Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Training large deep neural networks on massive datasets is  computationally very challenging.
There has been recent surge in interest in using large batch stochastic optimization methods to tackle this issue.
The most prominent algorithm in this line of research is LARS, which by  employing layerwise adaptive learning rates trains ResNet on ImageNet in a few minutes.
However, LARS performs poorly for attention models like BERT, indicating that its performance gains are not consistent across tasks.
In this paper, we first study a principled layerwise adaptation strategy to accelerate training of deep neural networks using large mini-batches.
Using this strategy, we develop a new layerwise adaptive large batch optimization technique called LAMB; we then provide convergence analysis of LAMB as well as LARS, showing convergence to a stationary point in general nonconvex settings.
Our empirical results demonstrate the superior performance of LAMB across various tasks such as BERT and ResNet-50 training with very little hyperparameter tuning.
In particular, for BERT training, our optimizer enables use of very large batch sizes of 32868 without any degradation of performance.  
By increasing the batch size to the memory limit of a TPUv3 Pod, BERT training time can be reduced from 3 days to just 76 minutes (Table 1).
With the advent of large scale datasets, training large deep neural networks, even using computationally efficient optimization methods like Stochastic gradient descent (SGD), has become particularly challenging.
For instance, training state-of-the-art deep learning models like BERT and ResNet-50 takes 3 days on 16 TPUv3 chips and 29 hours on 8 Tesla P100 gpus respectively (Devlin et al., 2018; He et al., 2016) .
Thus, there is a growing interest to develop optimization solutions to tackle this critical issue.
The goal of this paper is to investigate and develop optimization techniques to accelerate training large deep neural networks, mostly focusing on approaches based on variants of SGD.
Methods based on SGD iteratively update the parameters of the model by moving them in a scaled (negative) direction of the gradient calculated on a minibatch.
However, SGD's scalability is limited by its inherent sequential nature.
Owing to this limitation, traditional approaches to improve SGD training time in the context of deep learning largely resort to distributed asynchronous setup (Dean et al., 2012; Recht et al., 2011) .
However, the implicit staleness introduced due to the asynchrony limits the parallelization of the approach, often leading to degraded performance.
The feasibility of computing gradient on large minibatches in parallel due to recent hardware advances has seen the resurgence of simply using synchronous SGD with large minibatches as an alternative to asynchronous SGD.
However, naïvely increasing the batch size typically results in degradation of generalization performance and reduces computational benefits (Goyal et al., 2017) .
Synchronous SGD on large minibatches benefits from reduced variance of the stochastic gradients used in SGD.
This allows one to use much larger learning rates in SGD, typically of the order square root of the minibatch size.
Surprisingly, recent works have demonstrated that up to certain minibatch sizes, linear scaling of the learning rate with minibatch size can be used to further speed up the training Goyal et al. (2017) .
These works also elucidate two interesting aspects to enable the use of linear scaling in large batch synchronous SGD:
(i) linear scaling of learning rate is harmful during the initial phase; thus, a hand-tuned warmup strategy of slowly increasing the learning rate needs to be used initially, and
(ii) linear scaling of learning rate can be detrimental beyond a certain batch size.
Using these tricks, Goyal et al. (2017) was able to drastically reduce the training time of ResNet-50 model from 29 hours to 1 hour using a batch size of 8192.
While these works demonstrate the feasibility of this strategy for reducing the wall time for training large deep neural networks, they also highlight the need for an adaptive learning rate mechanism for large batch learning.
Variants of SGD using layerwise adaptive learning rates have been recently proposed to address this problem.
The most successful in this line of research is the LARS algorithm (You et al., 2017) , which was initially proposed for training RESNET.
Using LARS, ResNet-50 can be trained on ImageNet in just a few minutes!
However, it has been observed that its performance gains are not consistent across tasks.
For instance, LARS performs poorly for attention models like BERT.
Furthermore, theoretical understanding of the adaptation employed in LARS is largely missing.
To this end, we study and develop new approaches specially catered to the large batch setting of our interest.
Contributions.
More specifically, we make the following main contributions in this paper.
• Inspired by LARS, we investigate a general adaptation strategy specially catered to large batch learning and provide intuition for the strategy.
• Based on the adaptation strategy, we develop a new optimization algorithm (LAMB) for achieving adaptivity of learning rate in SGD.
Furthermore, we provide convergence analysis for both LARS and LAMB to achieve a stationary point in nonconvex settings.
We highlight the benefits of using these methods for large batch settings.
• We demonstrate the strong empirical performance of LAMB across several challenging tasks.
Using LAMB we scale the batch size in training BERT to more than 32k without degrading the performance; thereby, cutting the time down from 3 days to 76 minutes.
Ours is the first work to reduce BERT training wall time to less than couple of hours.
• We also demonstrate the efficiency of LAMB for training state-of-the-art image classification models like RESNET.
To the best of our knowledge, ours is first adaptive solver that can achieve state-of-the-art accuracy for RESNET-50 as adaptive solvers like Adam fail to obtain the accuracy of SGD with momentum for these tasks.
A fast optimizer for general applications and large-batch training.