Lucas Liebenwein

According to our database, Lucas Liebenwein authored at least 21 papers between 2017 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2022

Publisher Correction: Closed-form continuous-time neural networks.

Ramin M. Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Aaron Ray, Max Tschaikowski, Gerald Teschl, Daniela Rus

Nat. Mac. Intell., December, 2022

Closed-form continuous-time neural networks.

Ramin M. Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Aaron Ray, Max Tschaikowski, Gerald Teschl, Daniela Rus

Nat. Mac. Intell., November, 2022

Sensitivity-Informed Provable Pruning of Neural Networks.

Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus

SIAM J. Math. Data Sci., 2022

Pruning by Active Attention Manipulation.

Zahra Babaiee, Lucas Liebenwein, Ramin M. Hasani, Daniela Rus, Radu Grosu

CoRR, 2022

End-to-End Sensitivity-Based Filter Pruning.

Zahra Babaiee, Lucas Liebenwein, Ramin M. Hasani, Daniela Rus, Radu Grosu

CoRR, 2022

2021

Efficient Deep Learning: From Theory to Practice.

Lucas Liebenwein

PhD thesis, 2021

Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition.

Lucas Liebenwein, Alaa Maalouf, Oren Gal, Dan Feldman, Daniela Rus

CoRR, 2021

Closed-form Continuous-Depth Models.

Ramin M. Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Max Tschaikowski, Gerald Teschl, Daniela Rus

CoRR, 2021

Low-Regret Active learning.

Cenk Baykal, Lucas Liebenwein, Dan Feldman, Daniela Rus

CoRR, 2021

Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition.

Lucas Liebenwein, Alaa Maalouf, Dan Feldman, Daniela Rus

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Sparse Flows: Pruning Continuous-depth Models.

Lucas Liebenwein, Ramin M. Hasani, Alexander Amini, Daniela Rus

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy.

Lucas Liebenwein, Cenk Baykal, Brandon Carter, David Gifford, Daniela Rus

Proceedings of Machine Learning and Systems 2021, 2021

2020

Provable Filter Pruning for Efficient Neural Networks.

Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman, Daniela Rus

Proceedings of the 8th International Conference on Learning Representations, 2020

Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space.

Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Lucas Liebenwein, Ryan Sander, Sertac Karaman, Daniela Rus

Proceedings of the 4th Conference on Robot Learning, 2020

2019

Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts.

Björn Lütjens, Lucas Liebenwein, Katharina Kramer

CoRR, 2019

SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks.

Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus

CoRR, 2019

Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds.

Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Counterexample-Guided Safety Contracts for Autonomous Driving.

Jonathan A. DeCastro, Lucas Liebenwein, Cristian Ioan Vasile, Russ Tedrake, Sertac Karaman, Daniela Rus

Proceedings of the Algorithmic Foundations of Robotics XIII, 2018

Sampling-Based Approximation Algorithms for Reachability Analysis with Provable Guarantees.

Lucas Liebenwein, Cenk Baykal, Igor Gilitschenski, Sertac Karaman, Daniela Rus

Proceedings of the Robotics: Science and Systems XIV, 2018

2017

Training Support Vector Machines using Coresets.

Cenk Baykal, Lucas Liebenwein, Wilko Schwarting

CoRR, 2017

Compositional and Contract-Based Verification for Autonomous Driving on Road Networks.

Lucas Liebenwein, Wilko Schwarting, Cristian Ioan Vasile, Jonathan A. DeCastro, Javier Alonso-Mora, Sertac Karaman, Daniela Rus

Proceedings of the Robotics Research, The 18th International Symposium, 2017