Research
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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,042 papers · 148 categories

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20395978 · Oct 201919922001200920172026
48 results for resource contention

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.

We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) an…

2013-12-19abs ↗pdf ↗

Floating Content (FC) is a communication paradigm for the local dissemination of contextualized information through D2D connectivity, in a way which minimizes the use of resources while achieving some specified performance target. Existing approaches to FC dimensioning are based on unrealistic system assumptions that m…

2018-10-24abs ↗pdf ↗

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.

Big data, data science, deep learning, artificial intelligence are the key words of intense hype related with a job market in full evolution, that impose to adapt the contents of our university professional trainings. Which artificial intelligence is mostly concerned by the job offers? Which methodologies and technolog…

2018-09-28abs ↗pdf ↗

Paper shows pretrained models can categorize Thai social media content effectively.

problem Lack of labeled data for Thai social media content classification.
method Pretrained language models on a large noisy Thai social media corpus, fine-tuned for downstream tasks.
result State-of-the-art results achieved on Thai social text categorization tasks.

Organizations adapt ML models to new data types using existing resources.

problem Adapting ML models to new data types in evolving applications.
method Utilize organizational resources like statistics, knowledge bases, and existing services to create a common feature space.
result Reduces model development time from months to days.

Paper develops a new unsupervised scoring function for cross-lingual document alignment.

problem Aligning documents across different languages for NLP tasks.
method Uses cross-lingual sentence embeddings to compute semantic distances and guides document alignment.
result The proposed scoring function outperforms current methods by 7-22% on various language pairs.

A dataset for evaluating engagement with scientific video lectures.

problem Challenges in managing learning resources due to rapid creation of video lectures.
method Introduction of VLEngagement dataset with content-based and video-specific features, and metrics related to user engagement.
result The largest and most diverse publicly available dataset for understanding context-agnostic engagement in video lectures.

Generative models predict page quality without training, useful for low-resource settings.

problem Detecting low-quality content in web articles.
method Human evaluation and analysis of 500 million web articles.
result Generative models can predict page quality without training, useful for low-resource settings.

This work disentangles speech and non-speech components from found data.

problem Building robust acoustic models from found data with non-standard variations.
method Latent Stochastic Models and Multinode Latent Space Variational Autoencoders (VAE).
result Speech and music can be separated in the latent space of a VAE, independent of the language.

TiFL divides clients into tiers to improve federated learning performance.

problem Heterogeneity in resource and data quality impacts FL performance.
method TiFL divides clients into tiers based on training performance and selects clients from the same tier in each training round.
result TiFL achieves faster training performance with comparable or better test accuracy.

Predicts user traffic for better resource management in cellular networks.

problem Lack of research on traffic prediction in cellular networks at the user level.
method Statistical, rule-based, and deep machine learning methods.
result Deep machine learning outperforms linear statistical learning after a threshold number of previous observations.

OLPA optimizes online user-centric selection with probing, achieving near-optimal regret bounds.

problem Sequential decision-making with unknown resources and rewards.
method Probing-augmented user-centric selection (PUCS) framework, greedy probing algorithm, OLPA algorithm.
result OLPA achieves a near-optimal regret bound of O(T+ln2T)\mathcal{O}(\sqrt{T} + \ln^{2} T) for online settings.

Sparse activations in neural models correlate with frequent words, suggesting sparsity is natural.

problem Interpretability and resource efficiency in neural language models.
method Used the Taxi-Euclidean norm to measure sparsity and analyzed gradients and activations of frequent words.
result Frequent input words are associated with sparse activations, while frequent target words are associated with dispersed activations.

CoTj improves diffusion model quality and stability via graph planning.

problem Rigidity in diffusion models due to high-dimensional state space.
method Chain-of-Trajectories (CoTj) framework using Diffusion DNA for graph planning.
result CoTj discovers context-aware trajectories improving output quality and stability.

Proposes a framework for energy-efficient AIGC workload scheduling in cloud data centers.

problem Challenges of scheduling AIGC workloads for energy efficiency and quality control.
method Joint energy management and coordinated AIGC workload scheduling framework with diffusion model-aided reward shaping.
result Effective learning of scheduling policies under sparse environmental feedback.

A general nonlinear logistic equation has been proposed to model long-time saturation in industrial growth. An integral solution of this equation has been derived for any arbitrary degree of nonlinearity. A time scale for the onset of nonlinear saturation in industrial growth can be estimated from an equipartition cond…

2009-03-02abs ↗pdf ↗

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 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.

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…

2012-10-20abs ↗pdf ↗

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…

2018-09-28abs ↗pdf ↗

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.

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…

2018-10-15abs ↗pdf ↗

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.

Study uses few-shot learning to analyze claims and arguments in German debate on arms deliveries.

problem Limited data and computational resources for automated content analysis.
method Multilingual transformer model with adapter extension and few-shot learning.
result Parameter-efficient approach performs well on varying training set sizes.