921 citations · 9.3k across the 292 of their papers we have counts for
7 papers · 2 filters
Validate and Enable Machine Learning in Industrial AI
Hongbo Zou, Guangjing Chen, Pengtao Xie +10
Industrial Artificial Intelligence (Industrial AI) is an emerging concept which refers to the application of artificial intelligence to industry. Industrial AI promises more effici…
Squared Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations
Haohan Wang, Zeyi Huang, Xindi Wu +1
Data augmentation is one of the most popular techniques for improving the robustness of neural networks. In addition to directly training the model with original samples and augmen…
Iterative Graph Self-Distillation
Hanlin Zhang, Shuai Lin, Weiyang Liu +4
Recently, there has been increasing interest in the challenge of how to discriminatively vectorize graphs. To address this, we propose a method called Iterative Graph Self-Distilla…
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Maruan Al-Shedivat, Jennifer Gillenwater, Eric Xing +1
Federated learning is typically approached as an optimization problem, where the goal is to minimize a global loss function by distributing computation across client devices that p…
Dropout as a Regularizer of Interaction Effects
Benjamin Lengerich, Eric P. Xing, Rich Caruana
We examine Dropout through the perspective of interactions. This view provides a symmetry to explain Dropout: given variables, there are possible sets of va…
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting
Yue Wu, Pan Zhou, Andrew Gordon Wilson +2
Despite success on a wide range of problems related to vision, generative adversarial networks (GANs) often suffer from inferior performance due to unstable training, especially fo…