New document embedding method finds more relevant tech docs.
problem Computing document similarities for technical content.
method Hybrid approach using graph techniques to extract keyphrases and score sentences.
result Proposed methods outperform baselines up to 27% in NDCG.
New algorithm poLinUCB improves online learning in content recommendation platforms.
problem Improving efficiency in content recommendation platforms with post-serving context.
method Novel contextual bandit problem with post-serving contexts and a new algorithm, poLinUCB.
result Achieves tight regret under standard assumptions and significant benefit of utilizing post-serving contexts.
AI recovers lost art from x-rays.
problem Reconstructing lost artwork under layers of x-ray imaging.
method Neural style transfer applied to x-radiographs.
result Reconstructed lost artwork visible through x-rays.
HTMLPhish detects phishing web pages using deep learning on HTML content.
problem Growing phishing attacks on the web require new detection methods.
method HTMLPhish uses Convolutional Neural Networks (CNNs) to classify HTML documents as phishing or benign.
result HTMLPhish achieves over 93% accuracy in detecting phishing web pages.
Robust machine learning models improve DNA regulatory sequence prediction under various shifts.
problem Real-world applications of DNA regulatory sequence prediction involve shifts not captured by standard i.i.d. assumptions.
method Introduces a robustness framework combining simulation benchmarks and real data analysis.
result Models remain accurate and calibrated under mild shifts but show higher error and miscalibration under strong shifts.
PreBit predicts Bitcoin price movements using social media and financial data.
problem Predicting extreme price movements of Bitcoin due to its volatility and speculative trading.
method Hybrid model combining FinBERT embeddings of Twitter content with candlestick data and technical indicators.
result The hybrid model can predict significant market movements with a profitable trading strategy.
The paper studies bias and adaptivity of CART regression trees.
problem Bias and adaptivity of CART regression trees.
method Derives an interesting connection between bias and MDI measure of variable importance.
result Decision trees with CART have small bias and are adaptive to signal strength and direction.
Corrects errors and completes missing parts in a paper on tangent cones.
problem Incorrect inequalities and missing definitions in a paper on tangent cones.
method Completely rewrote sections 5.1-5.4, providing missing definitions and proofs.
result Corrected inequalities and completed missing parts in the paper.
Recently software development companies started to embrace Machine Learning (ML) techniques for introducing a series of advanced functionality in their products such as personalisation of the user experience, improved search, content recommendation and automation. The technical challenges for tackling these problems ar…
We develop a statistical framework to benchmark and select large language models based on their risks.
problem Benchmarking and selecting large language models based on their associated risks.
method A distributional framework using first and second order stochastic dominance, linked to mean-risk models in finance.
result Formalizes a risk-aware approach for model selection, balancing risk and utility.
The paper studies metrics on hyperkähler manifolds using sub-twistor constraints.
problem Understanding metrics on hyperkähler manifolds with non-holonomic constraints.
method Introducing and analyzing sub-conic metrics, showing they are sub-Finsler.
result Explicit description of Finsler norms for sub-twistor metrics.
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.
Sensory data are often comprised of independent content and transformation factors. For example, face images may have shapes as content and poses as transformation. To infer separately these factors from given data, various ``disentangling'' models have been proposed. However, many of these are supervised or semi-super…
We study the propagation of bosonic strings in singular target space-times. For describing this, we assume this target space to be the quotient of a smooth manifold M by a singular foliation F on it. Using the technical tool of a gauge theory, we propose a smooth functional for this scenario, such that the p…
In the industry of video content providers such as VOD and IPTV, predicting the popularity of video contents in advance is critical not only from a marketing perspective but also from a network optimization perspective. By predicting whether the content will be successful or not in advance, the content file, which is l…
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.
We consider visual domains in which a class label specifies the content of an image, and class-irrelevant properties that differentiate instances constitute the style. We present a domain-independent method that permits the open-ended recombination of style of one image with the content of another. Open ended simply me…
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.
AR app visualizes Quranic Surah al-Fil for Islamic education.
problem Lack of interactive and context-rich learning materials for Quranic studies.
method Research and development approach, including data collection, user requirement analysis, interface design, 3D asset creation, and integration of AR technology.
result AR application achieved high accuracy and user satisfaction, enhancing learner engagement and understanding.
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.
Text style transfer aims to modify the style of a sentence while keeping its content unchanged. Recent style transfer systems often fail to faithfully preserve the content after changing the style. This paper proposes a structured content preserving model that leverages linguistic information in the structured fine-gra…
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.
With rapid development of the Internet, web contents become huge. Most of the websites are publicly available, and anyone can access the contents from anywhere such as workplace, home and even schools. Nevertheless, not all the web contents are appropriate for all users, especially children. An example of these content…
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.
Much of the data being created on the web contains interactions between users and items. Stochastic blockmodels, and other methods for community detection and clustering of bipartite graphs, can infer latent user communities and latent item clusters from this interaction data. These methods, however, typically ignore t…
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.
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.
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.
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.
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.
This paper surveys cryptocurrency trading research, covering various aspects.
problem Understanding the unique nature and behavior of cryptocurrencies as assets.
method Comprehensive review of 146 research papers on cryptocurrency trading.
result Identifies promising open opportunities in cryptocurrency trading.
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.
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.
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.