Zobrazeno 1 - 10
of 29 831
pro vyhledávání: '"Olsson P"'
Using normalizing flows and reweighting, Boltzmann Generators enable equilibrium sampling from a Boltzmann distribution, defined by an energy function and thermodynamic state. In this work, we introduce Thermodynamic Interpolation (TI), which allows
Externí odkaz:
http://arxiv.org/abs/2411.10075
Autor:
Glazer, Elliot, Erdil, Ege, Besiroglu, Tamay, Chicharro, Diego, Chen, Evan, Gunning, Alex, Olsson, Caroline Falkman, Denain, Jean-Stanislas, Ho, Anson, Santos, Emily de Oliveira, Järviniemi, Olli, Barnett, Matthew, Sandler, Robert, Vrzala, Matej, Sevilla, Jaime, Ren, Qiuyu, Pratt, Elizabeth, Levine, Lionel, Barkley, Grant, Stewart, Natalie, Grechuk, Bogdan, Grechuk, Tetiana, Enugandla, Shreepranav Varma, Wildon, Mark
We introduce FrontierMath, a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians. The questions cover most major branches of modern mathematics -- from computationally intensiv
Externí odkaz:
http://arxiv.org/abs/2411.04872
Autor:
Rhee, Dongjoon, Kim, Kwan-Ho, Zheng, Jeffrey, Song, Seunguk, Peng, Lian-Mao, Olsson III, Roy H., Kang, Joohoon, Jariwala, Deep
Reconfigurable devices have garnered significant attention for alleviating the scaling requirements of conventional CMOS technology, as they require fewer components to construct circuits with similar function. Prior works required continuous voltage
Externí odkaz:
http://arxiv.org/abs/2411.03198
General state-space models (SSMs) are widely used in statistical machine learning and are among the most classical generative models for sequential time-series data. SSMs, comprising latent Markovian states, can be subjected to variational inference
Externí odkaz:
http://arxiv.org/abs/2411.02217
Deep Brain Stimulation (DBS) is a therapy widely used for treating the symptoms of neurological disorders. Electrical pulses are chronically delivered in DBS to a disease-specific brain target via a surgically implanted electrode. The stimulating con
Externí odkaz:
http://arxiv.org/abs/2410.17780
Accurate prediction of thermodynamic properties is essential in drug discovery and materials science. Molecular dynamics (MD) simulations provide a principled approach to this task, yet they typically rely on prohibitively long sequential simulations
Externí odkaz:
http://arxiv.org/abs/2410.10605
Autor:
Moufad, Badr, Janati, Yazid, Bedin, Lisa, Durmus, Alain, Douc, Randal, Moulines, Eric, Olsson, Jimmy
Diffusion models have recently shown considerable potential in solving Bayesian inverse problems when used as priors. However, sampling from the resulting denoising posterior distributions remains a challenge as it involves intractable terms. To tack
Externí odkaz:
http://arxiv.org/abs/2410.09945
Autor:
Abdelli, Khouloud, Lonardi, Matteo, Gripp, Jurgen, Boitier, Samuel Olsson Fabien, Layec, Patricia
We introduce a novel weather-adaptive approach for multi-step forecasting of multi-scale SOP changes in aerial fiber links. By harnessing the discrete wavelet transform and incorporating weather data, our approach improves forecasting accuracy by ove
Externí odkaz:
http://arxiv.org/abs/2409.03663
Autor:
Abdelli, Khouloud, Lonardi, Matteo, Gripp, Jurgen, Olsson, Samuel, Boitier, Fabien, Layec, Patricia
Publikováno v:
ECOC 2024
We propose a novel unsupervised anomaly detection approach using generative adversarial networks and SOP-derived spectrograms. Demonstrating remarkable efficacy, our method achieves over 97% accuracy on SOP datasets from both submarine and terrestria
Externí odkaz:
http://arxiv.org/abs/2409.03657
Publikováno v:
Computer Physics Communications 306 (2025) 109384
The program ftint is introduced which numerically evaluates dimensionally regulated integrals as they occur in the perturbative approach to the gradient-flow formalism in quantum field theory. It relies on sector decomposition in order to determine t
Externí odkaz:
http://arxiv.org/abs/2407.16529