Brian Axelrod | DeepAI

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∙ 09/07/2023

Random Expert Sampling for Deep Learning Segmentation of Acute Ischemic Stroke on Non-contrast CT

Purpose: Multi-expert deep learning training methods to automatically qu...

0 Sophie Ostmeier, et al. ∙

∙ 11/24/2022

Non-inferiority of Deep Learning Model to Segment Acute Stroke on Non-contrast CT Compared to Neuroradiologists

Purpose: To develop a deep learning model to segment the acute ischemic ...

0 Sophie Ostmeier, et al. ∙

∙ 09/26/2022

Evaluation of Medical Image Segmentation Models for Uncertain, Small or Empty Reference Annotations

Performance metrics for medical image segmentation models are used to me...

0 Sophie Ostmeier, et al. ∙

∙ 01/12/2022

On the Statistical Complexity of Sample Amplification

Given n i.i.d. samples drawn from an unknown distribution P, when is it ...

0 Brian Axelrod, et al. ∙

∙ 02/24/2020

Learning From Strategic Agents: Accuracy, Improvement, and Causality

In many predictive decision-making scenarios, such as credit scoring and...

0 Yonadav Shavit, et al. ∙

∙ 08/31/2019

Near-optimal Approximate Discrete and Continuous Submodular Function Minimization

In this paper we provide improved running times and oracle complexities ...

0 Brian Axelrod, et al. ∙

∙ 07/18/2019

A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families

We consider the problem of computing the maximum likelihood multivariate...

0 Brian Axelrod, et al. ∙

∙ 04/26/2019

Sample Amplification: Increasing Dataset Size even when Learning is Impossible

Given data drawn from an unknown distribution, D, to what extent is it p...

0 Brian Axelrod, et al. ∙

∙ 11/08/2018

An Efficient Algorithm for High-Dimensional Log-Concave Maximum Likelihood

The log-concave maximum likelihood estimator (MLE) problem answers: for ...

0 Brian Axelrod, et al. ∙

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