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ORCIDLucas Liebenwein
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2020 – today
- 2022
[j3]Ramin M. Hasani
, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Aaron Ray, Max Tschaikowski, Gerald Teschl, Daniela Rus:
Closed-form continuous-time neural networks. Nat. Mac. Intell. 4(11): 992-1003 (2022)- [j2]Ramin M. Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Aaron Ray, Max Tschaikowski, Gerald Teschl, Daniela Rus:
Publisher Correction: Closed-form continuous-time neural networks. Nat. Mac. Intell. 4(12): 1267 (2022) - [j1]Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus:
Sensitivity-Informed Provable Pruning of Neural Networks. SIAM J. Math. Data Sci. 4(1): 26-45 (2022) - [i13]Zahra Babaiee, Lucas Liebenwein, Ramin M. Hasani, Daniela Rus, Radu Grosu:
End-to-End Sensitivity-Based Filter Pruning. CoRR abs/2204.07412 (2022) - [i12]Zahra Babaiee, Lucas Liebenwein, Ramin M. Hasani, Daniela Rus, Radu Grosu:
Pruning by Active Attention Manipulation. CoRR abs/2210.11114 (2022) - 2021
- [b1]Lucas Liebenwein:
Efficient Deep Learning: From Theory to Practice. Massachusetts Institute of Technology, USA, 2021 - [c9]Lucas Liebenwein, Cenk Baykal, Brandon Carter, David Gifford, Daniela Rus:
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy. MLSys 2021 - [c8]Lucas Liebenwein, Alaa Maalouf, Dan Feldman, Daniela Rus:
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition. NeurIPS 2021: 5328-5344 - [c7]Lucas Liebenwein, Ramin M. Hasani, Alexander Amini, Daniela Rus:
Sparse Flows: Pruning Continuous-depth Models. NeurIPS 2021: 22628-22642 - [i11]Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Lucas Liebenwein, Ryan Sander, Sertac Karaman, Daniela Rus:
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space. CoRR abs/2102.09812 (2021) - [i10]Lucas Liebenwein, Cenk Baykal, Brandon Carter, David Gifford, Daniela Rus:
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy. CoRR abs/2103.03014 (2021) - [i9]Cenk Baykal, Lucas Liebenwein, Dan Feldman, Daniela Rus:
Low-Regret Active learning. CoRR abs/2104.02822 (2021) - [i8]Lucas Liebenwein, Ramin M. Hasani, Alexander Amini, Daniela Rus:
Sparse Flows: Pruning Continuous-depth Models. CoRR abs/2106.12718 (2021) - [i7]Ramin M. Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Max Tschaikowski, Gerald Teschl, Daniela Rus:
Closed-form Continuous-Depth Models. CoRR abs/2106.13898 (2021) - [i6]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) - 2020
- [c6]Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Lucas Liebenwein, Ryan Sander, Sertac Karaman, Daniela Rus:
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space. CoRL 2020: 1855-1870 - [c5]Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman, Daniela Rus:
Provable Filter Pruning for Efficient Neural Networks. ICLR 2020
2010 – 2019
- 2019
- [c4]Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus:
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds. ICLR (Poster) 2019 - [i5]Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus:
SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks. CoRR abs/1910.05422 (2019) - [i4]Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman, Daniela Rus:
Provable Filter Pruning for Efficient Neural Networks. CoRR abs/1911.07412 (2019) - [i3]Björn Lütjens, Lucas Liebenwein, Katharina Kramer:
Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts. CoRR abs/1912.07850 (2019) - 2018
- [c3]Lucas Liebenwein, Cenk Baykal, Igor Gilitschenski, Sertac Karaman, Daniela Rus:
Sampling-Based Approximation Algorithms for Reachability Analysis with Provable Guarantees. Robotics: Science and Systems 2018 - [c2]Jonathan A. DeCastro, Lucas Liebenwein, Cristian Ioan Vasile, Russ Tedrake, Sertac Karaman, Daniela Rus:
Counterexample-Guided Safety Contracts for Autonomous Driving. WAFR 2018: 939-955 - [i2]Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus:
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds. CoRR abs/1804.05345 (2018) - 2017
- [c1]Lucas Liebenwein, Wilko Schwarting, Cristian Ioan Vasile, Jonathan A. DeCastro, Javier Alonso-Mora, Sertac Karaman, Daniela Rus:
Compositional and Contract-Based Verification for Autonomous Driving on Road Networks. ISRR 2017: 163-181 - [i1]Cenk Baykal, Lucas Liebenwein, Wilko Schwarting:
Training Support Vector Machines using Coresets. CoRR abs/1708.03835 (2017)
Coauthor Index
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