Showing papers from Technical University of Munich / Pruna AI Show all papers
Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Solver-Aligned Initialization Learning differentiates through SCF solvers to train transferable ML initial guesses, reducing iterations by up to 37% on molecules up to 10× larger than training data.
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Diffusion LLMs amortize adversarial prompt optimization by directly generating diverse, transferable jailbreak prompts that bypass black-box target models.
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 2 on Hugging Face
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A multi-task virtual sensor model predicts diverse targets via shared representations, reducing computation up to 415x and memory 951x while improving accuracy over isolated and foundation alternatives.
Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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