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ORCIDJovita Lukasik
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2020 – today
- 2024
[c9]Gabriela Kadlecová, Jovita Lukasik, Martin Pilát, Petra Vidnerová, Mahmoud Safari, Roman Neruda, Frank Hutter:
Surprisingly Strong Performance Prediction with Neural Graph Features. ICML 2024- [c8]Jan Philipp Schneider, Mishal Fatima, Jovita Lukasik, Andreas Kolb, Margret Keuper, Michael Moeller:
Implicit Representations for Constrained Image Segmentation. ICML 2024 - [i10]Paul Gavrikov, Jovita Lukasik, Steffen Jung, Robert Geirhos, Bianca Lamm, Muhammad Jehanzeb Mirza, Margret Keuper, Janis Keuper:
Are Vision Language Models Texture or Shape Biased and Can We Steer Them? CoRR abs/2403.09193 (2024) - [i9]Gabriela Kadlecová, Jovita Lukasik, Martin Pilát, Petra Vidnerová, Mahmoud Safari, Roman Neruda, Frank Hutter:
Surprisingly Strong Performance Prediction with Neural Graph Features. CoRR abs/2404.16551 (2024) - 2023
- [b1]Jovita Lukasik:
Topology Learning for Prediction, Generation, and Robustness in Neural Architecture Search. University of Mannheim, Germany, 2023 - [j1]Jovita Lukasik, Paul Gavrikov, Janis Keuper, Margret Keuper:
Improving Native CNN Robustness with Filter Frequency Regularization. Trans. Mach. Learn. Res. 2023 (2023) - [c7]Jovita Lukasik
, Michael Moeller, Margret Keuper:
An Evaluation of Zero-Cost Proxies - From Neural Architecture Performance Prediction to Model Robustness. DAGM 2023: 624-638 - [c6]Steffen Jung, Jovita Lukasik, Margret Keuper:
Neural Architecture Design and Robustness: A Dataset. ICLR 2023 - [i8]Steffen Jung, Jovita Lukasik, Margret Keuper:
Neural Architecture Design and Robustness: A Dataset. CoRR abs/2306.06712 (2023) - [i7]Jovita Lukasik, Michael Möller, Margret Keuper:
An Evaluation of Zero-Cost Proxies - from Neural Architecture Performance to Model Robustness. CoRR abs/2307.09365 (2023) - 2022
- [c5]Jovita Lukasik, Steffen Jung, Margret Keuper:
Learning Where to Look - Generative NAS is Surprisingly Efficient. ECCV (23) 2022: 257-273 - [c4]Arber Zela, Julien Niklas Siems, Lucas Zimmer, Jovita Lukasik, Margret Keuper, Frank Hutter:
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks. ICLR 2022 - [i6]Jovita Lukasik, Steffen Jung, Margret Keuper:
Learning Where To Look - Generative NAS is Surprisingly Efficient. CoRR abs/2203.08734 (2022) - 2021
- [c3]Jovita Lukasik, David Friede, Arber Zela, Frank Hutter, Margret Keuper:
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search. IJCNN 2021: 1-8 - [i5]Jonas Geiping, Jovita Lukasik, Margret Keuper, Michael Moeller:
DARTS for Inverse Problems: a Study on Hyperparameter Sensitivity. CoRR abs/2108.05647 (2021) - 2020
- [c2]Jovita Lukasik, David Friede, Heiner Stuckenschmidt, Margret Keuper:
Neural Architecture Performance Prediction Using Graph Neural Networks. GCPR 2020: 188-201 - [c1]Jovita Lukasik, Margret Keuper, Maneesh Singh, Julian Yarkony:
A Benders Decomposition Approach to Correlation Clustering. MLHPC/AI4S@SC 2020: 9-16 - [i4]Julien Siems, Lucas Zimmer, Arber Zela, Jovita Lukasik, Margret Keuper, Frank Hutter:
NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search. CoRR abs/2008.09777 (2020) - [i3]Jovita Lukasik, David Friede, Arber Zela, Heiner Stuckenschmidt, Frank Hutter, Margret Keuper:
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search. CoRR abs/2010.04683 (2020) - [i2]Jovita Lukasik, David Friede, Heiner Stuckenschmidt, Margret Keuper:
Neural Architecture Performance Prediction Using Graph Neural Networks. CoRR abs/2010.10024 (2020)
2010 – 2019
- 2019
- [i1]David Friede, Jovita Lukasik, Heiner Stuckenschmidt, Margret Keuper:
A Variational-Sequential Graph Autoencoder for Neural Architecture Performance Prediction. CoRR abs/1912.05317 (2019)
Coauthor Index
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