Apple iPhone 17 Review: Close to Perfect
If you need to upgrade this year, the iPhone 17 comes close to perfection.
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Tado’s new AI features will adaptively heat your home
Smart thermostat company Tado is launching new AI-powered features that aim to help users heat their homes more efficiently. Tado says the AI Assist update builds on the company’s existing Auto Assist options by introducing “advanced machine-learning” that “learns, predicts,…
A first look at Kojima’s OD Xbox game
Hideo Kojima first announced he was teaming up with Xbox for a new game in 2022, before a teaser revealed the OD name and some cast last year. We’re now getting a better look at exactly what OD is, in…
Chewy Promo Codes: $20 Off | September 2025
Explore Chewy coupon codes for great discounts, like a $20 gift card with a purchase of $49 or more.
Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients
arXiv:2505.03432v2 Announce Type: replace-cross Abstract: Score-based Generative Models (SGMs) approximate a data distribution by perturbing it with Gaussian noise and subsequently denoising it via a learned reverse diffusion process. These models excel at modeling complex data distributions and generating diverse…
GEM-T: Generative Tabular Data via Fitting Moments
arXiv:2509.17752v1 Announce Type: cross Abstract: Tabular data dominates data science but poses challenges for generative models, especially when the data is limited or sensitive. We present a novel approach to generating synthetic tabular data based on the principle of maximum…
Significativity Indices for Agreement Values
arXiv:2504.15325v3 Announce Type: replace-cross Abstract: Agreement measures, such as Cohen’s kappa or intraclass correlation, gauge the matching between two or more classifiers. They are used in a wide range of contexts from medicine, where they evaluate the effectiveness of medical…
Bayesian Semi-supervised Inference via a Debiased Modeling Approach
arXiv:2509.17385v1 Announce Type: cross Abstract: Inference in semi-supervised (SS) settings has gained substantial attention in recent years due to increased relevance in modern big-data problems. In a typical SS setting, there is a much larger-sized unlabeled data, containing only observations…
Cover Learning for Large-Scale Topology Representation
arXiv:2503.09767v2 Announce Type: replace-cross Abstract: Classical unsupervised learning methods like clustering and linear dimensionality reduction parametrize large-scale geometry when it is discrete or linear, while more modern methods from manifold learning find low dimensional representation or infer local geometry by…
On Quantification of Borrowing of Information in Hierarchical Bayesian Models
arXiv:2509.17301v1 Announce Type: cross Abstract: In this work, we offer a thorough analytical investigation into the role of shared hyperparameters in a hierarchical Bayesian model, examining their impact on information borrowing and posterior inference. Our approach is rooted in a…