predictability of asylum court decisions.
Proceedings of the 16th edition of the International Conference on Artificial Intelligence and Law, 2017

2016
Singularity of the Hessian in Deep Learning.
CoRR, 2016

2015
Universality in halting time and its applications in optimization.
CoRR, 2015

Explorations on high dimensional landscapes.
Proceedings of the 3rd International Conference on Learning Representations, 2015


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