Papers
Overcoming Catastrophic Interference using Conceptor-Aided Backpropagation
Xu He, Herbert Jaeger
GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets
Jinsung Yoon, James Jordon, Mihaela van der Schaar
Spatially Transformed Adversarial Examples
Chaowei Xiao, Jun-Yan Zhu, Bo Li et al.
Learning to cluster in order to transfer across domains and tasks
Yen-Chang Hsu, Zhaoyang Lv, Zsolt Kira
Learning an Embedding Space for Transferable Robot Skills
Karol Hausman, Jost Tobias Springenberg, Ziyu Wang et al.
Mitigating Adversarial Effects Through Randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang et al.
Not-So-Random Features
Brian Bullins, Cyril Zhang, Yi Zhang
Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip
Feiwen Zhu, Jeff Pool, Michael Andersch et al.
Robustness of Classifiers to Universal Perturbations: A Geometric Perspective
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi et al.
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection
Bo Zong, Qi Song, Martin Renqiang Min et al.
Gradient Estimators for Implicit Models
Yingzhen Li, Richard E. Turner
Mixed Precision Training of Convolutional Neural Networks using Integer Operations
Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere et al.
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Yujun Lin, Song Han, Huizi Mao et al.
An Online Learning Approach to Generative Adversarial Networks
Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi et al.
Proximal Backpropagation
Thomas Frerix, Thomas Möllenhoff, Michael Moeller et al.
Decoupling the Layers in Residual Networks
Ricky Fok, Aijun An, Zana Rashidi et al.
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes
Erin Grant, Chelsea Finn, Sergey Levine et al.
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments
Maruan Al-Shedivat, Trapit Bansal, Yura Burda et al.
Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity
Tianyi Zhou, Jeff Bilmes
Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator Abstraction
Da Xiao, Jo-Yu Liao, Xingyuan Yuan
Generalizing Hamiltonian Monte Carlo with Neural Networks
Daniel Levy, Matt D. Hoffman, Jascha Sohl-Dickstein
Improving GAN Training via Binarized Representation Entropy (BRE) Regularization
Yanshuai Cao, Gavin Weiguang Ding, Kry Yik-Chau Lui et al.
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
Yeming Wen, Paul Vicol, Jimmy Ba et al.
Multi-Task Learning for Document Ranking and Query Suggestion
Wasi Uddin Ahmad, Kai-Wei Chang, Hongning Wang
cGANs with Projection Discriminator
Takeru Miyato, Masanori Koyama