Jovita Lukasik

Computer Science · Physical Sciences

4h-index131citations23works1.12yr avg
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Research Topics

Advanced Neural Network Applications(9), Adversarial Robustness in Machine Learning(5), Advanced Graph Neural Networks(4), Anomaly Detection Techniques and Applications(4), Machine Learning in Materials Science(4)

Publications23 total

ONNX-Net: Towards Universal Representations and Instant Performance Prediction for Neural Architectures
arXiv (Cornell University)·2025Open Access
Smooth Model Compression without Fine-Tuning
arXiv (Cornell University)·2025Open Access
Search for PDR and ISGQR in \(A\simeq 60\) and \(A=120\) Mass Regions
Acta Physica Polonica B Proceedings Supplement·2025Open Access
Can We Talk Models Into Seeing the World Differently?
Opus-HSO (Offenburg University of Applied Sciences)·2025
An Evaluation of Zero-Cost Proxies - from Neural Architecture Performance Prediction to Model Robustness
International Journal of Computer Vision·2024· 4 citedOpen Access
Surprisingly Strong Performance Prediction with Neural Graph Features
arXiv (Cornell University)·2024· 2 citedOpen Access
Can We Talk Models Into Seeing the World Differently?
arXiv (Cornell University)·2024Open Access
An Evaluation of Zero-Cost Proxies - From Neural Architecture Performance Prediction to Model Robustness
Lecture notes in computer science·2024· 3 cited
An Evaluation of Zero-Cost Proxies -- from Neural Architecture Performance to Model Robustness
arXiv (Cornell University)·2023Open Access
Neural Architecture Design and Robustness: A Dataset
arXiv (Cornell University)·2023· 1 citedOpen Access
Learning Where To Look -- Generative NAS is Surprisingly Efficient
arXiv (Cornell University)·2022· 1 citedOpen Access
Learning Where to Look – Generative NAS is Surprisingly Efficient
Lecture notes in computer science·2022
Is Differentiable Architecture Search truly a One-Shot Method?
arXiv (Cornell University)·2021Open Access
DARTS for Inverse Problems: a Study on Hyperparameter Sensitivity.
arXiv (Cornell University)·2021Open Access
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search
2021· 1 citedOpen Access
NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search
2021· 80 cited
Neural Architecture Performance Prediction Using Graph Neural Networks
Lecture notes in computer science·2021· 3 citedOpen Access
A Benders Decomposition Approach to Correlation Clustering
2020· 4 cited
Table of Contents
2020Open Access
Smooth Variational Graph Embeddings for Efficient Neural Architecture\n Search
arXiv (Cornell University)·2020Open Access
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Frequent Co-authors

Margret Keuper(16), Michael Moeller(7), Frank Hutter(5), Arber Zela(4), David Friede(4), Julien Siems(2), Heiner Stuckenschmidt(2), Steffen Jung(2), Paweł Pawłowski(2), Steffen Jung(2), Paul Gavrikov(2), M. Jehanzeb Mirza(2), Jonas Geiping(2), Lucas Zimmer(1), Lucas Zimmer(1), Maneesh Singh(1), Julian Yarkony(1), Gabriela Kadlecová(1), Martin Pilát(1), Petra Vidnerová(1)