arXiv:2510.17794v2 Announce Type: replace-cross Abstract: Modern probabilistic regressors often remain overconfident under distribution shift. We present Functional Distribution Networks (FDN), an input-conditioned distribution over network weights that induces predictive mixtures whose dispersion adapts to the input. FDN is trained with a beta-ELBO and Monte Carlo sampling. We further propose an evaluation protocol that cleanly separates interpolation from extrapolation and stresses OOD sanity checks (e.g., that predictive likelihood degrades under shift while in-distribution accuracy and calibration are maintained). On […]

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Clustering and dimensionality reduction have been crucial topics in machine learning and computer vision. Clustering high-dimensional data has been challenging for a long time due to the curse of dimensionality. For that reason, a more promising direction is the joint learning of dimension reduction and clustering. In this work, we propose a Manifold Learning Framework that learns dimensionality reduction and clustering simultaneously. The proposed framework is able to jointly learn the parameters of a dimension reduction technique (e.g. […]

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The wind-induced structural response forecasting capabilities of a novel transformer methodology are examined here. The model also provides a digital twin component for bridge structural health monitoring. Firstly, the approach uses the temporal characteristics of the system to train a forecasting model. Secondly, the vibration predictions are compared to the measured ones to detect large deviations. Finally, the identified cases are used as an early-warning indicator of structural change. The artificial intelligence-based model outperforms approaches for response forecasting […]

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Neural models get the hype. But that lightning-fast word suggestion as you type? That’s not a neural model. It’s a lookup table. And it’s beating transformers on the metric that matters. I’ve spent a lot of time thinking about prediction under pressure. When I was working on Indian-language typing for early Kindle devices, we had a problem that no amount of ML enthusiasm could solve: the hardware simply didn’t care about our ambitions. We had roughly 512 MB […]

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arXiv:2602.13232v1 Announce Type: new Abstract: We present PlotChain, a deterministic, generator-based benchmark for evaluating multimodal large language models (MLLMs) on engineering plot reading-recovering quantitative values from classic plots (e.g., Bode/FFT, step response, stress-strain, pump curves) rather than OCR-only extraction or free-form captioning. PlotChain contains 15 plot families with 450 rendered plots (30 per family), where every item is produced from known parameters and paired with exact ground truth computed directly from the generating process. A central contribution is […]

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This study surveys the historical development of regularization, tracing its evolution from stepwise regression in the 1960s to recent advancements in formal error control, structured penalties for non-independent features, Bayesian methods, and l0-based regularization (among other techniques). We empirically evaluate the performance of four canonical frameworks — Ridge, Lasso, ElasticNet, and Post-Lasso OLS — across 134,400 simulations spanning a 7-dimensional manifold grounded in eight production-grade machine learning models. Our findings demonstrate that for prediction accuracy when the sample-to-feature […]

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arXiv:2603.21683v1 Announce Type: cross Abstract: We study the approximation of operators acting on probability measures on a product space with prescribed marginal. Let $I$ be a label space endowed with a reference measure $lambda$, and define $cal M_lambda$ as the set of probability measures on $Itimes mathbb{R}^d$ with first marginal $lambda$. By disintegration, elements of $cal M_lambda$ correspond to families of labeled conditional distributions. Operators defined on this constrained measure space arise naturally in mean-field control problems with […]

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arXiv:2603.06700v1 Announce Type: new Abstract: Scientific images fundamentally differ from natural and AI-generated images in that they encode structured domain knowledge rather than merely depict visual scenes. Assessing their quality therefore requires evaluating not only perceptual fidelity but also scientific correctness and logical completeness. However, existing image quality assessment (IQA) paradigms primarily focus on perceptual distortions or image-text alignment, implicitly assuming that depicted content is factually valid. This assumption breaks down in scientific contexts, where visually plausible figures […]

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