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ORCIDSandeep Madireddy
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2020 – today
- 2024
[c17]Viktor Nilsson, Anirban Samaddar, Sandeep Madireddy, Pierre Nyquist:
REMEDI: Corrective Transformations for Improved Neural Entropy Estimation. ICML 2024- [c16]Yixuan Sun
, Elizabeth Cucuzzella, Steven Brus, Sri Hari Krishna Narayanan, Balasubramanya T. Nadiga, Luke Van Roekel, Jan Hückelheim, Sandeep Madireddy, Patrick Heimbach:
Parametric Sensitivities of a Wind-driven Baroclinic Ocean Using Neural Surrogates. PASC 2024: 6:1-6:10 - [i27]Viktor Nilsson, Anirban Samaddar, Sandeep Madireddy, Pierre Nyquist:
REMEDI: Corrective Transformations for Improved Neural Entropy Estimation. CoRR abs/2402.05718 (2024) - [i26]Yuan-Sen Ting, Tuan Dung Nguyen, Tirthankar Ghosal, Rui Pan, Hardik Arora, Zechang Sun, Tijmen de Haan, Nesar Ramachandra, Azton Wells, Sandeep Madireddy, Alberto Accomazzi:
AstroMLab 1: Who Wins Astronomy Jeopardy!? CoRR abs/2407.11194 (2024) - [i25]Zizhang Chen, Pengyu Hong, Sandeep Madireddy:
Question Rephrasing for Quantifying Uncertainty in Large Language Models: Applications in Molecular Chemistry Tasks. CoRR abs/2408.03732 (2024) - 2023
- [j5]Aleksandra Ciprijanovic, Ashia Lewis, Kevin Pedro, Sandeep Madireddy, Brian Nord, Gabriel N. Perdue, Stefan M. Wild:
DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection. Mach. Learn. Sci. Technol. 4(2): 25013 (2023) - [j4]Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Eseoghene Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Reddy Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Konidaris, Dhireesha Kudithipudi, Erik G. Learned-Miller, Seungwon Lee, Michael Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha:
A domain-agnostic approach for characterization of lifelong learning systems. Neural Networks 160: 274-296 (2023) - [c15]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: 10207-10222 - [c14]Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash:
Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures. CoLLAs 2023: 992-1008 - [c13]Yuming Liu, Angel Yanguas-Gil, Sandeep Madireddy, Yanjing Li:
Memristor-Spikelearn: A Spiking Neural Network Simulator for Studying Synaptic Plasticity under Realistic Device and Circuit Behaviors. DATE 2023: 1-6 - [i24]Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Eseoghene Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Reddy Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Dimitri Konidaris, Dhireesha Kudithipudi, Erik G. Learned-Miller, Seungwon Lee, Michael Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha:
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems. CoRR abs/2301.07799 (2023) - [i23]Aleksandra Ciprijanovic, Ashia Lewis, Kevin Pedro, Sandeep Madireddy, Brian Nord, Gabriel N. Perdue, Stefan M. Wild:
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection. CoRR abs/2302.02005 (2023) - [i22]Angel Yanguas-Gil, Sandeep Madireddy:
AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architectures. CoRR abs/2302.13210 (2023) - [i21]Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash:
Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures. CoRR abs/2308.04539 (2023) - [i20]Ray A. O. Sinurat, Anurag Reddy Daram, Haryadi S. Gunawi, Robert B. Ross, Sandeep Madireddy:
Towards Continually Learning Application Performance Models. CoRR abs/2310.16996 (2023) - [i19]Yixuan Sun, Elizabeth Cucuzzella, Steven Brus, Sri Hari Krishna Narayanan, Balu Nadiga, Luke Van Roekel, Jan Hückelheim, Sandeep Madireddy:
Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models. CoRR abs/2311.08421 (2023) - [i18]Tung Nguyen, Rohan Shah, Hritik Bansal, Troy Arcomano, Sandeep Madireddy, Romit Maulik, Veerabhadra Kotamarthi, Ian T. Foster, Aditya Grover:
Scaling transformer neural networks for skillful and reliable medium-range weather forecasting. CoRR abs/2312.03876 (2023) - 2022
- [j3]Aleksandra Ciprijanovic, Diana Kafkes, Gregory F. Snyder, F. Javier Sánchez, Gabriel Nathan Perdue, Kevin Pedro, Brian Nord, Sandeep Madireddy, Stefan M. Wild:
DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification. Mach. Learn. Sci. Technol. 3(3): 35007 (2022) - [j2]Dhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh C. Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Reddy Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou, Hava T. Siegelmann:
Biological underpinnings for lifelong learning machines. Nat. Mach. Intell. 4(3): 196-210 (2022) - [c12]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: 381-393 - [c11]Angel Yanguas-Gil, Sandeep Madireddy:
AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architectures. ICRC 2022: 24-29 - [c10]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: 16:1-16:14 - [i17]Anirban Samaddar, Sandeep Madireddy, Prasanna Balaprakash:
Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck. CoRR abs/2203.02592 (2022) - [i16]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 abs/2204.08180 (2022) - [i15]Sanket R. Jantre, Sandeep Madireddy, Shrijita Bhattacharya, Tapabrata Maiti, Prasanna Balaprakash:
Sequential Bayesian Neural Subnetwork Ensembles. CoRR abs/2206.00794 (2022) - [i14]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 abs/2210.00798 (2022) - [i13]Sumegha Premchandar, Sandeep Madireddy, Sanket R. Jantre, Prasanna Balaprakash:
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness. CoRR abs/2210.04083 (2022) - [i12]Aleksandra Ciprijanovic, Ashia Lewis, Kevin Pedro, Sandeep Madireddy, Brian Nord, Gabriel N. Perdue, Stefan M. Wild:
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection. CoRR abs/2211.00677 (2022) - [i11]Angel Yanguas-Gil, Sandeep Madireddy:
General policy mapping: online continual reinforcement learning inspired on the insect brain. CoRR abs/2211.16759 (2022) - 2021
- [j1]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): 25010 (2021) - [i10]Aleksandra Ciprijanovic, Diana Kafkes, K. Downey, S. Jenkins, Gabriel N. Perdue, Sandeep Madireddy, T. Johnston, Gregory F. Snyder, Brian Nord:
DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains. CoRR abs/2103.01373 (2021) - [i9]Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier M. Duarte, Philip C. Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bähr, Jürgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomás E. Müller-Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Kyle J. Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Thomas K. Warburton, Maria Acosta Flechas, Anthony Aportela, Thomas Calvet, Leonardo Cristella, Daniel Diaz, Caterina Doglioni, Maria Domenica Galati, Elham E Khoda, Farah Fahim, Davide Giri, Benjamin Hawks, Duc Hoang, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Iris Johnson, Raghav Kansal, Ryan Kastner, Erik Katsavounidis, Jeffrey D. Krupa, Pan Li, Sandeep Madireddy, Ethan Marx, Patrick McCormack, Andres Meza, Jovan Mitrevski, Mohammed Attia Mohammed, Farouk Mokhtar, Eric A. Moreno, Srishti Nagu, Rohin Narayan, Noah Palladino, Zhiqiang Que, Sang Eon Park, Subramanian Ramamoorthy, Dylan S. Rankin, Simon Rothman, Ashish Sharma, Sioni Summers, Pietro Vischia, Jean-Roch Vlimant, Olivia Weng:
Applications and Techniques for Fast Machine Learning in Science. CoRR abs/2110.13041 (2021) - [i8]Aleksandra Ciprijanovic, Diana Kafkes, Gabriel N. Perdue, Kevin Pedro, Gregory F. Snyder, F. Javier Sánchez, Sandeep Madireddy, Stefan M. Wild, Brian Nord:
Robustness of deep learning algorithms in astronomy - galaxy morphology studies. CoRR abs/2111.00961 (2021) - [i7]Aleksandra Ciprijanovic, Diana Kafkes, Gregory F. Snyder, F. Javier Sánchez, Gabriel Nathan Perdue, Kevin Pedro, Brian Nord, Sandeep Madireddy, Stefan M. Wild:
DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification. CoRR abs/2112.14299 (2021) - 2020
- [c9]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: 41-49 - [c8]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: 15-21 - [c7]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: 33 - [i6]Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash:
Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning. CoRR abs/2007.08159 (2020) - [i5]Aleksandra Ciprijanovic, Diana Kafkes, S. Jenkins, K. Downey, Gabriel N. Perdue, Sandeep Madireddy, T. Johnston, Brian Nord:
Domain adaptation techniques for improved cross-domain study of galaxy mergers. CoRR abs/2011.03591 (2020)
2010 – 2019
- 2019
- [c6]Sunwoo Lee, Qiao Kang, Sandeep Madireddy, Prasanna Balaprakash, Ankit Agrawal, Alok N. Choudhary, Richard Archibald, Wei-keng Liao:
Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time. IEEE BigData 2019: 830-839 - [c5]Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash:
Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning. ICONS 2019: 5:1-5:5 - [c4]Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Glenn K. Lockwood, Robert B. Ross, Shane Snyder, Stefan M. Wild:
Adaptive Learning for Concept Drift in Application Performance Modeling. ICPP 2019: 79:1-79:11 - [i4]Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash:
Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning. CoRR abs/1906.01668 (2019) - [i3]Romit Maulik, Vishwas Rao, Sandeep Madireddy, Bethany Lusch, Prasanna Balaprakash:
Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models. CoRR abs/1909.09144 (2019) - [i2]Sandeep Madireddy, Nan Li, Nesar Ramachandra, Prasanna Balaprakash, Salman Habib:
Modular Deep Learning Analysis of Galaxy-Scale Strong Lensing Images. CoRR abs/1911.03867 (2019) - [i1]Peihong Jiang, Hieu Doan, Sandeep Madireddy, Rajeev Surendran Assary, Prasanna Balaprakash:
Value-Added Chemical Discovery Using Reinforcement Learning. CoRR abs/1911.07630 (2019) - 2018
- [c3]Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Robert B. Ross, Shane Snyder, Stefan M. Wild:
Modeling I/O Performance Variability Using Conditional Variational Autoencoders. CLUSTER 2018: 109-113 - [c2]Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Robert B. Ross, Shane Snyder, Stefan M. Wild:
Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems. ISC 2018: 184-204 - 2017
- [c1]Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Robert B. Ross, Shane Snyder, Stefan M. Wild:
Analysis and Correlation of Application I/O Performance and System-Wide I/O Activity. NAS 2017: 1-10
Coauthor Index
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