System validates truthfulness of statements with evidence.
problem Unverified contents in accessible information sources.
method Inference method on a knowledge graph (KG) combined with ontologies.
result System provides valid and concise evidence for false statements.
Kernel analysis reveals rumor truth from diffusion patterns alone.
problem Detecting unverified rumors on Twitter using text and user identities.
method Graph kernels to extract diffusion patterns from Twitter cascade structures.
result Diffusion patterns are highly informative of rumor truth or falsehood.
Note refutes examples of Landsberg surfaces with vanishing flag curvature.
problem Verifying examples of Landsberg surfaces with specific curvature properties.
method Analyzing examples from Zhou's result to show they are Berwaldian.
result Examples of Landsberg surfaces with vanishing flag curvature are Berwaldian.
New model learns content and transformation separately from data.
problem Learning disentangled representations from data without explicit labels.
method Group-based variational autoencoders, assuming content and transformation groups.
result Model learns generalizable content representations from unseen data.
Study shows online learning algorithms incentivize low-quality content, proposing new algorithms to improve quality.
problem Online learning algorithms in content recommender systems incentivize producers to create low-quality content.
method Analyzed the game between producers and content quality, designed new learning algorithms to incentivize high effort and quality.
result New algorithms incentivize producers to invest high effort and achieve high user welfare, improving content quality.
Proposes a model to optimize feedback for content creators on social media.
problem Nurturing content creation on social media platforms.
method Modeling feedback distribution to incentivize creators and optimize newsfeed experience.
result Optimized newsfeed for content creators without compromising user experience.
Proposes a VAE variant for ordinal content factors.
problem Isolating ordinal-valued content factors in deep latent variable models.
method Introduces a partially ordered set (poset) structure and a conditional Gaussian spacing prior model.
result Significant improvements in content-style separation over previous non-ordinal approaches.
This research predicts the popularity of new video contents using a hybrid machine learning approach.
problem Predicting the popularity of new video contents before they are published.
method Hybrid machine learning approach combining XGBoosting and deep neural nets, using metadata and categorical embedding techniques.
result Achieved better performance than standalone methods, validated on a dataset from a top streaming service.
Method recombines image content and style from different images.
problem Recombining image content and style from different images.
method Constructs content embedding, uses VAE with leakage filtering to ensure separation of style and content.
result Synthesizes novel images with state-of-the-art performance on few-shot learning tasks.
Preserves content while changing style in text.
problem Text style transfer often fails to preserve content.
method Leverages linguistic information in structured supervisions.
result Significant improvement in content preservation and style transfer.
A mixture of CNNs improves adult content recognition.
problem Recognizing and restricting inappropriate images like pornography.
method A weighted sum of multiple CNN models trained using OLS.
result The proposed model outperforms single and average models.
Many businesses are using recommender systems for marketing outreach. Recommendation algorithms can be either based on content or driven by collaborative filtering. We study different ways to incorporate content information directly into the matrix factorization approach of collaborative filtering. These content-booste…
Spotify improves content mix using contextual bandits.
problem Skewed historical data and varying user preferences across contexts.
method Contextual bandits to dynamically learn optimal content type distribution.
result Improved precision and user engagement with under-represented content types.
A new model considers fatigue in online content recommendation systems.
problem Fatigue in users due to overexposure and boredom from similar recommendations.
method Proposed a fatigue-aware Dependent Click Model (DCM) and two learning algorithms.
result Developed algorithms with regret bounds for learning content relevance and fatigue effects.
New PCGML approach generates novel game content across multiple platformer domains.
problem Generating novel game content in new domains.
method Using a new affordance and path vocabulary, variational autoencoders trained on data from six platformer games produce new content with varying proportions of different domains.
result Captures latent level space spanning multiple domains and generates new content with varying proportions of different domains.
New model for recovering unverifiable signals from observers in decentralized networks.
problem Verifying the level of service provided by untrusted parties in decentralized networks.
method Formal model of signal elicitation, source identifiability condition, and peer prediction techniques.
result Existence of a strictly truthful mechanism for unverifiable signal recovery.
Enhances clustering of user-item interactions using item content.
problem Lack of item content in traditional clustering methods.
method Content-Augmented Stochastic Blockmodels (CASB).
result CASB provides highly accurate clusters with respect to community structure metrics.
New study shows personalized content recommendations can lead to polarization of user preferences.
problem Personalized content recommendations can alter user preferences, leading to polarization.
method Used a model of preference dynamics to explore how personalized content affects user preferences.
result Standard reward maximization algorithms achieve only constant regret in personalized recommendation environments.
A new approach uses deep learning to manage vehicular content efficiently.
problem Managing content replication and caching in vehicular networks efficiently.
method Data-driven, centralized approach using a Convolutional Neural Network (CNN).
result Effective strategies derived to modulate FC operation in space and adapt to mobility changes.
Study on heat content for submanifolds in sub-Riemannian geometry.
problem Understanding heat content for submanifolds in sub-Riemannian geometry.
method Existence of smooth tubular neighborhood, definition of relative heat content, approximation via smooth neighborhoods, asymptotic expansion analysis.
result Approximation of relative heat content fails to recover the exact expansion.
Enhances content moderation with culturally-aware models.
problem Global content moderation policies miss local cultural nuances.
method Fine-tuning encoder-decoder models on media-diet data.
result Improved accuracy in local violation detection and cultural alignment.
This work improves disentanglement by preventing style variables from encoding content-related features.
problem Disentanglement of content and style in data representations using Variational Autoencoders.
method Adversarial training with mutual information minimization to prevent content information leakage in style representations.
result The method efficiently separates content and style related attributes and generalizes to unseen data.
Efficiently transfers style to content without distorting the content structure.
problem Arbitrary style transfer in computer vision.
method Rigid alignment of style features to content features.
result High-quality stylized images with intact content structure.
Transforms web content for better visibility in AI-driven search engines.
problem Disruption of traditional SEO by generative AI search engines.
method Fine-tunes a BART-base transformer on synthetically generated training data.
result Significant improvements in ROUGE-L and BLEU scores, and substantial visibility gains in generative search responses.
Improves content allocation in educational platforms with sparse data.
problem Imbalanced content allocation and delayed convergence in adaptive strategies.
method Introduces WAPTS, an algorithm that refines Thompson Sampling for data-sparse environments.
result Demonstrates earlier and more reliable identification of promising treatments.
Modeling incentives for content creators on algorithm-curated platforms.
problem Maximizing exposure for content creators on algorithmic platforms.
method Formalized exposure game model, proving effects of algorithmic choices on equilibria, proposing tools for finding equilibria.
result Algorithmic choices significantly affect content exposure and creator behavior.
Simple method disentangles content and style from pre-trained vision models.
problem Learning interpretable features in visual representations.
method Probabilistic linear entanglement model and simple disentanglement algorithm.
result Method provably disentangles content and style features.
Study on heat content for domains with fractal boundaries.
problem Analyzing short-time asymptotics of heat content for domains with fractal boundaries.
method Developing mathematical analysis on de Gennes' hypothesis and exploring fractal curvatures.
result Fractal curvatures and their scaling exponents may emerge in the short-time heat content asymptotics of domains with fractal boundaries.
IGSD separates task-specific content channels in transformer components by comparing activation replacement with zero ablation.
problem Mechanistic interpretability of transformer components
method IGSD: paired-intervention framework for comparing activation replacement with zero ablation
result IGSD identifies an early-layer content channel in transformer components that standard importance methods underestimate.
The study finds that many social media content cascades recur with bursts of popularity over time.
problem Understanding the recurrence of content cascades on social media.
method Large-scale analysis of cascades on Facebook over extended time periods, using a simple model to simulate recurrence.
result Content virality drives recurrence, but beyond a certain popularity, the rate of recurrence decreases.
System filters inappropriate YouTube content for advertisers.
problem Inadequate detection of inappropriate content on YouTube ads.
method Proposes a system for identifying and filtering inappropriate content.
result Current countermeasures are ineffective in detecting inappropriate content.
Proposes a new model to measure trade impact and information content in fluctuating markets.
problem Measuring price impact and information content of trades in a time-varying market setting.
method Non-linear observation-driven model for dynamically estimating market impact and information content.
result Market impact shows intraday patterns with large fluctuations, some of which are exogenous.
Measures policy-violating content prevalence with ML-assisted sampling and LLM labeling.
problem Accurate measurement of content violations that are often rare and costly to label.
method Design-based measurement system using ML-assisted probability sampling and LLM labeling.
result Produces unbiased prevalence estimates with confidence intervals and dashboard drilldowns.
Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and (largely ad-hoc) hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recommendations…
Minimal surfaces can be transformed into others with unchanged bending content.
problem Understanding the deformation properties of minimal surfaces.
method Refined polar decomposition theorem to identify bending-neutral deformations.
result Every minimal surface can be transformed into another by a bending-neutral deformation.
Paper classifies brain signals using eigenvalues for 2D and 3D educational content questions.
problem Classifying brain signals for 2D and 3D educational content questions.
method Eigenvalues of covariance matrix used as features; KNN and SVM classifiers applied.
result No significant difference in learning, memory retention, and recall between 2D and 3D educational content.
Fast, format-agnostic web content detection for security.
problem Detecting malicious web content efficiently and accurately.
method Deep learning on static HTML tokens, avoiding complex parsing.
result 97.5% detection rate at 0.1% false positive rate.
Study heat content on RCD(K,N) spaces with specific boundary conditions.
problem Analyzing heat content in RCD(K,N) spaces with irregular boundaries.
method Proved first-order asymptotics using measured interior geodesic condition.
result Established first-order heat content asymptotics on RCD(K,N) spaces.
Classifies domains critical for heat content and exit-time moments.
problem Understanding critical domains for heat content and exit-time moments.
method First variation of heat content, constant flow property, isoparametric foliation.
result Domains critical for heat content at all times have constant flow property and isoparametric foliation.
Study heat content in sub-Riemannian manifolds, obtaining asymptotic expansion.
problem Heat content in sub-Riemannian manifolds with non-characteristic domains.
method Fourth-order asymptotic expansion, combining rough boundary temperature and stochastic completeness.
result Obtained a fourth-order asymptotic expansion for relative heat content.
Hybrid method for clustering text and graph data.
problem Discover latent connections and content associations in mixed data.
method Joint Nonnegative Matrix Factorization (NMF) for text and graph clustering.
result Higher quality clustering results compared to methods using only content or structure.
Detects anomalous behavior in social media users by analyzing content and connections.
problem Identifying disruptive patterns in user behavior on social media platforms.
method Joint representation learning of content and connection to detect anomalous behavior.
result Observed densely connected users engaging in local politics and exhibiting troll-like behavior.
Content based image retrieval, a technique which uses visual contents of image to search images from large scale image databases according to users' interests. This paper provides a comprehensive survey on recent technology used in the area of content based face image retrieval. Nowadays digital devices and photo shari…
MIXGAN combines concepts from different domains for new image generation.
problem Generating new images with mixed content and style from different domains.
method MIXGAN is a mixture generative adversarial network that learns content and style from two domains and generates new images combining them.
result MIXGAN effectively generates new images with mixed content and style from different domains.
RB-Modulation trains free diffusion models without external adapters.
problem Training-free personalization of diffusion models with style and content control.
method Stochastic optimal control with a style descriptor and cross-attention aggregation.
result Precise content and style extraction and control without external adapters.
CRAE recommends while learning content sequences, outperforming state-of-the-art methods.
problem Recommendation systems struggle with content information, especially in sequence generation tasks.
method Developed a hybrid denoising recurrent autoencoder (DRAE) for collaborative filtering (CF) that models content sequences.
result CRAE significantly outperforms state-of-the-art methods on recommendation and sequence generation tasks.
Kernel Mean Matching enhances GANs for content-addressable generation.
problem Creating models that can generate images consistent with specified examples.
method Kernel Mean Matching applied to GANs.
result The method generates images consistent with specified input sets while maintaining original model quality.
A movie multilayer network model captures narration from script, subtitles, and content.
problem Discovering content and stories in movies using network models.
method Developed a multilayer network model using visual and textual semantic cues.
result Demonstrated the effectiveness of the model on the Star Wars saga.