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Found 9 papers, 3 papers with code
Mapping the Increasing Use of LLMs in Scientific Papers
To address this gap, we conduct the first systematic, large-scale analysis across 950, 965 papers published between January 2020 and February 2024 on the arXiv, bioRxiv, and Nature portfolio journals, using a population-level statistical framework to measure the prevalence of LLM-modified content over time.
Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews
We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM).
Protein structure generation via folding diffusion
The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases.
Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality
The algorithm discriminates mortality with an AUC of 0. 83 (95% CI 0. 79-0. 87) surpassing the discrimination of the RCRI score with an AUC of 0. 67 (CI 0. 61-0. 72) in the held out test cohort.
High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning
Left ventricular hypertrophy (LVH) results from chronic remodeling caused by a broad range of systemic and cardiovascular disease including hypertension, aortic stenosis, hypertrophic cardiomyopathy, and cardiac amyloidosis.
Video-based AI for beat-to-beat assessment of cardiac function
Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease, screening for cardiotoxicity and decisions regarding the clinical management of patients with a critical illness.
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Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders
We define a flexible and differentiable family of warping metrics, which encompasses common metrics such as DTW, Edit Distance, Euclidean, etc.
Crowdsourcing Feature Discovery via Adaptively Chosen Comparisons
In addition we also ask the crowd to provide binary labels to the remaining examples based on the discovered features.
Contrastive Learning Using Spectral Methods
In many natural settings, the analysis goal is not to characterize a single data set in isolation, but rather to understand the difference between one set of observations and another.
