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Generative Adversarial Networks
In this chapter, you will learn about generative adversarial networks as well as how you can implement anomaly detection using them.
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Revolutionizing personalized medicine with generative AI: a systematic review
BackgroundPrecision medicine, targeting treatments to individual genetic and clinical profiles, faces challenges in data collection, costs, and...
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Generative AI Hardware
In Chapter 6 , we examined how various generative AI cloud services providers compete for customers and...
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Speech Enhancement with Generative Diffusion Models
AbstractAn alternative approach to speech denoising using generative diffusion models that model the distribution of training data is proposed. In...
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Generative artificial intelligence-enabled dynamic detection of rat nicotine-related circuits
Nicotine addiction circuits involve integrating specific brain regions that alter to frequent smoking. Detection of these circuits via fMRI...
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SinGRAV: Learning a Generative Radiance Volume from a Single Natural Scene
We present SinGRAV, an attempt to learn a generative radiance volume from multi-view observations of a single natural scene, in stark contrast to...
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Generative AI and Causality
This chapter explores the intersection of generative artificial intelligence (AI) and the principles of causality in machine learning, delving into...
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Examining generative AI user disclosure intention: an ELM perspective
Generative AI needs to collect massive information including personal information to train the model and improve the accuracy of answers. This may...
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Parameterization, algorithmic modeling, and fluid–structure interaction analysis for generative design of transcatheter aortic valves
Heart valves play a critical role in maintaining proper cardiovascular function in the human heart; however, valve diseases can lead to improper...
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On the adversarial robustness of generative autoencoders in the latent space
The generative autoencoders, such as the variational autoencoders or the adversarial autoencoders, have achieved great success in lots of real-world...
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Graph contrastive learning for recommendation with generative data augmentation
Graph Neural Networks (GNNs) have been successfully adopted in recommender systems by virtue of the message-passing that implicitly captures...
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Analyzing behavioral intentions toward Generative Artificial Intelligence: the case of ChatGPT
Generative artificial intelligence (AI) is an innovative AI technology that has garnered considerable attention worldwide. This study aimed to...
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Wavelet transform-assisted generative model for efficient 3d deep shape generation
Unsupervised deep learning has been widely employed to generate high-quality samples. While its potential for point clouds generation tasks has...
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Bi-GAE: A Bidirectional Generative Auto-Encoder
Improving the generative and representational capabilities of auto-encoders is a hot research topic. However, it is a challenge to jointly and...
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A novel generative corrective network structure for traffic forecasting
Traffic forecasting plays a critical role in intelligent transportation systems aiming to accurately estimate future short-term or long-term traffic...
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Public perception of generative AI on Twitter: an empirical study based on occupation and usage
The emergence of generative AI has sparked substantial discussions, with the potential to have profound impacts on society in all aspects. As...
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Investigating better context representations for generative question answering
Generating natural language answers for question-answering (QA) tasks has recently surged in popularity with the rise of task-based personalized...
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Sparse self-attention guided generative adversarial networks for time-series generation
Remarkable progress has been achieved in generative modeling for time-series data, where the dominating models are generally generative adversarial...
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A probabilistic generative model for tracking multi-knowledge concept mastery probability
Knowledge tracing aims to track students’ knowledge status over time to predict students’ future performance accurately. In a real environment,...
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Population synthesis for urban resident modeling using deep generative models
The impact of new real estate developments is strongly associated with its target population distribution, that is, the characteristics that define a...
