Small, correlated changes in synaptic connectivity may facilitate rapid motor learning
Barbara Feulner
Matt Perich
Raeed H. Chowdhury
Lee Miller
Juan A. Gallego
Claudia Clopath
2022-09-02
Nature Communications (published)
doi.org
Unified gene expression signature of novel NPM1 exon 5 mutations in acute myeloid leukemia
Véronique Lisi
Ève Blanchard
Michael Vladovsky
Éric Audemard
Albert Ferghaly
Sébastien Lemieux
Josée Hébert
Guy Sauvageau
Vincent-Philippe Lavallee
Visual Abstract
2022-09-02
Blood Advances (published)
doi.org
From YouTube to the brain: Transfer learning can improve brain-imaging predictions with deep learning
Nahiyan Malik
Danilo Bzdok
2022-09-01
Neural Networks (published)
doi.org
GCNFusion: An efficient graph convolutional network based model for information diffusion
Bahare Fatemi
Soheila Mehr Molaei
Shirui Pan
Samira Abbasgholizadeh-Rahimi
2022-09-01
Expert Systems with Applications (published)
doi.org
Interpretable domain adaptation using unsupervised feature selection on pre-trained source models
Luxin Zhang
Pascal Germain
Yacine Kessaci
C. Biernacki
2022-09-01
Neurocomputing (published)
doi.org
A novel domain adaptation theory with Jensen-Shannon divergence
Changjian Shui
Qi CHEN
Jun Wen
Fan Zhou
Christian Gagné
Boyu Wang
2022-09-01
Knowledge-Based Systems (published)
doi.org
The load planning and sequencing problem for double-stack trains
Moritz Ruf
Jean-François Cordeau
Emma Frejinger
2022-09-01
Journal of Rail Transport Planning & Management (published)
doi.org
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results
Raghav Mehta
Angelos Filos
Ujjwal Baid
Chiharu Sako
Richard McKinley
Michael Rebsamen
Katrin Dätwyler
Raphael Meier
Piotr Radojewski
Gowtham Krishnan Murugesan
Sahil Nalawade
Chandan Ganesh
Benjamin C. Wagner
Fang Frank Yu
Baowei Fei
Ananth J. Madhuranthakam
Joseph A. Maldjian
Laura Daza
Catalina Gómez
Pablo Arbeláez … (see 72 more)
Chengliang Dai
Shuo Wang
Hadrien Reynaud
Yuanhan Mo
Elsa Angelini
Yike Guo
Wenjia Bai
Subhashis Banerjee
Linmin Pei
Murat AK
Sarahi Rosas-González
Ilyess Zemmoura
Clovis Tauber
Minh H. Vu
Tufve Nyholm
Tommy Löfstedt
Laura Mora Ballestar
Veronica Vilaplana
Hugh McHugh
Gonzalo Maso Talou
Alan Wang
Jay Patel
Ken Chang
Katharina Hoebel
Mishka Gidwani
Nishanth Arun
Sharut Gupta
Mehak Aggarwal
Praveer Singh
Elizabeth R. Gerstner
Jayashree Kalpathy-Cramer
Nicolas Boutry
Alexis Huard
Lasitha Vidyaratne
Md Monibor Rahman
Khan M. Iftekharuddin
Joseph Chazalon
Elodie Puybareau
Guillaume Tochon
Jun Ma
Mariano Cabezas
Xavier Llado
Arnau Oliver
Liliana Valencia
Sergi Valverde
Mehdi Amian
Mohammadreza Soltaninejad
Andriy Myronenko
Ali Hatamizadeh
Xue Feng
Quan Dou
Nicholas Tustison
Craig Meyer
Nisarg A. Shah
Sanjay Talbar
Marc-André Weber
Abhishek Mahajan
Andras Jakab
Roland Wiest
Hassan M. Fathallah-Shaykh
Arash Nazeri
Mikhail Milchenko
Daniel Marcus
Aikaterini Kotrotsou
Rivka R. Colen
John Freymann
Justin Kirby
Christos Davatzikos
Bjoern Menze
Spyridon Bakas
Yarin Gal
Tal Arbel
2022-08-26
Machine Learning for Biomedical Imaging (published)
doi.org
arxiv.org
Fractal impedance for passive controllers: a framework for interaction robotics
Keyhan Kouhkiloui Babarahmati
Carlo Tiseo
Joshua Smith
Hsiu-Chin Lin
M. S. Erden
Michael Nalin Mistry
2022-08-25
Nonlinear Dynamics (published)
doi.org
arxiv.org
GaMPEN: A Machine-learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
Aritra Ghosh
C. Urry
Amrit Rau
Laurence Perreault-Levasseur
M. Cranmer
Kevin Schawinski
Dominic Stark
Chuan Tian
Ryan Ofman
T. Ananna
Connor Auge
Nico Cappelluti
D. Sanders
Ezequiel Treister
We introduce a novel machine-learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large num… (see more)bers of galaxies. The Galaxy Morphology Posterior Estimation Network (GaMPEN) estimates values and uncertainties for a galaxy’s bulge-to-total-light ratio (L B /L T ), effective radius (R e ), and flux (F). To estimate posteriors, GaMPEN uses the Monte Carlo Dropout technique and incorporates the full covariance matrix between the output parameters in its loss function. GaMPEN also uses a spatial transformer network (STN) to automatically crop input galaxy frames to an optimal size before determining their morphology. This will allow it to be applied to new data without prior knowledge of galaxy size. Training and testing GaMPEN on galaxies simulated to match z 0.25 galaxies in Hyper Suprime-Cam Wide g-band images, we demonstrate that GaMPEN achieves typical errors of 0.1 in L B /L T , 0.″17 (∼7%) in R e , and 6.3 × 104 nJy (∼1%) in F. GaMPEN's predicted uncertainties are well calibrated and accurate (5% deviation)—for regions of the parameter space with high residuals, GaMPEN correctly predicts correspondingly large uncertainties. We a
2022-08-23
The Astrophysical Journal (published)
doi.org
arxiv.org
Learning Shared Neural Manifolds from Multi-Subject FMRI Data
Jessie Huang
Je-chun Huang
Erica Lindsey Busch
Tom Wallenstein
Michal Gerasimiuk
Andrew Benz
Guillaume Lajoie
Guy Wolf
Nicholas Turk-Browne
Smita Krishnaswamy
Functional magnetic resonance imaging (fMRI) data is collected in millions of noisy, redundant dimensions. To understand how different brain… (see more)s process the same stimulus, we aim to denoise the fMRI signal via a meaningful embedding space that captures the data's intrinsic structure as shared across brains. We assume that stimulus-driven responses share latent features common across subjects that are jointly discoverable. Previous approaches to this problem have relied on linear methods like principal component analysis and shared response modeling. We propose a neural network called MRMD-AE (manifold-regularized multiple- decoder, autoencoder) that learns a common embedding from multi-subject fMRI data while retaining the ability to decode individual responses. Our latent common space represents an extensible manifold (where untrained data can be mapped) and improves classification accuracy of stimulus features of unseen timepoints, as well as cross-subject translation of fMRI signals.
2022-08-22
2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP) (published)
doi.org
openreview.net
Proteogenomics and Differential Ion Mobility Enable the Exploration of the Mutational Landscape in Colon Cancer Cells
Zhaoguan Wu
Eric Bonneil
Michael Belford
Cornelia Boeser
Maria-Virginia Ruiz Cuevas
Sébastien Lemieux
Jean-Jacques Dunyach
Pierre Thibault
2022-08-22
Analytical Chemistry (published)
doi.org