Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper.
Query: Comparing the Visual Representations and Performance of Humans and Deep Neural Networks
Comparing deep neural networks against humans: object recognition when the signal gets weaker
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
Deep Learning Human Mind for Automated Visual Classification
Deep networks for robust visual recognition
Towards The Deep Model : Understanding Visual Recognition Through Computational Models
Examining Representational Similarity in ConvNets and the Primate Visual Cortex
From generic to specific deep representations for visual recognition
Methods for Interpreting and Understanding Deep Neural Networks
Visualizing and Comparing Convolutional Neural Networks
High-Performance Neural Networks for Visual Object Classification
Evaluation of convolutional neural networks for visual recognition
Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers
DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?
Seeing it all: Convolutional network layers map the function of the human visual system
Perception Science in the Age of Deep Neural Networks
Neocognitron: A neural network model for a mechanism of visual pattern recognition
a fast , invariant representation for human action in the visual system .
Visualizing Higher-Layer Features of a Deep Network
Neocognitron: A Hierarchical Neural Network Capable of Visual Pattern Recognition
Multiple Object Recognition with Visual Attention
Segmentation of liver using marker watershed transform algorithm for CT scan images
Biodiesel from microalgae
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability
InfoGather: entity augmentation and attribute discovery by holistic matching with web tables