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Found 11 papers, 3 papers with code
Neural Greedy Pursuit for Feature Selection
NGP is efficient in selecting $N$ features when $N \ll P$, and it provides a notion of feature importance in a descending order following the sequential selection procedure.
Use of Deterministic Transforms to Design Weight Matrices of a Neural Network
For the existing SSFN, a part of each weight matrix is trained using a layer-wise convex optimization approach (a supervised training), while the other part is chosen as a random matrix instance (an unsupervised training).
Statistical model-based evaluation of neural networks
Using a statistical model-based data generation, we develop an experimental setup for the evaluation of neural networks (NNs).
A ReLU Dense Layer to Improve the Performance of Neural Networks
We use a combination of random weights and rectified linear unit (ReLU) activation function to add a ReLU dense (ReDense) layer to the trained neural network such that it can achieve a lower training loss.
A Low Complexity Decentralized Neural Net with Centralized Equivalence using Layer-wise Learning
We design a low complexity decentralized learning algorithm to train a recently proposed large neural network in distributed processing nodes (workers).
Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University
We provide a predictive analysis of the spread of COVID-19, also known as SARS-CoV-2, using the dataset made publicly available online by the Johns Hopkins University.
Asynchronous Decentralized Learning of a Neural Network
In this work, we exploit an asynchronous computing framework namely ARock to learn a deep neural network called self-size estimating feedforward neural network (SSFN) in a decentralized scenario.
High-dimensional Neural Feature Design for Layer-wise Reduction of Training Cost
We show that the proposed architecture is norm-preserving and provides an invertible feature vector, and therefore, can be used to reduce the training cost of any other learning method which employs linear projection to estimate the target.
SSFN -- Self Size-estimating Feed-forward Network with Low Complexity, Limited Need for Human Intervention, and Consistent Behaviour across Trials
We design a self size-estimating feed-forward network (SSFN) using a joint optimization approach for estimation of number of layers, number of nodes and learning of weight matrices.
R3Net: Random Weights, Rectifier Linear Units and Robustness for Artificial Neural Network
We consider a neural network architecture with randomized features, a sign-splitter, followed by rectified linear units (ReLU).
Progressive Learning for Systematic Design of Large Neural Networks
The developed network is expected to show good generalization power due to appropriate regularization and use of random weights in the layers.
