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ORCIDAlaa Maalouf
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2020 – today
- 2024
[j6]Alaa Maalouf
, Ninad Jadhav, Krishna Murthy Jatavallabhula, Makram Chahine, Daniel M. Vogt, Robert J. Wood, Antonio Torralba, Daniela Rus:
Follow Anything: Open-Set Detection, Tracking, and Following in Real-Time. IEEE Robotics Autom. Lett. 9(4): 3283-3290 (2024)- [j5]Alaa Maalouf, Gilad Eini, Ben Mussay, Dan Feldman, Margarita Osadchy:
A Unified Approach to Coreset Learning. IEEE Trans. Neural Networks Learn. Syst. 35(5): 6893-6905 (2024) - [c17]Noel Loo, Alaa Maalouf, Ramin M. Hasani, Mathias Lechner, Alexander Amini, Daniela Rus:
Large Scale Dataset Distillation with Domain Shift. ICML 2024 - [c16]Tsun-Hsuan Wang, Alaa Maalouf, Wei Xiao, Yutong Ban, Alexander Amini, Guy Rosman, Sertac Karaman, Daniela Rus:
Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models. ICRA 2024: 6687-6694 - [i23]Shiva Sreeram, Tsun-Hsuan Wang, Alaa Maalouf, Guy Rosman, Sertac Karaman, Daniela Rus:
Probing Multimodal LLMs as World Models for Driving. CoRR abs/2405.05956 (2024) - 2023
- [c15]Alaa Maalouf, Murad Tukan, Vladimir Braverman, Daniela Rus:
AutoCoreset: An Automatic Practical Coreset Construction Framework. ICML 2023: 23451-23466 - [c14]Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir Braverman, Dan Feldman:
Provable Data Subset Selection For Efficient Neural Networks Training. ICML 2023: 34533-34555 - [c13]Alaa Maalouf, Yotam Gurfinkel, Barak Diker, Oren Gal, Daniela Rus, Dan Feldman:
Deep Learning on Home Drone: Searching for the Optimal Architecture. ICRA 2023: 8208-8215 - [c12]Alaa Maalouf, Murad Tukan, Noel Loo, Ramin M. Hasani, Mathias Lechner, Daniela Rus:
On the Size and Approximation Error of Distilled Datasets. NeurIPS 2023 - [c11]Krishna Murthy Jatavallabhula, Alihusein Kuwajerwala, Qiao Gu, Mohd. Omama, Ganesh Iyer, Soroush Saryazdi, Tao Chen, Alaa Maalouf, Shuang Li, Nikhil Varma Keetha, Ayush Tewari, Joshua B. Tenenbaum, Celso Miguel de Melo, K. Madhava Krishna, Liam Paull, Florian Shkurti, Antonio Torralba:
ConceptFusion: Open-set multimodal 3D mapping. Robotics: Science and Systems 2023 - [i22]Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir Braverman, Dan Feldman:
Provable Data Subset Selection For Efficient Neural Network Training. CoRR abs/2303.05151 (2023) - [i21]Alaa Maalouf, Murad Tukan, Vladimir Braverman, Daniela Rus:
AutoCoreset: An Automatic Practical Coreset Construction Framework. CoRR abs/2305.11980 (2023) - [i20]Alaa Maalouf, Murad Tukan, Noel Loo, Ramin M. Hasani, Mathias Lechner, Daniela Rus:
On the Size and Approximation Error of Distilled Sets. CoRR abs/2305.14113 (2023) - [i19]Murad Tukan, Alaa Maalouf, Margarita Osadchy:
Dataset Distillation Meets Provable Subset Selection. CoRR abs/2307.08086 (2023) - [i18]Alaa Maalouf, Ninad Jadhav, Krishna Murthy Jatavallabhula, Makram Chahine, Daniel M. Vogt, Robert J. Wood, Antonio Torralba, Daniela Rus:
Follow Anything: Open-set detection, tracking, and following in real-time. CoRR abs/2308.05737 (2023) - [i17]Tsun-Hsuan Wang, Alaa Maalouf, Wei Xiao, Yutong Ban, Alexander Amini, Guy Rosman, Sertac Karaman, Daniela Rus:
Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models. CoRR abs/2310.17642 (2023) - 2022
- [b1]Alaa Maalouf:
MDL-BOOST: Constructing Efficient Machine/Deep Learning Systems via Data and Model Compression. University of Haifa, Israel, 2022 - [j4]Alaa Maalouf, Ibrahim Jubran, Dan Feldman:
Fast and Accurate Least-Mean-Squares Solvers for High Dimensional Data. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 9977-9994 (2022) - [c10]Alaa Maalouf, Murad Tukan, Eric Price, Daniel M. Kane, Dan Feldman:
Coresets for Data Discretization and Sine Wave Fitting. AISTATS 2022: 10622-10639 - [c9]Murad Tukan, Alaa Maalouf, Dan Feldman, Roi Poranne:
Obstacle Aware Sampling for Path Planning. IROS 2022: 13676-13683 - [c8]Murad Tukan, Loay Mualem, Alaa Maalouf:
Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions. NeurIPS 2022 - [i16]Alaa Maalouf, Murad Tukan, Eric Price, Daniel Kane, Dan Feldman:
Coresets for Data Discretization and Sine Wave Fitting. CoRR abs/2203.03009 (2022) - [i15]Murad Tukan, Alaa Maalouf, Dan Feldman, Roi Poranne:
Obstacle Aware Sampling for Path Planning. CoRR abs/2203.04075 (2022) - [i14]Murad Tukan, Loay Mualem, Alaa Maalouf:
Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions. CoRR abs/2209.08554 (2022) - [i13]Alaa Maalouf, Yotam Gurfinkel, Barak Diker, Oren Gal, Daniela Rus, Dan Feldman:
Deep Learning on Home Drone: Searching for the Optimal Architecture. CoRR abs/2209.11064 (2022) - 2021
- [j3]Murad Tukan, Alaa Maalouf, Matan Weksler, Dan Feldman:
No Fine-Tuning, No Cry: Robust SVD for Compressing Deep Networks. Sensors 21(16): 5599 (2021) - [j2]Alaa Maalouf, Ibrahim Jubran, Murad Tukan, Dan Feldman:
Coresets for the Average Case Error for Finite Query Sets. Sensors 21(19): 6689 (2021) - [j1]Ibrahim Jubran, Alaa Maalouf, Dan Feldman:
Overview of accurate coresets. WIREs Data Mining Knowl. Discov. 11(6) (2021) - [c7]Ibrahim Jubran, Alaa Maalouf, Ron Kimmel, Dan Feldman:
Provably Approximated Point Cloud Registration. ICCV 2021: 13249-13258 - [c6]Alaa Maalouf, Harry Lang, Daniela Rus, Dan Feldman:
Deep Learning meets Projective Clustering. ICLR 2021 - [c5]Lucas Liebenwein, Alaa Maalouf, Dan Feldman, Daniela Rus:
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition. NeurIPS 2021: 5328-5344 - [i12]Ibrahim Jubran, Alaa Maalouf, Ron Kimmel, Dan Feldman:
Provably Approximated ICP. CoRR abs/2101.03588 (2021) - [i11]Lucas Liebenwein, Alaa Maalouf, Oren Gal, Dan Feldman, Daniela Rus:
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition. CoRR abs/2107.11442 (2021) - [i10]Alaa Maalouf, Gilad Eini, Ben Mussay, Dan Feldman, Margarita Osadchy:
A Unified Approach to Coreset Learning. CoRR abs/2111.03044 (2021) - [i9]Alaa Maalouf, Ibrahim Jubran, Dan Feldman:
Introduction to Coresets: Approximated Mean. CoRR abs/2111.03046 (2021) - 2020
- [c4]Ibrahim Jubran, Murad Tukan, Alaa Maalouf, Dan Feldman:
Sets Clustering. ICML 2020: 4994-5005 - [c3]Alaa Maalouf, Adiel Statman, Dan Feldman:
Tight Sensitivity Bounds For Smaller Coresets. KDD 2020: 2051-2061 - [c2]Murad Tukan, Alaa Maalouf, Dan Feldman:
Coresets for Near-Convex Functions. NeurIPS 2020 - [i8]Ibrahim Jubran, Murad Tukan, Alaa Maalouf, Dan Feldman:
Sets Clustering. CoRR abs/2003.04135 (2020) - [i7]Alaa Maalouf, Ibrahim Jubran, Murad Tukan, Dan Feldman:
Faster PAC Learning and Smaller Coresets via Smoothed Analysis. CoRR abs/2006.05441 (2020) - [i6]Murad Tukan, Alaa Maalouf, Dan Feldman:
Coresets for Near-Convex Functions. CoRR abs/2006.05482 (2020) - [i5]Murad Tukan, Alaa Maalouf, Matan Weksler, Dan Feldman:
Compressed Deep Networks: Goodbye SVD, Hello Robust Low-Rank Approximation. CoRR abs/2009.05647 (2020) - [i4]Alaa Maalouf, Harry Lang, Daniela Rus, Dan Feldman:
Deep Learning Meets Projective Clustering. CoRR abs/2010.04290 (2020)
2010 – 2019
- 2019
- [c1]Alaa Maalouf, Ibrahim Jubran, Dan Feldman:
Fast and Accurate Least-Mean-Squares Solvers. NeurIPS 2019: 8305-8316 - [i3]Alaa Maalouf, Ibrahim Jubran, Dan Feldman:
Fast and Accurate Least-Mean-Squares Solvers. CoRR abs/1906.04705 (2019) - [i2]Alaa Maalouf, Adiel Statman, Dan Feldman:
Tight Sensitivity Bounds For Smaller Coresets. CoRR abs/1907.01433 (2019) - [i1]Ibrahim Jubran, Alaa Maalouf, Dan Feldman:
Introduction to Coresets: Accurate Coresets. CoRR abs/1910.08707 (2019)
Coauthor Index
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last updated on 2024-09-10 01:15 CEST by the dblp team
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