no code implementations • 26 May 2023 • Vaibhav Saxena, Kamal Rahimi Malekshan, Linh Tran, Yotto Koga
Most widely used methods learn to infer the object pose in a discriminative setup where the model filters useful information to infer the exact pose of the object.
2 code implementations • NeurIPS 2021 • Vaibhav Saxena, Jimmy Ba, Danijar Hafner
We introduce the Clockwork VAE (CW-VAE), a video prediction model that leverages a hierarchy of latent sequences, where higher levels tick at slower intervals.
no code implementations • 1 Jan 2021 • Vaibhav Saxena, Jimmy Ba, Danijar Hafner
Deep learning has shown promise for accurately predicting high-dimensional video sequences.
no code implementations • 24 Jun 2020 • Vaibhav Saxena, K. R. Jayaram, Saurav Basu, Yogish Sabharwal, Ashish Verma
We design a fast dynamic programming based optimizer to solve this problem in real-time to determine jobs that can be scaled up/down, and use this optimizer in an autoscaler to dynamically change the allocated resources and batch sizes of individual DL jobs.
no code implementations • 8 Mar 2019 • Vaibhav Saxena, Srinivasan Sivanandan, Pulkit Mathur
Adversarial methods for imitation learning have been shown to perform well on various control tasks.
no code implementations • 7 Aug 2017 • Minsik Cho, Ulrich Finkler, Sameer Kumar, David Kung, Vaibhav Saxena, Dheeraj Sreedhar
We train Resnet-101 on Imagenet 22K with 64 IBM Power8 S822LC servers (256 GPUs) in about 7 hours to an accuracy of 33. 8 % validation accuracy.