Attribute interactions in machine learning
Migracija podatkov Microsoft SharePoint v velikem sistemu
Digitalizacija avdiovizualnega arhiva
Generating lyrics using constrained random walks on a word network
In the paper we present an approach for automatic lyrics generation. From the American National Corpusof written texts we build a Word Network, which encodes word sequences. Lyrics are then generated by performing a constrained random walk over the Word Network. The constraints include the structure of the generated sentence, the rhythm of the line...
Integrative analysis of rhythmicity
From biological to socio-technical systems, rhythmic processes are pervasive in our environment. However, methods for their comprehensive analysis are prevalent only in specific fields that limit the transfer of knowledge across scientific disciplines. This hinders interdisciplinary research and integrative analyses of rhythms across different doma...
Unified platform for storing, retrieving, and analysing biomechanical applications data using graph database
Sensors and smart equipment are frequently used in biomechanical systems and applications in sports and rehabilitation to measure various physical quantities. Various sensors, measuring different parameters, can produce a large amount of data at high speeds and volumes that must be stored for real-time or post-processing and analysis. In addition t...
Mobiprox
Runtime-tunable context-dependent network compression would make mobile deep learning (DL) adaptable to often varying resource availability, input “difficulty”, or user needs. The existing compression techniques significantly reduce the memory, processing, and energy tax of DL, yet, the resulting models tend to be permanently impaired, sacrificing ...
Hierarchical learning of robotic contact policies
The paper addresses the issue of learning tasks where a robot maintains permanent contact with the environment. We propose a new methodology based on a hierarchical learning scheme coupled with task representation through directed graphs. These graphs are constituted of nodes and branches that correspond to the states and robotic actions, respectiv...
Positive unlabeled learning with tensor networks
Positive unlabeled learning is a binary classification problem with positive and unlabeled data. It is common in domains where negative labels are costly or impossible to obtain, e.g., medicine and personalized advertising. Most approaches to positive unlabeled learning apply to specific data types (e.g., images, categorical data) and can not gener...
Dimensionally-consistent equation discovery through probabilistic attribute grammars
Equation discovery, also known as symbolic regression, is a machine learning task of inducing closed-form equations from data and background knowledge. The latter takes various forms. Domain-specific knowledge can constrain the space of candidate equations to those that make sense in the scientific or engineering domain of use. Cross-domain knowled...
