Arjun Nair

According to our database1, Arjun Nair authored at least 13 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2023
TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection.
Trans. Mach. Learn. Res., 2023

A 3D deep learning classifier and its explainability when assessing coronary artery disease.
CoRR, 2023

2022
Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning.
CoRR, 2022

CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning.
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022

STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

2021
Is MC Dropout Bayesian?
CoRR, 2021

Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data.
CoRR, 2021

A Computationally Efficient Approach to Segmentation of the Aorta and Coronary Arteries Using Deep Learning.
IEEE Access, 2021

The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data.
Proceedings of the Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data, 2021

The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification.
Proceedings of the Predictive Intelligence in Medicine - 4th International Workshop, 2021

Patient-Specific 3d Cellular Automata Nodule Growth Synthesis In Lung Cancer Without The Need Of External Data.
Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021

2020
Bayesian Sampling Bias Correction: Training with the Right Loss Function.
CoRR, 2020

2019
Modelling Airway Geometry as Stock Market Data Using Bayesian Changepoint Detection.
Proceedings of the Machine Learning in Medical Imaging - 10th International Workshop, 2019


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