Letting Logos Speak: Leveraging Multiview Representation Learning for Data-Driven Logo Design

2019 ◽  
Author(s):  
Ryan Dew ◽  
Asim Ansari ◽  
Olivier Toubia
Keyword(s):  
Representation Learning ◽  
Data Driven ◽  
Logo Design ◽  
Multiview Representation

Letting Logos Speak: Leveraging Multiview Representation Learning for Data-Driven Branding and Logo Design

2021 ◽  
Author(s):  
Ryan Dew ◽  
Asim Ansari ◽  
Olivier Toubia
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Representation Learning ◽  
Data Driven ◽  
Logo Design ◽  
Variational Autoencoder ◽  
Multiview Representation

The authors develop a decision support system for design and branding based on a multimodal variational autoencoder that merges image, text, and ratings data.

scholarly journals An Exploratory Study of COVID-19 Information on Twitter in the Greater Region

2021 ◽  
Vol 5 (1) ◽  
pp. 5
Author(s):  
Ninghan Chen ◽  
Zhiqiang Zhong ◽  
Jun Pang
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Online Social Networks ◽  
Exploratory Study ◽  
Representation Learning ◽  
Data Driven ◽  
Learning Methods ◽  
Twitter Users ◽  
Over Time

The outbreak of the COVID-19 led to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out how Twitter users in the Greater Region (GR) and related countries react differently over time through conducting a data-driven exploratory study of COVID-19 information using machine learning and representation learning methods. We find that tweet volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 22 January 2020 to 5 June 2020, figuring out the main differences between GR and related countries.

scholarly journals Survey of Deep Representation Learning for Speech Emotion Recognition

2021 ◽  
Author(s):  
Siddique Latif ◽  
Rajib Rana ◽  
Sara Khalifa ◽  
Raja Jurdak ◽  
Junaid Qadir ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
General Setting ◽  
Representation Learning ◽  
Data Driven ◽  
Speech Emotion Recognition ◽  
Feature Engineering ◽  
Acoustic Features ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Hierarchical Representations

<div>Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual effort, which impedes generalisability and slows the pace of innovation. This has motivated the adoption of representation learning techniques that can automatically learn an intermediate representation of the input signal without any manual feature engineering. Representation learning has led to improved SER performance and enabled rapid innovation. Its effectiveness has further increased with advances in deep learning (DL), which has facilitated deep representation learning where hierarchical representations are automatically learned in a data-driven manner. This paper presents the first comprehensive survey on the important topic of deep representation learning for SER. We highlight various techniques, related challenges and identify important future areas of research. Our survey bridges the gap in the literature since existing surveys either focus on SER with hand-engineered features or representation learning in the general setting without focusing on SER.</div>

scholarly journals Hierarchical Linear Disentanglement of Data-Driven Conceptual Spaces

2020 ◽  
Author(s):  
Rana Alshaikh ◽  
Zied Bouraoui ◽  
Steven Schockaert
Keyword(s):  
Vector Space ◽  
Representation Learning ◽  
Data Driven ◽  
Vector Spaces ◽  
Conceptual Spaces ◽  
Semantic Features ◽  
Geometric Meaning ◽  
Domain Specific ◽  
Text Corpora ◽  
Quality Dimensions

Conceptual spaces are geometric meaning representations in which similar entities are represented by similar vectors. They are widely used in cognitive science, but there has been relatively little work on learning such representations from data. In particular, while standard representation learning methods can be used to induce vector space embeddings from text corpora, these differ from conceptual spaces in two crucial ways. First, the dimensions of a conceptual space correspond to salient semantic features, known as quality dimensions, whereas the dimensions of learned vector space embeddings typically lack any clear interpretation. This has been partially addressed in previous work, which has shown that it is possible to identify directions in learned vector spaces which capture semantic features. Second, conceptual spaces are normally organised into a set of domains, each of which is associated with a separate vector space. In contrast, learned embeddings represent all entities in a single vector space. Our hypothesis in this paper is that such single-space representations are sub-optimal for learning quality dimensions, due to the fact that semantic features are often only relevant to a subset of the entities. We show that this issue can be mitigated by identifying features in a hierarchical fashion. Intuitively, the top-level features split the vector space into different domains, making it possible to subsequently identify domain-specific quality dimensions.

scholarly journals Survey of Deep Representation Learning for Speech Emotion Recognition

2021 ◽  
Author(s):  
Siddique Latif ◽  
Rajib Rana ◽  
Sara Khalifa ◽  
Raja Jurdak ◽  
Junaid Qadir ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
General Setting ◽  
Representation Learning ◽  
Data Driven ◽  
Speech Emotion Recognition ◽  
Feature Engineering ◽  
Acoustic Features ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Hierarchical Representations

<div>Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual effort, which impedes generalisability and slows the pace of innovation. This has motivated the adoption of representation learning techniques that can automatically learn an intermediate representation of the input signal without any manual feature engineering. Representation learning has led to improved SER performance and enabled rapid innovation. Its effectiveness has further increased with advances in deep learning (DL), which has facilitated deep representation learning where hierarchical representations are automatically learned in a data-driven manner. This paper presents the first comprehensive survey on the important topic of deep representation learning for SER. We highlight various techniques, related challenges and identify important future areas of research. Our survey bridges the gap in the literature since existing surveys either focus on SER with hand-engineered features or representation learning in the general setting without focusing on SER.</div>

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