323 citations · 600 across the 9 of their papers we have counts for
Showing 2018Show all
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cs.LG2018★ 20 cited
Multi-Task Graph Autoencoders
Phi Vu Tran
We examine two fundamental tasks associated with graph representation learning: link prediction and node classification. We present a new autoencoder architecture capable of learni…
cs.CV2018★ 206 cited
Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning
David Mascharka, Philip Tran, Ryan Soklaski +1
Visual question answering requires high-order reasoning about an image, which is a fundamental capability needed by machine systems to follow complex directives. Recently, modular…
cs.LG2018★ 32 cited
Learning to Make Predictions on Graphs with Autoencoders
Phi Vu Tran
We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification. We present a novel autoencoder architecture…