Gopinath Chennupati

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 59

Top Topics

Gradient Boosting

Speech Recognition

Negative Matrix Factorization

Protein Structure

Top Venues

GECCO (Companion)

Publications

John Harvill, Rinat Khaziev, Scarlett Li, Randy Cogill, Lidan Wang, Gopinath Chennupati, Hari Thadakamalla
Significant ASR Error Detection for Conversational Voice Assistants. ICASSP (2024)
Aakriti Agrawal, Milind Rao, Anit Kumar Sahu, Gopinath Chennupati, Andreas Stolcke
Learning When to Trust Which Teacher for Weakly Supervised ASR. INTERSPEECH (2023)
Aakriti Agrawal, Milind Rao, Anit Kumar Sahu, Gopinath Chennupati, Andreas Stolcke
Learning When to Trust Which Teacher for Weakly Supervised ASR. CoRR (2023)
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 (2023)
Guruprasad V. Ramesh, Gopinath Chennupati, Milind Rao, Anit Kumar Sahu, Ariya Rastrow, Jasha Droppo
Federated Representation Learning for Automatic Speech Recognition. CoRR (2023)
Hamdy Abdelkhalik, Shamminuj Aktar, Yehia Arafa, Atanu Barai, Gopinath Chennupati, Nandakishore Santhi, Nishant Panda, Nirmal Prajapati, Nazmul Haque Turja, Stephan J. Eidenbenz, Abdel-Hameed A. Badawy
BB-ML: Basic Block Performance Prediction using Machine Learning Techniques. ICPADS (2023)
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)
Nasrin Akhter, Kazi Lutful Kabir, Gopinath Chennupati, Raviteja Vangara, Boian S. Alexandrov, Hristo N. Djidjev, Amarda Shehu
Improved Protein Decoy Selection via Non-Negative Matrix Factorization. IEEE ACM Trans. Comput. Biol. Bioinform. 19 (3) (2022)
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 (2022)
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)
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. J. Supercomput. 78 (2) (2022)
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 (2022)
Ganesh Tata, Gautham Krishna Gudur, Gopinath Chennupati, Mohammad Emtiyaz Khan
Can Calibration Improve Sample Prioritization? CoRR (2022)
Sunil Thulasidasan, Sushil Thapa, Sayera Dhaubhadel, Gopinath Chennupati, Tanmoy Bhattacharya, Jeff A. Bilmes
An Effective Baseline for Robustness to Distributional Shift. CoRR (2021)
Gopinath Chennupati, Nandakishore Santhi, Phillip Romero, Stephan J. Eidenbenz
Machine Learning-enabled Scalable Performance Prediction of Scientific Codes. ACM Trans. Model. Comput. Simul. 31 (2) (2021)
Sunil Thulasidasan, Sushil Thapa, Sayera Dhaubhadel, Gopinath Chennupati, Tanmoy Bhattacharya, Jeff A. Bilmes
An Effective Baseline for Robustness to Distributional Shift. ICMLA (2021)
Yehia Arafa, Abdel-Hameed A. Badawy, Ammar ElWazir, Atanu Barai, Ali Eker, Gopinath Chennupati, Nandakishore Santhi, Stephan J. Eidenbenz
Hybrid, scalable, trace-driven performance modeling of GPGPUs. SC (2021)
Raviteja Vangara, Manish Bhattarai, Erik Skau, Gopinath Chennupati, Hristo N. Djidjev, Thomas Tierney, James P. Smith, Valentin G. Stanev, Boian S. Alexandrov
Finding the Number of Latent Topics With Semantic Non-Negative Matrix Factorization. IEEE Access 9 (2021)
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 (2021)
Atanu Barai, Gopinath Chennupati, Nandakishore Santhi, Abdel-Hameed A. Badawy, Yehia Arafa, Stephan J. Eidenbenz
PPT-SASMM: Scalable Analytical Shared Memory Model: Predicting the Performance of Multicore Caches from a Single-Threaded Execution Trace. CoRR (2021)
Nasrin Akhter, Gopinath Chennupati, Hristo N. Djidjev, Amarda Shehu
Decoy selection for protein structure prediction via extreme gradient boosting and ranking. BMC Bioinform. (1) (2020)
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. CF (2020)
Kazi Lutful Kabir, Gopinath Chennupati, Raviteja Vangara, Hristo N. Djidjev, Boian S. Alexandrov, Amarda Shehu
Decoy Selection in Protein Structure Determination via Symmetric Non-negative Matrix Factorization. BIBM (2020)
Yehia Arafa, Abdel-Hameed A. Badawy, Gopinath Chennupati, Atanu Barai, Nandakishore Santhi, Stephan J. Eidenbenz
Fast, accurate, and scalable memory modeling of GPGPUs using reuse profiles. ICS (2020)
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 (2020)
Poornima Haridas, Gopinath Chennupati, Nandakishore Santhi, Phillip Romero, Stephan J. Eidenbenz
Code Characterization With Graph Convolutions and Capsule Networks. IEEE Access 8 (2020)
Raviteja Vangara, Erik Skau, Gopinath Chennupati, Hristo N. Djidjev, Thomas Tierney, James P. Smith, Manish Bhattarai, Valentin G. Stanev, Boian S. Alexandrov
Semantic Nonnegative Matrix Factorization with Automatic Model Determination for Topic Modeling. ICMLA (2020)
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 (2020)
Gopinath Chennupati, Nandakishore Santhi, Phillip Romero, Stephan J. Eidenbenz
Machine Learning Enabled Scalable Performance Prediction of Scientific Codes. CoRR (2020)
Gopinath Chennupati, Raviteja Vangara, Erik Skau, Hristo N. Djidjev, Boian S. Alexandrov
Distributed non-negative matrix factorization with determination of the number of latent features. J. Supercomput. 76 (9) (2020)
Manish Bhattarai, Gopinath Chennupati, Erik Skau, Raviteja Vangara, Hristo N. Djidjev, Boian S. Alexandrov
Distributed Non-Negative Tensor Train Decomposition. HPEC (2020)
Hector Carrillo-Cabada, Erik Skau, Gopinath Chennupati, Boian S. Alexandrov, Hristo N. Djidjev
An Out of Memory tSVD for Big-Data Factorization. IEEE Access 8 (2020)
Manish Bhattarai, Gopinath Chennupati, Erik Skau, Raviteja Vangara, Hristo N. Djidjev, Boian S. Alexandrov
Distributed Non-Negative Tensor Train Decomposition. CoRR (2020)
Nasrin Akhter, Gopinath Chennupati, Hristo N. Djidjev, Amarda Shehu
Decoy Selection for Protein Structure Prediction Via Extreme Gradient Boosting and Ranking. CoRR (2020)
Atanu Barai, Gopinath Chennupati, Nandakishore Santhi, Abdel-Hameed A. Badawy, Yehia Arafa, Stephan J. Eidenbenz
PPT-SASMM: Scalable Analytical Shared Memory Model: Predicting the Performance of Multicore Caches from a Single-Threaded Execution Trace. MEMSYS (2020)
Yehia Arafa, Ammar ElWazir, Abdelrahman Elkanishy, Youssef Aly, Ayatelrahman Elsayed, Abdel-Hameed A. Badawy, Gopinath Chennupati, Stephan J. Eidenbenz, Nandakishore Santhi
NVIDIA GPGPUs Instructions Energy Consumption. ISPASS (2020)
Yehia Arafa, Gopinath Chennupati, Atanu Barai, Abdel-Hameed A. Badawy, Nandakishore Santhi, Stephan J. Eidenbenz
GPUs Cache Performance Estimation using Reuse Distance Analysis. IPCCC (2019)
Yehia Arafa, Abdel-Hameed A. Badawy, Gopinath Chennupati, Nandakishore Santhi, Stephan J. Eidenbenz
POSTER: GPUs Pipeline Latency Analysis. ASAP (2019)
Yehia Arafa, Abdel-Hameed A. Badawy, Gopinath Chennupati, Nandakishore Santhi, Stephan J. Eidenbenz
Instructions' Latencies Characterization for NVIDIA GPGPUs. CoRR (2019)
Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff A. Bilmes, Gopinath Chennupati, Jamal Mohd-Yusof
Combating Label Noise in Deep Learning Using Abstention. CoRR (2019)
Sunil Thulasidasan, Gopinath Chennupati, Jeff A. Bilmes, Tanmoy Bhattacharya, Sarah Michalak
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks. CoRR (2019)
Atanu Barai, Gopinath Chennupati, Nandakishore Santhi, Abdel-Hameed A. Badawy, Stephan J. Eidenbenz
Modeling Shared Cache Performance of OpenMP Programs using Reuse Distance. CoRR (2019)
Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff A. Bilmes, Gopinath Chennupati, Jamal Mohd-Yusof
Combating Label Noise in Deep Learning using Abstention. ICML (2019)
Yehia Arafa, Abdel-Hameed A. Badawy, Gopinath Chennupati, Nandakishore Santhi, Stephan J. Eidenbenz
Low Overhead Instruction Latency Characterization for NVIDIA GPGPUs. HPEC (2019)
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)
Nasrin Akhter, Raviteja Vangara, Gopinath Chennupati, Boian S. Alexandrov, Hristo N. Djidjev, Amarda Shehu
Non-Negative Matrix Factorization for Selection of Near-Native Protein Tertiary Structures. BIBM (2019)
Yehia Arafa, Abdel-Hameed A. Badawy, Gopinath Chennupati, Nandakishore Santhi, Stephan J. Eidenbenz
PPT-GPU: Scalable GPU Performance Modeling. IEEE Comput. Archit. Lett. 18 (2019)
Gopinath Chennupati, Nandakishore Santhi, Stephan J. Eidenbenz
Scalable Performance Prediction of Codes with Memory Hierarchy and Pipelines. SIGSIM-PADS (2019)
Nasrin Akhter, Gopinath Chennupati, Hristo N. Djidjev, Amarda Shehu
Improved Decoy Selection via Machine Learning and Ranking. ICCABS (2018)
Mohammad Abu Obaida, Jason Liu, Gopinath Chennupati, Nandakishore Santhi, Stephan J. Eidenbenz
Parallel Application Performance Prediction Using Analysis Based Models and HPC Simulations. SIGSIM-PADS (2018)
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, Nikolai Sinitsyn, Pieter Swart, Marc Vuffray, Jim Wendelberger, Boram Yoon, Richard J. Zamora, Wei Zhu
Quantum Algorithm Implementations for Beginners. CoRR (2018)
Gopinath Chennupati, Stephan J. Eidenbenz, Alex Long, Olena Tkachenko, Joseph Zerr, Jason Liu
Imcsim: Parameterized Performance Prediction for Implicit Monte Carlo codes. WSC (2018)
Yehia Arafa, Abdel-Hameed A. Badawy, Gopinath Chennupati, Nandakishore Santhi, Stephan J. Eidenbenz
PPT-GPU: performance prediction toolkit for GPUs identifying the impact of caches: extended abstract. MEMSYS (2018)
Bhargava Kalla, Nandakishore Santhi, Abdel-Hameed A. Badawy, Gopinath Chennupati, Stephan J. Eidenbenz
A Probabilistic Monte Carlo Framework for Branch Prediction. CLUSTER (2017)
Gopinath Chennupati, Nandakishore Santhi, Stephan J. Eidenbenz, Sunil Thulasidasan
An analytical memory hierarchy model for performance prediction. WSC (2017)
Bhargava Kalla, Nandakishore Santhi, Abdel-Hameed A. Badawy, Gopinath Chennupati, Stephan J. Eidenbenz
Probabilistic Monte Carlo simulations for static branch prediction. IPCCC (2017)
Gopinath Chennupati, Nandakishore Santhi, Stephan J. Eidenbenz, Sunil Thulasidasan
AMM: Scalable Memory Reuse Model to Predict the Performance of Physics Codes. CLUSTER (2017)
Gopinath Chennupati, Nandakishore Santhi, Robert F. Bird, Sunil Thulasidasan, Abdel-Hameed A. Badawy, Satyajayant Misra, Stephan J. Eidenbenz
A Scalable Analytical Memory Model for CPU Performance Prediction. PMBS@SC (2017)
Gopinath Chennupati, R. Muhammad Atif Azad, Conor Ryan
Automatic lock-free parallel programming on multi-core processors. CEC (2016)
Gopinath Chennupati, R. Muhammad Atif Azad, Conor Ryan
Automatic Evolution of Parallel Sorting Programs on Multi-cores. EvoApplications (2015)
Gopinath Chennupati, R. Muhammad Atif Azad, Conor Ryan
On the Automatic Generation of Efficient Parallel Iterative Sorting Algorithms. GECCO (Companion) (2015)
Gopinath Chennupati, R. Muhammad Atif Azad, Conor Ryan
Synthesis of Parallel Iterative Sorts with Multi-Core Grammatical Evolution. GECCO (Companion) (2015)
Gopinath Chennupati, R. Muhammad Atif Azad, Conor Ryan
Performance Optimization of Multi-Core Grammatical Evolution Generated Parallel Recursive Programs. GECCO (2015)
Gopinath Chennupati, R. Muhammad Atif Azad, Conor Ryan
Automatic Evolution of Parallel Recursive Programs. EuroGP (2015)
Gopinath Chennupati, R. Muhammad Atif Azad, Conor Ryan
Predict the performance of GE with an ACO based machine learning algorithm. GECCO (Companion) (2014)
Gopinath Chennupati, Jeannie Fitzgerald, Conor Ryan
On the efficiency of Multi-core Grammatical Evolution (MCGE) evolving multi-core parallel programs. NaBIC (2014)
Gopinath Chennupati, Conor Ryan, R. Muhammad Atif Azad
Predict the success or failure of an evolutionary algorithm run. GECCO (Companion) (2014)
Gopinath Chennupati
eAnt-Miner : An Ensemble Ant-Miner to Improve the ACO Classification. CoRR (2014)
Gopinath Chennupati, R. Muhammad Atif Azad, Conor Ryan
Multi-core GE: automatic evolution of CPU based multi-core parallel programs. GECCO (Companion) (2014)