Record 16122025 · captured 2026-08-25
The world looked up Rob Reiner. 30 tracked subjects, 25 discussions, 25 papers. This record is frozen and will not change.
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What the most people looked up, ranked by Wikipedia pageviews for that day.
Robert Reiner was an American filmmaker, actor, and political activist. He directed a series of acclaimed studio films in a career that spanned comedy, drama, romance, and documentary. Reiner received numerous accolades, including winning two Primetime Emmy Aw
Tracy Reiner is an American former actress. She is known for her roles in When Harry Met Sally..., Masque of the Red Death, A League of Their Own, and Apollo 13.
Carole Penny Marshall was an American actress, film director, and producer. She starred as Laverne DeFazio on the television sitcom Laverne & Shirley from 1976 to 1983, and received three nominations for the Golden Globe Award for Best Actress – Television Ser
Carl Reiner was an American actor, author, comedian, director, and screenwriter whose career spanned seven decades. His awards and honors include 12 Primetime Emmy Awards, a Grammy Award, and the Mark Twain Prize for American Humor. He was inducted into the Te
On 14 December 2025, an antisemitic and Islamic State (IS)-inspired terrorist attack occurred at the Archer Park area of Bondi Beach in Sydney, New South Wales, Australia, during a celebration of the Jewish holiday of Hanukkah attended by around 1,000 people.
Dhurandhar is a 2025 Indian Hindi-language spy action thriller film written and directed by Aditya Dhar. It is produced by Aditya Dhar, Lokesh Dhar and Jyoti Deshpande under Jio Studios and B62 Studios. The film features an ensemble cast consisting of Ranveer
Being Charlie is a 2015 American drama film directed by Rob Reiner and written by his son, Nick Reiner, alongside Matt Elisofon. The film stars Nick Robinson, Common, Cary Elwes, Devon Bostick, Morgan Saylor, Susan Misner, and Ricardo Chavira.
Google Chrome is a cross-platform web browser developed by Google. It was launched in September 2008 for Microsoft Windows and was built with free software components from Apple WebKit and Mozilla Firefox. Versions for Linux, macOS, iOS, iPadOS, and Android we
Anthony Geary was an American actor. His career spanned more than four decades, and began in episodic television. He appeared as a guest on several primetime series and transitioned into a career predominantly in the soap opera genre. His first soap role was D
It: Welcome to Derry is an American supernatural horror television series based on Stephen King's 1986 novel It. Serving as a prequel to the films It (2017) and It Chapter Two (2019), the series was developed by Andy Muschietti, Barbara Muschietti and Jason Fu
Wake Up Dead Man is a 2025 American mystery film written and directed by Rian Johnson. It is the third film in the Knives Out series. The film stars Daniel Craig, who reprises his role as master detective Benoit Blanc as he investigates the death of a Catholic
Estelle Reiner was an American actress and singer, described by The New York Times as "matriarch of one of the leading families in American comedy". She was the wife of Carl Reiner and the mother of Rob Reiner, Lucas Reiner, and Annie Reiner.
Philip Michael Rivers is an American former professional football quarterback who played in the National Football League (NFL) for 18 seasons, primarily with the Chargers franchise. He played college football for the NC State Wolfpack, winning ACC Player of th
Spinal Tap II: The End Continues
Spinal Tap II: The End Continues is a 2025 American mockumentary comedy film directed by Rob Reiner and written by Reiner, Christopher Guest, Michael McKean and Harry Shearer. The sequel to This Is Spinal Tap (1984), it stars Guest, McKean, Shearer, and Reiner
This is a list of lists of deaths of significant people, organized by year. New deaths articles are added to their respective month and then linked below.
The Port Arthur massacre was a mass shooting that occurred on 28 April 1996 at Port Arthur, a tourist town in the Australian state of Tasmania. The perpetrator, Martin Bryant, murdered 35 people and wounded 23 others, in the deadliest massacre in modern Austra
The Princess Bride is a 1987 American fantasy-adventure-comedy film directed and co-produced by Rob Reiner and starring Cary Elwes, Robin Wright, Mandy Patinkin, André the Giant, Chris Sarandon, Christopher Guest, Wallace Shawn, Peter Falk, Fred Savage, Billy
Stand by Me is a 1986 American coming-of-age drama film directed by Rob Reiner. Based on Stephen King's 1982 novella The Body, the film is set in the fictional town of Castle Rock, Oregon, in 1959. Stand by Me stars Wil Wheaton, River Phoenix, Corey Feldman, a
Rachael Anna-Maie Carpani was an Australian actress best known for her role as Jodi Fountain-McLeod in McLeod's Daughters.
2025 Brown University shooting
On December 13, 2025, a mass shooting occurred at Brown University in Providence, Rhode Island, United States, during the second day of final examination week for the fall semester. The shooter, Cláudio Manuel Neves Valente, entered the Barus and Holley Buildi
This Is Spinal Tap is a 1984 American mockumentary comedy film directed by Rob Reiner in his feature directorial debut. Christopher Guest, Michael McKean, and Harry Shearer play members of the parody heavy metal band Spinal Tap, while Reiner plays Martin "Mart
Joshua Mathias O'Connor is an English actor. From 2016 to 2019, he had a major role portraying Larry Durrell in ITV's The Durrells. He had his breakthrough playing the lead role of a gay sheep farmer in Francis Lee's romantic drama God's Own Country (2017), fo
José Antonio Kast Rist is a Chilean lawyer and politician who has served as the 38th president of Chile since 2026. Kast previously served as a member of the Chamber of Deputies from 2002 to 2018, representing districts in the Santiago Metropolitan Region.
Hanukkah is a Jewish holiday that commemorates the Maccabean Revolt against the Seleucid Empire in the 2nd century BCE, when the Maccabees successfully recovered Jerusalem and the Second Temple.
Sardar Abdul Rehman Baloch, known by the alias Rehman Dakait, was a Pakistani gangster based in Karachi's Lyari neighbourhood who formed the Peoples' Aman Committee which was affiliated with the Pakistan People's Party. The Government of Sindh had set a reward
Lucas Joseph Reiner is an American painter, printmaker, photographer and filmmaker. He is most known for painting series that mix elements of representation, narrative, symbolism and abstraction. The work explores subjects such as the collision between organic
A Few Good Men is a 1992 American legal drama film, produced and directed by Rob Reiner and written by Aaron Sorkin, who adapted his 1989 play. It stars an ensemble cast including Tom Cruise, Jack Nicholson, Demi Moore, Kevin Bacon, Kevin Pollak, J. T. Walsh,
When Harry Met Sally... is a 1989 American romantic comedy film directed by Rob Reiner and written by Nora Ephron. Starring Billy Crystal, Meg Ryan, Carrie Fisher, and Bruno Kirby, it follows the title characters from the time they meet in Chicago and share a
John Felix Anthony Cena is an American actor, retired professional wrestler, and former rapper. He is signed to WWE as a brand ambassador. Cena wrestled for WWE for 24 years, becoming a record-setting 17-time world champion, before transitioning fully into his
Richard Wayne Van Dyke is an American actor, comedian, singer, dancer and writer. His work spans screen and stage, and his awards include six Emmy Awards, a Grammy Award, and a Tony Award. He was inducted into the Hollywood Walk of Fame in 1993, and then the T
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
The Optimal Approximation Factor in Density Estimation
Consider the following problem: given two arbitrary densities $q_1,q_2$ and a sample-access to an unknown target density $p$, find which of the $q_i$'s is closer to $p$ in total variation. A remarkable result due to Yatracos shows that this problem is tractable in the following sense: there exists an algorithm that uses $O(ε^{-2})$ samples from $p$ and outputs~$q_i$ such that with high probability, $TV(q_i,p) \le
Adaptive Risk Mitigation in Demand Learning
We study dynamic pricing of a product with an unknown demand distribution over a finite horizon. Departing from the standard no-regret learning environment in which prices can be adjusted at any time, we restrict price changes to predetermined points in time to reflect common retail practice. This constraint, coupled with demand model ambiguity and an unknown customer arrival pattern, imposes a high risk of revenue l
We Can Always Catch You: Detecting Adversarial Patched Objects WITH or WITHOUT Signature
Recently, object detection has proven vulnerable to adversarial patch attacks. The attackers holding a specially crafted patch can hide themselves from state-of-the-art detectors, e.g., YOLO, even in the physical world. This attack can bring serious security threats, such as escaping from surveillance cameras. How to effectively detect this kind of adversarial examples to catch potential attacks has become an importa
Comparing concepts of quantum and classical neural network models for image classification task
While quantum architectures are still under development, when available, they will only be able to process quantum data when machine learning algorithms can only process numerical data. Therefore, in the issues of classification or regression, it is necessary to simulate and study quantum systems that will transfer the numerical input data to a quantum form and enable quantum computers to use the available methods of
CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer
Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms. The missed screening-time detection is commonly caused by the tumor being obscured by its surrounding breast tissues, a phenomenon called masking. To study and benchmark mammographic masking of cancer, in this work we
TreeFlow: Going beyond Tree-based Gaussian Probabilistic Regression
The tree-based ensembles are known for their outstanding performance in classification and regression problems characterized by feature vectors represented by mixed-type variables from various ranges and domains. However, considering regression problems, they are primarily designed to provide deterministic responses or model the uncertainty of the output with Gaussian or parametric distribution. In this work, we intr
In the logic synthesis stage, structure transformations in the synthesis tool need to be combined into optimization sequences and act on the circuit to meet the specified circuit area and delay. However, logic synthesis optimization sequences are time-consuming to run, and predicting the quality of the results (QoR) against the synthesis optimization sequence for a circuit can help engineers find a better optimizatio
Enhancing Interpretability and Interactivity in Robot Manipulation: A Neurosymbolic Approach
In this paper we present a neurosymbolic architecture for coupling language-guided visual reasoning with robot manipulation. A non-expert human user can prompt the robot using unconstrained natural language, providing a referring expression (REF), a question (VQA), or a grasp action instruction. The system tackles all cases in a task-agnostic fashion through the utilization of a shared library of primitive skills. Ea
ExReg: Wide-range Photo Exposure Correction via a Multi-dimensional Regressor with Attention
Photo exposure correction is widely investigated, but fewer studies focus on correcting under- and over-exposed images simultaneously. Three issues remain open to handle and correct both under- and over-exposed images in a unified way. First, a locally-adaptive exposure adjustment may be more flexible instead of learning a global mapping. Second, it is an ill-posed problem to determine the suitable exposure values lo
A Survey on Uncertainty Quantification Methods for Deep Learning
Deep neural networks (DNNs) have achieved tremendous success in computer vision, natural language processing, and scientific and engineering domains. However, DNNs can make unexpected, incorrect, yet overconfident predictions, leading to serious consequences in high-stakes applications such as autonomous driving, medical diagnosis, and disaster response. Uncertainty quantification (UQ) estimates the confidence of DNN
WCCNet: Wavelet-context Cooperative Network for Efficient Multispectral Pedestrian Detection
Multispectral pedestrian detection is essential to various tasks especially autonomous driving, for which both the accuracy and computational cost are of paramount importance. Most existing approaches treat RGB and infrared modalities equally. They typically adopt two symmetrical backbones for multimodal feature extraction, which ignore the substantial differences between modalities and bring great difficulty for the
Learning NEAT Emergent Behaviors in Robot Swarms
When researching robot swarms, many studies observe complex group behavior emerging from the individual agents' simple local actions. However, the task of learning an individual policy to produce a desired group behavior remains a challenging problem. We present a method of training distributed robotic swarm algorithms to produce emergent behavior. Inspired by the biological evolution of emergent behavior in anim
A Neural-preconditioned Poisson Solver for Mixed Dirichlet and Neumann Boundary Conditions
We introduce a neural-preconditioned iterative solver for Poisson equations with mixed boundary conditions. Typical Poisson discretizations yield large, ill-conditioned linear systems. Iterative solvers can be effective for these problems, but only when equipped with powerful preconditioners. Unfortunately, effective preconditioners like multigrid require costly setup phases that must be re-executed every time domain
Physics-informed neural network for modeling dynamic linear elasticity
In this work, we present the physics-informed neural network (PINN) model applied particularly to dynamic problems in solid mechanics. We focus on forward and inverse problems. Particularly, we show how a PINN model can be used efficiently for material identification in a dynamic setting. In this work, we assume linear continuum elasticity. We show results for two-dimensional (2D) plane strain problem and then we pro
IRG: Modular Synthetic Relational Database Generation with Complex Relational Schemas
Relational databases (RDBs) are widely used by corporations and governments to store multiple related tables. Their relational schemas pose unique challenges to synthetic data generation for privacy-preserving data sharing, e.g., for collaborative analytical and data mining tasks, as well as software testing at various scales. Relational schemas typically include a set of primary and foreign key constraints to specif
PADS: Plug-and-Play 3D Human Pose Analysis via Diffusion Generative Modeling
Diffusion models have demonstrated impressive capabilities in modeling complex data distributions and are increasingly applied in various generative tasks. In this work, we propose Pose Analysis by Diffusion Synthesis PADS, a unified generative modeling framework for 3D human pose analysis. PADS first learns a task-agnostic 3D pose prior via unconditional diffusion synthesis and then performs training-free adaptation
"All of Me": Mining Users' Attributes from their Public Spotify Playlists
In the age of digital music streaming, playlists on platforms like Spotify have become an integral part of individuals' musical experiences. People create and publicly share their own playlists to express their musical tastes, promote the discovery of their favorite artists, and foster social connections. In this work, we aim to address the question: can we infer users' private attributes from their public Sp
Foveated Retinotopy Improves Classification and Localization in Convolutional Neural Networks
From falcons spotting preys to humans recognizing faces, rapid visual abilities depend on a foveated retinal organization which delivers high-acuity central vision while preserving low-resolution periphery. This organization is conserved along early visual pathways but remains underexplored in machine learning. Here we examine how embedding a foveated retinotopic transformation as a preprocessing layer impacts convol
An Efficient and Harmonized Framework for Balanced Cross-Domain Feature Integration
Despite significant advancements in image generation using advanced generative frameworks, cross-image integration of content and style remains a key challenge. Current generative models, while powerful, frequently depend on vague textual prompts to define styles--creating difficulties in balancing content semantics and style preservation. We propose a novel framework that utilizes customized models to learn style re
A Comprehensive Survey on Self-Supervised Learning for Recommendation
Recommender systems play a crucial role in tackling the challenge of information overload by delivering personalized recommendations based on individual user preferences. Deep learning techniques, such as RNNs, GNNs, and Transformer architectures, have significantly propelled the advancement of recommender systems by enhancing their comprehension of user behaviors and preferences. However, supervised learning methods
Counterfactual Explanations for Deep Learning-Based Traffic Forecasting
Deep learning models are widely used in traffic forecasting and have achieved state-of-the-art prediction accuracy. However, the black-box nature of those models makes the results difficult to interpret by users. This study aims to leverage an Explainable AI approach, counterfactual explanations, to enhance the explainability and usability of deep learning-based traffic forecasting models. Specifically, the goal is t
Fast Wrong-way Cycling Detection in CCTV Videos: Sparse Sampling is All You Need
Effective monitoring of unusual transportation behaviors, such as wrong-way cycling (i.e., riding a bicycle or e-bike against designated traffic flow), is crucial for optimizing law enforcement deployment and traffic planning. However, accurately recording all wrong-way cycling events is both unnecessary and infeasible in resource-constrained environments, as it requires high-resolution cameras for evidence collectio
Graph convolutional neural networks (GCNs) are powerful tools for learning graph-based knowledge representations from training data. However, they are vulnerable to small perturbations in the input graph, which makes them susceptible to input faults or adversarial attacks. This poses a significant problem for GCNs intended to be used in critical applications, which need to provide certifiably robust services even in
Tau Anomaly Detection in PET Imaging via Bilateral-Guided Deterministic Diffusion Model
The emergence of tau PET imaging over the last decade has enabled Alzheimer's disease (AD) researchers to examine tau pathology in vivo and more effectively characterize the disease trajectories of AD. Current tau PET analysis methods, however, typically perform inferences on large cortical ROIs and are limited in the detection of localized tau pathology that varies across subjects. In this work, we propose a nov
Probabilistically Plausible Counterfactual Explanations with Normalizing Flows
We present PPCEF, a novel method for generating probabilistically plausible counterfactual explanations (CFs). PPCEF advances beyond existing methods by combining a probabilistic formulation that leverages the data distribution with the optimization of plausibility within a unified framework. Compared to reference approaches, our method enforces plausibility by directly optimizing the explicit density function withou
Notable events recorded on this day and month across all years.