Lukas Geiger

According to our database, Lukas Geiger authored at least 5 papers between 2018 and 2021.

Collaborative distances:

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

2021

Larq Compute Engine: Design, Benchmark and Deploy State-of-the-Art Binarized Neural Networks.

Tom Bannink, Adam Hillier, Lukas Geiger, Tim de Bruin, Leon Overweel, Jelmer Neeven, Koen Helwegen

Proceedings of Machine Learning and Systems 2021, 2021

2020

Larq: An Open-Source Library for Training Binarized Neural Networks.

Lukas Geiger, Plumerai Team

J. Open Source Softw., 2020

Larq Compute Engine: Design, Benchmark, and Deploy State-of-the-Art Binarized Neural Networks.

Tom Bannink, Arash Bakhtiari, Adam Hillier, Lukas Geiger, Tim de Bruin, Leon Overweel, Jelmer Neeven, Koen Helwegen

CoRR, 2020

2019

Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization.

Koen Helwegen, James Widdicombe, Lukas Geiger, Zechun Liu, Kwang-Ting Cheng, Roeland Nusselder

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Generating and Refining Particle Detector Simulations Using the Wasserstein Distance in Adversarial Networks.

Martin Erdmann, Lukas Geiger, Jonas Glombitza, David Schmidt

Comput. Softw. Big Sci., November, 2018