Instruct: Given a paper title, retrieve paper titles that are cited by the given paper
Query: Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
An Architecture to Accelerate Convolution in Deep Neural Networks
Effnet: An Efficient Structure for Convolutional Neural Networks
Fast Algorithms for Convolutional Neural Networks
DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices
Analysis and Optimization of Convolutional Neural Network Architectures
Efficient Mobile Implementation of A CNN-based Object Recognition System
Learning Efficient Convolutional Networks through Network Slimming
CaffePresso: Accelerating Convolutional Networks on Embedded SoCs
Striving for Simplicity: The All Convolutional Net
SimNets: A Generalization of Convolutional Networks
Joint training of convolutional and non-convolutional neural networks
Systematic evaluation of convolution neural network advances on the Imagenet
Pruning Filters and Classes: Towards On-Device Customization of Convolutional Neural Networks
Data and Hardware Efficient Design for Convolutional Neural Network
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
Best Practices for Convolutional Neural Networks Applied to Object Recognition in Images
Large scale evolution of convolutional neural networks using volunteer computing
Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units
Squeezing Deep Learning into Mobile and Embedded Devices
SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis
Greening IoT with Fog: A Survey
Less Is More: Towards Compact CNNs
Minimizing area and energy of deep learning hardware design using collective low precision and structured compression
Wideband Multi-Mode CMOS VCO Design Using Coupled Inductors