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Anuj Karpatne
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2020 – today
- 2024
- [j29]Medha Sawhney, Bhas Karmarkar, Eric J. Leaman, Arka Daw, Anuj Karpatne, Bahareh Behkam
:
Motion Enhanced Multi-Level Tracker (MEMTrack): A Deep Learning-Based Approach to Microrobot Tracking in Dense and Low-Contrast Environments. Adv. Intell. Syst. 6(4) (2024) - [c33]Mridul Khurana, Arka Daw, M. Maruf, Josef C. Uyeda, Wasila M. Dahdul, Caleb Charpentier, Yasin Bakis, Henry L. Bart Jr., Paula M. Mabee, Hilmar Lapp, James P. Balhoff, Wei-Lun Chao, Charles V. Stewart, Tanya Y. Berger-Wolf, Anuj Karpatne:
Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolution. ECCV (89) 2024: 137-153 - [c32]Dipanjyoti Paul, Arpita Chowdhury, Xinqi Xiong, Feng-Ju Chang, David Edward Carlyn, Samuel Stevens, Kaiya Provost, Anuj Karpatne, Bryan Carstens, Daniel I. Rubenstein, Charles V. Stewart, Tanya Y. Berger-Wolf, Yu Su, Wei-Lun Chao:
A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis. ICLR 2024 - [c31]Mohannad Elhamod, Anuj Karpatne:
Neuro-Visualizer: A Novel Auto-Encoder-Based Loss Landscape Visualization Method With an Application in Knowledge-Guided Machine Learning. ICML 2024 - [i36]Anuj Karpatne, Xiaowei Jia, Vipin Kumar:
Knowledge-guided Machine Learning: Current Trends and Future Prospects. CoRR abs/2403.15989 (2024) - [i35]Kazi Sajeed Mehrab, M. Maruf, Arka Daw, Harish Babu Manogaran, Abhilash Neog, Mridul Khurana, Bahadir Altintas, Yasin Bakis, Elizabeth G. Campolongo, Matthew J. Thompson, Xiaojun Wang, Hilmar Lapp, Wei-Lun Chao, Paula M. Mabee, Henry L. Bart Jr., Wasila M. Dahdul, Anuj Karpatne:
Fish-Vista: A Multi-Purpose Dataset for Understanding & Identification of Traits from Images. CoRR abs/2407.08027 (2024) - [i34]Mridul Khurana, Arka Daw, M. Maruf, Josef C. Uyeda, Wasila M. Dahdul, Caleb Charpentier, Yasin Bakis, Henry L. Bart Jr., Paula M. Mabee, Hilmar Lapp, James P. Balhoff, Wei-Lun Chao, Charles V. Stewart, Tanya Y. Berger-Wolf, Anuj Karpatne:
Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolution. CoRR abs/2408.00160 (2024) - [i33]M. Maruf, Arka Daw, Kazi Sajeed Mehrab, Harish Babu Manogaran, Abhilash Neog, Medha Sawhney, Mridul Khurana, James P. Balhoff, Yasin Bakis, Bahadir Altintas, Matthew J. Thompson, Elizabeth G. Campolongo, Josef C. Uyeda, Hilmar Lapp, Henry L. Bart Jr., Paula M. Mabee, Yu Su, Wei-Lun Chao, Charles V. Stewart, Tanya Y. Berger-Wolf, Wasila M. Dahdul, Anuj Karpatne:
VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images. CoRR abs/2408.16176 (2024) - [i32]Harish Babu Manogaran, M. Maruf, Arka Daw, Kazi Sajeed Mehrab, Caleb Charpentier, Josef C. Uyeda, Wasila M. Dahdul, Matthew J. Thompson, Elizabeth G. Campolongo, Kaiya Provost, Paula M. Mabee, Hilmar Lapp, Anuj Karpatne:
What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits. CoRR abs/2409.02335 (2024) - [i31]Naveen Gupta, Medha Sawhney, Arka Daw, Youzuo Lin, Anuj Karpatne:
A Unified Framework for Forward and Inverse Problems in Subsurface Imaging using Latent Space Translations. CoRR abs/2410.11247 (2024) - 2023
- [j28]Ziyu Yao, Anuj Karpatne:
Welcome to AI Matters 9(1). AI Matters 9(1): 3 (2023) - [j27]Ziyu Yao, Anuj Karpatne:
Welcome to AI Matters 9(2). AI Matters 9(2): 3 (2023) - [j26]Ziyu Yao, Anuj Karpatne:
Welcome to AI Matters 9(3). AI Matters 9(3): 3 (2023) - [j25]Sanmay Das, Nicholas Mattei, John P. Dickerson, Sven Koenig, Louise A. Dennis, Larry R. Medsker, Ziyu Yao, Anuj Karpatne, Alan Tsang, Matt Luckcuck:
SIGAI Annual Report: July 1 2022 - August 30 2023. AI Matters 9(3): 4-9 (2023) - [c30]Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne:
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling. ICML 2023: 7264-7302 - [c29]Mohannad Elhamod
Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks. KDD 2023: 3966-3978 - [i30]Jie Bu, Kazi Sajeed Mehrab, Anuj Karpatne:
Let There Be Order: Rethinking Ordering in Autoregressive Graph Generation. CoRR abs/2305.15562 (2023) - [i29]Mohannad Elhamod, Mridul Khurana, Harish Babu Manogaran, Josef C. Uyeda, Meghan A. Balk, Wasila M. Dahdul, Yasin Bakis, Henry L. Bart Jr., Paula M. Mabee, Hilmar Lapp, James P. Balhoff, Caleb Charpentier, David Carlyn, Wei-Lun Chao, Charles V. Stewart, Daniel I. Rubenstein, Tanya Y. Berger-Wolf, Anuj Karpatne:
Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks. CoRR abs/2306.03228 (2023) - [i28]M. Maruf, Arka Daw, Amartya Dutta, Jie Bu, Anuj Karpatne:
Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation. CoRR abs/2308.11052 (2023) - [i27]Mohannad Elhamod, Anuj Karpatne:
Neuro-Visualizer: An Auto-encoder-based Loss Landscape Visualization Method. CoRR abs/2309.14601 (2023) - [i26]Medha Sawhney, Bhas Karmarkar, Eric J. Leaman, Arka Daw, Anuj Karpatne, Bahareh Behkam:
MEMTRACK: A Deep Learning-Based Approach to Microrobot Tracking in Dense and Low-Contrast Environments. CoRR abs/2310.09441 (2023) - [i25]Dipanjyoti Paul, Arpita Chowdhury, Xinqi Xiong, Feng-Ju Chang, David Carlyn, Samuel Stevens, Kaiya Provost, Anuj Karpatne, Bryan Carstens, Daniel I. Rubenstein, Charles V. Stewart, Tanya Y. Berger-Wolf, Yu Su, Wei-Lun Chao:
A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis. CoRR abs/2311.04157 (2023) - 2022
- [j24]Anuj Karpatne, Ziyu Yao:
Welcome to AI matters 8(1). AI Matters 8(1): 3 (2022) - [j23]Anuj Karpatne, Ziyu Yao:
Welcome to AI matters 8(2). AI Matters 8(2): 3 (2022) - [j22]Anuj Karpatne, Ziyu Yao:
Welcome to AI matters 8(3). AI Matters 8(3): 3 (2022) - [j21]Sanmay Das, Nicholas Mattei, John P. Dickerson, Sven Koenig, Louise A. Dennis, Larry R. Medsker, Ziyu Yao, Anuj Karpatne, Alan Tsang, Matt Luckcuck:
SIGAI Annual Report: July 1 2021 - June 30 2022. AI Matters 8(3): 4-7 (2022) - [j20]Anuj Karpatne, Ziyu Yao:
Welcome to AI matters 8(4). AI Matters 8(4): 3 (2022) - [j19]Mohannad Elhamod
CoPhy-PGNN: Learning Physics-guided Neural Networks with Competing Loss Functions for Solving Eigenvalue Problems. ACM Trans. Intell. Syst. Technol. 13(6): 92:1-92:23 (2022) - [c28]Arka Daw, Kyongmin Yeo, Anuj Karpatne, Levente J. Klein:
Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring. IEEE Big Data 2022: 4835-4841 - [i24]Sangeeta Srivastava, Samuel Olin, Viktor Podolskiy, Anuj Karpatne, Wei-Cheng Lee, Anish Arora:
Physics-Guided Problem Decomposition for Scaling Deep Learning of High-dimensional Eigen-Solvers: The Case of Schrödinger's Equation. CoRR abs/2202.05994 (2022) - [i23]Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne:
Rethinking the Importance of Sampling in Physics-informed Neural Networks. CoRR abs/2207.02338 (2022) - [i22]Arka Daw, Kyongmin Yeo, Anuj Karpatne, Levente J. Klein:
Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring. CoRR abs/2211.00864 (2022) - 2021
- [j18]Iolanda Leite, Anuj Karpatne:
Welcome to AI Matters 7(1). AI Matters 7(1): 4 (2021) - [j17]Iolanda Leite, Anuj Karpatne:
Welcome to AI Matters 7(2). AI Matters 7(2): 3-4 (2021) - [j16]Iolanda Leite, Anuj Karpatne:
Welcome to AI matters 7(3). AI Matters 7(3): 4 (2021) - [j15]Sanmay Das, Nicholas Mattei, John P. Dickerson, Sven Koenig, Louise A. Dennis, Larry R. Medsker, Todd W. Neller, Iolanda Leite, Anuj Karpatne, Alan Tsang:
SIGAI annual report: July 1 2020 - June 30 2021. AI Matters 7(3): 5-11 (2021) - [j14]Xiaowei Jia
Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles. Trans. Data Sci. 2(3): 20:1-20:26 (2021) - [c27]Mohannad Elhamod, Jie Bu, Christopher Singh, Matthew Redell, Abantika Ghosh, Viktor Podolskiy, Wei-Cheng Lee, Anuj Karpatne:
Learning Physics-guided Neural Networks with Competing Physics Loss: A Summary of Results in Solving Eigenvalue Problems. AAAI Spring Symposium: MLPS 2021 - [c26]Nikhil Muralidhar, Jie Bu, Ze Cao, Neil Raj, Naren Ramakrishnan
PhyFlow: Physics-Guided Deep Learning for Generating Interpretable 3D Flow Fields. ICDM 2021: 1246-1251 - [c25]Ioannis Papakis, Abhijit Sarkar
A Graph Convolutional Neural Network Based Approach for Traffic Monitoring Using Augmented Detections with Optical Flow. ITSC 2021: 2980-2986 - [c24]Arka Daw
PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics. KDD 2021: 237-247 - [c23]Rose Yu, Paris Perdikaris, Anuj Karpatne:
Physics-Guided AI for Large-Scale Spatiotemporal Data. KDD 2021: 4088-4089 - [c22]Jie Bu, Arka Daw, M. Maruf, Anuj Karpatne:
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM). NeurIPS 2021: 3491-3503 - [c21]M. Maruf, Anuj Karpatne:
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling Approach. SDM 2021: 271-279 - [c20]Jie Bu, Anuj Karpatne:
Quadratic Residual Networks: A New Class of Neural Networks for Solving Forward and Inverse Problems in Physics Involving PDEs. SDM 2021: 675-683 - [e1]Jonghyun Lee, Eric F. Darve, Peter K. Kitanidis, Michael W. Mahoney, Anuj Karpatne, Matthew W. Farthing, Tyler J. Hesser:
Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to - 24th, 2021. CEUR Workshop Proceedings 2964, CEUR-WS.org 2021 [contents] - [i21]Jie Bu, Anuj Karpatne:
Quadratic Residual Networks: A New Class of Neural Networks for Solving Forward and Inverse Problems in Physics Involving PDEs. CoRR abs/2101.08366 (2021) - [i20]Reza Sepasdar
A Data-Driven Approach to Full-Field Damage and Failure Pattern Prediction in Microstructure-Dependent Composites using Deep Learning. CoRR abs/2104.04485 (2021) - [i19]Arka Daw, M. Maruf, Anuj Karpatne:
PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics. CoRR abs/2106.02993 (2021) - [i18]Jie Bu, Arka Daw, M. Maruf, Anuj Karpatne:
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM). CoRR abs/2110.00684 (2021) - [i17]Austin Chennault, Andrey A. Popov, Amit N. Subrahmanya, Rachel Cooper, Anuj Karpatne, Adrian Sandu:
Adjoint-Matching Neural Network Surrogates for Fast 4D-Var Data Assimilation. CoRR abs/2111.08626 (2021) - 2020
- [j13]Amy McGovern, Iolanda Leite, Anuj Karpatne:
Welcome to AI Matters 6(1). AI Matters 6(1): 3-4 (2020) - [j12]Iolanda Leite, Anuj Karpatne:
Welcome to AI matters 6(2). AI Matters 6(2): 3-4 (2020) - [j11]Sanmay Das, Nicholas Mattei, John P. Dickerson, Sven Koenig, Louise A. Dennis, Larry R. Medsker, Todd W. Neller, Iolanda Leite, Anuj Karpatne:
SIGAI annual report: July 1 2019 - June 30 2020. AI Matters 6(2): 5-9 (2020) - [j10]Iolanda Leite, Anuj Karpatne:
Welcome to AI matters 6(3). AI Matters 6(3): 3-4 (2020) - [j9]Nikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan
Physics-Guided Deep Learning for Drag Force Prediction in Dense Fluid-Particulate Systems. Big Data 8(5): 431-449 (2020) - [c19]Xiaowei Jia, Jared Willard
Process Guided Deep Learning for Modeling Physical Systems: An Application in Lake Temperature Modeling. IGARSS 2020: 3494-3496 - [c18]Jeremy Leipzig
Biodiversity Image Quality Metadata Augments Convolutional Neural Network Classification of Fish Species. MTSR 2020: 3-12 - [c17]Arka Daw, R. Quinn Thomas
Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling. SDM 2020: 532-540 - [c16]Nikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan
PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly. SDM 2020: 559-567 - [i16]Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan S. Read, Jacob A. Zwart, Michael S. Steinbach, Vipin Kumar:
Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles. CoRR abs/2001.11086 (2020) - [i15]Jie Bu, Mohannad Elhamod, Christopher Singh, Matthew Redell, Wei-Cheng Lee, Anuj Karpatne:
Learning Neural Networks with Competing Physics Objectives: An Application in Quantum Mechanics. CoRR abs/2007.01420 (2020) - [i14]M. Maruf, Anuj Karpatne:
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling Approach. CoRR abs/2007.01423 (2020) - [i13]Ioannis Papakis, Abhijit Sarkar
GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization. CoRR abs/2010.00067 (2020)
2010 – 2019
- 2019
- [b1]Pang-Ning Tan, Michael S. Steinbach, Anuj Karpatne, Vipin Kumar:
Introduction to Data Mining (Second Edition). Pearson 2019 - [j8]Amy McGovern, Iolanda Leite, Anuj Karpatne:
Welcome to AI matters 5(4). AI Matters 5(4): 3-4 (2019) - [j7]Anuj Karpatne
Machine Learning for the Geosciences: Challenges and Opportunities. IEEE Trans. Knowl. Data Eng. 31(8): 1544-1554 (2019) - [c15]Xiaowei Jia, Mengdie Wang, Ankush Khandelwal, Anuj Karpatne, Vipin Kumar:
Recurrent Generative Networks for Multi-Resolution Satellite Data: An Application in Cropland Monitoring. IJCAI 2019: 2628-2634 - [c14]Xiaowei Jia, Sheng Li, Ankush Khandelwal, Guruprasad Nayak
Spatial Context-Aware Networks for Mining Temporal Discriminative Period in Land Cover Detection. SDM 2019: 513-521 - [c13]Xiaowei Jia, Guruprasad Nayak
Classifying Heterogeneous Sequential Data by Cyclic Domain Adaptation: An Application in Land Cover Detection. SDM 2019: 540-548 - [c12]Xiaowei Jia, Jared Willard
Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles. SDM 2019: 558-566 - [i12]Saurabh Agrawal, Saurabh Verma, Anuj Karpatne, Stefan Liess, Snigdhansu Chatterjee, Vipin Kumar:
A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series. CoRR abs/1906.01450 (2019) - [i11]Arka Daw, R. Quinn Thomas, Cayelan C. Carey, Jordan S. Read, Alison P. Appling, Anuj Karpatne:
Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling. CoRR abs/1911.02682 (2019) - [i10]Nikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan, Danesh K. Tafti, Anuj Karpatne:
Physics-guided Design and Learning of Neural Networks for Predicting Drag Force on Particle Suspensions in Moving Fluids. CoRR abs/1911.04240 (2019) - 2018
- [j6]Gowtham Atluri, Anuj Karpatne
Spatio-Temporal Data Mining: A Survey of Problems and Methods. ACM Comput. Surv. 51(4): 83:1-83:41 (2018) - [c11]Nikhil Muralidhar, Mohammad Raihanul Islam, Manish Marwah, Anuj Karpatne, Naren Ramakrishnan
Incorporating Prior Domain Knowledge into Deep Neural Networks. IEEE BigData 2018: 36-45 - [i9]Saurabh Agrawal, Saurabh Verma, Gowtham Atluri, Anuj Karpatne, Stefan Liess, Angus W. MacDonald III, Snigdhansu Chatterjee, Vipin Kumar:
Mining Sub-Interval Relationships In Time Series Data. CoRR abs/1802.06095 (2018) - [i8]Xiaowei Jia, Anuj Karpatne, Jared Willard, Michael S. Steinbach, Jordan S. Read, Paul C. Hanson, Hilary A. Dugan, Vipin Kumar:
Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to Monitoring Water Temperature And Quality In Lakes. CoRR abs/1810.02880 (2018) - [i7]Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan S. Read, Jacob Zwart, Michael S. Steinbach, Vipin Kumar:
Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles. CoRR abs/1810.13075 (2018) - 2017
- [j5]Anuj Karpatne, Gowtham Atluri, James H. Faghmous, Michael S. Steinbach
Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data. IEEE Trans. Knowl. Data Eng. 29(10): 2318-2331 (2017) - [c10]Xiaowei Jia, Yifan Hu, Ankush Khandelwal, Anuj Karpatne, Vipin Kumar:
Joint sparse auto-encoder: A semi-supervised spatio-temporal approach in mapping large-scale croplands. IEEE BigData 2017: 1173-1182 - [c9]Anuj Karpatne, Vipin Kumar:
Big Data in Climate: Opportunities and Challenges for Machine Learning. KDD 2017: 21-22 - [c8]Saurabh Agrawal, Gowtham Atluri, Anuj Karpatne, William Haltom, Stefan Liess
Tripoles: A New Class of Relationships in Time Series Data. KDD 2017: 697-706 - [i6]Anuj Karpatne, William Watkins, Jordan S. Read, Vipin Kumar:
Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling. CoRR abs/1710.11431 (2017) - [i5]Anuj Karpatne, Imme Ebert-Uphoff, Sai Ravela, Hassan Ali Babaie, Vipin Kumar:
Machine Learning for the Geosciences: Challenges and Opportunities. CoRR abs/1711.04708 (2017) - [i4]Gowtham Atluri, Anuj Karpatne, Vipin Kumar:
Spatio-Temporal Data Mining: A Survey of Problems and Methods. CoRR abs/1711.04710 (2017) - [i3]Ankush Khandelwal, Anuj Karpatne, Vipin Kumar:
ORBIT: Ordering Based Information Transfer Across Space and Time for Global Surface Water Monitoring. CoRR abs/1711.05799 (2017) - [i2]Xiaowei Jia, Ankush Khandelwal, Anuj Karpatne, Vipin Kumar:
Discovery of Shifting Patterns in Sequence Classification. CoRR abs/1712.07203 (2017) - 2016
- [c7]Xiaowei Jia, Xi C. Chen, Anuj Karpatne, Vipin Kumar:
Identifying dynamic changes with noisy labels in spatial-temporal data: A study on large-scale water monitoring application. IEEE BigData 2016: 1328-1333 - [p2]Anuj Karpatne, Ankush Khandelwal, Xi Chen, Varun Mithal, James H. Faghmous, Vipin Kumar:
Global Monitoring of Inland Water Dynamics: State-of-the-Art, Challenges, and Opportunities. Computational Sustainability 2016: 121-147 - [i1]Anuj Karpatne, Gowtham Atluri, James H. Faghmous, Michael S. Steinbach, Arindam Banerjee, Auroop R. Ganguly, Shashi Shekhar, Nagiza F. Samatova, Vipin Kumar:
Theory-guided Data Science: A New Paradigm for Scientific Discovery. CoRR abs/1612.08544 (2016) - 2015
- [j4]Anuj Karpatne, Stefan Liess
A Guide to Earth Science Data: Summary and Research Challenges. Comput. Sci. Eng. 17(6): 14-18 (2015) - [c6]Anuj Karpatne, Vipin Kumar:
Adaptive Heterogeneous Ensemble Learning Using the Context of Test Instances. ICDM 2015: 787-792 - [c5]Anuj Karpatne, Vipin Kumar:
Building Predictive Models for Noisy and Heterogeneous Data: An Application in Global Monitoring of Inland Water Dynamics. ICDM Workshops 2015: 1530-1531 - [c4]Anuj Karpatne, Ankush Khandelwal, Vipin Kumar:
Ensemble Learning Methods for Binary Classification with Multi-modality within the Classes. SDM 2015: 730-738 - 2014
- [c3]Anuj Karpatne, Ankush Khandelwal, Shyam Boriah, Vipin Kumar:
Predictive Learning in the Presence of Heterogeneity and Limited Training Data. SDM 2014: 253-261 - 2013
- [j3]Reshma Khemchandani
Twin support vector regression for the simultaneous learning of a function and its derivatives. Int. J. Mach. Learn. Cybern. 4(1): 51-63 (2013) - [j2]Reshma Khemchandani
Proximal support tensor machines. Int. J. Mach. Learn. Cybern. 4(6): 703-712 (2013) - [p1]Anuj Karpatne, James H. Faghmous, Jaya Kawale, Luke Styles, Mace Blank, Varun Mithal, Xi C. Chen, Ankush Khandelwal, Shyam Boriah, Karsten Steinhaeuser, Michael S. Steinbach
Earth Science Applications of Sensor Data. Managing and Mining Sensor Data 2013: 505-530 - 2012
- [c2]Anuj Karpatne, Mace Blank, Michael Lau, Shyam Boriah, Karsten Steinhaeuser, Michael S. Steinbach
Importance of vegetation type in forest cover estimation. CIDU 2012: 71-78 - [c1]Xi C. Chen, Anuj Karpatne, Yashu Chamber, Varun Mithal, Michael Lau, Karsten Steinhaeuser, Shyam Boriah, Michael S. Steinbach
A new data mining framework for forest fire mapping. CIDU 2012: 104-111 - 2011
- [j1]Reshma Khemchandani
Generalized eigenvalue proximal support vector regressor. Expert Syst. Appl. 38(10): 13136-13142 (2011)
Coauthor Index
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