Lukas Mauch

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 32

Top Topics

Convolutional Neural Network

Image Segmentation

Top Venues

Publications

Bac Nguyen, Stefan Uhlich, Fabien Cardinaux, Lukas Mauch, Marzieh Edraki, Aaron C. Courville
SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning. CoRR (2024)
Yassir Bendou, Giulia Lioi, Bastien Pasdeloup, Lukas Mauch, Ghouthi Boukli Hacene, Fabien Cardinaux, Vincent Gripon
LLM meets Vision-Language Models for Zero-Shot One-Class Classification. CoRR (2024)
Yihang Chen, Lukas Mauch
Order-Preserving GFlowNets. ICLR (2024)
Reda Bensaid, Vincent Gripon, François Leduc-Primeau, Lukas Mauch, Ghouthi Boukli Hacene, Fabien Cardinaux
A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation Models. CoRR (2024)
Pau Mulet Arabi, Alec Flowers, Lukas Mauch, Fabien Cardinaux
DBsurf: A Discrepancy Based Method for Discrete Stochastic Gradient Estimation. CoRR (2023)
Bac Nguyen Cong, Lukas Mauch
Efficient Training of Deep Equilibrium Models. CoRR (2023)
Yassir Bendou, Vincent Gripon, Bastien Pasdeloup, Giulia Lioi, Lukas Mauch, Fabien Cardinaux, Ghouthi Boukli Hacene
Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning. CoRR (2023)
Yassir Bendou, Vincent Gripon, Bastien Pasdeloup, Giulia Lioi, Lukas Mauch, Stefan Uhlich, Fabien Cardinaux, Ghouthi Boukli Hacene, Javier Alonso García
A Statistical Model for Predicting Generalization in Few-Shot Classification. EUSIPCO (2023)
Yihang Chen, Lukas Mauch
Order-Preserving GFlowNets. CoRR (2023)
Yassir Bendou, Vincent Gripon, Bastien Pasdeloup, Lukas Mauch, Stefan Uhlich, Fabien Cardinaux, Ghouthi Boukli Hacene, Javier Alonso García
A Statistical Model for Predicting Generalization in Few-Shot Classification. CoRR (2022)
Ghouthi Boukli Hacene, Lukas Mauch, Stefan Uhlich, Fabien Cardinaux
DNN Quantization with Attention. CoRR (2021)
Fabien Cardinaux, Stefan Uhlich, Kazuki Yoshiyama, Javier Alonso García, Lukas Mauch, Stephen Tiedemann, Thomas Kemp, Akira Nakamura
Iteratively Training Look-Up Tables for Network Quantization. IEEE J. Sel. Top. Signal Process. 14 (4) (2020)
Stefan Uhlich, Lukas Mauch, Fabien Cardinaux, Kazuki Yoshiyama, Javier Alonso García, Stephen Tiedemann, Thomas Kemp, Akira Nakamura
Mixed Precision DNNs: All you need is a good parametrization. ICLR (2020)
Lukas Mauch, Stephen Tiedemann, Javier Alonso García, Bac Nguyen Cong, Kazuki Yoshiyama, Fabien Cardinaux, Thomas Kemp
Efficient Sampling for Predictor-Based Neural Architecture Search. CoRR (2020)
Fabien Cardinaux, Stefan Uhlich, Kazuki Yoshiyama, Javier Alonso García, Lukas Mauch, Stephen Tiedemann, Thomas Kemp, Akira Nakamura
Iteratively Training Look-Up Tables for Network Quantization. CoRR (2019)
Stefan Uhlich, Lukas Mauch, Kazuki Yoshiyama, Fabien Cardinaux, Javier Alonso García, Stephen Tiedemann, Thomas Kemp, Akira Nakamura
Differentiable Quantization of Deep Neural Networks. CoRR (2019)
Alexander Bartler, Felix Wiewel, Lukas Mauch, Bin Yang
Training Variational Autoencoders with Discrete Latent Variables Using Importance Sampling. EUSIPCO (2019)
Lukas Mauch, Bin Yang
Least-Squares Based Layerwise Pruning Of Convolutional Neural Networks. SSP (2018)
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
Graphical User Interface for Medical Deep Learning - Application to Magnetic Resonance Imaging. APSIPA (2018)
Chunlai Wang, Jiawei Yu, Lukas Mauch, Bin Yang
Binary Segmentation Based Class Extension in Semantic Image Segmentation Using Convolutional Neural Networks. ICIP (2018)
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
A Machine-learning framework for automatic reference-free quality assessment in MRI. CoRR (2018)
Karim Said Barsim, Lukas Mauch, Bin Yang
Neural Network Ensembles to Real-time Identification of Plug-level Appliance Measurements. CoRR (2018)
Chunlai Wang, Lukas Mauch, Mehul Manoj Saxena, Bin Yang
On the contextual aspects of using deep convolutional neural network for semantic image segmentation. J. Electronic Imaging 27 (05) (2018)
Lukas Mauch, Chunlai Wang, Bin Yang
Subset selection for visualization of relevant image fractions for deep learning based semantic image segmentation. J. Frankl. Inst. 355 (4) (2018)
Lukas Mauch, Bin Yang
Deep Neural Network inference with reduced word length. CoRR (2018)
Thomas Kustner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang
Automatic Motion Artifact Detection for Whole-Body Magnetic Resonance Imaging. ICASSP (2018)
Alexander Bartler, Lukas Mauch, Bin Yang, Michael Reuter, Liviu Stoicescu
Automated Detection of Solar Cell Defects with Deep Learning. EUSIPCO (2018)
Lukas Mauch, Bin Yang
A novel layerwise pruning method for model reduction of fully connected deep neural networks. ICASSP (2017)
Lukas Mauch, Bin Yang
Selecting optimal layer reduction factors for model reduction of deep neural networks. ICASSP (2017)
Chunlai Wang, Lukas Mauch, Ze Guo, Bin Yang
On semantic image segmentation using deep convolutional neural network with shortcuts and easy class extension. IPTA (2016)
Lukas Mauch, Bin Yang
A novel DNN-HMM-based approach for extracting single loads from aggregate power signals. ICASSP (2016)
Lukas Mauch, Bin Yang
A new approach for supervised power disaggregation by using a deep recurrent LSTM network. GlobalSIP (2015)
Yixuan Wang, Lukas Mauch, Joachim Speidel
Bi-Directional DFEs for Plastic Optical Fiber Based In-Vehicle Infotainment System at 2-3Gbit/s. VTC Fall (2012)