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pro vyhledávání: '"Tamir, Ella"'
Autor:
Tamir, Ella, Solin, Arno
Learning dynamical systems from sparse observations is critical in numerous fields, including biology, finance, and physics. Even if tackling such problems is standard in general information fusion, it remains challenging for contemporary machine lea
Externí odkaz:
http://arxiv.org/abs/2406.00561
Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining prior knowledge. While Gaussian processes elegantly tackle these problems, they struggle with scalability and hand
Externí odkaz:
http://arxiv.org/abs/2403.10929
Deep neural networks (NNs) are known to lack uncertainty estimates and struggle to incorporate new data. We present a method that mitigates these issues by converting NNs from weight space to function space, via a dual parameterization. Importantly,
Externí odkaz:
http://arxiv.org/abs/2309.02195
The dynamic Schr\"odinger bridge problem provides an appealing setting for solving constrained time-series data generation tasks posed as optimal transport problems. It consists of learning non-linear diffusion processes using efficient iterative sol
Externí odkaz:
http://arxiv.org/abs/2301.13636
Simulation-based techniques such as variants of stochastic Runge-Kutta are the de facto approach for inference with stochastic differential equations (SDEs) in machine learning. These methods are general-purpose and used with parametric and non-param
Externí odkaz:
http://arxiv.org/abs/2110.15739
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