Tag: scalable
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From Shallow Bayesian Neural Networks to Gaussian Processes: General Convergence, Identifiability and Scalable Inference
From Shallow Bayesian Neural Networks to Gaussian Processes: General Convergence, Identifiability and Scalable Inference arXiv:2602.22492v1 Announce Type: new Abstract: In this work, we study scaling limits of shallow Bayesian neural networks (BNNs) via their connection to Gaussian processes (GPs), with an emphasis on statistical modeling, identifiability, and scalable inference. We first establish a general convergence…
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Tilt Matching for Scalable Sampling and Fine-Tuning
Tilt Matching for Scalable Sampling and Fine-Tuning arXiv:2512.21829v1 Announce Type: new Abstract: We propose a simple, scalable algorithm for using stochastic interpolants to sample from unnormalized densities and for fine-tuning generative models. The approach, Tilt Matching, arises from a dynamical equation relating the flow matching velocity to one targeting the same distribution tilted by a…
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Scalable Machine Learning Algorithms using Path Signatures
Scalable Machine Learning Algorithms using Path Signatures arXiv:2506.17634v1 Announce Type: new Abstract: The interface between stochastic analysis and machine learning is a rapidly evolving field, with path signatures – iterated integrals that provide faithful, hierarchical representations of paths – offering a principled and universal feature map for sequential and structured data. Rooted in rough path…