Techniques are described for reducing the number of parameters of a deep neural network model. According to one or more embodiments, a device can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The ...
You can indirectly tune over the number of layers by defining 10 blocks (with their tuning parameters) and wrapping each of them in aPipeOpBranch. Each of these branches can either add the block to the architecture or do nothing (PipeOpNop). You can then tune theout_featuresparameter of e...
Biased Dropout and Crossmap Dropout: Learning towards effective dropout regularization in convolutional neural network Training a deep neural network with a large number of parameters often leads to overfitting problem. Recently, Dropout has been introduced as a simple, yet... A Poernomo,DK Kang - ...
While DBSCAN has efficient implementations, it is highly sensitive to its hyperparameters which are hard to tune. Nonparametric Deep Clustering. Among the very few examples of deep methods that also find K are [11,52,66,74]. Some of them use an offline DPM inference ...
. In response to the above questions, the Alterable Kernel Convolution (AKConv) is explored in this work, which gives the convolution kernel an arbitrary number of parameters and arbitrary sampled shapes to provide richer options for the trade-off between network overhead and performance. In AK...
The recent surge of interest in Deep Neural Networks (DNNs) has led to increasingly complex networks that tax computational and memory resources. Many DNNs presently use 16-bit or 32-bit floating point operations. Significant performance and power gains can be obtained when DNN accelerators support...
Application of Minimal Radial Basis Function Neural Network to Distance Protection. optimum number of neurons in the hidden layer without resorting to trial and error; Adjustment of network parameters using a variant of extended Kalman ... Dash,K P.,Pradhan,... - 《IEEE Transactions on Power ...
Here we extend the deep RL framework in previous studies to tackle state and action spaces with mixed discrete and continuous parameters. We implement a clipped version of the Proximal Policy Optimization (PPO) algorithm41,42,43. See Algorithm 1 in the “Methods” section for the pseudo-code ...
In dilated-based convolution neural network, the convolution layer and the pooling layer have been replaced by dilated convolution, which can reduce the computation cost. The quantitative neural network based method quantizes the weight parameters to an integer power of two, which transforms the ...
we apply transfer learning for the first time in the CNV calling domain and create variant ECOLE models tailored towards certain label sets. First, we further tune the parameters of the ECOLE model (trained with the semi-ground truth) using only 4 human expert-labeled samples and generate the...