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Deep Generative Session-Based Recommender System
The inherent structural sequences in sessions and the mutual influence of complex variables in different time steps make deep generative models...
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A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue Information Extraction
This paper focuses on term-status pair extraction from medical dialogues (MD-TSPE), which is essential in diagnosis dialogue systems and the...
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Generative AI for Fire Safety
In the field of fire safety, Generative AI presents promising opportunities to enhance prevention, response, and recovery efforts. In this chapter,...
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Generative Modeling of Sparse Approximate Inverse Preconditioners
We present a new deep learning paradigm for the generation of sparse approximate inverse (SPAI) preconditioners for matrix systems arising from the...
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Exploring Variational Auto-encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI
Generative AI models for music and the arts in general are increasingly complex and hard to understand. The field of explainable AI (XAI) seeks to...
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Multidimensional Behavior Fusion: Joint Probabilistic Generative Modeling
In this work, we aim at building a bridge from coarse behavioral data to an effective, quick-response, and robust behavioral model for online...
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Generative Models for Missing Data
Missing data poses an ubiquitous challenge across a wide range of applications, stemming from a multitude of causes that are both diverse and...
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Generative hyperelasticity with physics-informed probabilistic diffusion fields
Many natural materials exhibit highly complex, nonlinear, anisotropic, and heterogeneous mechanical properties. Recently, it has been demonstrated...
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DeepNews: enhancing fake news detection using generative round network (GRN)
Fake news is a crucial issue in social media that spreads intentionally crafted false content designed to mislead the public over the digital...
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Self-supervised generative learning for sequential data prediction
For many real world applications, such as stock price prediction and video frame synthesis, sequential data prediction is a fundamental and...
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SSR-GAN: super resolution-based generative adversarial networks model for flood image enhancement
Floods, a common natural disaster, it affects more than half of all natural disasters, primarily due to high floods, high tides, heavy rainfall, and...
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Generative adversarial meta-learning knowledge graph completion for large-scale complex knowledge graphs
In the study of large-scale complex knowledge graphs, due to the incompleteness of knowledge and the existence of low-frequency knowledge samples,...
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Sense-Based Generative AI Demystified
The realm of Generative AI typically conjures images of algorithms churning out text, images, or music. However, the burgeoning field of sense-based...
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Generative adversarial network for newborn 3D skeleton part segmentation
Childbirth simulations have been studied in order to predict and prevent difficult delivery issues. The reconstruction of the maternal pelvic model,...
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Prompt Aloud!: Incorporating image-generative AI into STEAM class with learning analytics using prompt data
The rapid advancements in artificial intelligence (AI) have transformed various domains, including education. Generative AI models have garnered...
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Multi-Constraint Transferable Generative Adversarial Networks for Cross-Modal Brain Image Synthesis
Recent progress in generative models has led to the drastic growth of research in image generation. Existing approaches show visually compelling...
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Generative AI can fabricate advanced scientific visualizations: ethical implications and strategic mitigation framework
The advancement of generative AI has introduced transformative changes in the scientific domain. This technology, recognized for its ability to...
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Generating diverse clothed 3D human animations via a generative model
Data-driven garment animation is a current topic of interest in the computer graphics industry. Existing approaches generally establish the map**...
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Comparing the latent space of generative models
Different encodings of datapoints in the latent space of latent-vector generative models may result in more or less effective and disentangled...
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Urban tree generator: spatio-temporal and generative deep learning for urban tree localization and modeling
We present a vision-based algorithm that uses spatio-temporal satellite imagery, pattern recognition, procedural modeling, and deep learning to...
