Instruct: Given the title of a scientific paper, retrieve paper titles that are cited by the given paper
Query: Compact ConvNets with Ternary Weights and Binary Activations
Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
Heterogeneous Bitwidth Binarization in Convolutional Neural Networks
From Hashing to CNNs: Training BinaryWeight Networks via Hashing
Local Binary Convolutional Neural Networks
Sparsely Connected Convolutional Networks
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model
YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights
Striving for Simplicity: The All Convolutional Net
Convexified Convolutional Neural Networks
Effnet: An Efficient Structure for Convolutional Neural Networks
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
A fully connected layer elimination for a binarizec convolutional neural network on an FPGA
Network Sketching: Exploiting Binary Structure in Deep CNNs
Convex Neural Networks
Input Convex Neural Networks
NN compactor: Minimizing memory and logic resources for small neural networks
The Power of Sparsity in Convolutional Neural Networks
Deep neural networks are robust to weight binarization and other non-linear distortions
towards compact convnets via structure - sparsity regularized filter pruning .
the compact 3 d convolutional neural network for medical images .
NeuFlow: A runtime reconfigurable dataflow processor for vision
Unified Deep Supervised Domain Adaptation and Generalization
Four text classification algorithms compared on a Dutch corpus
Tubular linear permanent magnet motors: an overall comparison
Mobile robot SLAM for line-based environment representation