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Found 5 papers, 2 papers with code
Singularity: Planet-Scale, Preemptive and Elastic Scheduling of AI Workloads
At the heart of Singularity is a novel, workload-aware scheduler that can transparently preempt and elastically scale deep learning workloads to drive high utilization without impacting their correctness or performance, across a global fleet of AI accelerators (e. g., GPUs, FPGAs).
LRTuner: A Learning Rate Tuner for Deep Neural Networks
For example on ImageNet with Resnet-50, LRTuner shows up to 0. 2% absolute gains in test accuracy compared to the hand-tuned baseline schedule.
Unsupervised Clustering using Pseudo-semi-supervised Learning
In this paper, we propose a framework that leverages semi-supervised models to improve unsupervised clustering performance.
Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule
Several papers argue that wide minima generalize better than narrow minima.
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AutoLR: A Method for Automatic Tuning of Learning Rate
We compare favorably against state of the art learning rate schedules for the given dataset and models, including for ImageNet on Resnet-50, Cifar-10 on Resnet-18, and SQuAD fine-tuning on BERT.
