340 citations · 482 across the 9 of their papers we have counts for
10 papers
Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges
Lajos Muzsai, David Imolai, András Lukács
We present 'Random-Crypto', a procedurally generated cryptographic Capture The Flag (CTF) dataset designed to unlock the potential of Reinforcement Learning (RL) for LLM-based agen…
: A Python package implementing Whittle's likelihood estimation of the Hurst exponent
Bálint Csanády, Lóránt Nagy, András Lukács
This paper presents , a Python package implementing Whittle's likelihood method for estimating the Hurst exponent in fractional Brownian motion (fBm). While the theor…
HackSynth: LLM Agent and Evaluation Framework for Autonomous Penetration Testing
Lajos Muzsai, David Imolai, András Lukács
We introduce HackSynth, a novel Large Language Model (LLM)-based agent capable of autonomous penetration testing. HackSynth's dual-module architecture includes a Planner and a Summ…
Parameter Estimation of Long Memory Stochastic Processes with Deep Neural Networks
Bálint Csanády, Lóránt Nagy, Dániel Boros +5
We present a purely deep neural network-based approach for estimating long memory parameters of time series models that incorporate the phenomenon of long-range dependence. Paramet…
Enhancing pretraining efficiency for medical image segmentation via transferability metrics
Gábor Hidy, Bence Bakos, András Lukács
In medical image segmentation tasks, the scarcity of labeled training data poses a significant challenge when training deep neural networks. When using U-Net-style architectures, i…
LlamBERT: Large-scale low-cost data annotation in NLP
Bálint Csanády, Lajos Muzsai, Péter Vedres +2
Large Language Models (LLMs), such as GPT-4 and Llama 2, show remarkable proficiency in a wide range of natural language processing (NLP) tasks. Despite their effectiveness, the hi…
Deep learning the Hurst parameter of linear fractional processes and assessing its reliability
Dániel Boros, Bálint Csanády, Iván Ivkovic +3
This research explores the reliability of deep learning, specifically Long Short-Term Memory (LSTM) networks, for estimating the Hurst parameter in fractional stochastic processes.…
Dilated Convolutional Neural Networks for Lightweight Diacritics Restoration
Bálint Csanády, András Lukács
Diacritics restoration has become a ubiquitous task in the Latin-alphabet-based English-dominated Internet language environment. In this paper, we describe a small footprint 1D dil…
Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation
Gusztáv Gaál, Balázs Maga, András Lukács
Chest X-ray is the most common test among medical imaging modalities. It is applied for detection and differentiation of, among others, lung cancer, tuberculosis, and pneumonia, th…
Impact of non-Poisson activity patterns on spreading processes
Alexei Vazquez, Racz Balazs, Lukacs Andras +1
Halting a computer or biological virus outbreak requires a detailed understanding of the timing of the interactions between susceptible and infected individuals. While current spre…