Daniel Rubin — accepted AI-conference papers

2023

RaLEs: a Benchmark for Radiology Language Evaluations

Juan Manuel Zambrano Chaves, Nandita Bhaskhar, Maayane Attias, Jean-Benoit Delbrouck, Daniel Rubin, Andreas Markus Loening +2

NeurIPS 2023poster

The radiology report is the main form of communication between radiologists and other clinicians. Prior work in natural language processing in radiology reports has shown the value of developing methods tailored for individual tasks such as identifying reports with critical results or disease detect…

Cited by 17 Source ↗PDF ↗code ↗Scholar ↗

2022

Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning

Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Ehsan Adeli +2

CVPR 2022poster

Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution. Despite recent progress, there remain fundamental challenges such as the lack of convergence and the potential…

Cited by 224 PDF ↗code ↗Scholar ↗

2022

Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis

Siyi Tang, Jared Dunnmon, Khaled Kamal Saab, Xuan Zhang, Qianying Huang, Florian Dubost +2

ICLR 2022poster

Automated seizure detection and classification from electroencephalography (EEG) can greatly improve seizure diagnosis and treatment. However, several modeling challenges remain unaddressed in prior automated seizure detection and classification studies: (1) representing non-Euclidean data structure…

Cited by 150 Source ↗PDF ↗code ↗Scholar ↗

2017

Inferring Generative Model Structure with Static Analysis

Paroma Varma, Bryan D He, Payal Bajaj, Nishith Khandwala, Imon Banerjee, Daniel Rubin +1

NeurIPS 2017poster

Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline. A popular solution is combining multiple sources of weak supervision using generative models. The structure of these models affects the quality of the training labels…

Cited by 69 Source ↗PDF ↗Scholar ↗