25 citations · 87 across the 19 of their papers we have counts for
20 papers
Identifying Planetary Nebulae Candidates from IPHAS and VPHAS+ Surveys via YOLO Deep Learning Models
Titouan Sérandour, Anne-Laure Méalier, Bruno Colicchio +5
We present a deep learning detection method capable of effectively identifying planetary nebulae (PNe) on IPHAS and VPHAS+ surveys. Our detection pipeline is based on the YOLO arch…
Mixed Membership Models for Multilevel Functional Data
Donatello Telesca, Nicholas Marco, Emma Landry
Mixed membership models extend classical clustering by substituting the notion of uncertain membership with the notion of mixed membership. In particular, these models allow each o…
Bayesian Transfer Learning for High-Dimensional Linear Regression via Adaptive Shrinkage
Parsa Jamshidian, Donatello Telesca
We introduce BLAST, Bayesian Linear regression with Adaptive Shrinkage for Transfer, a Bayesian multi-source transfer learning framework for high-dimensional linear regression. The…
Modeling EEG Spectral Features through Warped Functional Mixed Membership Models
Emma Landry, Damla Senturk, Shafali Jeste +3
A common concern in the field of functional data analysis is the challenge of temporal misalignment, which is typically addressed using curve registration methods. Currently, most…
Covariate Adjusted Functional Mixed Membership Models
Nicholas Marco, Damla Şentürk, Shafali Jeste +3
Mixed membership models are a flexible class of probabilistic data representations used for unsupervised and semi-supervised learning, allowing each observation to partially belong…
PDXpower: A Power Analysis Tool for Experimental Design in Pre-clinical Xenograft Studies for Uncensored and Censored Outcomes
Shanpeng Li, Donatello Telesca, Harley I. Kornblum +5
In cancer research, leveraging patient-derived xenografts (PDXs) in pre-clinical experiments is a crucial approach for assessing innovative therapeutic strategies. Addressing the i…