Results 51 to 60 of about 22,594 (260)
Data‐Driven Materials Science for Energy‐Sustainable Applications
Advanced Materials, EarlyView.Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley +1 more source
Quantization Robust Pruning With Knowledge Distillation
IEEE Access, 2023 To resolve the problem that deep neural networks (DNN) require a large number of network parameters, many researchers have sought to compress the network.
Jangho Kim
doaj +1 more source
Spectral Pruning for Recurrent Neural Networks
CoRR, 2021 Recurrent neural networks (RNNs) are a class of neural networks used in sequential tasks. However, in general, RNNs have a large number of parameters and involve enormous computational costs by repeating the recurrent structures in many time steps. As a method to overcome this difficulty, RNN pruning has attracted increasing attention in recent years ...
Takashi Furuya +5 more
openaire +3 more sources
Scalable Task Planning via Large Language Models and Structured World Representations
Advanced Robotics Research, EarlyView.This work efficiently combines graph‐based world representations with the commonsense knowledge in Large Language Models to enhance planning techniques for the large‐scale environments that modern robots will need to face. Planning methods often struggle with computational intractability when solving task‐level problems in large‐scale environments ...
Rodrigo Pérez‐Dattari +4 more
wiley +1 more source
Structure Optimization in Deep Neural Networks with Synaptic Pruning Based on Connection Appraisal [PDF]
Computer and Knowledge EngineeringDeep neural networks typically require predefined architectures, which can lead to overfitting, underfitting, high computational costs, and storage overhead.
Aghil Ahmadi, Reza Mahboobi Esfanjani
doaj +1 more source
Improving Deep Echo State Network with Neuronal Similarity-Based Iterative Pruning Merging Algorithm
Applied Sciences, 2023 Recently, a layer-stacked ESN model named deep echo state network (DeepESN) has been established. As an interactional model of a recurrent neural network and deep neural network, investigations of DeepESN are of significant importance in both areas ...
Qingyu Shen, Hanwen Zhang, Yao Mao
doaj +1 more source
Advanced Robotics Research, EarlyView.Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor. Deformability, anatomical variance, and hygiene rules make conventional suction and scripted motions unreliable. We present ChicGrasp, an end‐to‐end hardware‐software co‐designed imitation learning framework, to offer a ...
Amirreza Davar +8 more
wiley +1 more source
Complexity of Deep Convolutional Neural Networks in Mobile Computing
Complexity, 2020 Neural networks employ massive interconnection of simple computing units called neurons to compute the problems that are highly nonlinear and could not be hard coded into a program.
Saad Naeem +3 more
doaj +1 more source
Machines, 2021 This paper introduces a new intelligent fault diagnosis method based on stack pruning sparse denoising autoencoder and convolutional neural network (sPSDAE-CNN).
Pu Yang +3 more
doaj +1 more source
Advanced Robotics Research, EarlyView.This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source