590 citations · 956 across the 6 of their papers we have counts for
6 papers
Enumerating Unique Computational Graphs via an Iterative Graph Invariant
Chris Ying
In this report, we describe a novel graph invariant for computational graphs (colored directed acylic graphs) and how we used it to generate all distinct computational graphs up to…
NAS-Bench-101: Towards Reproducible Neural Architecture Search
Chris Ying, Aaron Klein, Esteban Real +3
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 r…
Large-Batch Training for LSTM and Beyond
Yang You, Jonathan Hseu, Chris Ying +3
Large-batch training approaches have enabled researchers to utilize large-scale distributed processing and greatly accelerate deep-neural net (DNN) training. For example, by scalin…
Image Classification at Supercomputer Scale
Chris Ying, Sameer Kumar, Dehao Chen +2
Deep learning is extremely computationally intensive, and hardware vendors have responded by building faster accelerators in large clusters. Training deep learning models at petaFL…
Depth-Adaptive Computational Policies for Efficient Visual Tracking
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. However, it is harder to track an o…
Don't Decay the Learning Rate, Increase the Batch Size
Samuel L. Smith, Pieter-Jan Kindermans, Chris Ying +1
It is common practice to decay the learning rate. Here we show one can usually obtain the same learning curve on both training and test sets by instead increasing the batch size du…