5 citations · 10 across the 4 of their papers we have counts for
5 papers
PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication
Cheng Wan, Youjie Li, Cameron R. Wolfe +3
Graph Convolutional Networks (GCNs) is the state-of-the-art method for learning graph-structured data, and training large-scale GCNs requires distributed training across multiple a…
Exceeding the Limits of Visual-Linguistic Multi-Task Learning
Cameron R. Wolfe, Keld T. Lundgaard
By leveraging large amounts of product data collected across hundreds of live e-commerce websites, we construct 1000 unique classification tasks that share similarly-structured inp…
REX: Revisiting Budgeted Training with an Improved Schedule
John Chen, Cameron Wolfe, Anastasios Kyrillidis
Deep learning practitioners often operate on a computational and monetary budget. Thus, it is critical to design optimization algorithms that perform well under any budget. The lin…
E-Stitchup: Data Augmentation for Pre-Trained Embeddings
Cameron R. Wolfe, Keld T. Lundgaard
In this work, we propose data augmentation methods for embeddings from pre-trained deep learning models that take a weighted combination of a pair of input embeddings, as inspired…
Functional Generative Design of Mechanisms with Recurrent Neural Networks and Novelty Search
Cameron R. Wolfe, Cem C. Tutum, Risto Miikkulainen
Consumer-grade 3D printers have made it easier to fabricate aesthetic objects and static assemblies, opening the door to automated design of such objects. However, while static des…