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2020 – today
- 2024
[i22]Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot
, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, Sai Krishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampásek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin H. S. Segler, Michael M. Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio:
Generative Active Learning for the Search of Small-molecule Protein Binders. CoRR abs/2405.01616 (2024)- 2023
- [i21]Abhisek Kundu, Naveen K. Mellempudi, Dharma Teja Vooturi, Bharat Kaul, Pradeep Dubey:
AUTOSPARSE: Towards Automated Sparse Training of Deep Neural Networks. CoRR abs/2304.06941 (2023) - 2022
- [c14]Narendra Chaudhary, Sanchit Misra, Dhiraj D. Kalamkar, Alexander Heinecke, Evangelos Georganas, Barukh Ziv, Menachem Adelman, Bharat Kaul:
Accelerating Deep Learning based Identification of Chromatin Accessibility from noisy ATAC-seq Data. IPDPS Workshops 2022: 176-185 - 2021
- [j2]Anirban Santara, Sohan Rudra, Sree Aditya Buridi, Meha Kaushik, Abhishek Naik, Bharat Kaul, Balaraman Ravindran:
MADRaS : Multi Agent Driving Simulator. J. Artif. Intell. Res. 70: 1517-1555 (2021) - [j1]Sanket Tavarageri, Alexander Heinecke, Sasikanth Avancha, Bharat Kaul, Gagandeep Goyal, Ramakrishna Upadrasta:
PolyDL: Polyhedral Optimizations for Creation of High-performance DL Primitives. ACM Trans. Archit. Code Optim. 18(1): 11:1-11:27 (2021) - [c13]Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul:
SEERL: Sample Efficient Ensemble Reinforcement Learning. AAMAS 2021: 1100-1108 - [c12]Jacob R. Stevens, Dipankar Das, Sasikanth Avancha, Bharat Kaul, Anand Raghunathan:
GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks. DAC 2021: 955-960 - [i20]Jacob R. Stevens, Dipankar Das, Sasikanth Avancha, Bharat Kaul, Anand Raghunathan:
GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks. CoRR abs/2103.10836 (2021) - [i19]Sanket Tavarageri, Gagandeep Goyal, Sasikanth Avancha, Bharat Kaul, Ramakrishna Upadrasta:
AI Powered Compiler Techniques for DL Code Optimization. CoRR abs/2104.05573 (2021) - [i18]Narendra Chaudhary, Sanchit Misra, Dhiraj D. Kalamkar, Alexander Heinecke, Evangelos Georganas, Barukh Ziv, Menachem Adelman, Bharat Kaul:
Efficient and Generic 1D Dilated Convolution Layer for Deep Learning. CoRR abs/2104.08002 (2021) - 2020
- [c11]Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul:
ERLP: Ensembles of Reinforcement Learning Policies (Student Abstract). AAAI 2020: 13905-13906 - [c10]Eric Qin, Ananda Samajdar, Hyoukjun Kwon, Vineet Nadella, Sudarshan Srinivasan, Dipankar Das, Bharat Kaul, Tushar Krishna:
SIGMA: A Sparse and Irregular GEMM Accelerator with Flexible Interconnects for DNN Training. HPCA 2020: 58-70 - [i17]Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul:
SEERL: Sample Efficient Ensemble Reinforcement Learning. CoRR abs/2001.05209 (2020) - [i16]Sanket Tavarageri, Alexander Heinecke, Sasikanth Avancha, Gagandeep Goyal, Ramakrishna Upadrasta, Bharat Kaul:
PolyScientist: Automatic Loop Transformations Combined with Microkernels for Optimization of Deep Learning Primitives. CoRR abs/2002.02145 (2020) - [i15]Sanket Tavarageri, Alexander Heinecke, Sasikanth Avancha, Gagandeep Goyal, Ramakrishna Upadrasta, Bharat Kaul:
PolyDL: Polyhedral Optimizations for Creation of High Performance DL primitives. CoRR abs/2006.02230 (2020) - [i14]Anirban Santara, Sohan Rudra, Sree Aditya Buridi, Meha Kaushik, Abhishek Naik, Bharat Kaul, Balaraman Ravindran:
MADRaS : Multi Agent Driving Simulator. CoRR abs/2010.00993 (2020)
2010 – 2019
- 2019
- [c9]Ashish Ranjan, Shubham Jain, Jacob R. Stevens, Dipankar Das, Bharat Kaul, Anand Raghunathan:
X-MANN: A Crossbar based Architecture for Memory Augmented Neural Networks. DAC 2019: 130 - [c8]Jacob R. Stevens, Ashish Ranjan, Dipankar Das, Bharat Kaul, Anand Raghunathan:
Manna: An Accelerator for Memory-Augmented Neural Networks. MICRO 2019: 794-806 - [i13]Dhiraj D. 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:
A Study of BFLOAT16 for Deep Learning Training. CoRR abs/1905.12322 (2019) - [i12]Naveen Mellempudi, Sudarshan Srinivasan, Dipankar Das, Bharat Kaul:
Mixed Precision Training With 8-bit Floating Point. CoRR abs/1905.12334 (2019) - [i11]Sanket Tavarageri, Srinivas Sridharan, Bharat Kaul:
Automatic Model Parallelism for Deep Neural Networks with Compiler and Hardware Support. CoRR abs/1906.08168 (2019) - [i10]Sudarshan Srinivasan, Pradeep Janedula, Saurabh Dhoble, Sasikanth Avancha, Dipankar Das, Naveen Mellempudi, Bharat Daga, Martin Langhammer, Gregg Baeckler, Bharat Kaul:
High Performance Scalable FPGA Accelerator for Deep Neural Networks. CoRR abs/1908.11809 (2019) - [i9]Abhisek Kundu, Sudarshan Srinivasan, Eric C. Qin, Dhiraj D. Kalamkar, Naveen K. Mellempudi, Dipankar Das, Kunal Banerjee, Bharat Kaul, Pradeep Dubey:
K-TanH: Hardware Efficient Activations For Deep Learning. CoRR abs/1909.07729 (2019) - 2018
- [c7]Anirban Santara, Abhishek Naik, Balaraman Ravindran, Dipankar Das, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul:
RAIL: Risk-Averse Imitation Learning. AAMAS 2018: 2062-2063 - [c6]Apoorv Vyas, Nataraj Jammalamadaka, Xia Zhu, Dipankar Das, Bharat Kaul, Theodore L. Willke:
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-Out Classifiers. ECCV (8) 2018: 560-574 - [c5]Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere, Dhiraj D. Kalamkar, Sasikanth Avancha, Kunal Banerjee, Srinivas Sridharan, Karthik Vaidyanathan, Bharat Kaul, Evangelos Georganas, Alexander Heinecke, Pradeep Dubey, Jesús Corbal, Nikita Shustrov, Roman Dubtsov, Evarist Fomenko, Vadim O. Pirogov:
Mixed Precision Training of Convolutional Neural Networks using Integer Operations. ICLR (Poster) 2018 - [i8]Srinivas Sridharan, Karthikeyan Vaidyanathan, Dhiraj D. Kalamkar, Dipankar Das, Mikhail E. Smorkalov, Mikhail Shiryaev, Dheevatsa Mudigere, Naveen Mellempudi, Sasikanth Avancha, Bharat Kaul, Pradeep Dubey:
On Scale-out Deep Learning Training for Cloud and HPC. CoRR abs/1801.08030 (2018) - [i7]Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere, Dhiraj D. Kalamkar, Sasikanth Avancha, Kunal Banerjee, Srinivas Sridharan, Karthik Vaidyanathan, Bharat Kaul, Evangelos Georganas, Alexander Heinecke, Pradeep Dubey, Jesús Corbal, Nikita Shustrov, Roman Dubtsov, Evarist Fomenko, Vadim O. Pirogov:
Mixed Precision Training of Convolutional Neural Networks using Integer Operations. CoRR abs/1802.00930 (2018) - [i6]Apoorv Vyas, Nataraj Jammalamadaka, Xia Zhu, Dipankar Das, Bharat Kaul, Theodore L. Willke:
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers. CoRR abs/1809.03576 (2018) - 2017
- [c4]Swagath Venkataramani, Ashish Ranjan, Subarno Banerjee, Dipankar Das, Sasikanth Avancha, Ashok Jagannathan, Ajaya Durg, Dheemanth Nagaraj, Bharat Kaul, Pradeep Dubey, Anand Raghunathan:
ScaleDeep: A Scalable Compute Architecture for Learning and Evaluating Deep Networks. ISCA 2017: 13-26 - [i5]Naveen Mellempudi, Abhisek Kundu, Dipankar Das, Dheevatsa Mudigere, Bharat Kaul:
Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point. CoRR abs/1701.08978 (2017) - [i4]Naveen Mellempudi, Abhisek Kundu, Dheevatsa Mudigere, Dipankar Das, Bharat Kaul, Pradeep Dubey:
Ternary Neural Networks with Fine-Grained Quantization. CoRR abs/1705.01462 (2017) - [i3]Abhisek Kundu, Kunal Banerjee, Naveen Mellempudi, Dheevatsa Mudigere, Dipankar Das, Bharat Kaul, Pradeep Dubey:
Ternary Residual Networks. CoRR abs/1707.04679 (2017) - [i2]Anirban Santara, Abhishek Naik, Balaraman Ravindran, Dipankar Das, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul:
RAIL: Risk-Averse Imitation Learning. CoRR abs/1707.06658 (2017) - 2016
- [i1]Dipankar Das, Sasikanth Avancha, Dheevatsa Mudigere, Karthikeyan Vaidyanathan, Srinivas Sridharan, Dhiraj D. Kalamkar, Bharat Kaul, Pradeep Dubey:
Distributed Deep Learning Using Synchronous Stochastic Gradient Descent. CoRR abs/1602.06709 (2016) - 2015
- [c3]Dheevatsa Mudigere, Srinivas Sridharan, Anand M. Deshpande, Jongsoo Park, Alexander Heinecke, Mikhail Smelyanskiy, Bharat Kaul, Pradeep Dubey, Dinesh K. Kaushik, David E. Keyes:
Exploring Shared-Memory Optimizations for an Unstructured Mesh CFD Application on Modern Parallel Systems. IPDPS 2015: 723-732 - 2014
- [c2]Karthikeyan Vaidyanathan, Kiran Pamnany, Dhiraj D. Kalamkar, Alexander Heinecke, Mikhail Smelyanskiy, Jongsoo Park, Daehyun Kim, Aniruddha G. Shet, Bharat Kaul, Bálint Joó, Pradeep Dubey:
Improving Communication Performance and Scalability of Native Applications on Intel Xeon Phi Coprocessor Clusters. IPDPS 2014: 1083-1092 - 2012
- [c1]Dhiraj D. Kalamkar, Joshua D. Trzasko, Srinivas Sridharan, Mikhail Smelyanskiy, Daehyun Kim, Armando Manduca, Yunhong Shu, Matt A. Bernstein, Bharat Kaul, Pradeep Dubey:
High Performance Non-uniform FFT on Modern X86-based Multi-core Systems. IPDPS 2012: 449-460
Coauthor Index
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last updated on 2024-10-07 21:16 CEST by the dblp team
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