13 citations · 32 across the 3 of their papers we have counts for
3 papers
Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks
Ben Adlam, Charles Weill, Amol Kapoor
We investigate under and overfitting in Generative Adversarial Networks (GANs), using discriminators unseen by the generator to measure generalization. We find that the model capac…
AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles
Charles Weill, Javier Gonzalvo, Vitaly Kuznetsov +9
AdaNet is a lightweight TensorFlow-based (Abadi et al., 2015) framework for automatically learning high-quality ensembles with minimal expert intervention. Our framework is inspire…
Improving Neural Architecture Search Image Classifiers via Ensemble Learning
Vladimir Macko, Charles Weill, Hanna Mazzawi +1
Finding the best neural network architecture requires significant time, resources, and human expertise. These challenges are partially addressed by neural architecture search (NAS)…