Cenk Baykal

39 papers A* 9A 1C 3Journal 20Unranked 5
YearRankTypeTitle / Venue / Authors
2023 A* conf
Alternating Updates for Efficient Transformers.
NeurIPS
Cenk Baykal, Dylan J. Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang
2023 J jnl
Alternating Updates for Efficient Transformers.
CoRR
Cenk Baykal, Dylan J. Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang
2023 A* conf
Robust Active Distillation.
ICLR
Cenk Baykal, Khoa Trinh, Fotis Iliopoulos, Gaurav Menghani, Erik Vee
2023 A* conf
SLaM: Student-Label Mixing for Distillation with Unlabeled Examples.
NeurIPS
Vasilis Kontonis, Fotis Iliopoulos, Khoa Trinh, Cenk Baykal, Gaurav Menghani, Erik Vee
2023 J jnl
SLaM: Student-Label Mixing for Semi-Supervised Knowledge Distillation.
CoRR
Vasilis Kontonis, Fotis Iliopoulos, Khoa Trinh, Cenk Baykal, Gaurav Menghani, Erik Vee
2023 J jnl
The Power of External Memory in Increasing Predictive Model Capacity.
CoRR
Cenk Baykal, Dylan J. Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang
2022 A* conf
A Theoretical View on Sparsely Activated Networks.
NeurIPS
Cenk Baykal, Nishanth Dikkala, Rina Panigrahy, Cyrus Rashtchian, Xin Wang
2022 J jnl
A Theoretical View on Sparsely Activated Networks.
CoRR
Cenk Baykal, Nishanth Dikkala, Rina Panigrahy, Cyrus Rashtchian, Xin Wang
2022 J jnl
Bandit Sampling for Multiplex Networks.
CoRR
Cenk Baykal, Vamsi K. Potluru, Sameena Shah, Manuela M. Veloso
2022 J jnl
Robust Active Distillation.
CoRR
Cenk Baykal, Khoa Trinh, Fotis Iliopoulos, Gaurav Menghani, Erik Vee
2022 J jnl
Sensitivity-Informed Provable Pruning of Neural Networks.
SIAM J. Math. Data Sci.
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus
2022 A* conf
Weighted Distillation with Unlabeled Examples.
NeurIPS
Fotis Iliopoulos, Vasilis Kontonis, Cenk Baykal, Gaurav Menghani, Khoa Trinh, Erik Vee
2022 J jnl
Weighted Distillation with Unlabeled Examples.
CoRR
Fotis Iliopoulos, Vasilis Kontonis, Cenk Baykal, Gaurav Menghani, Khoa Trinh, Erik Vee
2021 J jnl
Graph Belief Propagation Networks.
CoRR
Junteng Jia, Cenk Baykal, Vamsi K. Potluru, Austin R. Benson
2021 — conf
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy.
MLSys
Lucas Liebenwein, Cenk Baykal, Brandon Carter, David Gifford, Daniela Rus
2021 J jnl
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy.
CoRR
Lucas Liebenwein, Cenk Baykal, Brandon Carter, David Gifford, Daniela Rus
2021 J jnl
Low-Regret Active learning.
CoRR
Cenk Baykal, Lucas Liebenwein, Dan Feldman, Daniela Rus
2021 J jnl
On coresets for support vector machines.
Theor. Comput. Sci.
Murad Tukan, Cenk Baykal, Dan Feldman, Daniela Rus
2021 — —
Sampling-based Algorithms for Fast and Deployable AI.
Cenk Baykal
2020 C conf
On Coresets for Support Vector Machines.
TAMC
Murad Tukan, Cenk Baykal, Dan Feldman, Daniela Rus
2020 J jnl
On Coresets for Support Vector Machines.
CoRR
Murad Tukan, Cenk Baykal, Dan Feldman, Daniela Rus
2020 A* conf
Provable Filter Pruning for Efficient Neural Networks.
ICLR
Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman, Daniela Rus
2019 J jnl
Asymptotically optimal kinematic design of robots using motion planning.
Auton. Robots
Cenk Baykal, Chris Bowen, Ron Alterovitz
2019 — conf
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds.
ICLR (Poster)
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus
2019 C conf
Deterministic Coresets for Stochastic Matrices with Applications to Scalable Sparse PageRank.
TAMC
Harry Lang, Cenk Baykal, Najib Abu Samra, Tony Tannous, Dan Feldman, Daniela Rus
2019 J jnl
Provable Filter Pruning for Efficient Neural Networks.
CoRR
Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman, Daniela Rus
2019 J jnl
Resilient Multi-Agent Consensus Using Wi-Fi Signals.
IEEE Control. Syst. Lett.
Stephanie Gil, Cenk Baykal, Daniela Rus
2019 J jnl
SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks.
CoRR
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus
2018 J jnl
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds.
CoRR
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus
2018 A* conf
Kinematic Design Optimization of a Parallel Surgical Robot to Maximize Anatomical Visibility via Motion Planning.
ICRA
Alan Kuntz, Chris Bowen, Cenk Baykal, Arthur W. Mahoney, Patrick L. Anderson, Fabien Maldonado, Robert J. Webster III, Ron Alterovitz
2018 — conf
Sampling-Based Approximation Algorithms for Reachability Analysis with Provable Guarantees.
Robotics: Science and Systems
Lucas Liebenwein, Cenk Baykal, Igor Gilitschenski, Sertac Karaman, Daniela Rus
2017 — conf
Asymptotically Optimal Design of Piecewise Cylindrical Robots using Motion Planning.
Robotics: Science and Systems
Cenk Baykal, Ron Alterovitz
2017 J jnl
Detection of AQM on Paths using Machine Learning Methods.
CoRR
Cenk Baykal, Wilko Schwarting, Alex Wallar
2017 A* conf
Persistent surveillance of events with unknown, time-varying statistics.
ICRA
Cenk Baykal, Guy Rosman, Sebastian Claici, Daniela Rus
2017 J jnl
Training Support Vector Machines using Coresets.
CoRR
Cenk Baykal, Lucas Liebenwein, Wilko Schwarting
2016 C conf
Persistent Surveillance of Events with Unknown Rate Statistics.
WAFR
Cenk Baykal, Guy Rosman, Kyle Kotowick, Mark Donahue, Daniela Rus
2015 A conf
Optimizing design parameters for sets of concentric tube robots using sampling-based motion planning.
IROS
Cenk Baykal, Luis G. Torres, Ron Alterovitz
2014 A* conf
Interactive-rate motion planning for concentric tube robots.
ICRA
Luis G. Torres, Cenk Baykal, Ron Alterovitz
2014 — conf
Participatory route planning.
SIGSPATIAL/GIS
David Wilkie, Cenk Baykal, Ming C. Lin