Learning to learn from data: Using deep adversarial learning to construct optimal statistical procedures
نویسندگان
چکیده
منابع مشابه
Integration of remote sensing and meteorological data to predict flooding time using deep learning algorithm
Accurate flood forecasting is a vital need to reduce its risks. Due to the complicated structure of flood and river flow, it is somehow difficult to solve this problem. Artificial neural networks, such as frequent neural networks, offer good performance in time series data. In recent years, the use of Long Short Term Memory networks hase attracted much attention due to the faults of frequent ne...
متن کاملAdaptive Filtering Strategy to Remove Noise from ECG Signals Using Wavelet Transform and Deep Learning
Introduction: Electrocardiogram (ECG) is a method to measure the electrical activity of the heart which is performed by placing electrodes on the surface of the body. Physicians use observation tools to detect and diagnose heart diseases, the same is performed on ECG signals by cardiologists. In particular, heart diseases are recognized by examining the graphic representation of heart signals w...
متن کاملAdaptive Filtering Strategy to Remove Noise from ECG Signals Using Wavelet Transform and Deep Learning
Introduction: Electrocardiogram (ECG) is a method to measure the electrical activity of the heart which is performed by placing electrodes on the surface of the body. Physicians use observation tools to detect and diagnose heart diseases, the same is performed on ECG signals by cardiologists. In particular, heart diseases are recognized by examining the graphic representation of heart signals w...
متن کاملLearning What Data to Learn
Machine learning is essentially the sciences of playing with data. An adaptive data selection strategy, enabling to dynamically choose different data at various training stages, can reach a more effective model in a more efficient way. In this paper, we propose a deep reinforcement learning framework, which we call Neural Data Filter (NDF), to explore automatic and adaptive data selection in th...
متن کاملLearn to Track: Deep Learning for Tractography
We show that deep learning techniques can be applied successfully to fiber tractography. Specifically, we use feed-forward and recurrent neural networks to learn the generation process of streamlines directly from diffusion-weighted imaging (DWI) data. Furthermore, we empirically study the behavior of the proposed models on a realistic white matter phantom with known ground truth. We show that ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Science Advances
سال: 2020
ISSN: 2375-2548
DOI: 10.1126/sciadv.aaw2140