Results 91 to 100 of about 124,149 (320)
Applications of QSPR and Machine Learning in Molecular Photonics
Advanced Optical Materials, EarlyView.Quantitative structureproperty relationships (QSPR) and machine learning (ML) are transforming photochemistry by enabling pre‐synthetic screening of photoactive molecules. This review outlines advances in data‐driven discovery of optical materials and functional dyes, identifies effective descriptors and models for photophysical processes, and provides
Andrey A. Buglak +2 more
wiley +1 more source
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 +6 more
wiley +1 more source
Multi-view Generative Adversarial Networks [PDF]
, 2017 Learning over multi-view data is a challenging problem with strong practical applications. Most related studies focus on the classification point of view and assume that all the views are available at any time. We consider an extension of this framework in two directions.
Chen, Mickaël, Denoyer, Ludovic
openaire +4 more sources
Stable Imitation of Multigait and Bipedal Motions for Quadrupedal Robots Over Uneven Terrains
Advanced Robotics Research, EarlyView.How are quadrupedal robots empowered to execute complex navigation tasks, including multigait and bipedal motions? Challenges in stability and real‐world adaptation persist, especially with uneven terrains and disturbances. This article presents an imitation learning framework that enhances adaptability and robustness by incorporating long short‐term ...
Erdong Xiao +3 more
wiley +1 more source
Adaptive Density Estimation for Generative Models [PDF]
, 2019 Unsupervised learning of generative models has seen tremendous progress over recent years, in particular due to generative adversarial networks (GANs), variational autoencoders, and flow-based models.
Alahari, Karteek +4 more
core +2 more sources
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 +5 more
wiley +1 more source
Advanced Robotics Research, EarlyView.This review systematically examines robotic systems for robot‐assisted transoral surgical procedures, classifying them based on transoral access depth, and evaluates their fundamental design principles, mechanical innovations, algorithmic advancements, and clinical implementation status.
Yuhao Shi +5 more
wiley +1 more source
Rapid measurements and phase transition detections made simple by AC-GANs
SciPost Physics CoreIn recent years, significant attention has been paid to using end-to-end neural networks for analyzing Monte Carlo data. However, the exploration of non-end-to-end generative adversarial neural networks remains limited.
Jiewei Ding, Ho-Kin Tang, Wing Chi Yu
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
Adversarial Example Detection and Classification With Asymmetrical Adversarial Training
, 2020 The vulnerabilities of deep neural networks against adversarial examples have become a significant concern for deploying these models in sensitive domains.
Kolouri, Soheil +2 more
core