155 citations · 156 across the 11 of their papers we have counts for
11 papers
Beyond Journals: Rethinking Research Evaluation in Hungarian Computer Science
János Tapolcai, Márk Jelasity, Lajos Rónyai +3
This study examines the role of top-tier conference publications in Hungarian computer science research. We show that the national scientometric practice, which is currently journa…
WMAttack: Automated Attack Search for Adversarial Evaluation of World-Model Agents
Zhixiang Guo, Siyuan Liang, Shi Fu +4
Despite the growing use of world models as decision-making agents, their adversarial robustness remains underexplored due to the lack of dedicated automated evaluation methods. A k…
When World Models Dream Wrong: Physical-Conditioned Adversarial Attacks against World Models
Zhixiang Guo, Siyuan Liang, Andras Balogh +4
Generative world models (WMs) are increasingly used to synthesize controllable, sensor-conditioned driving videos, yet their reliance on physical priors exposes novel attack surfac…
Verification of the Implicit World Model in a Generative Model via Adversarial Sequences
András Balogh, Márk Jelasity
Generative sequence models are typically trained on sample sequences from natural or formal languages. It is a crucial question whether -- or to what extent -- sample-based trainin…
Detecting Semantic Backdoors in a Mystery Shopping Scenario
Arpad Berta, Gabor Danner, Istvan Hegedus +1
Detecting semantic backdoors in classification models--where some classes can be activated by certain natural, but out-of-distribution inputs--is an important problem that has rece…
On the Brittleness of LLMs: A Journey around Set Membership
Lea Hergert, Gábor Berend, Mario Szegedy +2
Large language models (LLMs) achieve superhuman performance on complex reasoning tasks, yet often fail on much simpler problems, raising concerns about their reliability and interp…
No Soundness in the Real World: On the Challenges of the Verification of Deployed Neural Networks
Attila Szász, Balázs Bánhelyi, Márk Jelasity
The ultimate goal of verification is to guarantee the safety of deployed neural networks. Here, we claim that all the state-of-the-art verifiers we are aware of fail to reach this…
How not to Stitch Representations to Measure Similarity: Task Loss Matching versus Direct Matching
András Balogh, Márk Jelasity
Measuring the similarity of the internal representations of deep neural networks is an important and challenging problem. Model stitching has been proposed as a possible approach,…
Evaluating the Adversarial Robustness of Semantic Segmentation: Trying Harder Pays Off
Levente Halmosi, Bálint Mohos, Márk Jelasity
Machine learning models are vulnerable to tiny adversarial input perturbations optimized to cause a very large output error. To measure this vulnerability, we need reliable methods…
On Evaluating the Adversarial Robustness of Semantic Segmentation Models
Levente Halmosi, Mark Jelasity
Achieving robustness against adversarial input perturbation is an important and intriguing problem in machine learning. In the area of semantic image segmentation, a number of adve…
Gossip Learning with Linear Models on Fully Distributed Data
Róbert Ormándi, István Hegedüs, Márk Jelasity
Machine learning over fully distributed data poses an important problem in peer-to-peer (P2P) applications. In this model we have one data record at each network node, but without…