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Found 12 papers, 1 papers with code
Mutation-Based Adversarial Attacks on Neural Text Detectors
Neural text detectors aim to decide the characteristics that distinguish neural (machine-generated) from human texts.
A Mutation-based Text Generation for Adversarial Machine Learning Applications
Many natural language related applications involve text generation, created by humans or machines.
Benchmark Assessment for DeepSpeed Optimization Library
Deep Learning (DL) models are widely used in machine learning due to their performance and ability to deal with large datasets while producing high accuracy and performance metrics.
Dynamic Feature Alignment for Semi-supervised Domain Adaptation
Most research on domain adaptation has focused on the purely unsupervised setting, where no labeled examples in the target domain are available.
Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate
We believe the proposed method will benefit astronomy and cosmology, where a large number of unlabeled multi-band images are available, but acquiring image labels is costly.
Dynamic Image for 3D MRI Image Alzheimer's Disease Classification
We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer's disease classification.
Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging
A key challenge in training neural networks for a given medical imaging task is often the difficulty of obtaining a sufficient number of manually labeled examples.
Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification
Recent works have shown that deep neural networks can achieve super-human performance in a wide range of image classification tasks in the medical imaging domain.
Unsupervised Domain Adaptation for Mammogram Image Classification: A Promising Tool for Model Generalization
Generalization is one of the key challenges in the clinical validation and application of deep learning models to medical images.
Defense-PointNet: Protecting PointNet Against Adversarial Attacks
Despite remarkable performance across a broad range of tasks, neural networks have been shown to be vulnerable to adversarial attacks.
2D Convolutional Neural Networks for 3D Digital Breast Tomosynthesis Classification
Automated methods for breast cancer detection have focused on 2D mammography and have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in clinical practice.
Joint 2D-3D Breast Cancer Classification
Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females.
