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- Dhagash Mehta
University of Notre Dame, USA
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Publication search results
found 43 matches
- 2023
- Edgar A. Bernal, Jonathan D. Hauenstein, Dhagash Mehta, Margaret H. Regan, Tingting Tang:
Machine learning the real discriminant locus. J. Symb. Comput. 115: 409-426 (2023) - Jonathan D. Hauenstein, Yang-Hui He, Ilias S. Kotsireas, Dhagash Mehta, Tingting Tang:
Special issue on Algebraic Geometry and Machine Learning. J. Symb. Comput. 118: 93-94 (2023) - Bhaskarjit Sarmah, Dhagash Mehta, Stefano Pasquali, Tianjie Zhu:
Towards reducing hallucination in extracting information from financial reports using Large Language Models. AIMLSystems 2023: 39:1-39:5 - Dhruv Desai, Ashmita Dhiman, Tushar Sharma, Deepika Sharma, Dhagash Mehta, Stefano Pasquali:
Quantifying Outlierness of Funds from their Categories using Supervised Similarity. ICAIF 2023: 655-663 - Dhruv Desai, Ashmita Dhiman, Tushar Sharma, Deepika Sharma, Dhagash Mehta, Stefano Pasquali:
Quantifying Outlierness of Funds from their Categories using Supervised Similarity. CoRR abs/2308.06882 (2023) - Bhaskarjit Sarmah, Tianjie Zhu, Dhagash Mehta, Stefano Pasquali:
Towards reducing hallucination in extracting information from financial reports using Large Language Models. CoRR abs/2310.10760 (2023) - Joshua Rosaler, Dhruv Desai, Bhaskarjit Sarmah, Dimitrios Vamvourellis, Deran Onay, Dhagash Mehta, Stefano Pasquali:
Towards Enhanced Local Explainability of Random Forests: a Proximity-Based Approach. CoRR abs/2310.12428 (2023) - 2022
- Dhagash Mehta, Tianran Chen, Tingting Tang, Jonathan D. Hauenstein:
The Loss Surface of Deep Linear Networks Viewed Through the Algebraic Geometry Lens. IEEE Trans. Pattern Anal. Mach. Intell. 44(9): 5664-5680 (2022) - Jerinsh Jeyapaulraj, Dhruv Desai, Dhagash Mehta, Peter Chu, Stefano Pasquali, Philip Sommer:
Supervised similarity learning for corporate bonds using Random Forest proximities. ICAIF 2022: 411-419 - Dimitrios Vamvourellis, Máté Tóth, Dhruv Desai, Dhagash Mehta, Stefano Pasquali:
Learning Mutual Fund Categorization using Natural Language Processing. ICAIF 2022: 87-95 - 2021
- Cynthia Pagliaro, Dhagash Mehta, Han-Tai Shiao, Shaofei Wang, Luwei Xiong:
Investor behavior modeling by analyzing financial advisor notes: a machine learning perspective. ICAIF 2021: 23:1-23:8 - Vipul Satone, Dhruv Desai, Dhagash Mehta:
Fund2Vec: mutual funds similarity using graph learning. ICAIF 2021: 16:1-16:8 - Vipul Satone, Dhruv Desai, Dhagash Mehta:
Fund2Vec: Mutual Funds Similarity using Graph Learning. CoRR abs/2106.12987 (2021) - 2020
- Dhagash Mehta, Dhruv Desai, Jithin Pradeep:
Machine learning fund categorizations. ICAIF 2020: 13:1-13:8 - Dhagash Mehta, Dhruv Desai, Jithin Pradeep:
Machine Learning Fund Categorizations. CoRR abs/2006.00123 (2020) - Edgar A. Bernal, Jonathan D. Hauenstein, Dhagash Mehta, Margaret H. Regan, Tingting Tang:
Machine learning the real discriminant locus. CoRR abs/2006.14078 (2020) - 2019
- Tianran Chen, Jakub Marecek, Dhagash Mehta, Matthew E. Niemerg:
Three Formulations of the Kuramoto Model as a System of Polynomial Equations. Allerton 2019: 810-815 - Heather A. Harrington, Dhagash Mehta, Helen M. Byrne, Jonathan D. Hauenstein:
Decomposing the Parameter Space of Biological Networks via a Numerical Discriminant Approach. MC 2019: 114-131 - 2018
- Christian Knoll, Dhagash Mehta, Tianran Chen, Franz Pernkopf:
Fixed Points of Belief Propagation - An Analysis via Polynomial Homotopy Continuation. IEEE Trans. Pattern Anal. Mach. Intell. 40(9): 2124-2136 (2018) - Tianran Chen, Robert Davis, Dhagash Mehta:
Counting Equilibria of the Kuramoto Model Using Birationally Invariant Intersection Index. SIAM J. Appl. Algebra Geom. 2(4): 489-507 (2018) - Shaunak D. Bopardikar, Dhagash Mehta, Jonathan D. Hauenstein:
Optimal Configurations in Coverage Control with Polynomial Costs. CoRR abs/1801.10285 (2018) - Dhagash Mehta, Xiaojun Zhao, Edgar A. Bernal, David J. Wales:
The Loss Surface of XOR Artificial Neural Networks. CoRR abs/1804.02411 (2018) - Dhagash Mehta, Tianran Chen, Tingting Tang, Jonathan D. Hauenstein:
The loss surface of deep linear networks viewed through the algebraic geometry lens. CoRR abs/1810.07716 (2018) - Timothy E. Wang, Jack Gu, Dhagash Mehta, Xiaojun Zhao, Edgar A. Bernal:
Towards Robust Deep Neural Networks. CoRR abs/1810.11726 (2018) - 2017
- Andrew J. Ballard, Ritankar Das, Stefano Martiniani, Dhagash Mehta, Levent Sagun, Jacob D. Stevenson, David J. Wales:
Perspective: Energy Landscapes for Machine Learning. CoRR abs/1703.07915 (2017) - Souvik Chandra, Dhagash Mehta, Aranya Chakrabortty:
Locating Power Flow Solution Space Boundaries: A Numerical Polynomial Homotopy Approach. CoRR abs/1704.04792 (2017) - 2016
- Dhagash Mehta:
Introduction to numerical polynomial homotopy continuation methods for the power systems practitioners. ACC 2016: 1768 - Dhagash Mehta, Daniel K. Molzahn, Konstantin S. Turitsyn:
Recent advances in computational methods for the power flow equations. ACC 2016: 1753-1765 - Daniel K. Molzahn, Dhagash Mehta, Matthew E. Niemerg:
Toward topologically based upper bounds on the number of power flow solutions. ACC 2016: 5927-5932 - Tianran Chen, Dhagash Mehta, Matthew E. Niemerg:
A Network Topology Dependent Upper Bound on the Number of Equilibria of the Kuramoto Model. CoRR abs/1603.05905 (2016)
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