Prasanna Balaprakash

ORCID

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 138

Top Venues

CoRR

CLUSTER

ISC

Publications

Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta, Pei Zhang, David Rogers, Jonghyun Bae, Khaled Z. Ibrahim, Ashwin M. Aji, Karl W. Schulz, Jorda Polo, Prasanna Balaprakash
Scalable Training of Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN. CoRR (2024)
Aristeidis Tsaris, Chengming Zhang, Xiao Wang, Junqi Yin, Siyan Liu, Moetasim Ashfaq, Ming Fan, Jong-Youl Choi, Mohamed Wahib, Dan Lu, Prasanna Balaprakash, Fei-Yue Wang
Sequence Length Scaling in Vision Transformers for Scientific Images on Frontier. CoRR (2024)
Xingfu Wu, John R. Tramm, Jeffrey Larson, John-Luke Navarro, Prasanna Balaprakash, Brice Videau, Michael Kruse, Paul D. Hovland, Valerie E. Taylor, Mary W. Hall
Integrating ytopt and libEnsemble to Autotune OpenMC. CoRR (2024)
Romain Egele, Felix Mohr, Tom J. Viering, Prasanna Balaprakash
The Unreasonable Effectiveness Of Early Discarding After One Epoch In Neural Network Hyperparameter Optimization. CoRR (2024)
Thomas Randall, Jaehoon Koo, Brice Videau, Michael Kruse, Xingfu Wu, Paul D. Hovland, Mary W. Hall, Rong Ge, Prasanna Balaprakash
Transfer-Learning-Based Autotuning Using Gaussian Copula. CoRR (2024)
Sami Khairy, Prasanna Balaprakash
Multi-fidelity reinforcement learning with control variates. Neurocomputing 597 (2024)
Xin Liu, Mario Rüttgers, Alessio Quercia, Romain Egele, Elisabeth Pfaehler, Rushikesh Shende, Marcel Aach, Wolfgang Schröder, Prasanna Balaprakash, Andreas Lintermann
Refining computer tomography data with super-resolution networks to increase the accuracy of respiratory flow simulations. Future Gener. Comput. Syst. 159 (2024)
Hongwei Jin, George Papadimitriou, Krishnan Raghavan, Pawel Zuk, Prasanna Balaprakash, Cong Wang, Anirban Mandal, Ewa Deelman
Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning. CoRR (2024)
Yixuan Sun, Olaperi Yeside Sowunmi, Romain Egele, Sri Hari Krishna Narayanan, Luke Van Roekel, Prasanna Balaprakash
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach. CoRR (2024)
Tanwi Mallick, Jane MacFarlane, Prasanna Balaprakash
Uncertainty Quantification for Traffic Forecasting Using Deep-Ensemble-Based Spatiotemporal Graph Neural Networks. IEEE Trans. Intell. Transp. Syst. 25 (8) (2024)
Hongwei Jin, Prasanna Balaprakash, Allen Zou, Pieter Ghysels, Aditi S. Krishnapriyan, Adam Mate, Arthur K. Barnes, Russell Bent
Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement. CoRR (2024)
Romain Egele, Felix Mohr, Tom J. Viering, Prasanna Balaprakash
The unreasonable effectiveness of early discarding after one epoch in neural network hyperparameter optimization. Neurocomputing 597 (2024)
Adarsha Balaji, Ramyad Hadidi, Gregory Kollmer, Mohammed E. Fouda, Prasanna Balaprakash
Network architecture search of X-ray based scientific applications. CoRR (2024)
Anindya Bhaduri, Nesar Ramachandra, Sandipp Krishnan Ravi, Lele Luan, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang
Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data. J. Comput. Inf. Sci. Eng. 24 (4) (2024)
Romain Egele, Júlio C. S. Jacques Júnior, Jan N. van Rijn, Isabelle Guyon, Xavier Baró, Albert Clapés, Prasanna Balaprakash, Sergio Escalera, Thomas B. Moeslund, Jun Wan
AI Competitions and Benchmarks: Dataset Development. CoRR (2024)
Krishnan Raghavan, George Papadimitriou, Hongwei Jin, Anirban Mandal, Mariam Kiran, Prasanna Balaprakash, Ewa Deelman
Advancing Anomaly Detection in Computational Workflows with Active Learning. CoRR (2024)
Xiao Wang, Aristeidis Tsaris, Siyan Liu, Jong-Youl Choi, Ming Fan, Wei Zhang, Junqi Yin, Moetasim Ashfaq, Dan Lu, Prasanna Balaprakash
ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability. CoRR (2024)
William F. Godoy, Pedro Valero-Lara, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter
Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation. CoRR (2023)
Romit Maulik, Romain Egele, Krishnan Raghavan, Prasanna Balaprakash
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles. CoRR (2023)
Lele Luan, Nesar Ramachandra, Sandipp Krishnan Ravi, Anindya Bhaduri, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang
Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field. CoRR (2023)
Sahil Bhola, Suraj Pawar, Prasanna Balaprakash, Romit Maulik
Multi-fidelity reinforcement learning framework for shape optimization. J. Comput. Phys. 482 (2023)
George Papadimitriou, Hongwei Jin, Cong Wang, Krishnan Raghavan, Anirban Mandal, Prasanna Balaprakash, Ewa Deelman
Flow-Bench: A Dataset for Computational Workflow Anomaly Detection. CoRR (2023)
Thomas Randall, Jaehoon Koo, Brice Videau, Michael Kruse, Xingfu Wu, Paul D. Hovland, Mary W. Hall, Rong Ge, Prasanna Balaprakash
Transfer-learning-based Autotuning using Gaussian Copula. ICS (2023)
Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Mariam Kiran, Ewa Deelman, Prasanna Balaprakash
Graph neural networks for detecting anomalies in scientific workflows. Int. J. High Perform. Comput. Appl. 37 (3-4) (2023)
Shengli Jiang, Shiyi Qin, Reid C. Van Lehn, Prasanna Balaprakash, Victor M. Zavala
Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search. CoRR (2023)
Xingfu Wu, Prasanna Balaprakash, Michael Kruse, Jaehoon Koo, Brice Videau, Paul D. Hovland, Valerie E. Taylor, Brad Geltz, Siddhartha Jana, Mary W. Hall
ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales. CoRR (2023)
Romain Egele, Isabelle Guyon, Yixuan Sun, Prasanna Balaprakash
Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization? ESANN (2023)
Prasanna Balaprakash
Scalable Automated Design and Development of Deep Neural Networks for Scientific and Engineering Applications. IPDPS Workshops (2023)
Krishnan Raghavan, Prasanna Balaprakash
Learning Continually on a Sequence of Graphs - The Dynamical System Way. CoRR (2023)
Romain Égelé, Isabelle Guyon, Venkatram Vishwanath, Prasanna Balaprakash
Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization. e-Science (2023)
Sami Khairy, Prasanna Balaprakash
Multi-Fidelity Reinforcement Learning with Control Variates. ESANN (2023)
Adarsha Balaji, Prasanna Balaprakash
Application - Hardware Co-Optimization of Crossbar-Based Neuromorphic Systems. ICMLA (2023)
Pedro Valero-Lara, Alexis Huante, Mustafa Al-Lail, William F. Godoy, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter
Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation. CoRR (2023)
Romain Egele, Isabelle Guyon, Yixuan Sun, Prasanna Balaprakash
Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization? CoRR (2023)
Romain Egele, Tyler Chang, Yixuan Sun, Venkatram Vishwanath, Prasanna Balaprakash
Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives. CoRR (2023)
Prasanna Balaprakash
iWAPT2023 Invited Speaker Optimizing HPC Systems for Scientific Applications: Machine Learning Approaches to Performance Tuning and Anomaly Detection. IPDPS Workshops (2023)
Anirban Samaddar, Sandeep Madireddy, Prasanna Balaprakash, Taps Maiti, Gustavo de los Campos, Ian Fischer
Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck. AISTATS (2023)
Weiheng Zhong, Tanwi Mallick, Jane MacFarlane, Hadi Meidani, Prasanna Balaprakash
Graph Pyramid Autoformer for Long- Term Traffic Forecasting. ICMLA (2023)
Tanwi Mallick, Joshua David Bergerson, Duane R. Verner, John K. Hutchison, Leslie-Anne Levy, Prasanna Balaprakash
Analyzing the impact of climate change on critical infrastructure from the scientific literature: A weakly supervised NLP approach. CoRR (2023)
Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Ewa Deelman, Prasanna Balaprakash
Self-supervised Learning for Anomaly Detection in Computational Workflows. CoRR (2023)
Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures. CoRR (2023)
Sajal Dash, Isaac Lyngaas, Junqi Yin, Xiao Wang, Romain Egele, Guojing Cong, Feiyi Wang, Prasanna Balaprakash
Optimizing Distributed Training on Frontier for Large Language Models. CoRR (2023)
Alec J. Linot, Joshua W. Burby, Qi Tang, Prasanna Balaprakash, Michael D. Graham, Romit Maulik
Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems. J. Comput. Phys. 474 (2023)
Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures. CoLLAs (2023)
Jongeun Kim, Sven Leyffer, Prasanna Balaprakash
Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics. INFORMS J. Comput. 35 (6) (2023)
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan A. Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael W. Irvin, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi A. Hanson, Thomas E. Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton D. Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin M. Aji, Angela Dalton, Michael J. Schulte, Karl Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. CoRR (2023)
William F. Godoy, Pedro Valero-Lara, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter
Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation. ICPP Workshops (2023)
Matthieu Dorier, Romain Egele, Prasanna Balaprakash, Jaehoon Koo, Sandeep Madireddy, Srinivasan Ramesh, Allen D. Malony, Robert B. Ross
HPC Storage Service Autotuning Using Variational- Autoencoder -Guided Asynchronous Bayesian Optimization. CLUSTER (2022)
Mihailo Isakov, Mikaela Currier, Eliakin Del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Glenn K. Lockwood, Michel A. Kinsy
A Taxonomy of Error Sources in HPC I/O Machine Learning Models. SC (2022)
Orcun Yildiz, Henry Chan, Krishnan Raghavan, William Judge, Mathew J. Cherukara, Prasanna Balaprakash, Subramanian Sankaranarayanan, Tom Peterka
Automated Continual Learning of Defect Identification in Coherent Diffraction Imaging. AI4S (2022)
Alec J. Linot, Joshua W. Burby, Qi Tang, Prasanna Balaprakash, Michael D. Graham, Romit Maulik
Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems. CoRR (2022)
Romain Egele, Joceran Gouneau, Venkatram Vishwanath, Isabelle Guyon, Prasanna Balaprakash
Asynchronous Distributed Bayesian Optimization at HPC Scale. CoRR (2022)
Fangfang Xia, Jonathan E. Allen, Prasanna Balaprakash, Thomas S. Brettin, Cristina Garcia-Cardona, Austin Clyde, Judith D. Cohn, James H. Doroshow, Xiaotian Duan, Veronika Dubinkina, Yvonne A. Evrard, Ya Ju Fan, Jason Gans, Stewart He, Pinyi Lu, Sergei Maslov, Alexander Partin, Maulik Shukla, Eric A. Stahlberg, Justin M. Wozniak, Hyun Seung Yoo, George F. Zaki, Yitan Zhu, Rick Stevens
A cross-study analysis of drug response prediction in cancer cell lines. Briefings Bioinform. 23 (1) (2022)
Sami Khairy, Prasanna Balaprakash
Multifidelity Reinforcement Learning with Control Variates. CoRR (2022)
Dominic Yang, Prasanna Balaprakash, Sven Leyffer
Modeling design and control problems involving neural network surrogates. Comput. Optim. Appl. 83 (3) (2022)
Romain Egele, Romit Maulik, Krishnan Raghavan, Bethany Lusch, Isabelle Guyon, Prasanna Balaprakash
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification. ICPR (2022)
Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Patrycja Krawczuk, Loïc Pottier, Mariam Kiran, Ewa Deelman, Prasanna Balaprakash
Workflow Anomaly Detection with Graph Neural Networks. WORKS@SC (2022)
Anirban Samaddar, Sandeep Madireddy, Prasanna Balaprakash
Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck. CoRR (2022)
Sahil Bhola, Suraj Pawar, Prasanna Balaprakash, Romit Maulik
Multi-fidelity reinforcement learning framework for shape optimization. CoRR (2022)
Sanket Jantre, Sandeep Madireddy, Shrijita Bhattacharya, Tapabrata Maiti, Prasanna Balaprakash
Sequential Bayesian Neural Subnetwork Ensembles. CoRR (2022)
Sami Khairy, Prasanna Balaprakash, Lin X. Cai, H. Vincent Poor
Data-Driven Random Access Optimization in Multi-Cell IoT Networks Using NOMA. IEEE Trans. Wirel. Commun. 21 (7) (2022)
Weiheng Zhong, Tanwi Mallick, Hadi Meidani, Jane MacFarlane, Prasanna Balaprakash
Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting. CoRR (2022)
R. Krishnan, Prasanna Balaprakash
Continual Learning via Dynamic Programming. ICPR (2022)
Nathan A Garland, Romit Maulik, Qi Tang, Xian-Zhu Tang, Prasanna Balaprakash
Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling. Mach. Learn. Sci. Technol. 3 (4) (2022)
Mihailo Isakov, Mikaela Currier, Eliakin Del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Glenn K. Lockwood, Michel A. Kinsy
A Taxonomy of Error Sources in HPC I/O Machine Learning Models. CoRR (2022)
Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul D. Hovland, Valerie Taylor, Mary W. Hall
Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimization. Concurr. Comput. Pract. Exp. 34 (20) (2022)
Matthieu Dorier, Romain Egele, Prasanna Balaprakash, Jaehoon Koo, Sandeep Madireddy, Srinivasan Ramesh, Allen D. Malony, Robert B. Ross
HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization. CoRR (2022)
Tanwi Mallick, Prasanna Balaprakash, Jane MacFarlane
Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting. CoRR (2022)
Sumegha Premchandar, Sandeep Madireddy, Sanket Jantre, Prasanna Balaprakash
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness. CoRR (2022)
Grant Getzelman, Prasanna Balaprakash
Learning to Switch Optimizers for Quadratic Programming. ACML (2021)
Krishnan Raghavan, Prasanna Balaprakash
Formalizing the Generalization-Forgetting Trade-off in Continual Learning. NeurIPS (2021)
Sami Khairy, Prasanna Balaprakash, Lin X. Cai, Yu Cheng
Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV Based Random Access IoT Networks With NOMA. IEEE J. Sel. Areas Commun. 39 (4) (2021)
Romain Egele, Romit Maulik, Krishnan Raghavan, Prasanna Balaprakash, Bethany Lusch
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification. CoRR (2021)
Yudong Yao, Henry Chan, Subramanian Sankaranarayanan, Prasanna Balaprakash, Ross J. Harder, Mathew J. Cherukara
AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale Coherent Imaging. CoRR (2021)
Yixuan Sun, Tanwi Mallick, Prasanna Balaprakash, Jane MacFarlane
A data-centric weak supervised learning for highway traffic incident detection. CoRR (2021)
Krishnan Raghavan, Prasanna Balaprakash
Formalizing the Generalization-Forgetting Trade-off in Continual Learning. CoRR (2021)
Dominic Yang, Prasanna Balaprakash, Sven Leyffer
Modeling Design and Control Problems Involving Neural Network Surrogates. CoRR (2021)
Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul D. Hovland, Valerie E. Taylor, Mary W. Hall
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization (extended version). CoRR (2021)
Romain Égelé, Prasanna Balaprakash, Isabelle Guyon, Venkatram Vishwanath, Fangfang Xia, Rick Stevens, Zhengying Liu
AgEBO-tabular: joint neural architecture and hyperparameter search with autotuned data-parallel training for tabular data. SC (2021)
Sami Khairy, Prasanna Balaprakash, Lin X. Cai, H. Vincent Poor
Learning-Based Distributed Random Access for Multi-Cell IoT Networks with NOMA. CoRR (2021)
Sandeep Madireddy, Ji Hwan Park, Sunwoo Lee, Prasanna Balaprakash, Shinjae Yoo, Wei-keng Liao, Cory D. Hauck, M. Paul Laiu, Richard Archibald
In situ compression artifact removal in scientific data using deep transfer learning and experience replay. Mach. Learn. Sci. Technol. 2 (2) (2021)
Jaehoon Koo, Prasanna Balaprakash, Michael Kruse, Xingfu Wu, Paul D. Hovland, Mary W. Hall
Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations. PMBS (2021)
Jongeun Kim, Sven Leyffer, Prasanna Balaprakash
Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics. CoRR (2021)
Jaehoon Koo, Prasanna Balaprakash, Michael Kruse, Xingfu Wu, Paul D. Hovland, Mary W. Hall
Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations. CoRR (2021)
Romit Maulik, Romain Egele, Bethany Lusch, Prasanna Balaprakash
Recurrent neural network architecture search for geophysical emulation. SC (2020)
Yi He, Prasanna Balaprakash, Yanjing Li
FIdelity: Efficient Resilience Analysis Framework for Deep Learning Accelerators. MICRO (2020)
R. Krishnan, Prasanna Balaprakash
Meta Continual Learning via Dynamic Programming. CoRR (2020)
Sami Khairy, Prasanna Balaprakash, Lin X. Cai, Yu Cheng
Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV based Random Access IoT Networks with NOMA. CoRR (2020)
Mihailo Isakov, Eliakin Del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Michel A. Kinsy
HPC I/O throughput bottleneck analysis with explainable local models. SC (2020)
Tanwi Mallick, Mariam Kiran, Bashir Mohammed, Prasanna Balaprakash
Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks. CoRR (2020)
Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul D. Hovland, Valerie E. Taylor, Mary W. Hall
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization. PMBS@SC (2020)
Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia
Towards On-Chip Bayesian Neuromorphic Learning. CoRR (2020)
Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, Prasanna Balaprakash
Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems. AAAI (2020)
Romain Egele, Prasanna Balaprakash, Venkatram Vishwanath, Isabelle Guyon, Zhengying Liu
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data. CoRR (2020)
Eliakin Del Rosario, Mikaela Currier, Mihailo Isakov, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Kevin Harms, Shane Snyder, Michel A. Kinsy
Gauge: An Interactive Data-Driven Visualization Tool for HPC Application I/O Performance Analysis. PDSW@SC (2020)
Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning. CoRR (2020)
Shengli Jiang, Prasanna Balaprakash
Graph Neural Network Architecture Search for Molecular Property Prediction. CoRR (2020)
Mihailo Isakov, Eliakin Del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Michel A. Kinsy
Toward Generalizable Models of I/O Throughput. ROSS@SC (2020)
Shengli Jiang, Prasanna Balaprakash
Graph Neural Network Architecture Search for Molecular Property Prediction. IEEE BigData (2020)
Tanwi Mallick, Prasanna Balaprakash, Eric Rask, Jane MacFarlane
Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting. CoRR (2020)

Prasanna Balaprakash.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Prasanna Balaprakash