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pro vyhledávání: '"JAKOB, H"'
Amortized simulation-based inference (SBI) methods train neural networks on simulated data to perform Bayesian inference. While this approach avoids the need for tractable likelihoods, it often requires a large number of simulations and has been chal
Externí odkaz:
http://arxiv.org/abs/2411.02728
Timescales of neural activity are diverse across and within brain areas, and experimental observations suggest that neural timescales reflect information in dynamic environments. However, these observations do not specify how neural timescales are sh
Externí odkaz:
http://arxiv.org/abs/2409.02684
Autor:
Dax, Maximilian, Green, Stephen R., Gair, Jonathan, Gupte, Nihar, Pürrer, Michael, Raymond, Vivien, Wildberger, Jonas, Macke, Jakob H., Buonanno, Alessandra, Schölkopf, Bernhard
Mergers of binary neutron stars (BNSs) emit signals in both the gravitational-wave (GW) and electromagnetic (EM) spectra. Famously, the 2017 multi-messenger observation of GW170817 led to scientific discoveries across cosmology, nuclear physics, and
Externí odkaz:
http://arxiv.org/abs/2407.09602
Autor:
Kapoor, Jaivardhan, Schulz, Auguste, Vetter, Julius, Pei, Felix, Gao, Richard, Macke, Jakob H.
Modern datasets in neuroscience enable unprecedented inquiries into the relationship between complex behaviors and the activity of many simultaneously recorded neurons. While latent variable models can successfully extract low-dimensional embeddings
Externí odkaz:
http://arxiv.org/abs/2407.08751
Publikováno v:
The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS) 2024
A central aim in computational neuroscience is to relate the activity of large populations of neurons to an underlying dynamical system. Models of these neural dynamics should ideally be both interpretable and fit the observed data well. Low-rank rec
Externí odkaz:
http://arxiv.org/abs/2406.16749
Amortized Bayesian inference trains neural networks to solve stochastic inference problems using model simulations, thereby making it possible to rapidly perform Bayesian inference for any newly observed data. However, current simulation-based amorti
Externí odkaz:
http://arxiv.org/abs/2404.09636
Autor:
Bischoff, Sebastian, Darcher, Alana, Deistler, Michael, Gao, Richard, Gerken, Franziska, Gloeckler, Manuel, Haxel, Lisa, Kapoor, Jaivardhan, Lappalainen, Janne K, Macke, Jakob H, Moss, Guy, Pals, Matthijs, Pei, Felix, Rapp, Rachel, Sağtekin, A Erdem, Schröder, Cornelius, Schulz, Auguste, Stefanidi, Zinovia, Toyota, Shoji, Ulmer, Linda, Vetter, Julius
Publikováno v:
Transactions on Machine Learning Research (TMLR) 2024
Generative models are invaluable in many fields of science because of their ability to capture high-dimensional and complicated distributions, such as photo-realistic images, protein structures, and connectomes. How do we evaluate the samples these m
Externí odkaz:
http://arxiv.org/abs/2403.12636
Autor:
Beck, Jonas, Bosch, Nathanael, Deistler, Michael, Kadhim, Kyra L., Macke, Jakob H., Hennig, Philipp, Berens, Philipp
Ordinary differential equations (ODEs) are widely used to describe dynamical systems in science, but identifying parameters that explain experimental measurements is challenging. In particular, although ODEs are differentiable and would allow for gra
Externí odkaz:
http://arxiv.org/abs/2402.12231
Scientific modeling applications often require estimating a distribution of parameters consistent with a dataset of observations - an inference task also known as source distribution estimation. This problem can be ill-posed, however, since many diff
Externí odkaz:
http://arxiv.org/abs/2402.07808
Simulation-based inference (SBI) provides a powerful framework for inferring posterior distributions of stochastic simulators in a wide range of domains. In many settings, however, the posterior distribution is not the end goal itself -- rather, the
Externí odkaz:
http://arxiv.org/abs/2312.02674