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https://arxiv.org/abs/1910.06239v2 Two-sample Testing Using Deep Learning We propose a two-sample testing procedure based on learned deep neural network representations. To this end, we define two test statistics that perform an asymptotic location test on data samples mapped onto a hidden layer. The tests are consistent and asy arxiv.org abstract 본 논문은 학습된 Neural Network 표현을 기반으로 2-샘플 테스트 절차를 제..

https://arxiv.org/abs/2002.04803v1 Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligen Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processi..

https://arxiv.org/abs/2002.11102v2 On Feature Normalization and Data Augmentation Modern neural network training relies heavily on data augmentation for improved generalization. After the initial success of label-preserving augmentations, there has been a recent surge of interest in label-perturbing approaches, which combine features an arxiv.org abstract 본 논문은 특징 일반화에 의해 추출되고 첫번째와 두번째 모메트를 활용하는..

https://arxiv.org/abs/2003.02597v1 AI outperformed every dermatologist: Improved dermoscopic melanoma diagnosis through customizing batch logic and loss function i Melanoma, one of most dangerous types of skin cancer, re-sults in a very high mortality rate. Early detection and resection are two key points for a successful cure. Recent research has used artificial intelligence to classify melanom..

https://arxiv.org/abs/2003.02800v1 Pruning Filters while Training for Efficiently Optimizing Deep Learning Networks Modern deep networks have millions to billions of parameters, which leads to high memory and energy requirements during training as well as during inference on resource-constrained edge devices. Consequently, pruning techniques have been proposed that remo arxiv.org abstract 현대의 딥 ..