Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: Geometrization of deep networks for the interpretability of deep learning systems
How deep learning works --The geometry of deep learning
Influence-Directed Explanations for Deep Convolutional Networks
Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks
Graph Spectral Regularization for Neural Network Interpretability.
Testing Deep Neural Networks
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning.
On the Number of Linear Regions of Deep Neural Networks
A unified view of gradient-based attribution methods for Deep Neural Networks
O N THE EXPRESSIVE POWER OF DEEP NEURAL NETWORKS
Learning the Number of Neurons in Deep Networks
Why Deep Neural Networks?
A Spline Theory of Deep Networks.
A Survey of Evaluation Methods and Measures for Interpretable Machine Learning.
Axiomatic Attribution for Deep Networks
CDeepEx: Contrastive Deep Explanations
Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization
Understanding symmetries in deep networks
Explaining Explanations: An Overview of Interpretability of Machine Learning
Interpretability in Machine Learning – Principles and Practice
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research
T2FA: Transparent Two-Factor Authentication
Administrative hierarchy and urban land expansion in transitional China
Single star identification and attitude determination in tracking mode
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications