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VAE-GNA: a variational autoencoder with Gaussian neurons in the latent space and attention mechanisms
Variational autoencoders (VAEs) are generative models known for learning compact and continuous latent representations of data. While they have...
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LightCapsGNN: light capsule graph neural network for graph classification
Graph neural networks (GNNs) have achieved excellent performances in many graph-related tasks. However, they need appropriate pooling operations to...
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An ensemble of self-supervised teachers for minimal student model with auto-tuned hyperparameters via improved Bayesian optimization
Due to a growing demand for efficient deep learning models capable of both high performance and reduced costs in terms of computation, model...
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Semantic proximity assessment in Bhojpuri and Maithili: a word embedding perspective
Natural Language Processing has been extensively researched for languages with abundant resources like English and Spanish, but low-resource...
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Advanced techniques for automated emotion recognition in dogs from video data through deep learning
Inter-species emotional relationships, particularly the symbiotic interaction between humans and dogs, are complex and intriguing. Humans and dogs...
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Knowledge graph embedding closed under composition
Knowledge Graph Embedding (KGE) has attracted increasing attention. Relation patterns, such as symmetry and inversion, have received considerable...
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Mmds: multimodal benchmark dataset for suspicious profile detection on twitter social network
In the era of widespread social media usage, detecting and mitigating suspicious profiles is essential for maintaining social platform integrity....
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Towards effective urban region-of-interest demand modeling via graph representation learning
Identifying the region’s functionalities and what the specific Point-of-Interest (POI) needs is essential for effective urban planning. However, due...
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Personalization of OLAP queries for hierarchical visualization under constraints
Decision-makers, whether at the corporate or enterprise level, do not have the same vision of all decision-making data since their needs vary greatly...
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Short-term POI recommendation with personalized time-weighted latent ranking
In this paper, we formulate a novel Point-of-interest (POI) recommendation task to recommend a set of new POIs for visit in a short period following...
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Rule learning by modularity
In this paper, we present a modular methodology that combines state-of-the-art methods in (stochastic) machine learning with well-established methods...
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Spatio-temporal wind speed forecasting with approximate Bayesian uncertainty quantification
The prediction of short- and long-term wind speed has great utility for the industry, especially for wind energy generation. Deep neural networks can...
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Temporal analysis of topic modeling output by machine learning techniques
Topic modeling is widely recognized as one of the most effective and significant methods of unsupervised text analysis. This method facilitates...
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A novel discrete slash family of distributions with application to epidemiology informatics data
This study puts forward a new class of discrete distribution that can be used by the epidemiologists and medical scientists to model data relating to...
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PROUD: PaRetO-gUided diffusion model for multi-objective generation
Recent advancements in the realm of deep generative models focus on generating samples that satisfy multiple desired properties. However, prevalent...
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Evaluating collective action theory-based model to simulate mobs
A mob is an event that is organized via social media, email, SMS, or other forms of digital communication technologies in which a group of people...
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Ant Colony Optimization for solving Directed Chinese Postman Problem
The Chinese Postman Problem (CPP) is a well-known optimization problem involving determining the shortest route, modeling the system as an undirected...
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Lyapunov-guided representation of recurrent neural network performance
Recurrent neural networks (RNN) are ubiquitous computing systems for sequences and multivariate time-series data. While several robust RNN...
