5 citations · 12 across the 5 of their papers we have counts for
4 papers · 1 filter
Cold Start Streaming Learning for Deep Networks
Cameron R. Wolfe, Anastasios Kyrillidis
The ability to dynamically adapt neural networks to newly-available data without performance deterioration would revolutionize deep learning applications. Streaming learning (i.e.,…
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…
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…