Bharat Kaul

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 35

Top Topics

Final Classification

Avoid Overfitting

Top Venues

Publications

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 (2024)
Abhisek Kundu, Naveen K. Mellempudi, Dharma Teja Vooturi, Bharat Kaul, Pradeep Dubey
AUTOSPARSE: Towards Automated Sparse Training of Deep Neural Networks. CoRR (2023)
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)
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 (2021)
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) (2021)
Jacob R. Stevens, Dipankar Das, Sasikanth Avancha, Bharat Kaul, Anand Raghunathan
GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks. CoRR (2021)
Jacob R. Stevens, Dipankar Das, Sasikanth Avancha, Bharat Kaul, Anand Raghunathan
GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks. DAC (2021)
Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
SEERL: Sample Efficient Ensemble Reinforcement Learning. AAMAS (2021)
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 (2021)
Sanket Tavarageri, Gagandeep Goyal, Sasikanth Avancha, Bharat Kaul, Ramakrishna Upadrasta
AI Powered Compiler Techniques for DL Code Optimization. CoRR (2021)
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)
Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
SEERL: Sample Efficient Ensemble Reinforcement Learning. CoRR (2020)
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 (2020)
Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
ERLP: Ensembles of Reinforcement Learning Policies (Student Abstract). AAAI (2020)
Sanket Tavarageri, Alexander Heinecke, Sasikanth Avancha, Gagandeep Goyal, Ramakrishna Upadrasta, Bharat Kaul
PolyDL: Polyhedral Optimizations for Creation of High Performance DL primitives. CoRR (2020)
Anirban Santara, Sohan Rudra, Sree Aditya Buridi, Meha Kaushik, Abhishek Naik, Bharat Kaul, Balaraman Ravindran
MADRaS : Multi Agent Driving Simulator. CoRR (2020)
Jacob R. Stevens, Ashish Ranjan, Dipankar Das, Bharat Kaul, Anand Raghunathan
Manna: An Accelerator for Memory-Augmented Neural Networks. MICRO (2019)
Naveen Mellempudi, Sudarshan Srinivasan, Dipankar Das, Bharat Kaul
Mixed Precision Training With 8-bit Floating Point. CoRR (2019)
Sanket Tavarageri, Srinivas Sridharan, Bharat Kaul
Automatic Model Parallelism for Deep Neural Networks with Compiler and Hardware Support. CoRR (2019)
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 (2019)
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 (2019)
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)
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 (2019)
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)
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 (2018)
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 (2018)
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 (2018)
Anirban Santara, Abhishek Naik, Balaraman Ravindran, Dipankar Das, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
RAIL: Risk-Averse Imitation Learning. AAMAS (2018)
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)
Naveen Mellempudi, Abhisek Kundu, Dipankar Das, Dheevatsa Mudigere, Bharat Kaul
Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point. CoRR (2017)
Anirban Santara, Abhishek Naik, Balaraman Ravindran, Dipankar Das, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
RAIL: Risk-Averse Imitation Learning. CoRR (2017)
Naveen Mellempudi, Abhisek Kundu, Dheevatsa Mudigere, Dipankar Das, Bharat Kaul, Pradeep Dubey
Ternary Neural Networks with Fine-Grained Quantization. CoRR (2017)
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)
Abhisek Kundu, Kunal Banerjee, Naveen Mellempudi, Dheevatsa Mudigere, Dipankar Das, Bharat Kaul, Pradeep Dubey
Ternary Residual Networks. CoRR (2017)
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 (2016)
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)
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)
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)