Generative Kaleidoscopic Networks
February 2024
Harsh Shrivastava
Senior Applied Scientist
Publications
Publications by Year
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Federated Learning with Neural Graphical Models
Urszula Chajewska, Harsh Shrivastava
Workshop on Advancements in Federated Learning (ECML-PKDD 2023) | September 2023
Neural Graphical Models
Harsh Shrivastava, Urszula Chajewska
European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2023) | September 2023
Are uGLAD? Time will tell!
Shima Imani, Harsh Shrivastava
Workshop on Advanced Analytics and Learning on Temporal Data (ECML 2023) | September 2023
Methods for Recovering Conditional Independence Graphs: A Survey
Harsh Shrivastava, Urszula Chajewska
The Journal of Artificial Intelligence Research (JAIR) | September 2023
Knowledge Propagation over Conditional Independence Graphs
Urszula Chajewska, Harsh Shrivastava
August 2023
Neural Graph Revealers
Harsh Shrivastava, Urszula Chajewska
Workshop Machine Learning for Multimodal Healthcare Data (ICML 2023) | July 2023
MathPrompter: Mathematical Reasoning using Large Language Models
Shima Imani, Liang Du, Harsh Shrivastava
2023 Meeting of the Association for Computational Linguistics | July 2023
DiversiGATE: A Comprehensive Framework for Reliable Large Language Models
Shima Imani, Ali Beyram, Harsh Shrivastava
June 2023
Reconstruction of Gene Regulatory Networks using Sparse Graph Recovery Models
April 2023
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uGLAD: A Deep Learning Model to Recover Conditional Independence Graphs
Harsh Shrivastava, Urszula Chajewska, Robin Abraham, Xinshi Chen
Workshop on New Frontiers in Graph Learning (NeurIPS 2022) | December 2022
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Antman: Sparse low-rank compression to accelerate rnn inference
Samyam Rajbhandari, Harsh Shrivastava, Yuxiong He
arXiv preprint arXiv:1910.01740 | September 2019
Publications by Research Area
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Generative Kaleidoscopic Networks
February 2024
Federated Learning with Neural Graphical Models
Urszula Chajewska, Harsh Shrivastava
Workshop on Advancements in Federated Learning (ECML-PKDD 2023) | September 2023
Neural Graphical Models
Harsh Shrivastava, Urszula Chajewska
European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2023) | September 2023
Are uGLAD? Time will tell!
Shima Imani, Harsh Shrivastava
Workshop on Advanced Analytics and Learning on Temporal Data (ECML 2023) | September 2023
Methods for Recovering Conditional Independence Graphs: A Survey
Harsh Shrivastava, Urszula Chajewska
The Journal of Artificial Intelligence Research (JAIR) | September 2023
Knowledge Propagation over Conditional Independence Graphs
Urszula Chajewska, Harsh Shrivastava
August 2023
Neural Graph Revealers
Harsh Shrivastava, Urszula Chajewska
Workshop Machine Learning for Multimodal Healthcare Data (ICML 2023) | July 2023
MathPrompter: Mathematical Reasoning using Large Language Models
Shima Imani, Liang Du, Harsh Shrivastava
2023 Meeting of the Association for Computational Linguistics | July 2023
DiversiGATE: A Comprehensive Framework for Reliable Large Language Models
Shima Imani, Ali Beyram, Harsh Shrivastava
June 2023
Reconstruction of Gene Regulatory Networks using Sparse Graph Recovery Models
April 2023
uGLAD: A Deep Learning Model to Recover Conditional Independence Graphs
Harsh Shrivastava, Urszula Chajewska, Robin Abraham, Xinshi Chen
Workshop on New Frontiers in Graph Learning (NeurIPS 2022) | December 2022
Antman: Sparse low-rank compression to accelerate rnn inference
Samyam Rajbhandari, Harsh Shrivastava, Yuxiong He
arXiv preprint arXiv:1910.01740 | September 2019
-
Federated Learning with Neural Graphical Models
Urszula Chajewska, Harsh Shrivastava
Workshop on Advancements in Federated Learning (ECML-PKDD 2023) | September 2023
Are uGLAD? Time will tell!
Shima Imani, Harsh Shrivastava
Workshop on Advanced Analytics and Learning on Temporal Data (ECML 2023) | September 2023
Knowledge Propagation over Conditional Independence Graphs
Urszula Chajewska, Harsh Shrivastava
August 2023
uGLAD: A Deep Learning Model to Recover Conditional Independence Graphs
Harsh Shrivastava, Urszula Chajewska, Robin Abraham, Xinshi Chen
Workshop on New Frontiers in Graph Learning (NeurIPS 2022) | December 2022
-
Federated Learning with Neural Graphical Models
Urszula Chajewska, Harsh Shrivastava
Workshop on Advancements in Federated Learning (ECML-PKDD 2023) | September 2023
Neural Graphical Models
Harsh Shrivastava, Urszula Chajewska
European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2023) | September 2023
Knowledge Propagation over Conditional Independence Graphs
Urszula Chajewska, Harsh Shrivastava
August 2023
Neural Graph Revealers
Harsh Shrivastava, Urszula Chajewska
Workshop Machine Learning for Multimodal Healthcare Data (ICML 2023) | July 2023
Reconstruction of Gene Regulatory Networks using Sparse Graph Recovery Models
April 2023
-
MathPrompter: Mathematical Reasoning using Large Language Models
Shima Imani, Liang Du, Harsh Shrivastava
2023 Meeting of the Association for Computational Linguistics | July 2023
DiversiGATE: A Comprehensive Framework for Reliable Large Language Models
Shima Imani, Ali Beyram, Harsh Shrivastava
June 2023
Publications by Type
-
Generative Kaleidoscopic Networks
February 2024
Knowledge Propagation over Conditional Independence Graphs
Urszula Chajewska, Harsh Shrivastava
August 2023
DiversiGATE: A Comprehensive Framework for Reliable Large Language Models
Shima Imani, Ali Beyram, Harsh Shrivastava
June 2023
Reconstruction of Gene Regulatory Networks using Sparse Graph Recovery Models
April 2023
-
Federated Learning with Neural Graphical Models
Urszula Chajewska, Harsh Shrivastava
Workshop on Advancements in Federated Learning (ECML-PKDD 2023) | September 2023
Neural Graphical Models
Harsh Shrivastava, Urszula Chajewska
European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2023) | September 2023
Are uGLAD? Time will tell!
Shima Imani, Harsh Shrivastava
Workshop on Advanced Analytics and Learning on Temporal Data (ECML 2023) | September 2023
Neural Graph Revealers
Harsh Shrivastava, Urszula Chajewska
Workshop Machine Learning for Multimodal Healthcare Data (ICML 2023) | July 2023
MathPrompter: Mathematical Reasoning using Large Language Models
Shima Imani, Liang Du, Harsh Shrivastava
2023 Meeting of the Association for Computational Linguistics | July 2023
uGLAD: A Deep Learning Model to Recover Conditional Independence Graphs
Harsh Shrivastava, Urszula Chajewska, Robin Abraham, Xinshi Chen
Workshop on New Frontiers in Graph Learning (NeurIPS 2022) | December 2022
-
Methods for Recovering Conditional Independence Graphs: A Survey
Harsh Shrivastava, Urszula Chajewska
The Journal of Artificial Intelligence Research (JAIR) | September 2023
Antman: Sparse low-rank compression to accelerate rnn inference
Samyam Rajbhandari, Harsh Shrivastava, Yuxiong He
arXiv preprint arXiv:1910.01740 | September 2019