Results 61 to 70 of about 151,060 (281)

Learning‐Based Soft Robotic Grasping: Recent Progress and Remaining Challenges

Advanced Robotics Research, EarlyView.
This review analyzes learning‐based soft robotic grasping from a pipeline‐oriented perspective, encompassing soft gripper design, multimodal sensing, and learning‐based planning and control. It surveys key neural network architectures and benchmark datasets and identifies critical challenges such as sim‐to‐real transfer, generalization, and continual ...
Arnab Majumder, Jelizaveta Konstantinova, Graham Deacon, Kaspar Althoefer   +3 more
wiley   +1 more source

Cosine Prompt-Based Class Incremental Semantic Segmentation for Point Clouds

Algorithms
Although current 3D semantic segmentation methods have achieved significant success, they suffer from catastrophic forgetting when confronted with dynamic, open environments.
Lei Guo, Hongye Li, Min Pang, Kaowei Liu, Xie Han, Fengguang Xiong   +5 more
doaj   +1 more source

Overcoming Catastrophic Forgetting Using Sparse Coding and Meta Learning

IEEE Access, 2021
Continuous learning occurs naturally in human beings. However, Deep Learning methods suffer from a problem known as Catastrophic Forgetting (CF) that consists of a model drastically decreasing its performance on previously learned tasks when it is ...
Julio Hurtado, Hans Lobel, Alvaro Soto
doaj   +1 more source

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

Advanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan, Hengheng Zhao, Haotian Wang, Zhanjie Liu, Qian Li, Weifeng Zhang, Qingsong Zhao, Liqun Zhang, Jun Liu   +8 more
wiley   +1 more source

Solutions to the Catastrophic Forgetting Problem [PDF]

, 2022
In this paper we review three kinds of proposed solutions to the catastrophic forgetting problem in neural networks. The solutions are based on reducing hidden unit overlap, rehearsal, and pseudorehearsal mechanisms. We compare the methods and identify some underlying similarities.
openaire   +1 more source

Advances and Perspectives in Graphene‐Based Quantum Dots Enabled Neuromorphic Devices

Advanced Science, EarlyView.
Graphene‐based QDs are zero‐dimensional carbon nanomaterials with pronounced quantum confinement and tunable electronic structures. Herein, we summarize their synthesis strategies and functionalization methods, and highlight their functional roles and operating mechanisms in devices, as well as recent advances in neuromorphic electronics. We anticipate
Yulin Zhen, Wei Zeng, Zherui Zhao, JiYu Zhao, Ziyan Huang, Zhuo Chen, Kui Zhou, Guanglong Ding, Su‐Ting Han, Ye Zhou   +9 more
wiley   +1 more source

Battery‐Inspired Electrochemical Synapses for Neuromorphic Applications

Advanced Science, EarlyView.
Battery‐ion‐inspired synaptic devices integrate diverse electrolyte systems (solid, ion‐gel, liquid) with transition metal oxides, two‐dimensional materials, and organic channels to regulate ion‐electron coupling. By tailoring ion transport and host interactions, these platforms enable controllable plasticity and energy‐efficient implementations for ...
Won Woo Lee, SeongCheol Jang, Wangmyung Choi, Jaewon Park, Jaehyun Hur, Hocheon Yoo, Hyun‐Suk Kim   +6 more
wiley   +1 more source

Self-Organizing Multiple Readouts for Reservoir Computing

IEEE Access, 2023
With advancements in deep learning (DL), artificial intelligence (AI) technology has become an indispensable tool. However, the application of DL incurs significant computational costs, making it less viable for edge AI scenarios.
Yuichiro Tanaka, Hakaru Tamukoh
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

Continual learning and catastrophic forgetting

Preprint of a book chapter; 21 pages, 4 ...
Gido M. van de Ven, Nicholas Soures, Dhireesha Kudithipudi   +2 more
openaire   +3 more sources