List of reference papers

Here is a list of the papers in the past year that related to this topic. You can also look at the reference paper page and accepted paper page from our 2016 workshop to see papers from previous year.

Feel free to email bjiang [at] berkeley.edu if you know any other related papers. This will greatly help us find out the audience and contributors for the workshop.

Related workshops

VADL 2017: Workshop on Visual Analytics for Deep Learning

2016 Workshop on Human Interpretability in Machine Learning at ICML

ACCV 2016 Workshop on Interpretation and Visualization of Deep Neural Nets

The Workshop on Machine Learning and Interpretability at ICANN 2016

Future of Interactive Learning Machines Workshop at NIPS 2016

Interpretable ML for Complex Systems NIPS 2016 Workshop

IJCAI 2016 workshop on Interactive Machine Learning: Connecting Humans and Machines

Human Centred Machine Learning at CHI 2016

CNN & Vision

Cooperative Training of Descriptor and Generator Network Jianwen Xie, Yang Lu, Ruiqi Gao, Song-Chun Zhu, and Ying Nian Wu

Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space Anh Nguyen, Jason Yosinski, Yoshua Bengio, Alexey Dosovitskiy, and Jeff Clune

Synthesizing the preferred inputs for neurons in neural networks via deep generator networks Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune

A Neural Representation of Sketch Drawings David Ha, Douglas Eck

Visualizing Deep Neural Network Decisions: Prediction Difference Analysis Luisa M Zintgraf, Taco S Cohen, Tameem Adel, and Max Welling

VisualBackProp: visualizing CNNs for autonomous driving Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski, Bernhard Firner, Larry Jackel, Urs Muller, and Karol Zieba

Network Dissection: Quantifying Interpretability of Deep Visual Representations David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, Antonio Torralba

Understanding Intra-Class Knowledge Inside CNN Donglai Wei, Bolei Zhou, Antonio Torrabla, William Freeman

Inverting visual representations with convolutional networks Alexey Dosovitskiy, Thomas Brox

Others

ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation Fred Hohman, Nathan Hodas, and Duen Horng Chau

Visualizing the Hidden Activity of Artificial Neural Networks Paulo E. Rauber, Samuel G. Fadel, Alexandre X. Falcao, and Alexandru C. Telea

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

The Mythos of Model Interpretability Zachary C. Lipton

Project Magenta: Make Music and Art Using Machine Learning

Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders Jesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Douglas Eck, Karen Simonyan, and Mohammad Norouzi

Visualizing and Understanding Neural Machine Translation Yanzhuo Ding, Yang Liu, Huanbo Luan and Maosong Sun