Lukas Mauch

According to our database, Lukas Mauch authored at least 31 papers between 2012 and 2024.

Collaborative distances:

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

2024

SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning.

Bac Nguyen, Stefan Uhlich, Fabien Cardinaux, Lukas Mauch, Marzieh Edraki, Aaron C. Courville

CoRR, 2024

LLM meets Vision-Language Models for Zero-Shot One-Class Classification.

Yassir Bendou, Giulia Lioi, Bastien Pasdeloup, Lukas Mauch, Ghouthi Boukli Hacene, Fabien Cardinaux, Vincent Gripon

CoRR, 2024

A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation Models.

Reda Bensaid, Vincent Gripon, François Leduc-Primeau, Lukas Mauch, Ghouthi Boukli Hacene, Fabien Cardinaux

CoRR, 2024

Order-Preserving GFlowNets.

Yihang Chen, Lukas Mauch

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning.

Yassir Bendou, Vincent Gripon, Bastien Pasdeloup, Giulia Lioi, Lukas Mauch, Fabien Cardinaux, Ghouthi Boukli Hacene

CoRR, 2023

DBsurf: A Discrepancy Based Method for Discrete Stochastic Gradient Estimation.

Pau Mulet Arabi, Alec Flowers, Lukas Mauch, Fabien Cardinaux

CoRR, 2023

Efficient Training of Deep Equilibrium Models.

Bac Nguyen Cong, Lukas Mauch

CoRR, 2023

A Statistical Model for Predicting Generalization in Few-Shot Classification.

Yassir Bendou, Vincent Gripon, Bastien Pasdeloup, Giulia Lioi, Lukas Mauch, Stefan Uhlich, Fabien Cardinaux, Ghouthi Boukli Hacene, Javier Alonso García

Proceedings of the 31st European Signal Processing Conference, 2023

2022

A Statistical Model for Predicting Generalization in Few-Shot Classification.

Yassir Bendou, Vincent Gripon, Bastien Pasdeloup, Lukas Mauch, Stefan Uhlich, Fabien Cardinaux, Ghouthi Boukli Hacene, Javier Alonso García

CoRR, 2022

2021

DNN Quantization with Attention.

Ghouthi Boukli Hacene, Lukas Mauch, Stefan Uhlich, Fabien Cardinaux

CoRR, 2021

2020

Iteratively Training Look-Up Tables for Network Quantization.

Fabien Cardinaux, Stefan Uhlich, Kazuki Yoshiyama, Javier Alonso García, Lukas Mauch, Stephen Tiedemann, Thomas Kemp, Akira Nakamura

IEEE J. Sel. Top. Signal Process., 2020

Efficient Sampling for Predictor-Based Neural Architecture Search.

Lukas Mauch, Stephen Tiedemann, Javier Alonso García, Bac Nguyen Cong, Kazuki Yoshiyama, Fabien Cardinaux, Thomas Kemp

CoRR, 2020

Mixed Precision DNNs: All you need is a good parametrization.

Stefan Uhlich, Lukas Mauch, Fabien Cardinaux, Kazuki Yoshiyama, Javier Alonso García, Stephen Tiedemann, Thomas Kemp, Akira Nakamura

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

2019

Differentiable Quantization of Deep Neural Networks.

Stefan Uhlich, Lukas Mauch, Kazuki Yoshiyama, Fabien Cardinaux, Javier Alonso García, Stephen Tiedemann, Thomas Kemp, Akira Nakamura

CoRR, 2019

Training Variational Autoencoders with Discrete Latent Variables Using Importance Sampling.

Alexander Bartler, Felix Wiewel, Lukas Mauch, Bin Yang

Proceedings of the 27th European Signal Processing Conference, 2019

2018

Subset selection for visualization of relevant image fractions for deep learning based semantic image segmentation.

Lukas Mauch, Chunlai Wang, Bin Yang

J. Frankl. Inst., 2018

On the contextual aspects of using deep convolutional neural network for semantic image segmentation.

Chunlai Wang, Lukas Mauch, Mehul Manoj Saxena, Bin Yang

J. Electronic Imaging, 2018

Deep Neural Network inference with reduced word length.

Lukas Mauch, Bin Yang

CoRR, 2018

A Machine-learning framework for automatic reference-free quality assessment in MRI.

Thomas Küstner, Sergios Gatidis, Annika Liebgott, Martin Schwartz, Lukas Mauch, Petros Martirosian, Holger Schmidt, Nina F. Schwenzer, Konstantin Nikolaou, Fabian Bamberg, Bin Yang, Fritz Schick

CoRR, 2018

Neural Network Ensembles to Real-time Identification of Plug-level Appliance Measurements.

Karim Said Barsim, Lukas Mauch, Bin Yang

CoRR, 2018

Least-Squares Based Layerwise Pruning Of Convolutional Neural Networks.

Lukas Mauch, Bin Yang

Proceedings of the 2018 IEEE Statistical Signal Processing Workshop, 2018

Binary Segmentation Based Class Extension in Semantic Image Segmentation Using Convolutional Neural Networks.

Chunlai Wang, Jiawei Yu, Lukas Mauch, Bin Yang

Proceedings of the 2018 IEEE International Conference on Image Processing, 2018

Automatic Motion Artifact Detection for Whole-Body Magnetic Resonance Imaging.

Thomas Kustner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang

Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Automated Detection of Solar Cell Defects with Deep Learning.

Alexander Bartler, Lukas Mauch, Bin Yang, Michael Reuter, Liviu Stoicescu

Proceedings of the 26th European Signal Processing Conference, 2018

Graphical User Interface for Medical Deep Learning - Application to Magnetic Resonance Imaging.

Sebastian Milde, Annika Liebgott, Ziwei Wu, Wenyi Feng, Jiahuan Yang, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang, Thomas Kustner

Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2018

2017

A novel layerwise pruning method for model reduction of fully connected deep neural networks.

Lukas Mauch, Bin Yang

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

Selecting optimal layer reduction factors for model reduction of deep neural networks.

Lukas Mauch, Bin Yang

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

2016

On semantic image segmentation using deep convolutional neural network with shortcuts and easy class extension.

Chunlai Wang, Lukas Mauch, Ze Guo, Bin Yang

Proceedings of the Sixth International Conference on Image Processing Theory, 2016

A novel DNN-HMM-based approach for extracting single loads from aggregate power signals.

Lukas Mauch, Bin Yang

Proceedings of the 2016 IEEE International Conference on Acoustics, 2016

2015

A new approach for supervised power disaggregation by using a deep recurrent LSTM network.

Lukas Mauch, Bin Yang

Proceedings of the 2015 IEEE Global Conference on Signal and Information Processing, 2015

2012

Bi-Directional DFEs for Plastic Optical Fiber Based In-Vehicle Infotainment System at 2-3Gbit/s.

Yixuan Wang, Lukas Mauch, Joachim Speidel

Proceedings of the 76th IEEE Vehicular Technology Conference, 2012