Farhad Pourkamali-Anaraki

54 papers A* 2B 2C 6Misc 2Journal 39Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction. CoRR Farhad Pourkamali-Anaraki
2025	C	`conf`	Kolmogorov-Arnold Networks in Low-Data Regimes: A Comparative Study with Multilayer Perceptrons. ICMLA Farhad Pourkamali-Anaraki
2025	C	`conf`	Out-of-the-Box Uncertainty: Reducing Confident Errors with Dirichlet Classifiers. ICMLA Courtney Franzen, Farhad Pourkamali-Anaraki
2025	J	`jnl`	Probabilistic Neural Networks (PNNs) with t-Distributed Outputs: Adaptive Prediction Intervals Beyond Gaussian Assumptions. CoRR Farhad Pourkamali-Anaraki
2024	J	`jnl`	Adaptive Activation Functions for Predictive Modeling with Sparse Experimental Data. CoRR Farhad Pourkamali-Anaraki, Tahamina Nasrin, Robert E. Jensen, Amy M. Peterson, Christopher J. Hansen
2024	J	`jnl`	Adaptive Conformal Prediction Intervals Using Data-Dependent Weights With Application to Seismic Response Prediction. IEEE Access Parisa Hajibabaee, Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili
2024	J	`jnl`	Adaptive activation functions for predictive modeling with sparse experimental data. Neural Comput. Appl. Farhad Pourkamali-Anaraki, Tahamina Nasrin, Robert E. Jensen, Amy M. Peterson, Christopher J. Hansen
2024	J	`jnl`	Kolmogorov-Arnold Networks in Low-Data Regimes: A Comparative Study with Multilayer Perceptrons. CoRR Farhad Pourkamali-Anaraki
2024	J	`jnl`	Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning. IEEE Access Farhad Pourkamali-Anaraki, Jamal F. Husseini, Scott E. Stapleton
2024	J	`jnl`	Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning. CoRR Farhad Pourkamali-Anaraki, Jamal F. Husseini, Scott E. Stapleton
2024	J	`jnl`	Special Issue: Big Scientific Data and Machine Learning in Science and Engineering. Big Data Farhad Pourkamali-Anaraki
2024	J	`jnl`	Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generation. CoRR Farhad Pourkamali-Anaraki, Jamal F. Husseini, Evan J. Pineda, Brett A. Bednarcyk, Scott E. Stapleton
2024	J	`jnl`	Two-stage surrogate modeling for data-driven design optimization with application to composite microstructure generation. Eng. Appl. Artif. Intell. Farhad Pourkamali-Anaraki, Jamal F. Husseini, Evan J. Pineda, Brett A. Bednarcyk, Scott E. Stapleton
2023	C	`conf`	Advancing Precision Medicine: An Evaluative Study of Feature Selection Methods. ICMLA Carol M. Kiekhaefer, Farhad Pourkamali-Anaraki
2023	C	`conf`	Evaluating Regression Models with Partial Data: A Sampling Approach. CoDIT Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili
2023	J	`jnl`	Evaluation of classification models in limited data scenarios with application to additive manufacturing. Eng. Appl. Artif. Intell. Farhad Pourkamali-Anaraki, Tahamina Nasrin, Robert E. Jensen, Amy M. Peterson, Christopher J. Hansen
2022	J	`jnl`	D-CBRS: Accounting For Intra-Class Diversity in Continual Learning. CoRR Yasin Findik, Farhad Pourkamali-Anaraki
2022	B	`conf`	D-CBRS: Accounting for Intra-Class Diversity in Continual Learning. ICIP Yasin Findik, Farhad Pourkamali-Anaraki
2022	J	`jnl`	Structural uncertainty quantification with partial information. Expert Syst. Appl. Mohammad Amin Hariri-Ardebili, Farhad Pourkamali-Anaraki
2021	J	`jnl`	Adaptive Data Compression for Classification Problems. IEEE Access Farhad Pourkamali-Anaraki, Walter D. Bennette
2021	C	`conf`	An Empirical Evaluation of the t-SNE Algorithm for Data Visualization in Structural Engineering. ICMLA Parisa Hajibabaee, Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili
2021	J	`jnl`	An Empirical Evaluation of the t-SNE Algorithm for Data Visualization in Structural Engineering. CoRR Parisa Hajibabaee, Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili
2021	J	`jnl`	Call for Special Issue Papers: Big Scientific Data and Machine Learning in Science and Engineering: Deadline for Manuscript Submission: February 1, 2022. Big Data Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili, Stephen Becker, Maziar Raissi
2021	J	`jnl`	Call for Special Issue Papers: Big Scientific Data and Machine Learning in Science and Engineering: Deadline for Manuscript Submission: February 1, 2022. Big Data Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili, Stephen Becker, Maziar Raissi
2021	J	`jnl`	Call for Special Issue Papers: Big Scientific Data and Machine Learning in Science and Engineering: Deadline for Manuscript Submission: February 1, 2022. Big Data Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili, Stephen Becker, Maziar Raissi
2021	J	`jnl`	Kernel Matrix Approximation on Class-Imbalanced Data With an Application to Scientific Simulation. IEEE Access Parisa Hajibabaee, Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili
2021	J	`jnl`	Neural Networks and Imbalanced Learning for Data-Driven Scientific Computing With Uncertainties. IEEE Access Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili
2020	J	`jnl`	A Unified NMPC Scheme for MAVs Navigation With 3D Collision Avoidance Under Position Uncertainty. IEEE Robotics Autom. Lett. Sina Sharif Mansouri, Christoforos Kanellakis, Björn Lindqvist, Farhad Pourkamali-Anaraki, Ali-Akbar Agha-Mohammadi, Joel Burdick, George Nikolakopoulos
2020	J	`jnl`	A Unified NMPC Scheme for MAVs Navigation with 3D Collision Avoidance under Position Uncertainty. CoRR Sina Sharif Mansouri, Christoforos Kanellakis, Björn Lindqvist, Farhad Pourkamali-Anaraki, Ali-Akbar Agha-Mohammadi, Joel Burdick, George Nikolakopoulos
2020	J	`jnl`	Efficient Solvers for Sparse Subspace Clustering. Signal Process. Farhad Pourkamali-Anaraki, James Folberth, Stephen Becker
2020	C	`conf`	Kernel Ridge Regression Using Importance Sampling with Application to Seismic Response Prediction. ICMLA Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili, Lydia Morawiec
2020	J	`jnl`	Kernel Ridge Regression Using Importance Sampling with Application to Seismic Response Prediction. CoRR Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili, Lydia Morawiec
2020	J	`jnl`	Scalable Spectral Clustering with Nystrom Approximation: Practical and Theoretical Aspects. CoRR Farhad Pourkamali-Anaraki
2020	J	`jnl`	Uncertainty Quantification of Structural Systems with Subset of Data. CoRR Mohammad Amin Hariri-Ardebili, Farhad Pourkamali-Anaraki, Siamak Sattar
2020	—	`conf`	Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud. MED Sina Sharif Mansouri, Farhad Pourkamali-Anaraki, Miguel Castano, Ali-Akbar Agha-Mohammadi, Joel Burdick, George Nikolakopoulos
2020	J	`jnl`	Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud. CoRR Sina Sharif Mansouri, Farhad Pourkamali-Anaraki, Miguel Castano Arranz, Ali-Akbar Agha-Mohammadi, Joel Burdick, George Nikolakopoulos
2019	J	`jnl`	Improved fixed-rank Nyström approximation via QR decomposition: Practical and theoretical aspects. Neurocomputing Farhad Pourkamali-Anaraki, Stephen Becker
2019	—	`conf`	Large-Scale Sparse Subspace Clustering Using Landmarks. MLSP Farhad Pourkamali-Anaraki
2019	J	`jnl`	Large-Scale Sparse Subspace Clustering Using Landmarks. CoRR Farhad Pourkamali-Anaraki
2019	J	`jnl`	The Effectiveness of Variational Autoencoders for Active Learning. CoRR Farhad Pourkamali-Anaraki, Michael B. Wakin
2018	J	`jnl`	Efficient Solvers for Sparse Subspace Clustering. CoRR Farhad Pourkamali-Anaraki, Stephen Becker
2018	A*	`conf`	Randomized Clustered Nystrom for Large-Scale Kernel Machines. AAAI Farhad Pourkamali-Anaraki, Stephen Becker, Michael B. Wakin
2017	J	`jnl`	Improved Fixed-Rank Nyström Approximation via QR Decomposition: Practical and Theoretical Aspects. CoRR Farhad Pourkamali-Anaraki, Stephen Becker
2017	J	`jnl`	Preconditioned Data Sparsification for Big Data With Applications to PCA and K-Means. IEEE Trans. Inf. Theory Farhad Pourkamali-Anaraki, Stephen Becker
2016	—	`conf`	A randomized approach to efficient kernel clustering. GlobalSIP Farhad Pourkamali-Anaraki, Stephen Becker
2016	J	`jnl`	Estimation of the sample covariance matrix from compressive measurements. IET Signal Process. Farhad Pourkamali-Anaraki
2016	J	`jnl`	Randomized Clustered Nystrom for Large-Scale Kernel Machines. CoRR Farhad Pourkamali-Anaraki, Stephen Becker
2015	J	`jnl`	Efficient Dictionary Learning via Very Sparse Random Projections. CoRR Farhad Pourkamali-Anaraki, Stephen Becker, Shannon M. Hughes
2015	J	`jnl`	Estimation of the sample covariance matrix from compressive measurements. CoRR Farhad Pourkamali-Anaraki
2015	J	`jnl`	Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means. CoRR Farhad Pourkamali-Anaraki, Stephen Becker
2014	Misc	`conf`	Efficient recovery of principal components from compressive measurements with application to Gaussian mixture model estimation. ICASSP Farhad Pourkamali-Anaraki, Shannon M. Hughes
2014	A*	`conf`	Memory and Computation Efficient PCA via Very Sparse Random Projections. ICML Farhad Pourkamali-Anaraki, Shannon M. Hughes
2013	Misc	`conf`	Compressive K-SVD. ICASSP Farhad Pourkamali-Anaraki, Shannon M. Hughes
2013	B	`conf`	Kernel compressive sensing. ICIP Farhad Pourkamali-Anaraki, Shannon M. Hughes