Instruct: Given a paper title, retrieve paper titles that are cited by the given paper
Query: Residual Connections Encourage Iterative Inference
Residual Networks are Exponential Ensembles of Relatively Shallow Networks
Iterative Visual Reasoning Beyond Convolutions
What Do We Understand About Convolutional Networks?
Competitive Inner-Imaging Squeeze and Excitation for Residual Network
Learning the Structure of Deep Convolutional Networks
Deep Networks with Stochastic Depth
Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation
Scale-invariant learning and convolutional networks
Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing
Understanding Deep Architectures using a Recursive Convolutional Network
How far can we go without convolution: Improving fully-connected networks
FractalNet: Ultra-Deep Neural Networks without Residuals
Advances in optimizing recurrent networks
Identity Mappings in Deep Residual Networks
Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible
The Shattered Gradients Problem: If resnets are the answer, then what is the question?
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Residual Attention Network for Image Classification
Learning Strict Identity Mappings in Deep Residual Networks
Deeper Depth Prediction with Fully Convolutional Residual Networks
A Low-Cost Gate Driver Design Using Bootstrap Capacitors for Multilevel MOSFET Inverters
Efficient learning of sparse representations with an energy-based model
An Analysis of Scale Invariance in Object Detection - SNIP
Acute Sexual Assault in the Pediatric and Adolescent Population.
Applying multimedia design principles enhances learning in medical education