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Check-N-Run: A Checkpointing System for Training Recommendation Models

Oct 17, 2020
Assaf Eisenman, Kiran Kumar Matam, Steven Ingram, Dheevatsa Mudigere, Raghuraman Krishnamoorthi, Murali Annavaram, Krishnakumar Nair, Misha Smelyanskiy


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SEERL: Sample Efficient Ensemble Reinforcement Learning

Jan 15, 2020
Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul


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Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems

Sep 25, 2019
Antonio Ginart, Maxim Naumov, Dheevatsa Mudigere, Jiyan Yang, James Zou


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Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems

Sep 04, 2019
Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov, Jiyan Yang

* 20 pages, 11 figures, 4 tables 

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The Architectural Implications of Facebook's DNN-based Personalized Recommendation

Jun 18, 2019
Udit Gupta, Xiaodong Wang, Maxim Naumov, Carole-Jean Wu, Brandon Reagen, David Brooks, Bradford Cottel, Kim Hazelwood, Bill Jia, Hsien-Hsin S. Lee, Andrey Malevich, Dheevatsa Mudigere, Mikhail Smelyanskiy, Liang Xiong, Xuan Zhang

* 11 pages 

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A Study of BFLOAT16 for Deep Learning Training

Jun 13, 2019
Dhiraj Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi, Dipankar Das, Kunal Banerjee, Sasikanth Avancha, Dharma Teja Vooturi, Nataraj Jammalamadaka, Jianyu Huang, Hector Yuen, Jiyan Yang, Jongsoo Park, Alexander Heinecke, Evangelos Georganas, Sudarshan Srinivasan, Abhisek Kundu, Misha Smelyanskiy, Bharat Kaul, Pradeep Dubey


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Deep Learning Recommendation Model for Personalization and Recommendation Systems

May 31, 2019
Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii, Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko, Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong, Misha Smelyanskiy

* 10 pages, 6 figures 

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Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy

Oct 26, 2018
Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere, Alejandro Ribeiro, Martin Takáč


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A Progressive Batching L-BFGS Method for Machine Learning

May 30, 2018
Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang

* ICML 2018. 25 pages, 17 figures, 2 tables 

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Mixed Precision Training of Convolutional Neural Networks using Integer Operations

Feb 23, 2018
Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere, Dhiraj Kalamkar, Sasikanth Avancha, Kunal Banerjee, Srinivas Sridharan, Karthik Vaidyanathan, Bharat Kaul, Evangelos Georganas, Alexander Heinecke, Pradeep Dubey, Jesus Corbal, Nikita Shustrov, Roma Dubtsov, Evarist Fomenko, Vadim Pirogov

* Published as a conference paper at ICLR 2018 

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On Scale-out Deep Learning Training for Cloud and HPC

Jan 24, 2018
Srinivas Sridharan, Karthikeyan Vaidyanathan, Dhiraj Kalamkar, Dipankar Das, Mikhail E. Smorkalov, Mikhail Shiryaev, Dheevatsa Mudigere, Naveen Mellempudi, Sasikanth Avancha, Bharat Kaul, Pradeep Dubey

* Accepted in SysML 2018 conference 

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RAIL: Risk-Averse Imitation Learning

Nov 29, 2017
Anirban Santara, Abhishek Naik, Balaraman Ravindran, Dipankar Das, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul

* Accepted for presentation in Deep Reinforcement Learning Symposium at NIPS 2017 

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Ternary Residual Networks

Oct 31, 2017
Abhisek Kundu, Kunal Banerjee, Naveen Mellempudi, Dheevatsa Mudigere, Dipankar Das, Bharat Kaul, Pradeep Dubey


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Ternary Neural Networks with Fine-Grained Quantization

May 30, 2017
Naveen Mellempudi, Abhisek Kundu, Dheevatsa Mudigere, Dipankar Das, Bharat Kaul, Pradeep Dubey


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On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Feb 09, 2017
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang

* Accepted as a conference paper at ICLR 2017 

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Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point

Feb 01, 2017
Naveen Mellempudi, Abhisek Kundu, Dipankar Das, Dheevatsa Mudigere, Bharat Kaul


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Distributed Hessian-Free Optimization for Deep Neural Network

Jan 15, 2017
Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takáč


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Distributed Deep Learning Using Synchronous Stochastic Gradient Descent

Feb 22, 2016
Dipankar Das, Sasikanth Avancha, Dheevatsa Mudigere, Karthikeyan Vaidynathan, Srinivas Sridharan, Dhiraj Kalamkar, Bharat Kaul, Pradeep Dubey


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Identification of Helicopter Dynamics based on Flight Data using Nature Inspired Techniques

Nov 12, 2014
S. N. Omkar, Dheevatsa Mudigere, J Senthilnath, M. Vijaya Kumar


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