5 papers
Practical Hybrid Quantum Language Models with Observable Readout on Real Hardware
Stefan Balauca, Ada-Astrid Balauca, Adrian Iftene
Hybrid quantum-classical models represent a crucial step toward leveraging near-term quantum devices for sequential data processing. We present Quantum Recurrent Neural Networks (Q…
MixAT: Combining Continuous and Discrete Adversarial Training for LLMs
Csaba Dékány, Stefan Balauca, Robin Staab +2
Despite recent efforts in Large Language Model (LLM) safety and alignment, current adversarial attacks on frontier LLMs can still consistently force harmful generations. Although a…
Understanding Museum Exhibits using Vision-Language Reasoning
Ada-Astrid Balauca, Sanjana Garai, Stefan Balauca +8
Museums serve as repositories of cultural heritage and historical artifacts from diverse epochs, civilizations, and regions, preserving well-documented collections that encapsulate…
Gaussian Loss Smoothing Enables Certified Training with Tight Convex Relaxations
Stefan Balauca, Mark Niklas Müller, Yuhao Mao +3
Training neural networks with high certified accuracy against adversarial examples remains an open challenge despite significant efforts. While certification methods can effectivel…
CTBENCH: A Library and Benchmark for Certified Training
Yuhao Mao, Stefan Balauca, Martin Vechev
Training certifiably robust neural networks is an important but challenging task. While many algorithms for (deterministic) certified training have been proposed, they are often ev…