Search Results for author: Rajat Monga

Found 13 papers, 6 papers with code

TRACE: A Time-Relational Approximate Cubing Engine for Fast Data Insights

no code implementations12 Jan 2024 Suharsh Sivakumar, Jonathan Shen, Rajat Monga

A large class of data questions can be modeled as identifying important slices of data driven by user defined metrics.

TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning

1 code implementation27 Feb 2019 Akshay Agrawal, Akshay Naresh Modi, Alexandre Passos, Allen Lavoie, Ashish Agarwal, Asim Shankar, Igor Ganichev, Josh Levenberg, Mingsheng Hong, Rajat Monga, Shanqing Cai

TensorFlow Eager is a multi-stage, Python-embedded domain-specific language for hardware-accelerated machine learning, suitable for both interactive research and production.

BIG-bench Machine Learning

Revisiting Distributed Synchronous SGD

no code implementations19 Feb 2017 Xinghao Pan, Jianmin Chen, Rajat Monga, Samy Bengio, Rafal Jozefowicz

Distributed training of deep learning models on large-scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the cost of additional noise introduced from asynchrony.

Stochastic Optimization

Revisiting Distributed Synchronous SGD

4 code implementations4 Apr 2016 Jianmin Chen, Xinghao Pan, Rajat Monga, Samy Bengio, Rafal Jozefowicz

Distributed training of deep learning models on large-scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the cost of additional noise introduced from asynchrony.

Stochastic Optimization

Beyond Short Snippets: Deep Networks for Video Classification

1 code implementation CVPR 2015 Joe Yue-Hei Ng, Matthew Hausknecht, Sudheendra Vijayanarasimhan, Oriol Vinyals, Rajat Monga, George Toderici

Convolutional neural networks (CNNs) have been extensively applied for image recognition problems giving state-of-the-art results on recognition, detection, segmentation and retrieval.

Action Recognition Classification +4

Deep Networks With Large Output Spaces

no code implementations23 Dec 2014 Sudheendra Vijayanarasimhan, Jonathon Shlens, Rajat Monga, Jay Yagnik

Deep neural networks have been extremely successful at various image, speech, video recognition tasks because of their ability to model deep structures within the data.

Video Recognition

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