12 citations · 19 across the 4 of their papers we have counts for
5 papers
Clockwork Variational Autoencoders
Vaibhav Saxena, Jimmy Ba, Danijar Hafner
Deep learning has enabled algorithms to generate realistic images. However, accurately predicting long video sequences requires understanding long-term dependencies and remains an…
Effective Elastic Scaling of Deep Learning Workloads
Vaibhav Saxena, K. R. Jayaram, Saurav Basu +2
The increased use of deep learning (DL) in academia, government and industry has, in turn, led to the popularity of on-premise and cloud-hosted deep learning platforms, whose goals…
Dyna-AIL : Adversarial Imitation Learning by Planning
Vaibhav Saxena, Srinivasan Sivanandan, Pulkit Mathur
Adversarial methods for imitation learning have been shown to perform well on various control tasks. However, they require a large number of environment interactions for convergenc…
Efficient Training of Convolutional Neural Nets on Large Distributed Systems
Sameer Kumar, Dheeraj Sreedhar, Vaibhav Saxena +2
Deep Neural Networks (DNNs) have achieved im- pressive accuracy in many application domains including im- age classification. Training of DNNs is an extremely compute- intensive pr…
PowerAI DDL
Minsik Cho, Ulrich Finkler, Sameer Kumar +3
As deep neural networks become more complex and input datasets grow larger, it can take days or even weeks to train a deep neural network to the desired accuracy. Therefore, distri…