Zobrazeno 1 - 10
of 204
pro vyhledávání: '"Beam, Andrew L."'
Causal inference is a critical task across fields such as healthcare, economics, and the social sciences. While recent advances in machine learning, especially those based on the deep-learning architectures, have shown potential in estimating causal
Externí odkaz:
http://arxiv.org/abs/2410.10044
Autor:
Palepu, Anil, Beam, Andrew L.
In this paper, we introduce a novel regularization scheme on contrastive language-image pre-trained (CLIP) medical vision models. Our approach is based on the observation that on many medical imaging tasks text tokens should only describe a small num
Externí odkaz:
http://arxiv.org/abs/2212.06710
Autor:
Palepu, Anil, Beam, Andrew L
Deep learning models trained in a fully supervised manner have been shown to rely on so-called "shortcut" features. Shortcut features are inputs that are associated with the outcome of interest in the training data, but are either no longer associate
Externí odkaz:
http://arxiv.org/abs/2206.07155
The No Unmeasured Confounding Assumption is widely used to identify causal effects in observational studies. Recent work on proximal inference has provided alternative identification results that succeed even in the presence of unobserved confounders
Externí odkaz:
http://arxiv.org/abs/2205.09824
We propose a selective learning method using meta-learning and deep reinforcement learning for medical image interpretation in the setting of limited labeling resources. Our method, MedSelect, consists of a trainable deep learning selector that uses
Externí odkaz:
http://arxiv.org/abs/2103.14339
The Large Scale Visual Recognition Challenge based on the well-known Imagenet dataset catalyzed an intense flurry of progress in computer vision. Benchmark tasks have propelled other sub-fields of machine learning forward at an equally impressive pac
Externí odkaz:
http://arxiv.org/abs/2010.01149
Autor:
Antropova, Natalia, Beam, Andrew L., Beaulieu-Jones, Brett K., Chen, Irene, Chivers, Corey, Dalca, Adrian, Finlayson, Sam, Fiterau, Madalina, Fries, Jason Alan, Ghassemi, Marzyeh, Hughes, Mike, Jedynak, Bruno, Kandola, Jasvinder S., McDermott, Matthew, Naumann, Tristan, Schulam, Peter, Shamout, Farah, Yahi, Alexandre
This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held on December 8, 2018 in Montreal, Canada.
Comment: Machine Learni
Comment: Machine Learni
Externí odkaz:
http://arxiv.org/abs/1811.07216
Biomedical association studies are increasingly done using clinical concepts, and in particular diagnostic codes from clinical data repositories as phenotypes. Clinical concepts can be represented in a meaningful, vector space using word embedding mo
Externí odkaz:
http://arxiv.org/abs/1811.01294
Autor:
Ghassemi, Marzyeh, Naumann, Tristan, Schulam, Peter, Beam, Andrew L., Chen, Irene Y., Ranganath, Rajesh
Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. However, learning in a clinical setting presents unique challenges that co
Externí odkaz:
http://arxiv.org/abs/1806.00388
The discovery of adversarial examples has raised concerns about the practical deployment of deep learning systems. In this paper, we demonstrate that adversarial examples are capable of manipulating deep learning systems across three clinical domains
Externí odkaz:
http://arxiv.org/abs/1804.05296