I work as a postdoctoral researcher in the Computer Vision group supervised by Prof. Dr. Michael Moeller at the University of Siegen since April 2023. Previously, I was a Ph.D. candidate supervised by Prof Dr.-Ing. Margret Keuper in the Computer Vision and Machine Learning group at the Max-Planck-Institute for Informatics and in the focus group of Computer Vision in the Data and Web Science Group at the University of Mannheim.
Together with Aaron Klein, Arber Zela, Giovanni Zappella and Rhea Sukthanker I also organize the AutoML Seminars as part of the ELLIS units Berlin and Freiburg. I was part of the organization team of the second workshop on neural architecture search @ ICLR 2021.
My research mainly focuses on efficient, unsupervised graph representations and embeddings for supervised surrogate models for neural architecture search.
News
- 06/2024 Gabriela and me will present a Tutorial at AutoML 2024: Towards Zero-Cost Proxies for Performance Prediction beyond CIFAR-10!
- 05/2024 Two papers were accepted to ICML 2024!
- 04/2024 I gave a talk about Neural Architecture Search – Towards Prediction beyond (CIFAR-10 Accuracy) Benchmarks at the Cambridge Image Analysis Group
- 03/2024 I am part of the publication chair at ECCV 2024!
- 12/2023 Our paper Improving Native CNN Robustness with Filter Frequency Regularization was accepted at TMLR!
- 11/2023 I presented my work about Multi-Objective Performance Prediction for Neural Architecture Search at the Doctoral Consortium at BMVC 2023!
- 10/2023 We have one workshop paper (Implicit Representations for Image Segmentation) accepted to Unireps@NeurIPS
- 09/2023 I was a panelist on the panel discussion for AutoML,Trustworthiness & Alignment at AutoML 2023
- 07/2023 Our paper An Evaluation of Zero-Cost Proxies - from Neural Architecture Performance to Model Robustness was accepted at GCPR 2023
Publications
Surprisingly Strong Performance Prediction with Neural Graph Features
G. Kadlecová, J. Lukasik, M. Pilát, P. Vidnerová, M. Safari, R. Neruda, F. Hutter
ICML 2024
PDF | Blog
Implicit Representations for Constrained Image Segmentation
J. P. Schneider, M. Fatima, J. Lukasik, A. Kolb, M. Keuper, M. Moeller
ICML 2024
PDF | Project Page
Improving Native CNN Robustness with Filter Frequency Regularization
J. Lukasik*, P. Gavrikov*, J.Keuper, M. Keuper
TMLR 2023
PDF
Implicit Representations for Image Segmentation
J. P. Schneider, M. Fatima, J. Lukasik, A. Kolb, M. Keuper, M. Moeller
Unireps@NeurIPS 2023
An Evaluation of Zero-Cost Proxies - from Neural Architecture Performance to Model Robustness
J. Lukasik, M. Moeller, M. Keuper
GCPR 2023 (oral)
PDF
Differentiable Architecture Search: a One-Shot Method?
J. Lukasik*, J. Geiping*, M. Moeller, M. Keuper
AutoML Conference 2023 Workshops
PDF
Neural Architecture Design and Robustness: A Dataset
S. Jung*, J. Lukasik*, M. Keuper
ICLR 2023
Website | PDF | Code
Learning Where to Look - Generative NAS is Surprisingly Efficient
J. Lukasik* , S. Jung*, M. Keuper
ECCV 2022
PDF | Code | Poster
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks
A. Zela*, JN. Siems*, L. Zimmer*, J. Lukasik, M. Keuper, F. Hutter
ICLR 2022
PDF
DARTS for Inverse Problems: a Study on Sensitivity
J. Geiping*, J. Lukasik*, M. Keuper, M. Moeller
NeurIPS workshop on Inverse Problems 2021
PDF | Poster
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search
J. Lukasik, D. Friede, A. Zela, F. Hutter, M. Keuper
IJCNN 2021
PDF | Code
Neural Architecture Performance Prediction Using Graph Neural Networks
J. Lukasik, D. Friede, H. Stuckenschmidt, M. Keuper
GCPR 2020
PDF