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End-to-End Sensitivity-Based Filter Pruning


Apr 15, 2022
Zahra Babaiee, Lucas Liebenwein, Ramin Hasani, Daniela Rus, Radu Grosu

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


Jul 23, 2021
Lucas Liebenwein, Alaa Maalouf, Oren Gal, Dan Feldman, Daniela Rus

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Closed-form Continuous-Depth Models


Jun 25, 2021
Ramin Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Max Tschaikowski, Gerald Teschl, Daniela Rus

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* 17 pages 

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Sparse Flows: Pruning Continuous-depth Models


Jun 24, 2021
Lucas Liebenwein, Ramin Hasani, Alexander Amini, Daniela Rus

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Low-Regret Active learning


Apr 06, 2021
Cenk Baykal, Lucas Liebenwein, Dan Feldman, Daniela Rus

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


Mar 04, 2021
Lucas Liebenwein, Cenk Baykal, Brandon Carter, David Gifford, Daniela Rus

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* Published in MLSys 2021 

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


Feb 19, 2021
Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Lucas Liebenwein, Ryan Sander, Sertac Karaman, Daniela Rus

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* Wilko, Tim, and Igor contributed equally to this work; published in Conference on Robot Learning 2020 

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


Dec 17, 2019
Björn Lütjens, Lucas Liebenwein, Katharina Kramer

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* Published at 2019 NeurIPS Workshop on Tackling Climate Change with Machine Learning 

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Provable Filter Pruning for Efficient Neural Networks


Nov 18, 2019
Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman, Daniela Rus

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


Oct 11, 2019
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus

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* First two authors contributed equally 

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