299 citations · 370 across the 9 of their papers we have counts for
9 papers
Enhancing the Ranking Context of Dense Retrieval Methods through Reciprocal Nearest Neighbors
George Zerveas, Navid Rekabsaz, Carsten Eickhoff
Sparse annotation poses persistent challenges to training dense retrieval models; for example, it distorts the training signal when unlabeled relevant documents are used spuriously…
Parameter-efficient Modularised Bias Mitigation via AdapterFusion
Deepak Kumar, Oleg Lesota, George Zerveas +4
Large pre-trained language models contain societal biases and carry along these biases to downstream tasks. Current in-processing bias mitigation approaches (like adversarial train…
Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG
İlkay Yıldız Potter, George Zerveas, Carsten Eickhoff +1
Epilepsy is one of the most common neurological disorders, typically observed via seizure episodes. Epileptic seizures are commonly monitored through electroencephalogram (EEG) rec…
Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model
Shaden Smith, Mostofa Patwary, Brandon Norick +17
Pretrained general-purpose language models can achieve state-of-the-art accuracies in various natural language processing domains by adapting to downstream tasks via zero-shot, few…
CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking
George Zerveas, Navid Rekabsaz, Daniel Cohen +1
Contrastive learning has been the dominant approach to training dense retrieval models. In this work, we investigate the impact of ranking context - an often overlooked aspect of l…
Extracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures
Aaron S. Eisman, Nishant R. Shah, Carsten Eickhoff +4
Anginal symptoms can connote increased cardiac risk and a need for change in cardiovascular management. This study evaluated the potential to extract these symptoms from physician…
A Transformer-based Framework for Multivariate Time Series Representation Learning
George Zerveas, Srideepika Jayaraman, Dhaval Patel +2
In this work we propose for the first time a transformer-based framework for unsupervised representation learning of multivariate time series. Pre-trained models can be potentially…
Brown University at TREC Deep Learning 2019
George Zerveas, Ruochen Zhang, Leila Kim +1
This paper describes Brown University's submission to the TREC 2019 Deep Learning track. We followed a 2-phase method for producing a ranking of passages for a given input query: I…
Improving Clinical Predictions through Unsupervised Time Series Representation Learning
Xinrui Lyu, Matthias Hueser, Stephanie L. Hyland +2
In this work, we investigate unsupervised representation learning on medical time series, which bears the promise of leveraging copious amounts of existing unlabeled data in order…