Dinh, N. de Freitas, et al., "Predicting parameters in deep learning," in Advances in Neural Informa- tion Processing Systems, pp. 2148-2156, 2013.Misha Denil, Babak Shakibi, Laurent Dinh, and Nando de Freitas. 2013. Predicting parameters in deep learning. In Advances in Neural ...
PREDICTING MACHINE LEARNING OR DEEP LEARNING MODEL TRAINING TIME Herein are techniques for exploring hyperparameters of a machine learning model (MLM) and to train a regressor to predict a time needed to train the MLM based on a hyperparameter configuration and a dataset. In an embodiment that ...
Tiresias使用一个商业入侵防御系统(Symantec's intrusion prevention product)收集的包含34亿个安全事件的数据集。当产品检测到与预定义特征相匹配的网络级或系统级活动时,将记录与安全事件相关的元信息。从系统记录的元信息中,提取出以下信息,anonymized machine ID,timestamp,security event ID,event description,system ...
题目:Deep Learning Face Representation from Predicting 10,000 Classes 主要内容:通过深度学习来进行图像高级特征表示(DeepID),进而进行人脸的分类。 优点:在人脸验证上面做,可以很好的扩展到其他的应用,并且夸数据库有效性;在数据库中的类别越多时,其泛化能力越强,特征比较少,不像其他特征好几K甚至上M,好的泛化...
This paper proposes to learn a set of high-level feature representations through deep learning, referred to as Deep hidden IDentity features (DeepID), for face verification. We argue that DeepID can be effectively learned through challenging multi-class face identification tasks, whilst they can be...
You can specify various parameters for main.lua e.g. set learning rate by -LearningRate. Take a look at main.lua for the options. Short explanation of the code: 1_data.lua reads the training and validation data; 2_model.lua specify the model; 3_loss.lua specify the loss function; 4...
题目:Deep Learning Face Representation from Predicting 10,000 Classes 主要内容:通过深度学习来进行图像高级特征表示(DeepID),进而进行人脸的分类。 长处:在人脸验证上面做,能够非常好的扩展到其它的应用,而且夸数据库有效性;在数据库中的类别越多时,其泛化能力越强,特征比較少,不像其它特征好几K甚至上M,好的泛...
总结——A multimodal deep learning framework for predicting drug–drug interaction events DDIMDL首先利用药物的化学亚结构、靶点、酶和通路四类特征,分别构建基于深度神经网络的子模型,然后采用联合DNN框架将这些子模型组合起来,学习药物-药物对的跨模表征,预测DDI事件。 基于相似性的方法是这些方法中的一个主要类别...
Recently, an RBP predictor, RBPPred, is proposed in our group to predict RBPs. However, RBPPred is too slow for that it needs to generate PSSM matrix as its feature. Herein, based on the protein feature of RBPPred and Convolutional Neural Network (CNN), we develop a deep learning model...
We find this result is robust to smoothing method (Gaussian or Lowess), a range of different smoothing parameters, different rolling window sizes for variance and lag-1 autocorrelation, and sample error in the experimental data (Supplementary Figs. 12–15). However, smoothing with a bandwidth ...