1 code implementation • 23 Apr 2024 • Nirupan Ananthamurugan, Dat Duong, Philip George, Ankita Gupta, Sandeep Tata, Beliz Gunel
Summarizing comparative opinions about entities (e. g., hotels, phones) from a set of source reviews, often referred to as contrastive summarization, can considerably aid users in decision making.
no code implementations • 25 Mar 2024 • Beliz Gunel, James B. Wendt, Jing Xie, Yichao Zhou, Nguyen Vo, Zachary Fisher, Sandeep Tata
Users often struggle with decision-making between two options (A vs B), as it usually requires time-consuming research across multiple web pages.
no code implementations • 14 Oct 2022 • Jeffrey Dominic, Nandita Bhaskhar, Arjun D. Desai, Andrew Schmidt, Elka Rubin, Beliz Gunel, Garry E. Gold, Brian A. Hargreaves, Leon Lenchik, Robert Boutin, Akshay S. Chaudhari
Although supervised learning has enabled high performance for image segmentation, it requires a large amount of labeled training data, which can be difficult to obtain in the medical imaging field.
1 code implementation • 21 Apr 2022 • Beliz Gunel, Arda Sahiner, Arjun D. Desai, Akshay S. Chaudhari, Shreyas Vasanawala, Mert Pilanci, John Pauly
Unrolled neural networks have enabled state-of-the-art reconstruction performance and fast inference times for the accelerated magnetic resonance imaging (MRI) reconstruction task.
no code implementations • 7 Jan 2022 • Beliz Gunel, Navneet Potti, Sandeep Tata, James B. Wendt, Marc Najork, Jing Xie
Automating information extraction from form-like documents at scale is a pressing need due to its potential impact on automating business workflows across many industries like financial services, insurance, and healthcare.
1 code implementation • 3 Nov 2021 • Arjun D Desai, Beliz Gunel, Batu M Ozturkler, Harris Beg, Shreyas Vasanawala, Brian A Hargreaves, Christopher Ré, John M Pauly, Akshay S Chaudhari
Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstruction.
no code implementations • NeurIPS Workshop Deep_Invers 2021 • Philip M Adamson, Beliz Gunel, Jeffrey Dominic, Arjun D Desai, Daniel Spielman, Shreyas Vasanawala, John M. Pauly, Akshay Chaudhari
Self-supervised learning (SSL) has become a popular pre-training tool due to its ability to capture generalizable and domain-specific feature representations of the underlying data for downstream tasks.
1 code implementation • ICLR 2021 • Beliz Gunel, Jingfei Du, Alexis Conneau, Ves Stoyanov
Our proposed fine-tuning objective leads to models that are more robust to different levels of noise in the fine-tuning training data, and can generalize better to related tasks with limited labeled data.
1 code implementation • NAACL 2021 • Jingfei Du, Edouard Grave, Beliz Gunel, Vishrav Chaudhary, Onur Celebi, Michael Auli, Ves Stoyanov, Alexis Conneau
Unsupervised pre-training has led to much recent progress in natural language understanding.
no code implementations • 27 Jun 2020 • Beliz Gunel, Chenguang Zhu, Michael Zeng, Xuedong Huang
In this work, we propose a novel architecture that extends Transformer encoder-decoder architecture in order to improve on these shortcomings.
no code implementations • ICLR 2019 • Albert Gu, Frederic Sala, Beliz Gunel, Christopher Ré
The quality of the representations achieved by embeddings is determined by how well the geometry of the embedding space matches the structure of the data.