Search Results for author:
Found 19 papers, 12 papers with code
Scaling up ridge regression for brain encoding in a massive individual fMRI dataset
This paper evaluates different parallelization techniques to reduce the training time of brain encoding with ridge regression on the CNeuroMod Friends dataset, one of the largest deep fMRI resource currently available.
Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication study
This study is part of a larger project investigating the replicability of potential neuroimaging biomarkers of PD.
Classification of Anomalies in Telecommunication Network KPI Time Series
The increasing complexity and scale of telecommunication networks have led to a growing interest in automated anomaly detection systems.
Numerical Uncertainty of Convolutional Neural Networks Inference for Structural Brain MRI Analysis
This paper investigates the numerical uncertainty of Convolutional Neural Networks (CNNs) inference for structural brain MRI analysis.
Numerical Stability of DeepGOPlus Inference
However, recent works have highlighted numerical stability challenges in DNNs, which also relates to their known sensitivity to noise injection.
Dynamic Ensemble Size Adjustment for Memory Constrained Mondrian Forest
Supervised learning algorithms generally assume the availability of enough memory to store data models during the training and test phases.
Mondrian Forest for Data Stream Classification Under Memory Constraints
Supervised learning algorithms generally assume the availability of enough memory to store their data model during the training and test phases.
Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics
The variability in the perturbed networks was captured in an augmented dataset, which was then used for an age classification task.
A Recommender System for Scientific Datasets and Analysis Pipelines
We investigate the feasibility of a collaborative filtering system to recommend pipelines and datasets based on provenance records from previous executions.
Accurate simulation of operating system updates in neuroimaging using Monte-Carlo arithmetic
Overall, our results establish that FL accurately simulates results variability due to OS updates, and is a practical framework to quantify numerical uncertainty in neuroimaging.
Reducing numerical precision preserves classification accuracy in Mondrian Forests
Mondrian Forests are a powerful data stream classification method, but their large memory footprint makes them ill-suited for low-resource platforms such as connected objects.
A benchmark of data stream classification for human activity recognition on connected objects
We measure both classification performance and resource consumption (runtime, memory, and power) of five usual stream classification algorithms, implemented in a consistent library, and applied to two real human activity datasets and to three synthetic datasets.
Can we Estimate Truck Accident Risk from Telemetric Data using Machine Learning?
We discuss the difficulties of using telemetric data for the estimation of the risk of accidents that could explain this negative result.
A performance comparison of Dask and Apache Spark for data-intensive neuroimaging pipelines
In the past few years, neuroimaging has entered the Big Data era due to the joint increase in image resolution, data sharing, and study sizes.
Distributed, Parallel, and Cluster Computing Performance
High-Resolution Road Vehicle Collision Prediction for the City of Montreal
Road accidents are an important issue of our modern societies, responsible for millions of deaths and injuries every year in the world.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
Subject Cross Validation in Human Activity Recognition
We conclude that Human Activity Recognition systems should be evaluated by Subject Cross Validation, and that overlapping windows are not worth their extra computational cost.
Data models for service failure prediction in supply-chain networks
We aim to predict and explain service failures in supply-chain networks, more precisely among last-mile pickup and delivery services to customers.
Predicting computational reproducibility of data analysis pipelines in large population studies using collaborative filtering
We formulate the problem as a collaborative filtering process, with constraints on the construction of the training set.
NeuroStorm: Accelerating Brain Science Discovery in the Cloud
Neuroscientists are now able to acquire data at staggering rates across spatiotemporal scales.
