Versus Trending Now Say More Look Up In My Bag Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable @Home Mashable Selects Top creators, ranked Gift Ideas For Everyone On Your List Creator Playbook AI at School All Series Brennan Lee Mulligan returns with'Dimension 20: Toylight' trailer Dropout's'Game Changer' finale: Sam Reich reveals the secrets of'Last Talent Standing' 19:43 On Mashable's, hosts Kristy Puchko (Mashable's Entertainment Editor) and Mark Stetson (Senior Creative Producer) bring humor and their trusted insights to the biggest shows, films, digital trends, and cultural moments. From viral-worthy rants and passionate raves to smart recaps and first-look teasers, they cover what everyone is talking about. Celebrity guests join the conversation for real talk about their careers, upcoming projects, and what's trending online. Davies and Horne go deep on their approach to handling chaos agents like Rich Ayoade and Jason Mantzoukas, the most iconic outfits throughout the show's Plus, Greg opens up about his obsession with Caramac bars! However, for some of the contestants, their particular brand of comedy is subverting the rules and expectations.

Say More Safety Net Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Versus Mashable Selects Creator Playbook In My Bag Trending Now Back to School Good Connection: Uplifting stories for a digital age Switch Off All Series Dropout's'Game Changer' finale: Sam Reich reveals the secrets of'Last Talent Standing' Plus, we asked how he wants to die. Kristy Puchko is the Entertainment Editor at Mashable. Based in New York City, she's an established film critic and entertainment reporter who has traveled the world on assignment, covered a variety of film festivals, co-hosted movie-focused podcasts, and interviewed a wide array of performers and filmmakers. Helen Mirren plays iconic author Patricia Highsmith in'A Talent for Murder' trailer On Mashable's, hosts Kristy Puchko (Mashable's Entertainment Editor) and Mark Stetson (Senior Creative Producer) bring humor and their trusted insights to the biggest shows, films, digital trends, and cultural moments. From viral-worthy rants and passionate raves to smart recaps and first-look teasers, they cover what everyone is talking about.

Say More Look Up Mashable Selects Mashable Voices Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Switch Off Trending Now In My Bag All Series Most of my conversations are convincing people to dance or to bake. Kristy Puchko is the Entertainment Editor at Mashable. Based in New York City, she's an established film critic and entertainment reporter who has traveled the world on assignment, covered a variety of film festivals, co-hosted movie-focused podcasts, and interviewed a wide array of performers and filmmakers. Brennan Lee Mulligan returns with'Dimension 20: Toylight' trailer Netflix's'My Brilliant Career' trailer looks like Eloise Bridgerton in the Australian bush 2:45 On Mashable's, hosts Kristy Puchko (Mashable's Entertainment Editor) and Mark Stetson (Senior Creative Producer) bring humor and their trusted insights to the biggest shows, films, digital trends, and cultural moments. From viral-worthy rants and passionate raves to smart recaps and first-look teasers, they cover what everyone is talking about.

Versus Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Creator Hub Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now Safety Net In My Bag VidCon with Mashable All Series Brennan Lee Mulligan returns with'Dimension 20: Toylight' trailer The Dropout actual play franchise is bringing toys and carnage into the dome. Kristy Puchko is the Entertainment Editor at Mashable. Based in New York City, she's an established film critic and entertainment reporter who has traveled the world on assignment, covered a variety of film festivals, co-hosted movie-focused podcasts, and interviewed a wide array of performers and filmmakers. LAIKA's'Wildwood' teaser trailer promises a gorgeous stop-motion fairy tale Already in 2026, Brennan Lee Mulligan's actual play show has offered Dropout subscribers and . The first trailer for promises that this season will reteam Mulligan with his Intrepid Heroes: Emily Axford, Ally Beardsley, Brian Murphy, Zac Oyama, Siobhan Thompson, and Lou Wilson.

Effective sub-typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts. However, subtyping temporal EHR datasets is known to be challenging due to inherent EHR issues, including complexity and irregularity. In this study, we propose a self-supervised Mamba-based model that learns effective EHR representations and enables enhanced patient subtyping. We evaluate the proposed model on public and private real-world EHR datasets to classify the data based on the available labels and subtype patients based on the representations learned from the model. Through an extensive set of experiments, we demonstrate that our model's design choices lead to better performance compared to competitive baseline models for prediction. Moreover, we evaluate several clustering techniques to demonstrate that our findings offer valuable insights into subtyping patients based on temporal records from EHR models\footnote{Our implementations are available at https://github.com/healthylaife/triplet_mamba.

Uncertainty quantification (UQ) in scientific machine learning is increasingly critical as neural networks are widely adopted to tackle complex problems across diverse scientific disciplines. For physics-informed neural networks (PINNs), a prominent model in scientific machine learning, uncertainty is typically quantified using Bayesian or dropout methods. However, both approaches suffer from a fundamental limitation: the prior distribution or dropout rate required to construct honest confidence sets cannot be determined without additional information. In this paper, we propose a novel method within the framework of extended fiducial inference (EFI) to provide rigorous uncertainty quantification for PINNs. The proposed method leverages a narrow-neck hyper-network to learn the parameters of the PINN and quantify their uncertainty based on imputed random errors in the observations. This approach overcomes the limitations of Bayesian and dropout methods, enabling the construction of honest confidence sets based solely on observed data. This advancement represents a significant breakthrough for PINNs, greatly enhancing their reliability, interpretability, and applicability to real-world scientific and engineering challenges. Moreover, it establishes a new theoretical framework for EFI, extending its application to large-scale models, eliminating the need for sparse hyper-networks, and significantly improving the automaticity and robustness of statistical inference.

The success of the machine learning field has reliably depended on training on large datasets. While effective, this trend comes at an extraordinary cost. This is due to two deeply intertwined factors: the size of models and the size of datasets. While promising research efforts focus on reducing the size of models, the other half of the equation remains fairly mysterious. Indeed, it is surprising that the standard approach to training remains to iterate over and over, uniformly sampling the training dataset.

Consistency learning with feature perturbation is a widely used strategy in semi-supervised medical image segmentation. However, many existing perturbation methods rely on dropout, and thus require a careful manual tuning of the dropout rate, which is a sensitive hyperparameter and often difficult to optimize and may lead to suboptimal regularization. To overcome this limitation, we propose VQ-Seg, the first approach to employ vector quantization (VQ) to discretize the feature space and introduce a novel and controllable Quantized Perturbation Module (QPM) that replaces dropout.

Traditional neural networks provide deterministic predictions without inherent uncertainty estimates. While Bayesian Neural Networks (BNNs) offer a principled approach to uncertainty quantification, their computational complexity limits scalability. Monte Carlo (MC) Dropout, initially introduced as a regularization technique, has been shown to approximate Bayesian inference by enabling probabilistic modeling through multiple stochastic forward passes. In this work, we enhance uncertainty estimation in deep learning by integrating a Dirichlet-based framework within MC Dropout. Specifically, we leverage the formulation proposed by Sensoy et al. (2018), where class probabilities are modeled using a Dirichlet distribution, allowing for a more informative uncertainty representation. The proposed approach maintains the computational efficiency of MC Dropout while improving the quality of uncertainty estimates. We discuss the theoretical foundations of our method and compare it with existing uncertainty quantification techniques. The results highlight the effectiveness of the proposed method in producing well-calibrated uncertainty estimates, offering a practical solution for uncertainty-aware deep learning models.

We develop a mean-field theory of dropout as a perturbation of critical signal propagation at the edge of chaos. Dropout shifts the perfect-alignment fixed point, making the depth scale for information propagation finite even at critical initialization. We derive critical and crossover scaling laws for correlation decay and establish that smooth activations and kinked, ReLU-like activations constitute distinct universality classes, with different critical exponents and a universal two-parameter scaling collapse in detuning and dropout strength. The distinction traces to the analytic structure of the correlation map: smooth activations admit a Taylor expansion near perfect alignment, while kinked activations develop a branch point with universal non-analyticity. As a corollary, the framework yields saturated dropout profiles under fixed budget; a rank-flow tie-breaker then selects front-loaded schedules, substantially reducing held-out test loss at no extra computational cost, with accuracy gains as a consistent secondary effect. We test the predictions in MLPs and Vision Transformers and discuss CNN/ResNet extensions.