Search Results for author: Justin Lovelace

Found 6 papers, 5 papers with code

IncDSI: Incrementally Updatable Document Retrieval

1 code implementation19 Jul 2023 Varsha Kishore, Chao Wan, Justin Lovelace, Yoav Artzi, Kilian Q. Weinberger

Differentiable Search Index is a recently proposed paradigm for document retrieval, that encodes information about a corpus of documents within the parameters of a neural network and directly maps queries to corresponding documents.

Retrieval

Latent Diffusion for Language Generation

1 code implementation NeurIPS 2023 Justin Lovelace, Varsha Kishore, Chao Wan, Eliot Shekhtman, Kilian Q. Weinberger

We then demonstrate that continuous diffusion models can be learned in the latent space of the language autoencoder, enabling us to sample continuous latent representations that can be decoded into natural language with the pretrained decoder.

Text Generation

Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network

1 code implementation ACL 2021 Justin Lovelace, Denis Newman-Griffis, Shikhar Vashishth, Jill Fain Lehman, Carolyn Penstein Rosé

We develop a deep convolutional network that utilizes textual entity representations and demonstrate that our model outperforms recent KG completion methods in this challenging setting.

Knowledge Graph Completion Re-Ranking

Learning to Generate Clinically Coherent Chest X-Ray Reports

1 code implementation Findings of the Association for Computational Linguistics 2020 Justin Lovelace, Bobak Mortazavi

Automated radiology report generation has the potential to reduce the time clinicians spend manually reviewing radiographs and streamline clinical care.

Text Generation

Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention

1 code implementation25 Jul 2020 Justin Lovelace, Nathan C. Hurley, Adrian D. Haimovich, Bobak J. Mortazavi

We identify risk factors for both readmission and mortality outcomes and demonstrate that our framework can be used to develop dynamic problem lists that present clinical problems along with their quantitative importance.

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