Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era
Over the last decade, deep learning (DL) methods have been extremely successful and
widely used to develop artificial intelligence (AI) in almost every domain, especially after it …
widely used to develop artificial intelligence (AI) in almost every domain, especially after it …
Gradient-based learning applied to document recognition
Multilayer neural networks trained with the back-propagation algorithm constitute the best
example of a successful gradient based learning technique. Given an appropriate network …
example of a successful gradient based learning technique. Given an appropriate network …
[PDF][PDF] 神经网络七十年: 回顾与展望
焦李成， 杨淑媛， 刘芳， 王士刚， 冯志玺 - 计算机学报, 2016 - cjc.ict.ac.cn
Hodykin-Huxley 方程, 感知器模型与自适应滤波器, 再到六十年代的自组织映射网络,
神经认知机, 自适应共振网络, 许多神经计算模型都发展成为信号处理, 计算机视觉 …
神经认知机, 自适应共振网络, 许多神经计算模型都发展成为信号处理, 计算机视觉 …
[HTML][HTML] Deep convolutional neural network models for weed detection in polyhouse grown bell peppers
Conventional weed management approaches are inefficient and non-suitable for integration
with smart agricultural machinery. Automatic identification and classification of weeds can …
with smart agricultural machinery. Automatic identification and classification of weeds can …
Large-scale methods for distributionally robust optimization
We propose and analyze algorithms for distributionally robust optimization of convex losses
with conditional value at risk (CVaR) and $\chi^ 2$ divergence uncertainty sets. We prove …
with conditional value at risk (CVaR) and $\chi^ 2$ divergence uncertainty sets. We prove …
Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China
Assessments of landslide disasters are becoming increasingly urgent. The aim of this study
is to investigate a convolutional neural network (CNN) framework for landslide susceptibility …
is to investigate a convolutional neural network (CNN) framework for landslide susceptibility …
[HTML][HTML] Breast cancer multi-classification from histopathological images with structured deep learning model
Automated breast cancer multi-classification from histopathological images plays a key role
in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is …
in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is …
[PDF][PDF] 深度卷积神经网络的发展及其在计算机视觉领域的应用
张顺， 龚怡宏， 王进军 - 计算机学报, 2019 - cjc.ict.ac.cn
2)(西安交通大学人工智能与机器人研究所, 陕西西安, 710049) 摘要作为类脑计算领域的一个
重要研究成果, 深度卷积神经网络已经广泛应用到计算机视觉, 自然语言处理, 信息检索 …
重要研究成果, 深度卷积神经网络已经广泛应用到计算机视觉, 自然语言处理, 信息检索 …
[HTML][HTML] Deep learning with convolutional neural networks applied to electromyography data: A resource for the classification of movements for prosthetic hands
Natural control methods based on surface electromyography (sEMG) and pattern
recognition are promising for hand prosthetics. However, the control robustness offered by …
recognition are promising for hand prosthetics. However, the control robustness offered by …
Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer
Canine mammary tumors (CMTs) have high incidences and mortality rates in dogs. They are
also considered excellent models for human breast cancer studies. Diagnoses of both …
also considered excellent models for human breast cancer studies. Diagnoses of both …