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pro vyhledávání: '"Falkner BE"'
Autor:
Falkner, Sebastian, Coretti, Alessandro, Peters, Baron, Bolhuis, Peter G., Dellago, Christoph
Rare event sampling algorithms are essential for understanding processes that occur infrequently on the molecular scale, yet they are important for the long-time dynamics of complex molecular systems. One of these algorithms, transition path sampling
Externí odkaz:
http://arxiv.org/abs/2408.03054
This paper presents our approaches for the SMM4H24 Shared Task 5 on the binary classification of English tweets reporting children's medical disorders. Our first approach involves fine-tuning a single RoBERTa-large model, while the second approach en
Externí odkaz:
http://arxiv.org/abs/2406.07759
Autor:
Coretti, Alessandro, Falkner, Sebastian, Weinreich, Jan, Dellago, Christoph, von Lilienfeld, O. Anatole
Publikováno v:
KIM REVIEW, Volume 2, Article 03, 2024
The paper by No\'e et al. [F. No\'e, S. Olsson, J. K\"ohler and H. Wu, Science, 365:6457 (2019)] introduced the concept of Boltzmann Generators (BGs), a deep generative model that can produce unbiased independent samples of many-body systems. They ca
Externí odkaz:
http://arxiv.org/abs/2404.16566
Autor:
Tighineanu, Petru, Grossberger, Lukas, Baireuther, Paul, Skubch, Kathrin, Falkner, Stefan, Vinogradska, Julia, Berkenkamp, Felix
Meta-learning is a powerful approach that exploits historical data to quickly solve new tasks from the same distribution. In the low-data regime, methods based on the closed-form posterior of Gaussian processes (GP) together with Bayesian optimizatio
Externí odkaz:
http://arxiv.org/abs/2312.00742
Neural Combinatorial Optimization has been researched actively in the last eight years. Even though many of the proposed Machine Learning based approaches are compared on the same datasets, the evaluation protocol exhibits essential flaws and the sel
Externí odkaz:
http://arxiv.org/abs/2310.04140
In recent years new deep learning approaches to solve combinatorial optimization problems, in particular NP-hard Vehicle Routing Problems (VRP), have been proposed. The most impactful of these methods are sequential neural construction approaches whi
Externí odkaz:
http://arxiv.org/abs/2309.17089
Autor:
Chen, Fei, Van Nguyen, Hoa, Taggart, David A., Falkner, Katrina, Rezatofighi, S. Hamid, Ranasinghe, Damith C.
Today, the most widespread, widely applicable technology for gathering data relies on experienced scientists armed with handheld radio telemetry equipment to locate low-power radio transmitters attached to wildlife from the ground. Although aerial ro
Externí odkaz:
http://arxiv.org/abs/2308.08104
Bayesian optimization (BO) is a popular method to optimize costly black-box functions. While traditional BO optimizes each new target task from scratch, meta-learning has emerged as a way to leverage knowledge from related tasks to optimize new tasks
Externí odkaz:
http://arxiv.org/abs/2307.03565
The computer simulation of many molecular processes is complicated by long time scales caused by rare transitions between long-lived states. Here, we propose a new approach to simulate such rare events, which combines transition path sampling with en
Externí odkaz:
http://arxiv.org/abs/2302.08757
Recent work on deep clustering has found new promising methods also for constrained clustering problems. Their typically pairwise constraints often can be used to guide the partitioning of the data. Many problems however, feature cluster-level constr
Externí odkaz:
http://arxiv.org/abs/2302.05134