Lucas Liebenwein

Orcid: 0000-0002-3229-6665

According to our database¹, Lucas Liebenwein authored at least 21 papers between 2017 and 2022.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

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PhD thesis

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Bibliography

2022

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

[BibT_eX]

[DOI]

Nat. Mac. Intell., December, 2022

Closed-form continuous-time neural networks.

[BibT_eX]

[DOI]

Nat. Mac. Intell., November, 2022

Sensitivity-Informed Provable Pruning of Neural Networks.

[BibT_eX]

[DOI]

SIAM J. Math. Data Sci., 2022

Pruning by Active Attention Manipulation.

[BibT_eX]

[DOI]

CoRR, 2022

End-to-End Sensitivity-Based Filter Pruning.

[BibT_eX]

[DOI]

CoRR, 2022

2021

Efficient Deep Learning: From Theory to Practice.

[BibT_eX]

[DOI]

Lucas Liebenwein

PhD thesis, 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

Closed-form Continuous-Depth Models.

[BibT_eX]

[DOI]

CoRR, 2021

Low-Regret Active learning.

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the Fourth Conference on Machine Learning and Systems, 2021

2020

Provable Filter Pruning for Efficient Neural Networks.

[BibT_eX]

[DOI]

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

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Björn Lütjens

Lucas Liebenwein

Katharina Kramer

CoRR, 2019

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

[BibT_eX]

[DOI]

CoRR, 2019

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

[BibT_eX]

[DOI]

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

2018

Counterexample-Guided Safety Contracts for Autonomous Driving.

[BibT_eX]

[DOI]

Proceedings of the Algorithmic Foundations of Robotics XIII, 2018

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

[BibT_eX]

[DOI]

Proceedings of the Robotics: Science and Systems XIV, 2018

2017

Training Support Vector Machines using Coresets.

[BibT_eX]

[DOI]

Cenk Baykal

Lucas Liebenwein

Wilko Schwarting

CoRR, 2017

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

[BibT_eX]

[DOI]

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

Lucas Liebenwein

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