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Found 6 papers, 2 papers with code
Efficient Forward Architecture Search
We propose a neural architecture search (NAS) algorithm, Petridish, to iteratively add shortcut connections to existing network layers.
Anytime Neural Network: a Versatile Trade-off Between Computation and Accuracy
We present an approach for anytime predictions in deep neural networks (DNNs).
Log-DenseNet: How to Sparsify a DenseNet
Skip connections are increasingly utilized by deep neural networks to improve accuracy and cost-efficiency.
Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing
Experimentally, the adaptive weights induce more competitive anytime predictions on multiple recognition data-sets and models than non-adaptive approaches including weighing all losses equally.
Gradient Boosting on Stochastic Data Streams
To generalize from batch to online, we first introduce the definition of online weak learning edge with which for strongly convex and smooth loss functions, we present an algorithm, Streaming Gradient Boosting (SGB) with exponential shrinkage guarantees in the number of weak learners.
Efficient Feature Group Sequencing for Anytime Linear Prediction
We theoretically guarantee that our algorithms achieve near-optimal linear predictions at each budget when a feature group is chosen.
