7 citations · 8 across the 4 of their papers we have counts for
8 papers
Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks
Arijit Sehanobish, Kawshik Kannan, Nabila Abraham +2
Large pretrained Transformer-based language models like BERT and GPT have changed the landscape of Natural Language Processing (NLP). However, fine tuning such models still require…
Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports
Arijit Sehanobish, McCullen Sandora, Nabila Abraham +7
Pretrained Transformer based models finetuned on domain specific corpora have changed the landscape of NLP. However, training or fine-tuning these models for individual tasks can b…
Efficient Extraction of Pathologies from C-Spine Radiology Reports using Multi-Task Learning
Arijit Sehanobish, Nathaniel Brown, Ishita Daga +7
Pretrained Transformer based models finetuned on domain specific corpora have changed the landscape of NLP. Generally, if one has multiple tasks on a given dataset, one may finetun…
Permutation invariant networks to learn Wasserstein metrics
Arijit Sehanobish, Neal Ravindra, David van Dijk
Understanding the space of probability measures on a metric space equipped with a Wasserstein distance is one of the fundamental questions in mathematical analysis. The Wasserstein…
Self-supervised edge features for improved Graph Neural Network training
Arijit Sehanobish, Neal G. Ravindra, David van Dijk
Graph Neural Networks (GNN) have been extensively used to extract meaningful representations from graph structured data and to perform predictive tasks such as node classification…
Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks
Arijit Sehanobish, Neal G. Ravindra, David van Dijk
A molecular and cellular understanding of how SARS-CoV-2 variably infects and causes severe COVID-19 remains a bottleneck in developing interventions to end the pandemic. We sought…