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ORCIDFarhad Pourkamali-Anaraki
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2020 – today
- 2024
[j17]Parisa Hajibabaee
, Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili:
Adaptive Conformal Prediction Intervals Using Data-Dependent Weights With Application to Seismic Response Prediction. IEEE Access 12: 53579-53597 (2024)- [j16]Farhad Pourkamali-Anaraki:
Special Issue: Big Scientific Data and Machine Learning in Science and Engineering. Big Data 12(4): 269 (2024) - [j15]Farhad Pourkamali-Anaraki, Jamal F. Husseini, Evan J. Pineda, Brett A. Bednarcyk, Scott E. Stapleton:
Two-stage surrogate modeling for data-driven design optimization with application to composite microstructure generation. Eng. Appl. Artif. Intell. 138: 109436 (2024) - [j14]Farhad Pourkamali-Anaraki, Tahamina Nasrin, Robert E. Jensen, Amy M. Peterson, Christopher J. Hansen:
Adaptive activation functions for predictive modeling with sparse experimental data. Neural Comput. Appl. 36(29): 18297-18311 (2024) - [i19]Farhad Pourkamali-Anaraki, Jamal F. Husseini, Evan J. Pineda, Brett A. Bednarcyk, Scott E. Stapleton:
Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generation. CoRR abs/2401.02008 (2024) - [i18]Farhad Pourkamali-Anaraki, Tahamina Nasrin, Robert E. Jensen, Amy M. Peterson, Christopher J. Hansen:
Adaptive Activation Functions for Predictive Modeling with Sparse Experimental Data. CoRR abs/2402.05401 (2024) - [i17]Farhad Pourkamali-Anaraki, Jamal F. Husseini, Scott E. Stapleton:
Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning. CoRR abs/2402.13945 (2024) - [i16]Farhad Pourkamali-Anaraki:
Kolmogorov-Arnold Networks in Low-Data Regimes: A Comparative Study with Multilayer Perceptrons. CoRR abs/2409.10463 (2024) - 2023
- [j13]Farhad Pourkamali-Anaraki, Tahamina Nasrin, Robert E. Jensen, Amy M. Peterson, Christopher J. Hansen:
Evaluation of classification models in limited data scenarios with application to additive manufacturing. Eng. Appl. Artif. Intell. 126: 106983 (2023) - [c13]Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili:
Evaluating Regression Models with Partial Data: A Sampling Approach. CoDIT 2023: 1882-1887 - [c12]Carol M. Kiekhaefer, Farhad Pourkamali-Anaraki:
Advancing Precision Medicine: An Evaluative Study of Feature Selection Methods. ICMLA 2023: 1052-1059 - 2022
- [j12]Mohammad Amin Hariri-Ardebili, Farhad Pourkamali-Anaraki:
Structural uncertainty quantification with partial information. Expert Syst. Appl. 198: 116736 (2022) - [c11]Yasin Findik, Farhad Pourkamali-Anaraki:
D-CBRS: Accounting for Intra-Class Diversity in Continual Learning. ICIP 2022: 2531-2535 - [i15]Yasin Findik, Farhad Pourkamali-Anaraki:
D-CBRS: Accounting For Intra-Class Diversity in Continual Learning. CoRR abs/2207.05897 (2022) - 2021
- [j11]Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili:
Neural Networks and Imbalanced Learning for Data-Driven Scientific Computing With Uncertainties. IEEE Access 9: 15334-15350 (2021) - [j10]Parisa Hajibabaee, Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili:
Kernel Matrix Approximation on Class-Imbalanced Data With an Application to Scientific Simulation. IEEE Access 9: 83579-83591 (2021) - [j9]Farhad Pourkamali-Anaraki, Walter D. Bennette:
Adaptive Data Compression for Classification Problems. IEEE Access 9: 157654-157669 (2021) - [j8]Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili, Stephen Becker, Maziar Raissi:
Call for Special Issue Papers: Big Scientific Data and Machine Learning in Science and Engineering: Deadline for Manuscript Submission: February 1, 2022. Big Data 9(4): 326-327 (2021) - [j7]Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili, Stephen Becker, Maziar Raissi:
Call for Special Issue Papers: Big Scientific Data and Machine Learning in Science and Engineering: Deadline for Manuscript Submission: February 1, 2022. Big Data 9(5): 404-405 (2021) - [j6]Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili, Stephen Becker, Maziar Raissi:
Call for Special Issue Papers: Big Scientific Data and Machine Learning in Science and Engineering: Deadline for Manuscript Submission: February 1, 2022. Big Data 9(6): 409-410 (2021) - [c10]Parisa Hajibabaee, Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili:
An Empirical Evaluation of the t-SNE Algorithm for Data Visualization in Structural Engineering. ICMLA 2021: 1674-1680 - [i14]Parisa Hajibabaee, Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili:
An Empirical Evaluation of the t-SNE Algorithm for Data Visualization in Structural Engineering. CoRR abs/2109.08795 (2021) - 2020
- [j5]Sina Sharif Mansouri, Christoforos Kanellakis, Björn Lindqvist, Farhad Pourkamali-Anaraki, Ali-akbar Agha-mohammadi, Joel Burdick, George Nikolakopoulos:
A Unified NMPC Scheme for MAVs Navigation With 3D Collision Avoidance Under Position Uncertainty. IEEE Robotics Autom. Lett. 5(4): 5740-5747 (2020) - [j4]Farhad Pourkamali-Anaraki, James Folberth, Stephen Becker:
Efficient Solvers for Sparse Subspace Clustering. Signal Process. 172: 107548 (2020) - [c9]Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili, Lydia Morawiec:
Kernel Ridge Regression Using Importance Sampling with Application to Seismic Response Prediction. ICMLA 2020: 511-518 - [c8]Sina Sharif Mansouri, Farhad Pourkamali-Anaraki, Miguel Castano, Ali-Akbar Agha-Mohammadi, Joel Burdick, George Nikolakopoulos:
Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud. MED 2020: 802-807 - [i13]Sina Sharif Mansouri, Farhad Pourkamali-Anaraki, Miguel Castano Arranz, Ali-akbar Agha-mohammadi, Joel Burdick, George Nikolakopoulos:
Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud. CoRR abs/2006.04225 (2020) - [i12]Farhad Pourkamali-Anaraki:
Scalable Spectral Clustering with Nystrom Approximation: Practical and Theoretical Aspects. CoRR abs/2006.14470 (2020) - [i11]Sina Sharif Mansouri, Christoforos Kanellakis, Björn Lindqvist, Farhad Pourkamali-Anaraki, Ali-akbar Agha-mohammadi, Joel Burdick, George Nikolakopoulos:
A Unified NMPC Scheme for MAVs Navigation with 3D Collision Avoidance under Position Uncertainty. CoRR abs/2007.15879 (2020) - [i10]Mohammad Amin Hariri-Ardebili, Farhad Pourkamali-Anaraki, Siamak Sattar:
Uncertainty Quantification of Structural Systems with Subset of Data. CoRR abs/2008.04382 (2020) - [i9]Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili, Lydia Morawiec:
Kernel Ridge Regression Using Importance Sampling with Application to Seismic Response Prediction. CoRR abs/2009.09136 (2020)
2010 – 2019
- 2019
- [j3]Farhad Pourkamali-Anaraki, Stephen Becker:
Improved fixed-rank Nyström approximation via QR decomposition: Practical and theoretical aspects. Neurocomputing 363: 261-272 (2019) - [c7]Farhad Pourkamali-Anaraki:
Large-Scale Sparse Subspace Clustering Using Landmarks. MLSP 2019: 1-6 - [i8]Farhad Pourkamali-Anaraki:
Large-Scale Sparse Subspace Clustering Using Landmarks. CoRR abs/1908.00683 (2019) - [i7]Farhad Pourkamali-Anaraki, Michael B. Wakin:
The Effectiveness of Variational Autoencoders for Active Learning. CoRR abs/1911.07716 (2019) - 2018
- [c6]Farhad Pourkamali-Anaraki, Stephen Becker, Michael B. Wakin:
Randomized Clustered Nystrom for Large-Scale Kernel Machines. AAAI 2018: 3960-3967 - [i6]Farhad Pourkamali-Anaraki, Stephen Becker:
Efficient Solvers for Sparse Subspace Clustering. CoRR abs/1804.06291 (2018) - 2017
- [j2]Farhad Pourkamali-Anaraki, Stephen Becker:
Preconditioned Data Sparsification for Big Data With Applications to PCA and K-Means. IEEE Trans. Inf. Theory 63(5): 2954-2974 (2017) - [i5]Farhad Pourkamali-Anaraki, Stephen Becker:
Improved Fixed-Rank Nyström Approximation via QR Decomposition: Practical and Theoretical Aspects. CoRR abs/1708.03218 (2017) - 2016
- [j1]Farhad Pourkamali-Anaraki:
Estimation of the sample covariance matrix from compressive measurements. IET Signal Process. 10(9): 1089-1095 (2016) - [c5]Farhad Pourkamali-Anaraki, Stephen Becker:
A randomized approach to efficient kernel clustering. GlobalSIP 2016: 207-211 - [i4]Farhad Pourkamali-Anaraki, Stephen Becker:
Randomized Clustered Nystrom for Large-Scale Kernel Machines. CoRR abs/1612.06470 (2016) - 2015
- [i3]Farhad Pourkamali-Anaraki, Stephen Becker, Shannon M. Hughes:
Efficient Dictionary Learning via Very Sparse Random Projections. CoRR abs/1504.01169 (2015) - [i2]Farhad Pourkamali-Anaraki, Stephen Becker:
Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means. CoRR abs/1511.00152 (2015) - [i1]Farhad Pourkamali-Anaraki:
Estimation of the sample covariance matrix from compressive measurements. CoRR abs/1512.08887 (2015) - 2014
- [c4]Farhad Pourkamali-Anaraki, Shannon M. Hughes:
Efficient recovery of principal components from compressive measurements with application to Gaussian mixture model estimation. ICASSP 2014: 2332-2336 - [c3]Farhad Pourkamali-Anaraki, Shannon M. Hughes:
Memory and Computation Efficient PCA via Very Sparse Random Projections. ICML 2014: 1341-1349 - 2013
- [c2]Farhad Pourkamali-Anaraki, Shannon M. Hughes:
Compressive K-SVD. ICASSP 2013: 5469-5473 - [c1]Farhad Pourkamali-Anaraki, Shannon M. Hughes:
Kernel compressive sensing. ICIP 2013: 494-498
Coauthor Index
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