28 citations · 39 across the 4 of their papers we have counts for
3 papers · 1 filter
Log-DenseNet: How to Sparsify a DenseNet
Hanzhang Hu, Debadeepta Dey, Allison Del Giorno +2
Skip connections are increasingly utilized by deep neural networks to improve accuracy and cost-efficiency. In particular, the recent DenseNet is efficient in computation and param…
Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing
Hanzhang Hu, Debadeepta Dey, Martial Hebert +1
This work considers the trade-off between accuracy and test-time computational cost of deep neural networks (DNNs) via \emph{anytime} predictions from auxiliary predictions. Specif…
Gradient Boosting on Stochastic Data Streams
Hanzhang Hu, Wen Sun, Arun Venkatraman +2
Boosting is a popular ensemble algorithm that generates more powerful learners by linearly combining base models from a simpler hypothesis class. In this work, we investigate the p…