Search Results for author: Andrew Ng

Found 20 papers, 6 papers with code

How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems

no code implementations13 May 2023 Cara Van Uden, Jeremy Irvin, Mars Huang, Nathan Dean, Jason Carr, Andrew Ng, Curtis Langlotz

In addition, we experiment with different transfer learning strategies to effectively adapt these pretrained models to new tasks and healthcare systems.

Self-Supervised Learning Transfer Learning

Effective Data Fusion with Generalized Vegetation Index: Evidence from Land Cover Segmentation in Agriculture

no code implementations7 May 2020 Hao Sheng, Xiao Chen, Jingyi Su, Ram Rajagopal, Andrew Ng

How can we effectively leverage the domain knowledge from remote sensing to better segment agriculture land cover from satellite images?

Neural Text Style Transfer via Denoising and Reranking

no code implementations WS 2019 Joseph Lee, Ziang Xie, Cindy Wang, Max Drach, Dan Jurafsky, Andrew Ng

We introduce a simple method for text style transfer that frames style transfer as denoising: we synthesize a noisy corpus and treat the source style as a noisy version of the target style.

Denoising Style Transfer +1

Countdown Regression: Sharp and Calibrated Survival Predictions

1 code implementation21 Jun 2018 Anand Avati, Tony Duan, Sharon Zhou, Kenneth Jung, Nigam H. Shah, Andrew Ng

Probabilistic survival predictions from models trained with Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high variance.

Decision Making Mortality Prediction +2

Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction

no code implementations NAACL 2018 Ziang Xie, Guillaume Genthial, Stanley Xie, Andrew Ng, Dan Jurafsky

Translation-based methods for grammar correction that directly map noisy, ungrammatical text to their clean counterparts are able to correct a broad range of errors; however, such techniques are bottlenecked by the need for a large parallel corpus of noisy and clean sentence pairs.

Denoising Machine Translation +1

Improving Palliative Care with Deep Learning

no code implementations17 Nov 2017 Anand Avati, Kenneth Jung, Stephanie Harman, Lance Downing, Andrew Ng, Nigam H. Shah

The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care.

Driverseat: Crowdstrapping Learning Tasks for Autonomous Driving

no code implementations7 Dec 2015 Pranav Rajpurkar, Toki Migimatsu, Jeff Kiske, Royce Cheng-Yue, Sameep Tandon, Tao Wang, Andrew Ng

While emerging deep-learning systems have outclassed knowledge-based approaches in many tasks, their application to detection tasks for autonomous technologies remains an open field for scientific exploration.

Autonomous Driving Lane Detection

Deep learning for class-generic object detection

no code implementations24 Dec 2013 Brody Huval, Adam Coates, Andrew Ng

We investigate the use of deep neural networks for the novel task of class generic object detection.

object-detection Object Detection

Reasoning With Neural Tensor Networks for Knowledge Base Completion

no code implementations NeurIPS 2013 Richard Socher, Danqi Chen, Christopher D. Manning, Andrew Ng

We assess the model by considering the problem of predicting additional true relations between entities given a partial knowledge base.

Knowledge Base Completion Tensor Networks

Tuned Models of Peer Assessment in MOOCs

no code implementations9 Jul 2013 Chris Piech, Jonathan Huang, Zhenghao Chen, Chuong Do, Andrew Ng, Daphne Koller

In massive open online courses (MOOCs), peer grading serves as a critical tool for scaling the grading of complex, open-ended assignments to courses with tens or hundreds of thousands of students.

Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (2009)

no code implementations13 Jun 2012 Jeff Bilmes, Andrew Ng

This is the Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, which was held in Montreal, QC, Canada, June 18 - 21 2009.

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