[HTML][HTML] Inference in the age of big data: Future perspectives on neuroscience
Neuroscience is undergoing faster changes than ever before. Over 100 years our field
qualitatively described and invasively manipulated single or few organisms to gain …
qualitatively described and invasively manipulated single or few organisms to gain …
Topic modeling in marketing: recent advances and research opportunities
M Reisenbichler, T Reutterer - Journal of Business Economics, 2019 - Springer
Using a probabilistic approach for exploring latent patterns in high-dimensional co-
occurrence data, topic models offer researchers a flexible and open framework for soft …
occurrence data, topic models offer researchers a flexible and open framework for soft …
Election campaigning on social media: Politicians, audiences, and the mediation of political communication on Facebook and Twitter
Although considerable research has concentrated on online campaigning, it is still unclear
how politicians use different social media platforms in political communication. Focusing on …
how politicians use different social media platforms in political communication. Focusing on …
Unsupervised grouped axial data modeling via hierarchical Bayesian nonparametric models with Watson distributions
This paper aims at proposing an unsupervised hierarchical nonparametric Bayesian
framework for modeling axial data (ie, observations are axes of direction) that can be …
framework for modeling axial data (ie, observations are axes of direction) that can be …
Learning natural coding conventions
Every programmer has a characteristic style, ranging from preferences about identifier
naming to preferences about object relationships and design patterns. Coding conventions …
naming to preferences about object relationships and design patterns. Coding conventions …
Hierarchical implicit models and likelihood-free variational inference
Implicit probabilistic models are a flexible class of models defined by a simulation process
for data. They form the basis for models which encompass our understanding of the physical …
for data. They form the basis for models which encompass our understanding of the physical …
Structured Event Memory: A neuro-symbolic model of event cognition.
Humans spontaneously organize a continuous experience into discrete events and use the
learned structure of these events to generalize and organize memory. We introduce the …
learned structure of these events to generalize and organize memory. We introduce the …
[图书][B] Plan, activity, and intent recognition: Theory and practice
Plan recognition, activity recognition, and intent recognition together combine and unify
techniques from user modeling, machine vision, intelligent user interfaces, human/computer …
techniques from user modeling, machine vision, intelligent user interfaces, human/computer …
Machine learning in solar physics
A Asensio Ramos, MCM Cheung, I Chifu… - Living Reviews in Solar …, 2023 - Springer
The application of machine learning in solar physics has the potential to greatly enhance our
understanding of the complex processes that take place in the atmosphere of the Sun. By …
understanding of the complex processes that take place in the atmosphere of the Sun. By …
Bayesian models of graphs, arrays and other exchangeable random structures
The natural habitat of most Bayesian methods is data represented by exchangeable
sequences of observations, for which de Finetti's theorem provides the theoretical …
sequences of observations, for which de Finetti's theorem provides the theoretical …