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Gopinath Chennupati
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2020 – today
- 2024
- [c40]John Harvill, Rinat Khaziev, Scarlett Li, Randy Cogill, Lidan Wang, Gopinath Chennupati
, Hari Thadakamalla:
Significant ASR Error Detection for Conversational Voice Assistants. ICASSP 2024: 11606-11610 - 2023
- [j10]Maksim Ekin Eren, Juston S. Moore, Erik Skau, Elisabeth Moore, Manish Bhattarai, Gopinath Chennupati, Boian S. Alexandrov:
General-purpose Unsupervised Cyber Anomaly Detection via Non-negative Tensor Factorization. DTRAP 4(1): 6:1-6:28 (2023) - [c39]Milind Rao, Gopinath Chennupati, Gautam Tiwari, Anit Kumar Sahu, Anirudh Raju, Ariya Rastrow, Jasha Droppo:
Federated Self-Learning with Weak Supervision for Speech Recognition. ICASSP 2023: 1-5 - [c38]Hamdy Abdelkhalik, Shamminuj Aktar, Yehia Arafa, Atanu Barai, Gopinath Chennupati, Nandakishore Santhi, Nishant Panda, Nirmal Prajapati
BB-ML: Basic Block Performance Prediction using Machine Learning Techniques. ICPADS 2023: 1975-1982 - [c37]Aakriti Agrawal, Milind Rao, Anit Kumar Sahu, Gopinath Chennupati, Andreas Stolcke:
Learning When to Trust Which Teacher for Weakly Supervised ASR. INTERSPEECH 2023: 381-385 - [i20]Aakriti Agrawal, Milind Rao, Anit Kumar Sahu, Gopinath Chennupati, Andreas Stolcke:
Learning When to Trust Which Teacher for Weakly Supervised ASR. CoRR abs/2306.12012 (2023) - [i19]Milind Rao, Gopinath Chennupati, Gautam Tiwari, Anit Kumar Sahu, Anirudh Raju, Ariya Rastrow, Jasha Droppo:
Federated Self-Learning with Weak Supervision for Speech Recognition. CoRR abs/2306.12015 (2023) - [i18]Guruprasad V. Ramesh, Gopinath Chennupati, Milind Rao, Anit Kumar Sahu, Ariya Rastrow, Jasha Droppo:
Federated Representation Learning for Automatic Speech Recognition. CoRR abs/2308.02013 (2023) - 2022
- [j9]Nasrin Akhter
Improved Protein Decoy Selection via Non-Negative Matrix Factorization. IEEE ACM Trans. Comput. Biol. Bioinform. 19(3): 1670-1682 (2022) - [j8]Atanu Barai
PPT-Multicore: performance prediction of OpenMP applications using reuse profiles and analytical modeling. J. Supercomput. 78(2): 2354-2385 (2022) - [c36]Gopinath Chennupati, Milind Rao, Gurpreet Chadha, Aaron Eakin, Anirudh Raju, Gautam Tiwari, Anit Kumar Sahu, Ariya Rastrow, Jasha Droppo, Andy Oberlin, Buddha Nandanoor, Prahalad Venkataramanan, Zheng Wu, Pankaj Sitpure:
ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale. KDD 2022: 2780-2788 - [i17]Shamminuj Aktar, Hamdy Abdelkhalik, Nazmul Haque Turja, Yehia Arafa, Atanu Barai, Nishant Panda, Gopinath Chennupati, Nandakishore Santhi, Stephan J. Eidenbenz, Abdel-Hameed A. Badawy:
BB-ML: Basic Block Performance Prediction using Machine Learning Techniques. CoRR abs/2202.07798 (2022) - [i16]Gopinath Chennupati, Milind Rao, Gurpreet Chadha, Aaron Eakin, Anirudh Raju, Gautam Tiwari, Anit Kumar Sahu, Ariya Rastrow, Jasha Droppo, Andy Oberlin, Buddha Nandanoor, Prahalad Venkataramanan, Zheng Wu, Pankaj Sitpure:
ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale. CoRR abs/2207.09078 (2022) - [i15]Ganesh Tata, Gautham Krishna Gudur, Gopinath Chennupati, Mohammad Emtiyaz Khan:
Can Calibration Improve Sample Prioritization? CoRR abs/2210.06592 (2022) - 2021
- [j7]Raviteja Vangara
Finding the Number of Latent Topics With Semantic Non-Negative Matrix Factorization. IEEE Access 9: 117217-117231 (2021) - [j6]Gopinath Chennupati
Machine Learning-enabled Scalable Performance Prediction of Scientific Codes. ACM Trans. Model. Comput. Simul. 31(2): 11:1-11:28 (2021) - [c35]Sunil Thulasidasan, Sushil Thapa, Sayera Dhaubhadel, Gopinath Chennupati, Tanmoy Bhattacharya, Jeff A. Bilmes:
An Effective Baseline for Robustness to Distributional Shift. ICMLA 2021: 278-285 - [c34]Yehia Arafa, Abdel-Hameed A. Badawy, Ammar ElWazir, Atanu Barai, Ali Eker, Gopinath Chennupati, Nandakishore Santhi
Hybrid, scalable, trace-driven performance modeling of GPGPUs. SC 2021: 53 - [i14]Atanu Barai
PPT-SASMM: Scalable Analytical Shared Memory Model: Predicting the Performance of Multicore Caches from a Single-Threaded Execution Trace. CoRR abs/2103.10635 (2021) - [i13]Atanu Barai, Yehia Arafa, Abdel-Hameed A. Badawy, Gopinath Chennupati, Nandakishore Santhi, Stephan J. Eidenbenz:
PPT-Multicore: Performance Prediction of OpenMP applications using Reuse Profiles and Analytical Modeling. CoRR abs/2104.05102 (2021) - [i12]Sunil Thulasidasan, Sushil Thapa, Sayera Dhaubhadel, Gopinath Chennupati, Tanmoy Bhattacharya, Jeff A. Bilmes:
An Effective Baseline for Robustness to Distributional Shift. CoRR abs/2105.07107 (2021) - 2020
- [j5]Hector Carrillo-Cabada
An Out of Memory tSVD for Big-Data Factorization. IEEE Access 8: 107749-107759 (2020) - [j4]Poornima Haridas
Code Characterization With Graph Convolutions and Capsule Networks. IEEE Access 8: 136307-136315 (2020) - [j3]Nasrin Akhter, Gopinath Chennupati
Decoy selection for protein structure prediction via extreme gradient boosting and ranking. BMC Bioinform. 21-S(1): 189 (2020) - [j2]Gopinath Chennupati
Distributed non-negative matrix factorization with determination of the number of latent features. J. Supercomput. 76(9): 7458-7488 (2020) - [c33]Kazi Lutful Kabir, Gopinath Chennupati, Raviteja Vangara, Hristo N. Djidjev
Decoy Selection in Protein Structure Determination via Symmetric Non-negative Matrix Factorization. BIBM 2020: 23-28 - [c32]Yehia Arafa, Ammar ElWazir, Abdelrahman Elkanishy, Youssef Aly, Ayatelrahman Elsayed, Abdel-Hameed A. Badawy, Gopinath Chennupati
Verified instruction-level energy consumption measurement for NVIDIA GPUs. CF 2020: 60-70 - [c31]Manish Bhattarai
Distributed Non-Negative Tensor Train Decomposition. HPEC 2020: 1-10 - [c30]Raviteja Vangara, Erik Skau, Gopinath Chennupati, Hristo N. Djidjev, Thomas Tierney, James P. Smith, Manish Bhattarai
Semantic Nonnegative Matrix Factorization with Automatic Model Determination for Topic Modeling. ICMLA 2020: 328-335 - [c29]Yehia Arafa, Abdel-Hameed A. Badawy, Gopinath Chennupati, Atanu Barai
Fast, accurate, and scalable memory modeling of GPGPUs using reuse profiles. ICS 2020: 31:1-31:12 - [c28]Yehia Arafa, Ammar ElWazir, Abdelrahman Elkanishy, Youssef Aly, Ayatelrahman Elsayed, Abdel-Hameed A. Badawy, Gopinath Chennupati
NVIDIA GPGPUs Instructions Energy Consumption. ISPASS 2020: 110-112 - [c27]Atanu Barai
PPT-SASMM: Scalable Analytical Shared Memory Model: Predicting the Performance of Multicore Caches from a Single-Threaded Execution Trace. MEMSYS 2020: 341-351 - [i11]Yehia Arafa, Ammar ElWazir, Abdelrahman Elkanishy, Youssef Aly, Ayatelrahman Elsayed, Abdel-Hameed A. Badawy, Gopinath Chennupati, Stephan J. Eidenbenz, Nandakishore Santhi:
Verified Instruction-Level Energy Consumption Measurement for NVIDIA GPUs. CoRR abs/2002.07795 (2020) - [i10]Manish Bhattarai, Gopinath Chennupati, Erik Skau, Raviteja Vangara, Hristo N. Djidjev, Boian S. Alexandrov:
Distributed Non-Negative Tensor Train Decomposition. CoRR abs/2008.01340 (2020) - [i9]Sayera Dhaubhadel, Jamaludin Mohd-Yusof, Kumkum Ganguly, Gopinath Chennupati, Sunil Thulasidasan, Nicolas W. Hengartner, Brent J. Mumphrey, Eric B. Durbin, Jennifer A. Doherty, Mireille Lemieux, Noah Schaefferkoetter, Georgia D. Tourassi, Linda Coyle, Lynne Penberthy, Benjamin McMahon, Tanmoy Bhattacharya:
Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports. CoRR abs/2009.05094 (2020) - [i8]Nasrin Akhter, Gopinath Chennupati, Hristo N. Djidjev, Amarda Shehu:
Decoy Selection for Protein Structure Prediction Via Extreme Gradient Boosting and Ranking. CoRR abs/2010.01441 (2020) - [i7]Gopinath Chennupati, Nandakishore Santhi, Phillip Romero, Stephan J. Eidenbenz:
Machine Learning Enabled Scalable Performance Prediction of Scientific Codes. CoRR abs/2010.04212 (2020)
2010 – 2019
- 2019
- [j1]Yehia Arafa
PPT-GPU: Scalable GPU Performance Modeling. IEEE Comput. Archit. Lett. 18: 55-58 (2019) - [c26]Yehia Arafa, Abdel-Hameed A. Badawy, Gopinath Chennupati, Nandakishore Santhi
POSTER: GPUs Pipeline Latency Analysis. ASAP 2019: 139 - [c25]Nasrin Akhter, Raviteja Vangara
Non-Negative Matrix Factorization for Selection of Near-Native Protein Tertiary Structures. BIBM 2019: 70-73 - [c24]Yehia Arafa, Abdel-Hameed A. Badawy, Gopinath Chennupati, Nandakishore Santhi
Low Overhead Instruction Latency Characterization for NVIDIA GPGPUs. HPEC 2019: 1-8 - [c23]Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff A. Bilmes, Gopinath Chennupati, Jamal Mohd-Yusof:
Combating Label Noise in Deep Learning using Abstention. ICML 2019: 6234-6243 - [c22]Yehia Arafa, Gopinath Chennupati, Atanu Barai
GPUs Cache Performance Estimation using Reuse Distance Analysis. IPCCC 2019: 1-8 - [c21]Sunil Thulasidasan, Gopinath Chennupati, Jeff A. Bilmes, Tanmoy Bhattacharya, Sarah Michalak:
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks. NeurIPS 2019: 13888-13899 - [c20]Gopinath Chennupati, Nandakishore Santhi
Scalable Performance Prediction of Codes with Memory Hierarchy and Pipelines. SIGSIM-PADS 2019: 13-24 - [i6]Yehia Arafa, Abdel-Hameed A. Badawy, Gopinath Chennupati, Nandakishore Santhi, Stephan J. Eidenbenz:
Instructions' Latencies Characterization for NVIDIA GPGPUs. CoRR abs/1905.08778 (2019) - [i5]Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff A. Bilmes, Gopinath Chennupati, Jamal Mohd-Yusof:
Combating Label Noise in Deep Learning Using Abstention. CoRR abs/1905.10964 (2019) - [i4]Sunil Thulasidasan, Gopinath Chennupati, Jeff A. Bilmes, Tanmoy Bhattacharya, Sarah Michalak:
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks. CoRR abs/1905.11001 (2019) - [i3]Atanu Barai
Modeling Shared Cache Performance of OpenMP Programs using Reuse Distance. CoRR abs/1907.12666 (2019) - 2018
- [c19]Nasrin Akhter, Gopinath Chennupati, Hristo N. Djidjev
Improved Decoy Selection via Machine Learning and Ranking. ICCABS 2018: 1 - [c18]Yehia Arafa, Abdel-Hameed A. Badawy, Gopinath Chennupati, Nandakishore Santhi
PPT-GPU: performance prediction toolkit for GPUs identifying the impact of caches: extended abstract. MEMSYS 2018: 301-302 - [c17]Mohammad Abu Obaida, Jason Liu
Parallel Application Performance Prediction Using Analysis Based Models and HPC Simulations. SIGSIM-PADS 2018: 49-59 - [c16]Gopinath Chennupati, Stephan J. Eidenbenz, Alex Long, Olena Tkachenko, Joseph Zerr, Jason Liu
Imcsim: Parameterized Performance Prediction for Implicit Monte Carlo codes. WSC 2018: 491-502 - [p1]Gopinath Chennupati, R. Muhammad Atif Azad
Synthesis of Parallel Programs on Multi-Cores. Handbook of Grammatical Evolution 2018: 289-315 - [i2]Patrick J. Coles, Stephan J. Eidenbenz, Scott Pakin, Adetokunbo Adedoyin, John Ambrosiano, Petr M. Anisimov, William Casper, Gopinath Chennupati, Carleton Coffrin, Hristo N. Djidjev, David Gunter, Satish Karra, Nathan Lemons, Shizeng Lin, Andrey Y. Lokhov, Alexander Malyzhenkov, David Dennis Lee Mascarenas, Susan M. Mniszewski, Balu Nadiga, Dan O'Malley, Diane Oyen, Lakshman Prasad, Randy Roberts, Philip Romero, Nandakishore Santhi
Quantum Algorithm Implementations for Beginners. CoRR abs/1804.03719 (2018) - 2017
- [c15]Gopinath Chennupati
AMM: Scalable Memory Reuse Model to Predict the Performance of Physics Codes. CLUSTER 2017: 649-650 - [c14]Bhargava Kalla, Nandakishore Santhi
A Probabilistic Monte Carlo Framework for Branch Prediction. CLUSTER 2017: 651-652 - [c13]Bhargava Kalla, Nandakishore Santhi
Probabilistic Monte Carlo simulations for static branch prediction. IPCCC 2017: 1-4 - [c12]Gopinath Chennupati, Nandakishore Santhi
A Scalable Analytical Memory Model for CPU Performance Prediction. PMBS@SC 2017: 114-135 - [c11]Gopinath Chennupati, Nandakishore Santhi
An analytical memory hierarchy model for performance prediction. WSC 2017: 908-919 - 2016
- [c10]Gopinath Chennupati
Automatic lock-free parallel programming on multi-core processors. CEC 2016: 4143-4150 - 2015
- [c9]Gopinath Chennupati
Automatic Evolution of Parallel Recursive Programs. EuroGP 2015: 167-178 - [c8]Gopinath Chennupati
Automatic Evolution of Parallel Sorting Programs on Multi-cores. EvoApplications 2015: 706-717 - [c7]Gopinath Chennupati
Performance Optimization of Multi-Core Grammatical Evolution Generated Parallel Recursive Programs. GECCO 2015: 1007-1014 - [c6]Gopinath Chennupati
Synthesis of Parallel Iterative Sorts with Multi-Core Grammatical Evolution. GECCO (Companion) 2015: 1059-1066 - [c5]Gopinath Chennupati
On the Automatic Generation of Efficient Parallel Iterative Sorting Algorithms. GECCO (Companion) 2015: 1369-1370 - 2014
- [c4]Gopinath Chennupati
Predict the success or failure of an evolutionary algorithm run. GECCO (Companion) 2014: 131-132 - [c3]Gopinath Chennupati
Multi-core GE: automatic evolution of CPU based multi-core parallel programs. GECCO (Companion) 2014: 1041-1044 - [c2]Gopinath Chennupati
Predict the performance of GE with an ACO based machine learning algorithm. GECCO (Companion) 2014: 1353-1360 - [c1]Gopinath Chennupati
On the efficiency of Multi-core Grammatical Evolution (MCGE) evolving multi-core parallel programs. NaBIC 2014: 238-243 - [i1]Gopinath Chennupati
eAnt-Miner : An Ensemble Ant-Miner to Improve the ACO Classification. CoRR abs/1409.2710 (2014)
Coauthor Index
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last updated on 2024-11-20 20:59 CET by the dblp team
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