Do you agree? Data normalization isn’t the finish line. Harmonization is. Even after basic normalization, datasets can drift ...
Systematic analyses show that normative model performance strongly depends on sample size and covariate distributions, larger samples yield more stable fits, while misaligned covariates introduce ...
A University of Hawaiʻi at Mānoa student-led team has developed a new algorithm to help scientists determine direction in ...
Dung Thuy Nguyen (Vanderbilt University), Ngoc N. Tran (Vanderbilt University), Taylor T. Johnson (Vanderbilt University), Kevin Leach (Vanderbilt University) PAPER PBP: Post-Training Backdoor ...
Risk prediction has been used in the primary prevention of cardiovascular disease for >3 decades. Contemporary cardiovascular risk assessment relies on multivariable models, which integrate ...
ABSTRACT: Machine learning-based weather forecasting models are of paramount importance for almost all sectors of human activity. However, incorrect weather forecasts can have serious consequences on ...
See https://arxiv.org/abs/1709.09603 for details. [2GPUs] pyhon3 train.py --model=resnet --depth=40 --widen_factor=10 --optimizer=adamg --grassmann=True --learnRate=0 ...
Machine Learning Practical - Coursework 2: Analysing problems with the VGG deep neural network architectures (with 8 and 38 hidden layers) on the CIFAR100 dataset by monitoring gradient flow during ...
Abstract: Batch normalization (BN) has proven to be a critical component in speeding up the training of deep spiking neural networks in deep learning. However, conventional BN implementations face ...
According to Yann LeCun (@ylecun), choosing a batch size of 1 in machine learning training can be optimal depending on the definition of 'optimal' (source: @ylecun, July 11, 2025). This approach, ...
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