no code implementations • 28 Jul 2022 • Nabeel Sarwar, Wilson Gregory, George A Kevrekidis, Soledad Villar, Bianca Dumitrascu
Single-cell RNA-seq data allow the quantification of cell type differences across a growing set of biological contexts.
1 code implementation • 2 Apr 2022 • Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu
Units equivariance (or units covariance) is the exact symmetry that follows from the requirement that relationships among measured quantities of physics relevance must obey self-consistent dimensional scalings.
1 code implementation • 24 Sep 2021 • Jacob D. Moss, Felix L. Opolka, Bianca Dumitrascu, Pietro Lió
Physically-inspired latent force models offer an interpretable alternative to purely data driven tools for inference in dynamical systems.
no code implementations • 6 Jul 2021 • Adrien Hallou, Hannah Yevick, Bianca Dumitrascu, Virginie Uhlmann
Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis.
1 code implementation • 11 Oct 2019 • Federico Camerlenghi, Bianca Dumitrascu, Federico Ferrari, Barbara E. Engelhardt, Stefano Favaro
The problem of maximizing cell type discovery under budget constraints is a fundamental challenge for the collection and analysis of single-cell RNA-sequencing (scRNA-seq) data.
Applications
no code implementations • 1 Jun 2019 • Li-Fang Cheng, Bianca Dumitrascu, Michael Zhang, Corey Chivers, Michael Draugelis, Kai Li, Barbara E. Engelhardt
However, capturing the short-term effects of drugs and therapeutic interventions on patient physiological state remains challenging.
1 code implementation • 24 May 2019 • Michael Minyi Zhang, Bianca Dumitrascu, Sinead A. Williamson, Barbara E. Engelhardt
Many machine learning problems can be framed in the context of estimating functions, and often these are time-dependent functions that are estimated in real-time as observations arrive.
no code implementations • NeurIPS 2018 • Bianca Dumitrascu, Karen Feng, Barbara E. Engelhardt
We address the problem of regret minimization in logistic contextual bandits, where a learner decides among sequential actions or arms given their respective contexts to maximize binary rewards.
1 code implementation • 27 Mar 2017 • Li-Fang Cheng, Gregory Darnell, Bianca Dumitrascu, Corey Chivers, Michael E Draugelis, Kai Li, Barbara E. Engelhardt
In the scenario of real-time monitoring of hospital patients, high-quality inference of patients' health status using all information available from clinical covariates and lab tests is essential to enable successful medical interventions and improve patient outcomes.