Quality follows upgrading
Tag: math.PR
arXiv:2603.23783v1 Announce Type: new Abstract: Adapting large-scale foundation models to new domains with limited supervision remains a fundamental challenge due to latent distribution mismatch, unstable …
arXiv:2603.19463v1 Announce Type: new Abstract: We develop deep learning-based approximation methods for fully nonlinear second-order PDEs on separable Hilbert spaces, such as HJB equations for …
arXiv:2603.17365v1 Announce Type: new Abstract: Internal noise in deep networks is usually inherited from heuristics such as dropout, hard masking, or additive perturbation. We ask …
arXiv:2603.11118v1 Announce Type: new Abstract: The superposition of arrival processes is a fundamental yet analytically intractable operation in queueing networks when inputs are general non-renewal …
arXiv:2603.10078v1 Announce Type: new Abstract: Stochastic port-Hamiltonian systems represent open dynamical systems with dissipation, inputs, and stochastic forcing in an energy based form. We introduce …
arXiv:2603.10079v1 Announce Type: new Abstract: We analyse SGD training of a shallow, fully connected network in the NTK scaling and provide a quantitative theory of …
arXiv:2603.09310v1 Announce Type: new Abstract: We study training algorithms with data following a Gaussian mixture model. For a specific family of such algorithms, we present …
arXiv:2603.05559v1 Announce Type: new Abstract: Decision makers exploiting photonic chaotic dynamics obtained by semiconductor lasers provide an ultrafast approach to solving multi-armed bandit problems by …
arXiv:2603.02356v1 Announce Type: new Abstract: We explore the question of how to learn an optimal search strategy within the example of a parking problem where …
arXiv:2602.22271v1 Announce Type: new Abstract: Self-attention is usually described as a flexible, content-adaptive way to mix a token with information from its past. We re-interpret …
