1 code implementation • ACL 2022 • Jean-Benoit Delbrouck, Khaled Saab, Maya Varma, Sabri Eyuboglu, Pierre Chambon, Jared Dunnmon, Juan Zambrano, Akshay Chaudhari, Curtis Langlotz
There is a growing need to model interactions between data modalities (e. g., vision, language) — both to improve AI predictions on existing tasks and to enable new applications.
1 code implementation • 2 Jun 2022 • Fernando Paolo, Tsu-ting Tim Lin, Ritwik Gupta, Bryce Goodman, Nirav Patel, Daniel Kuster, David Kroodsma, Jared Dunnmon
Unsustainable fishing practices worldwide pose a major threat to marine resources and ecosystems.
Ranked #1 on Holdout Set on xView3-SAR
1 code implementation • 14 Apr 2022 • Siyi Tang, Amara Tariq, Jared Dunnmon, Umesh Sharma, Praneetha Elugunti, Daniel Rubin, Bhavik N. Patel, Imon Banerjee
Measures to predict 30-day readmission are considered an important quality factor for hospitals as accurate predictions can reduce the overall cost of care by identifying high risk patients before they are discharged.
2 code implementations • ICLR 2022 • Sabri Eyuboglu, Maya Varma, Khaled Saab, Jean-Benoit Delbrouck, Christopher Lee-Messer, Jared Dunnmon, James Zou, Christopher Ré
In this work, we address these challenges by first designing a principled evaluation framework that enables a quantitative comparison of SDMs across 1, 235 slice discovery settings in three input domains (natural images, medical images, and time-series data).
no code implementations • 3 Aug 2021 • Anirudh Joshi, Sabri Eyuboglu, Shih-Cheng Huang, Jared Dunnmon, Arjun Soin, Guido Davidzon, Akshay Chaudhari, Matthew P Lungren
FDG PET/CT imaging is a resource intensive examination critical for managing malignant disease and is particularly important for longitudinal assessment during therapy.
no code implementations • 11 Apr 2020 • Zhaobin Kuang, Frederic Sala, Nimit Sohoni, Sen Wu, Aldo Córdova-Palomera, Jared Dunnmon, James Priest, Christopher Ré
To relax these assumptions, we propose Ivy, a new method to combine IV candidates that can handle correlated and invalid IV candidates in a robust manner.
no code implementations • 27 Sep 2019 • Luke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro, Christopher Ré
Machine learning models for medical image analysis often suffer from poor performance on important subsets of a population that are not identified during training or testing.
no code implementations • 26 Mar 2019 • Jared Dunnmon, Alexander Ratner, Nishith Khandwala, Khaled Saab, Matthew Markert, Hersh Sagreiya, Roger Goldman, Christopher Lee-Messer, Matthew Lungren, Daniel Rubin, Christopher Ré
Labeling training datasets has become a key barrier to building medical machine learning models.
no code implementations • ICLR Workshop LLD 2019 • Khaled Saab, Jared Dunnmon, Alexander Ratner, Daniel Rubin, Christopher Re
Supervised machine learning models for high-value computer vision applications such as medical image classification often require large datasets labeled by domain experts, which are slow to collect, expensive to maintain, and static with respect to changes in the data distribution.
no code implementations • 13 Feb 2019 • Swetava Ganguli, Jared Dunnmon, Darren Hau
Obtaining reliable data describing local Food Security Metrics (FSM) at a granularity that is informative to policy-makers requires expensive and logistically difficult surveys, particularly in the developing world.
no code implementations • 13 Feb 2019 • Jared Dunnmon, Swetava Ganguli, Darren Hau, Brooke Husic
The ability to obtain accurate food security metrics in developing areas where relevant data can be sparse is critically important for policy makers tasked with implementing food aid programs.
1 code implementation • 5 Oct 2018 • Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, Christopher Ré
Snorkel MeTaL: A framework for training models with multi-task weak supervision
Ranked #1 on Semantic Textual Similarity on SentEval
1 code implementation • NeurIPS 2017 • Alexander J. Ratner, Henry R. Ehrenberg, Zeshan Hussain, Jared Dunnmon, Christopher Ré
Data augmentation is a ubiquitous technique for increasing the size of labeled training sets by leveraging task-specific data transformations that preserve class labels.
no code implementations • 17 May 2017 • Darvin Yi, Rebecca Lynn Sawyer, David Cohn III, Jared Dunnmon, Carson Lam, Xuerong Xiao, Daniel Rubin
Breast cancer has the highest incidence and second highest mortality rate for women in the US.