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Mats L. Richter
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2020 – today
- 2024
- [j2]Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, Mats L. Richter, Quentin Gregory Anthony, Eugene Belilovsky, Timothée Lesort, Irina Rish:
Simple and Scalable Strategies to Continually Pre-train Large Language Models. Trans. Mach. Learn. Res. 2024 (2024) - [c8]Pablo Pernias, Dominic Rampas, Mats L. Richter, Christopher Pal, Marc Aubreville:
Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models. ICLR 2024 - [i9]Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, Mats L. Richter, Quentin Anthony, Timothée Lesort, Eugene Belilovsky, Irina Rish:
Simple and Scalable Strategies to Continually Pre-train Large Language Models. CoRR abs/2403.08763 (2024) - [i8]Matthew Fortier, Mats L. Richter, Oliver Sonnentag, Chris Pal:
CarbonSense: A Multimodal Dataset and Baseline for Carbon Flux Modelling. CoRR abs/2406.04940 (2024) - 2023
- [c7]Leila Malihi, Ursula Hübner, Mats L. Richter, Maurice Moelleken, Mareike Przysucha, Dorothee Busch, Jan Heggemann, Guido Hafer, Stefan Wiemeyer, Gunther Heidemann, Joachim Dissemond, Cornelia Erfurt-Berge, Carlotta B. C. Barkhau, Achim Hendriks, Jens Hüsers
:
Can Synthetic Images Improve CNN Performance in Wound Image Classification? MIE 2023: 927-931 - [i7]Kshitij Gupta, Benjamin Thérien, Adam Ibrahim, Mats L. Richter, Quentin Anthony, Eugene Belilovsky, Irina Rish, Timothée Lesort:
Continual Pre-Training of Large Language Models: How to (re)warm your model? CoRR abs/2308.04014 (2023) - 2022
- [b1]Mats L. Richter:
Towards efficient convolutional neural architecture design: exploring the properties of neural architectures using spectral decomposition and logistic regression probes. University of Osnabrück, Germany, 2022, pp. 1-196 - [j1]Justin Shenk
Delve: Neural Network Feature Variance Analysis. J. Open Source Softw. 7(69): 3992 (2022) - [c6]Leila Malihi, Jens Hüsers, Mats L. Richter, Maurice Moelleken, Mareike Przysucha, Dorothee Busch, Jan Heggemann, Guido Hafer, Stefan Wiemeyer, Gunther Heidemann, Joachim Dissemond, Cornelia Erfurt-Berge, Ursula Hübner:
Automatic Wound Type Classification with Convolutional Neural Networks. ICIMTH 2022: 281-284 - [c5]Jens Hüsers, Maurice Moelleken, Mats L. Richter, Mareike Przysucha, Leila Malihi, Dorothee Busch, Nina-Alexandra Götz, Jan Heggemann, Guido Hafer, Stefan Wiemeyer, Birgit Babitsch, Gunther Heidemann, Joachim Dissemond, Cornelia Erfurt-Berge, Ursula Hübner:
An Image Based Object Recognition System for Wound Detection and Classification of Diabetic Foot and Venous Leg Ulcers. MIE 2022: 63-67 - [i6]Mats L. Richter, Christopher Pal:
Receptive Field Refinement for Convolutional Neural Networks Reliably Improves Predictive Performance. CoRR abs/2211.14487 (2022) - 2021
- [c4]Mats L. Richter, Justin Shenk, Wolf Byttner, Anna Wiedenroth, Mikael Huss:
Feature Space Saturation during Training. BMVC 2021: 17 - [c3]Julius Schöning
AI-Based Crop Rotation for Sustainable Agriculture Worldwide. GHTC 2021: 142-146 - [c2]Mats L. Richter, Wolf Byttner, Ulf Krumnack, Anna Wiedenroth
(Input) Size Matters for CNN Classifiers. ICANN (2) 2021: 133-144 - [c1]Mats L. Richter, Julius Schöning
Should You Go Deeper? Optimizing Convolutional Neural Network Architectures without Training. ICMLA 2021: 964-971 - [i5]Mats L. Richter, Wolf Byttner, Ulf Krumnack, Ludwig Schallner, Justin Shenk:
Size Matters. CoRR abs/2102.01582 (2021) - [i4]Mats L. Richter, Leila Malihi, Anne-Kathrin Patricia Windler, Ulf Krumnack:
Exploring the Properties and Evolution of Neural Network Eigenspaces during Training. CoRR abs/2106.09526 (2021) - [i3]Mats L. Richter, Julius Schöning
Should You Go Deeper? Optimizing Convolutional Neural Network Architectures without Training by Receptive Field Analysis. CoRR abs/2106.12307 (2021) - 2020
- [i2]Justin Shenk, Mats L. Richter, Wolf Byttner, Anders Arpteg, Mikael Huss:
Feature Space Saturation during Training. CoRR abs/2006.08679 (2020)
2010 – 2019
- 2019
- [i1]Justin Shenk, Mats L. Richter, Anders Arpteg, Mikael Huss:
Spectral Analysis of Latent Representations. CoRR abs/1907.08589 (2019)
Coauthor Index
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