2022年6月15日,岳峰课题组在Science Advances上发表了题为EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps的工作。该项工作基于深度学习和集成学习策略...
Abstract 背景:目前对cross-framework conversion中的inconsistencies和security bugs的研究少有 本文:TensorScope Github:https://github.com/tensorscopepro/Tensorscope Task: test cross-frame APIs in Machine Learning Libraries Method: 1. Differential Testing among Machine Learning Libraries 2. joint constraint an...
Wang X, Luan Y, Yue F. EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps[J]. Science Advances, 2022, 8(24): eabn9215.
Wang X, Luan Y, Yue F. EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps[J]. Science Advances, 2022, 8(24): eabn9215.
A deep-learning framework for predicting a full range of structural variations from bulk and single-cell contact maps - XiaoTaoWang/EagleC
介绍完这些内容之后,很多人自然会对优化的最为广泛的应用——深度学习(deep learning)感兴趣。的确,无论是处理计算机视觉(Computer Vision,CV)相关问题的卷积神经网络(Convolutional Neural Network,CNN),还是处理自然语言处理(Natural Language Processing,NLP)相关问题的循环神经网络(Recurrent Neural Network,RNN),它们的...
The aim of this paper is to propose a deep learning framework for micro-calcification detection in 2D mammography and in 2D synthetic mammography (C-view) from digital breast tomosynthesis (DBT). The dataset analyzed for 2D mammograms is the INbreast dataset that consists of 410 digital images ...
Deep learning framework for wearable activity recognition based on convolutional and LSTM recurrent layers. In this repository it is presented the architecture of DeepConvLSTM: a deep framework for wearable activity recognition based on convolutional and LSTM recurrent units. To obtain a detailed descripti...
In this work, 123 COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays… 2663 the transfer learning strategy to pre-trained ResNet34 and HRNet model was applied for several reasons, including: (1) to avoid the overfitting problem due to unavailability...
Machine Learning Framework: Eager mode framework: e.g.: PyTorch, JAX 命令式运行定义方法(imperative define-by-run) approach 机器学习模型表达为每次运行时所需的代码learning model is represented as code that is executed each time one wants to run the model 优点:更易于理解,可以使用标准工具进行调试 ...