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We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection ...

We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset. Our findings are three-fold...

A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of ...

Materie / parole chiave: Computer science; Artificial intelligence; Machine learning; Multiphysics; Inference; Artificial neural network; Physical law; Field (mathematics); Discretization; Kernel method; Deep learning; Theoretical computer scien...

Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) plays a central role in understanding evolutionary history from molecular sequence data. Visualizing and analyzing the MCMC-generated samples from the...

Materie / parole chiave: Cross-validation; Bayesian probability; Computer science; Model validation; Variable-order Bayesian network; Artificial intelligence; Data mining; Econometrics; Bayesian inference; Machine learning; Mathematics; Data sci...

UNLABELLED: RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical...

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors ...

Materie / parole chiave: Inference; Transcriptome; Abundance (ecology); Expression (computer science); Computer science; Computational biology; Biology; Feature (linguistics); Gene expression; Gene; Artificial intelligence; Genetics

TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor-Flow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. ...

The rapid expansion of conversational artificial intelligence (AI) usage in healthcare, nutrition, and digital health education has made prompting a routine part of professional and personal practice. However, each AI pr...

IntroductionArtificial intelligence (AI) is increasingly embedded in journalism, yet audience responses may depend on both AI provenance, meaning who or what is presented as having written the story, and transparency cue...

In India's mountainous areas, maternal mortality is still a serious public health concern, especially in Uttarakhand, where access to healthcare is hampered by geographical obstacles. UttaraRisk-Next, a multi-task ensemb...

Artificial intelligence systems increasingly interpret human communication in high-stakes settings such as mental health, legal reasoning, and cybersecurity contexts. Yet current large language models often produce fluen...

Abstract In the power operation scenarios, there are problems such as large differences in target scales, small targets susceptible to occlusion, and limited computational resources for edge devices. This leads to insuff...

Pragmatic ambiguity poses a major challenge for machine translation in low-resource languages like Akan, where a single English phrase may represent multiple pragmatic contexts and vice versa. To address this gap, we dev...

Abstract Purpose To design a coach-interpretable movement quality score for badminton footwork training using an adaptive neuro-fuzzy inference system. Methodology We derived compact biomechanical descriptors of timing, ...

Agentic tool-augmented large language models are a common design for therapeutic decision support. The model can issue structured calls to external resources during inference, such as drug label endpoints, and then cite ...

Quantifying modality contributions in Vision-Language Models (VLMs) remains challenging. Existing approaches rely on perturbation or gradient-based methods, which conflate inherent modality informativeness with model-spe...

When making sampling distribution inferences about the parameter of the data, θ, it is appropriate to ignore the process that causes missing data if the missing data are ‘missing at random’ and the observed data are...

The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybri...

IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have con...

Algebra of Vectors and Matrices. Probability Theory, Tools and Techniques. Continuous Probability Models. The Theory of Least Squares and Analysis of Variance. Criteria and Methods of Estimation. Large Sample Theory and ...

This paper gives a systematic application of maximum likelihood inference concerning cointegration vectors in non-stationary vector valued autoregressive time series models with Gaussian errors, where the model includes ...