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Found 11 papers, 2 papers with code
OccFlowNet: Towards Self-supervised Occupancy Estimation via Differentiable Rendering and Occupancy Flow
Additionally, we introduce occupancy flow as a mechanism to handle dynamic objects in the scene and ensure their temporal consistency.
pyAKI - An Open Source Solution to Automated KDIGO classification
This work introduces pyAKI as an open-source solution for implementing the KDIGO criteria for AKI diagnosis using time series data with high accuracy and performance.
Momentum-SAM: Sharpness Aware Minimization without Computational Overhead
The recently proposed optimization algorithm for deep neural networks Sharpness Aware Minimization (SAM) suggests perturbing parameters before gradient calculation by a gradient ascent step to guide the optimization into parameter space regions of flat loss.
Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks
Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines.
From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modeling
The brain-age gap is one of the most investigated risk markers for brain changes across disorders.
Seeing biodiversity: perspectives in machine learning for wildlife conservation
Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices.
An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling
The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain changes, growing into a cornerstone of biological age-research.
The Impact of Activation Sparsity on Overfitting in Convolutional Neural Networks
This tendency is preserved across network architectures and reguralisation strategies so that our measures can be used as a reliable indicator for overfitting while decoupling the network's generalisation capabilities from its loss-based definition.
Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks
For comparison, state-of-the-art models using preprocessed neuroimaging data are trained and validated on the same samples.
Exploiting the Full Capacity of Deep Neural Networks while Avoiding Overfitting by Targeted Sparsity Regularization
Overfitting is one of the most common problems when training deep neural networks on comparatively small datasets.
PHOTONAI -- A Python API for Rapid Machine Learning Model Development
PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development.
