Concept

Latent Dirichlet allocation

Related publications (143)

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MATHICSE Technical Report : Analysis of a class of Multi-Level Markov Chain Monte Carlo algorithms based on Independent Metropolis-Hastings

Fabio Nobile, Juan Pablo Madrigal Cianci

In this work, we present, analyze, and implement a class of Multi-Level Markov chain Monte Carlo (ML-MCMC) algorithms based on independent Metropolis-Hastings proposals for Bayesian inverse problems. In this context, the likelihood function involves solvin ...

MATHICSE2021

We tackle the task of diverse 3D human motion prediction, that is, forecasting multiple plausible future 3D poses given a sequence of observed 3D poses. In this context, a popular approach consists of using a Conditional Variational Autoencoder (CVAE). How ...

IEEE2021

Effects of eight neuropsychiatric copy number variants on human brain structure

Bogdan Draganski, Lester Melie Garcia, Borja Rodriguez Herreros, Kuldeep Kumar, Claudia Modenato

Many copy number variants (CNVs) confer risk for the same range of neurodevelopmental symptoms and psychiatric conditions including autism and schizophrenia. Yet, to date neuroimaging studies have typically been carried out one mutation at a time, showing ...

SPRINGERNATURE2021

Person Retrieval in Surveillance Videos Via Deep Attribute Mining and Reasoning

Zhen Wei, Zhiye Wang, Peixia Li

Person retrieval largely relies on the appearance features of pedestrians. This task is rather more difficult in surveillance videos due to the limitations of extracting robust appearance features brought by the cross-view and cross-camera data with lower ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

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Data augmentation is a widely adopted technique for avoiding overfitting when training deep neural networks. However, this approach requires domain-specific knowledge and is often limited to a fixed set of hard-coded transformations. Recently, several work ...

IEEE2021

Low-Rank Subspaces for Unsupervised Entity Linking

Robert West, Akhil Arora, Alberto Garcia Duran

Entity linking is an important problem with many applications. Most previous solutions were designed for settings where annotated training data is available, which is, however, not the case in numerous domains. We propose a light-weight and scalable entity ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

Learning Urban Nightlife Routines from Mobile Data

Daniel Gatica-Perez, Thanh Trung Phan

The use of smartphone sensing for public health studies is appealing to understand routines. We present an approach to learn nightlife routines in a smartphone sensing dataset volunteered by 184 young people (1586 weekend nights with location data captured ...

ACM2020

A Dirichlet process mixture model of discrete choice: Comparisons and a case study on preferences for shared automated vehicles

This paper i) compares parametric and semi-parametric representations of unobserved heterogeneity in hierarchical Bayesian logit models and ii) applies these methods to infer distributions of willingness to pay for features of shared automated vehicle (SAV ...

2020

Small-variance asymptotics for non-parametric online robot learning

Sylvain Calinon, Ajay Kumar Tanwani

Small-variance asymptotics is emerging as a useful technique for inference in large-scale Bayesian non-parametric mixture models. This paper analyzes the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small-va ...

SAGE PUBLICATIONS LTD2019

DeepSuccess - Predict the Success of Tech Startups

Davide Di Dio

Many interesting applications emerged with the increasing popularity of deep learning. This project explored natural language processing and visualization techniques as well as two neural network architectures to classify ICOs. The first network focused on ...

2019