Results 91 to 100 of about 3,042,897 (294)

Generative Adversarial Networks and Other Generative Models

, 2022
Generative networks are fundamentally different in their aim and methods compared to CNNs for classification, segmentation, or object detection. They have initially not been meant to be an image analysis tool, but to produce naturally looking images. The
Wenzel, Markus
core  

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication

, 2021
Generated data and checkpoints for Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and ...
Gasper Begus
core   +1 more source

Coevolution of Generative Adversarial Networks [PDF]

, 2019
Published in EvoApplications ...
Victor Costa, Nuno Lourenço 0002, Penousal Machado   +2 more
openaire   +3 more sources

SemanticST: A Scalable Multi‐Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi‐Sample Integration in Spatial Transcriptomics

Advanced Science, EarlyView.
Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi, Ahmadreza Argha, Nona Farbehi, Ivan Bakhshayeshi, Thantrira Porntaveetus, Youqiong Ye, Nigel H. Lovell, Hamid Alinejad‐Rokny   +7 more
wiley   +1 more source

Generative Artificial Intelligence (AI) for Cybersecurity

Future Internet
Generative artificial intelligence (AI), including large language models (LLMs), generative adversarial networks, autoencoders, and related generative learning methods, is increasingly shaping cybersecurity research and practice [...]
Li Yang, Raha Moraffah
doaj   +1 more source

Neuromorphic Devices and Computing for Sensing, Memory, and Control

Advanced Science, EarlyView.
This review introduces neuromorphic devices made from diverse materials. These devices mimic neuronal functions and architectures and, when integrated with artificial or biological computing, can form closed loops with neurons for pressure, optical, acoustic, and biochemical sensing and modulation.
Zhengguang Zhu, Nicholas Schaffer, Xiao Yang   +2 more
wiley   +1 more source

Data for paper "Quality Aware Conditional Generative Adversarial Networks for Precipitation Nowcasting" submitted to AAI.

, 2023
Dataset for paper "Quality Aware Conditional Generative Adversarial Networks for Precipitation Nowcasting" submitted to Applied Artificial ...
Jonnalagadda, Jahnavi
core   +1 more source

Structural Divergence Between the Moltbook AI‐Agent Network and Human Social Networks

Advanced Science, EarlyView.
Analysis of the Moltbook AI‐agent network reveals a striking combination of familiar global scaling and distinct internal organization. Attention is highly concentrated, reciprocity is limited, connected triads are suppressed, and communities are strongly modular.
Wenpin Hou, Zhicheng Ji
wiley   +1 more source

A PRIMER ON GENERATIVE ADVERSARIAL NETWORKS

International Journal of Innovative Research in Computer Science & Technology, 2020
Generative Adversarial Networks (GANs) is a type of deep neural network architecture that utilizes unsupervised machine learning to generate data. They were presented in 2014, in a paper by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. This paper will introduce the core components of GANs.
Dr. Vikas Thada, Mr. Utpal Shrivastava, Jyotsna Sharma, Kuwar Prateek Singh, Manda Ranadeep   +4 more
openaire   +1 more source