Search Results for author:
Found 9 papers, 6 papers with code
Enhancing the Ranking Context of Dense Retrieval Methods through Reciprocal Nearest Neighbors
Sparse annotation poses persistent challenges to training dense retrieval models; for example, it distorts the training signal when unlabeled relevant documents are used spuriously as negatives in contrastive learning.
Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG
Epileptic seizures are commonly monitored through electroencephalogram (EEG) recordings due to their routine and low expense collection.
Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model
Next, we detail the training process, the design of our training corpus, and our data curation techniques, which we believe is a key ingredient to the success of the model.
Ranked #35 on
Language Modelling
on LAMBADA
CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking
Contrastive learning has been the dominant approach to training dense retrieval models.
Extracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures
Anginal symptoms can connote increased cardiac risk and a need for change in cardiovascular management.
A Transformer-based Framework for Multivariate Time Series Representation Learning
In this work we propose for the first time a transformer-based framework for unsupervised representation learning of multivariate time series.
Ranked #2 on
Time Series Classification
on Insectwingbeat
Brown University at TREC Deep Learning 2019
This paper describes Brown University's submission to the TREC 2019 Deep Learning track.
Improving Clinical Predictions through Unsupervised Time Series Representation Learning
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 to eventually assist clinical decision making.
