Search Results for author: Chris Ying

Found 5 papers, 3 papers with code

NAS-Bench-101: Towards Reproducible Neural Architecture Search

4 code implementations25 Feb 2019 Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, Frank Hutter

Recent advances in neural architecture search (NAS) demand tremendous computational resources, which makes it difficult to reproduce experiments and imposes a barrier-to-entry to researchers without access to large-scale computation.

Neural Architecture Search

Large-Batch Training for LSTM and Beyond

1 code implementation24 Jan 2019 Yang You, Jonathan Hseu, Chris Ying, James Demmel, Kurt Keutzer, Cho-Jui Hsieh

LEGW enables Sqrt Scaling scheme to be useful in practice and as a result we achieve much better results than the Linear Scaling learning rate scheme.

Image Classification at Supercomputer Scale

no code implementations16 Nov 2018 Chris Ying, Sameer Kumar, Dehao Chen, Tao Wang, Youlong Cheng

Deep learning is extremely computationally intensive, and hardware vendors have responded by building faster accelerators in large clusters.

Classification General Classification +1

Depth-Adaptive Computational Policies for Efficient Visual Tracking

no code implementations1 Jan 2018 Chris Ying, Katerina Fragkiadaki

Current convolutional neural networks algorithms for video object tracking spend the same amount of computation for each object and video frame.

Frame Video Object Tracking +1

Don't Decay the Learning Rate, Increase the Batch Size

3 code implementations ICLR 2018 Samuel L. Smith, Pieter-Jan Kindermans, Chris Ying, Quoc V. Le

We can further reduce the number of parameter updates by increasing the learning rate $\epsilon$ and scaling the batch size $B \propto \epsilon$.

Cannot find the paper you are looking for? You can Submit a new open access paper.