IEEE

Continual Learning for Object Classification: Integrating AutoML for Binary Classification Tasks Within a Modular Dynamic Architecture

Abstract: For humans it is quite easy to identify a new object after learning to identify existing ones, but not for a machine. Deep neural networks (DNN) are the foundation of the current ...
GitHub

feature penalty not supported in CUDA

In the following example, I expected CUDA version, similar to the CPU version, to also output feature importance to be "0 600" when feature penalty is 0 on the first feature. import numpy as np import ...
eLife

Fast evolutionary turnover and overlapping variances of sex-biased gene expression patterns defy a simple binary sex classification of somatic tissues

This study presents data on sex differences in gene expression across organs of four mice taxa. The authors have generated a unique and convincing dataset that fills a gap left by previous studies.
Frontiers

Comparative analysis of machine learning techniques on the BraTS dataset for brain tumor classification

1 Information Statistics Center, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China 2 School of Computer Science and Technology, Hubei Business ...
Scientific Research Publishing

Saito, T. and Rehmsmeier, M. (2015) The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets.

ABSTRACT: Accurately predicting individual responses to antidepressant treatment is a critical step toward achieving personalized psychiatry and minimizing the ...
GitHub

ONNX cannot save the XGBoost binary classifier properly when trained on an imbalanced dataset.

ONNX cannot properly save an XGBoost binary classification model when it is trained on an imbalanced dataset. When I create the dataset for the XGBoost binary classification model like this: ...
INSPIRE

Exploring quantum neural networks for binary classification on MNIST dataset: A swap test approach

In this study, we propose a novel modularized Quantum Neural Network (mQNN) model tailored to address the binary classification problem on the MNIST dataset. The mQNN organizes input information using ...