Lucas Liebenwein

27 papers A* 3C 2Misc 1Journal 17Unranked 3
YearRankTypeTitle / Venue / Authors
2025 J jnl
Nemotron-Flash: Towards Latency-Optimal Hybrid Small Language Models.
CoRR
Yonggan Fu, Xin Dong, Shizhe Diao, Matthijs Van Keirsbilck, Hanrong Ye, Wonmin Byeon, Yashaswi Karnati, Lucas Liebenwein, Hannah Zhang, Nikolaus Binder, Maksim Khadkevich, Alexander Keller, Jan Kautz, Yingyan (Celine) Lin, Pavlo Molchanov
2022 J jnl
Closed-form continuous-time neural networks.
Nat. Mac. Intell.
Ramin M. Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Aaron Ray, Max Tschaikowski, Gerald Teschl, Daniela Rus
2022 J jnl
End-to-End Sensitivity-Based Filter Pruning.
CoRR
Zahra Babaiee, Lucas Liebenwein, Ramin M. Hasani, Daniela Rus, Radu Grosu
2022 J jnl
Pruning by Active Attention Manipulation.
CoRR
Zahra Babaiee, Lucas Liebenwein, Ramin M. Hasani, Daniela Rus, Radu Grosu
2022 J jnl
Publisher Correction: Closed-form continuous-time neural networks.
Nat. Mac. Intell.
Ramin M. Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Aaron Ray, Max Tschaikowski, Gerald Teschl, Daniela Rus
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
2021 J jnl
Closed-form Continuous-Depth Models.
CoRR
Ramin M. Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Max Tschaikowski, Gerald Teschl, Daniela Rus
2021 A* conf
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition.
NeurIPS
Lucas Liebenwein, Alaa Maalouf, Dan Feldman, Daniela Rus
2021 J jnl
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition.
CoRR
Lucas Liebenwein, Alaa Maalouf, Oren Gal, Dan Feldman, Daniela Rus
2021 J jnl
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space.
CoRR
Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Lucas Liebenwein, Ryan Sander, Sertac Karaman, Daniela Rus
2021 — —
Efficient Deep Learning: From Theory to Practice.
Lucas Liebenwein
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 A* conf
Sparse Flows: Pruning Continuous-depth Models.
NeurIPS
Lucas Liebenwein, Ramin M. Hasani, Alexander Amini, Daniela Rus
2021 J jnl
Sparse Flows: Pruning Continuous-depth Models.
CoRR
Lucas Liebenwein, Ramin M. Hasani, Alexander Amini, Daniela Rus
2020 Misc conf
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space.
CoRL
Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Lucas Liebenwein, Ryan Sander, Sertac Karaman, Daniela Rus
2020 A* conf
Provable Filter Pruning for Efficient Neural Networks.
ICLR
Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman, Daniela Rus
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 J jnl
Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts.
CoRR
Björn Lütjens, Lucas Liebenwein, Katharina Kramer
2019 J jnl
Provable Filter Pruning for Efficient Neural Networks.
CoRR
Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman, 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 C conf
Counterexample-Guided Safety Contracts for Autonomous Driving.
WAFR
Jonathan A. DeCastro, Lucas Liebenwein, Cristian Ioan Vasile, Russ Tedrake, Sertac Karaman, 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 — 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 C conf
Compositional and Contract-Based Verification for Autonomous Driving on Road Networks.
ISRR
Lucas Liebenwein, Wilko Schwarting, Cristian Ioan Vasile, Jonathan A. DeCastro, Javier Alonso-Mora, Sertac Karaman, Daniela Rus
2017 J jnl
Training Support Vector Machines using Coresets.
CoRR
Cenk Baykal, Lucas Liebenwein, Wilko Schwarting