Results 71 to 80 of about 2,921,969 (298)

On‐Chip Photonic Neural Network Architectures

Advanced Optical Materials, EarlyView.
This review presents a comprehensive overview of on‐chip photonic neural network architectures, covering key photonic building blocks, representative network types, and emerging applications. Recent advances, implementation challenges, and future directions are examined, highlighting the potential of integrated photonics to enable ultrafast, energy ...
Seokjin Hong, Berkay Neseli, Seungyeon Cho, Seungsoo Lee, Yeonjun Kim, Seungyoon Choi, Hyo‐Hoon Park, Hamza Kurt   +7 more
wiley   +1 more source

Generative Adversarial Networks GAN Overview

Jisuanji kexue yu tansuo, 2020
As a new unsupervised learning algorithm framework, generative adversarial networks (GAN) has been favored by more and more researchers, and it has become a research hotspot. GAN is inspired by the two-person zero-sum game theory in game theory.
LIANG Junjie, WEI Jianjing, JIANG Zhengfeng
doaj   +1 more source

oswaldoludwig/Adversarial-Learning-for-Generative-Conversational-Agents: Adversarial Learning for Generative Conversational Agents

, 2017
<p>This repository presents a new adversarial learning method for generative conversational agents (GCA) besides a new model of GCA. Our method assumes the GCA as a generator that aims at fooling a discriminator that labels dialogues as human ...
Oswaldo Ludwig
core   +1 more source

Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks

, 2022
In the age of the Internet of Things (IoT), large numbers of sensors and edge devices are deployed in various application scenarios; Therefore, collaborative learning is widely used in IoT to implement crowd intelligence by inviting multiple participants
Michael Blumenstein, Ying Zhao, Di Wu, Yu, Shui, Zhang, Jiale, Jiale Zhang, Chen, Junjun, Shui Yu, Wu, Di, Junjun Chen, Zhao, Ying, Blumenstein, Michael   +11 more
core   +1 more source

Prescribed Generative Adversarial Networks

CoRR, 2019
Generative adversarial networks (GANs) are a powerful approach to unsupervised learning. They have achieved state-of-the-art performance in the image domain. However, GANs are limited in two ways. They often learn distributions with low support---a phenomenon known as mode collapse---and they do not guarantee the existence of a probability density ...
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei, Michalis K. Titsias   +3 more
openaire   +2 more sources

Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space

Advanced Robotics Research, EarlyView.
A quadruped robot masters dynamic jumps through constrained spaces with animal‐inspired moves and intelligent vision control. This hierarchical learning approach combines imitation of biological agility with real‐time trajectory planning. Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating ...
Zeren Luo, Jiahui Zhang, Yimin Han, Ji Ma, Minghao Lu, Ioannis Havoutis, Peng Lu   +6 more
wiley   +1 more source

Information Transmission Strategies for Self‐Organized Robotic Aggregation

Advanced Robotics Research, EarlyView.
In this review, we discuss how information transmission influences the neighbor‐based self‐organized aggregation of swarm robots. We focus specifically on local interactions regarding information transfer and categorize previous studies based on the functions of the information exchanged.
Shu Leng, Zihan Li, Yifeng Shao, Jinhao Liu, Li Yu, Shuguang Li   +5 more
wiley   +1 more source

Review of Application of Generative Adversarial Networks in Image Restoration [PDF]

Jisuanji kexue yu tansuo
With the rapid development of generative adversarial networks, many image restoration problems that are difficult to solve based on traditional methods have gained new research approaches.
GONG Ying, XU Wentao, ZHAO Ce, WANG Binjun
doaj   +1 more source

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source

A State‐Adaptive Koopman Control Framework for Real‐Time Deformable Tool Manipulation in Robotic Environmental Swabbing

Advanced Robotics Research, EarlyView.
This work presents a state‐adaptive Koopman linear quadratic regulator framework for real‐time manipulation of a deformable swab tool in robotic environmental sampling. By combining Koopman linearization, tactile sensing, and centroid‐based force regulation, the system maintains stable contact forces and high coverage across flat and inclined surfaces.
Siavash Mahmoudi, Amirreza Davar, Dongyi Wang   +2 more
wiley   +1 more source