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Deep Learning for Polymer Property Prediction
This chapter explores deep learning methods for polymer property prediction, focusing on classification and regression tasks. It examines neural...
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Imbalanced Learning: Semi-Supervised Graph Imbalanced Regression
Data imbalance is easily found in annotated data when the observations of certain continuous label values are difficult to collect for regression...
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Neural Wave Machines
Traveling waves have been measured at a diversity of regions and scales in the brain, however a consensus as to their computational purpose has yet...
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Latent Traversal as Potential Flows
Despite the significant recent progress in deep generative models, the underlying structure of their latent spaces is still poorly understood,...
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Generalized Confidentiality
Today’s large-scale data management systems need to address distributed applications’ confidentiality and scalability requirements among a set of...
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Verifiability
In many cross-enterprise systems, it is essential for enterprises to verify transactions initiated by others to meet global constraints while...
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Analysis
In this chapter, we shall discuss the different works that attempted to analyse hate content (and its variants) across different social media...
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Generative Modeling: Data-Centric Learning from Unlabeled Graphs with Diffusion Model
Graph property prediction tasks are important and numerous. While each task offers a small size of labeled examples, unlabeled graphs have been...
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Deep Learning for Inverse Polymer Design
This chapter presents neural network approaches for inverse polymer design, enabling the generation of polymers with or without specific constraints....
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Background
This chapter introduces the background knowledge of structured representation learning which aims to enforce beneficial inductive biases to learned...
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Conclusion
In the preceding seven chapters we have discussed generalized forms of structure in neural network representations, built models to respect this...
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Topographic Variational Autoencoders
In this chapter we seek to bridge the concepts of topographic organization and equivariance in neural networks. To accomplish this, we introduce the...
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Scalability
Scalability remains a significant barrier to the widespread adoption of blockchain systems in business. Permissioned blockchain systems often utilize...
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Preliminaries
Blockchain systems are global-scale peer-to-peer systems that integrate many techniques and protocols from cryptography, distributed systems, and...
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Introduction
Networked applications, the web, and diverse applications on mobile platforms have attracted hundreds of millions of end-users [1, 2] who interact...
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Introduction
There is an abundance of the definition of the term ‘hate speech’ in the literature. The United Nations defines it as follows
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Challenges in Hate Speech Identification
The previous chapter explored the evaluation of hate speech detection models, primarily focusing on their performance on held-out (test) data. While...
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Flow Factorized Representation Learning
A prominent goal of representation learning research is to achieve representations which are factorized in a useful manner with respect to the ground...
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Introduction
State of the art artificial neural networks have reached incredible levels of performance, yet still, these models perform far below our expectations...
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Unsupervised Factorized Representation Learning Based on Sparse Transformation Analysis
There is a vast literature on representation learning based on principles such as coding efficiency, statistical independence, causality,...