List of reference papers

Since this is the first workshop on visualzation for deep learning, here is a list of the papers in the past that related to this topic. Feel free to email bjiang [at] if you know any other related papers. This will greatly help us find out the audience and contributors for the workshop.

————CNN & Vision—————

Visualizing and Understanding Convolutional Networks Matthew D. Zeiler, Rob Fergus

Visualizing Higher-Layer Features of a Deep Network Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pascal Vincent

Evaluating the visualization of what a Deep Neural Network has learned Wojciech Samek, Alexander Binder, Gregoire Montavon, Sebastian Bach, Klaus-Robert Muller

Explaining NonLinear Classification Decisions with Deep Taylor Decomposition Gregoire Montavon, Sebastian Bach, Alexander Binder, Wojciech Samek, Klaus-Robert Muller

Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks Anh Nguyen, Jason Yosinski, Jeff Clune

Understanding Neural Networks Through Deep Visualization Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, Hod Lipson

Inceptionism: Going Deeper into Neural Networks Alexander Mordvintsev, Christopher Olah, Mike Tyka

Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network Misha Denil, Alban Demiraj, Nal Kalchbrenner, Phil Blunsom, Nando de Freitas

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan, Andrea Vedaldi, Andrew Zisserman

Understanding Deep Image Representations by Inverting Them Aravindh Mahendran, Andrea Vedaldi

Inverting Visual Representations with Convolutional Networks Alexey Dosovitskiy, Thomas Brox

Generative modeling of convolutional neural networks Jifeng Dai, Yang Lu, Ying Nian Wu

AverageExplorer: Interactive Exploration and Alignment of Visual Data Collections Jun-Yan Zhu, Yong Jae Lee, Alexei Efros

HOGgles: Visualizing Object Detection Features Carl Vondrick, Aditya Khosla, Hamed Pirsiavash, Tomasz Malisiewicz, Antonio Torralba

Object Detectors Emerge in Deep Scene CNNs Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba

Learning Deep Features for Discriminative Localization Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva and Antonio Torralba

Do Convnets Learn Correspondence? Jonathan Long, Ning Zhang, Trevor Darrell

Towards Better Analysis of Deep Convolutional Neural Networks Mengchen Liu, Jiaxin Shi, Zhen Li, Chongxuan Li, Jun Zhu, Shixia Liu

A New Method to Visualize Deep Neural Networks Luisa M. Zintgraf, Taco S. Cohen, Max Welling

An Interactive Node-Link Visualization of Convolutional Neural Networks Adam W. Harley

ML-o-scope: a diagnostic visualization system for deep machine learning pipelines Daniel Bruckner

Visualizing and comparing convolutional neural networks Wei Yu, Kuiyuan Yang, Yalong Bai, Yong Rui, Hongxun Yao

Discovering internal representations from object-CNNs using population encoding Jianyu Wang, Zhishuai Zhang, Vittal Premachandran, Alan Yuille

————RNN, Attention & Others—————

Visualizing and Understanding Recurrent Networks Andrej Karpathy, Justin Johnson, Li Fei-Fei

Visualizing and Understanding Neural Models in NLP Jiwei Li, Xinlei Chen, Eduard Hovy and Dan Jurafsky

Understanding the difficulty of training deep feedforward neural networks Xavier Glorot, Yoshua Bengio

Qualitatively characterizing neural network optimization problems Ian J. Goodfellow, Oriol Vinyals, Andrew M. Saxe

Adversarial Perturbations of Deep Neural Networks David Warde-Farley, Ian Goodfellow

The Loss Surfaces of Multilayer Networks Anna Choromanska, Mikael Henaff, Michael Mathieu, Gerard Ben Arous, Yann LeCun

Graying the black box: Understanding DQNs Tom Zahavy, Nir Ben Zrihem, Shie Mannor

Recurrent Models of Visual Attention Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu

Power to the People: The Role of Humans in Interactive Machine Learning Saleema Amershi, Maya Cakmak, W. Bradley Knox, Todd Kulesza

wevi: Word embedding visual inspector Xin Rong

Tensorflow Playground Daniel Smilkov, Shan Carter

Neural Networks, Manifolds, and Topology Christopher Olah