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Found 9 papers, 0 papers with code
Intrusion Detection System in Smart Home Network Using Bidirectional LSTM and Convolutional Neural Networks Hybrid Model
In this paper, we proposed an intrusion detection system (IDS) to detect anomalies in a smart home network using a bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) hybrid model.
Convolutional Neural Network Pruning with Structural Redundancy Reduction
Based on this finding, we then propose a network pruning approach that identifies structural redundancy of a CNN and prunes filters in the selected layer(s) with the most redundancy.
A Rule-Based Model for Victim Prediction
Social network analysis is employed to measure the influence of peers on the outcome variable.
Investigating Channel Pruning through Structural Redundancy Reduction -- A Statistical Study
Most existing channel pruning methods formulate the pruning task from a perspective of inefficiency reduction which iteratively rank and remove the least important filters, or find the set of filters that minimizes some reconstruction errors after pruning.
Speeding up convolutional networks pruning with coarse ranking
In specific, with the proposed method, 75% and 54% of the total computation time for the whole pruning procedure can be reduced for AlexNet on CIFAR-10, and for VGG-16 on ImageNet, respectively.
Single-shot Channel Pruning Based on Alternating Direction Method of Multipliers
In this work, we propose a novel single-shot channel pruning approach based on alternating direction methods of multipliers (ADMM), which can eliminate the need for complex iterative pruning and fine-tuning procedure and achieve a target compression ratio with only one run of pruning and fine-tuning.
Fast-converging Conditional Generative Adversarial Networks for Image Synthesis
Building on top of the success of generative adversarial networks (GANs), conditional GANs attempt to better direct the data generation process by conditioning with certain additional information.
Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis
However, the application of these systems to model cell movement is still challenging and requires a comprehensive understanding of regulation networks at the right scales.
