Results 181 to 190 of about 228,260 (299)

Learning Compact Representations of Constraint Networks

Passive constraint acquisition aims to learn constraint networks from examples of solutions and non-solutions. There typically exist many constraint networks that are consistent with a given set of examples, so the performance of an acquisition system is critically dependent on its ability to determine which network will generalize the best to unseen ...
Christian Bessiere, Clément Carbonnel, Areski Himeur   +2 more
openaire   +1 more source

Texoskeletons: Developing the Fundamental Technologies for Creating Intelligent Soft Robotic Clothing With Integrated 1D Sensors and Actuators

Advanced Functional Materials, EarlyView.
ABSTRACT Traditional wearable exoskeletons rely on rigid structures, which limit comfort, flexibility, and everyday usability. This work introduces the fundamental technologies to create the first soft, lightweight, intelligent textile‐based exoskeletons (Texoskeletons) built using 1D sensors and actuators.
Amy Lukomiak, Rameesh Bulathsinghala, Jack Varga, Shuxin Meng, Kalana Marasinghe, Imaad Refai, Biyon Fernando, Amitkumar Patel, Chamika M. Halloluwa‐Arachchige, Carlos Oliveira, Arash M. Shahidi, Zahra Rahemtulla, Alexander Turner, Ziyun Ding, Bernard X. W. Liew, Andy Kerr, Ezio Preatoni, Theo Hughes‐Riley, Ishara Dharmasena, Pasindu Lugoda   +19 more
wiley   +1 more source

Modular artificial neural network for prediction of petrophysical properties from well log data

, 1996
This paper reports the application of Kohonen's Self-Organizing Map (SOM) and Learning Vector Quantization (LVQ) algorithms, and the commonly used Back Propagation Neural Network (BPNN) to the prediction of petrophysical properties from well log data ...
Wong, K.W., Crocker, H., Eren, H., Charlebois, R., Fung, C.C.   +4 more
core  

DeeWaNA: An Unsupervised Network Representation Learning Framework Integrating Deepwalk and Neighborhood Aggregation for Node Classification. [PDF]

Entropy (Basel)
Xu X, Lu X, Wang J.
europepmc   +1 more source

Representation and learning in feedforward neural networks

CWI Quarterly, 1993
Summary: The paper gives an introduction to feedforward neural networks. The aim is to present some of the basics of artificial neural networks, with a particular emphasis on the following two central issues. The first central issue of this paper is: in what sense do artificial neural networks represent mathematical functions, and what mathematical ...
openaire   +3 more sources

Machine Learning‐Assisted Inverse Design of Soft and Multifunctional Hybrid Liquid Metal Composites

Advanced Functional Materials, EarlyView.
A machine learning framework is presented for inverse design of synthesizable multifunctional composites containing both liquid metal and solid inclusions. By integrating physics‐based modeling, data‐driven prediction, and Bayesian optimization, the approach enables intelligent design of experiments to identify optimal compositions and realize these ...
Lijun Zhou, Yunsik Ohm, Ren‐Mian Chin, Olivia Kerr, Krithika Manohar, Mohammad H. Malakooti   +5 more
wiley   +1 more source

Graph Representation Learning for Social Networks.

, 2021
Online social networks provide a rich source of information about millions of users worldwide. However, due to sparsity and complex structure, analyzing these networks is quite challenging and expensive. Recently, graph embedding emerged to map networked data into low-dimensional representations, i.e. vector embeddings.
openaire   +1 more source

Integrated Field‐Free SOT Domain‐Wall Synapses and MTJ Stochastic Neurons for Hardware Boltzmann Machines

Advanced Functional Materials, EarlyView.
Field‐free spin‐orbit torque domain‐wall synapses integrated with stochastic MTJ neurons enable compact hardware Boltzmann machines. Leveraging intrinsic stochasticity and multi‐level conductance, the system achieves efficient probabilistic learning with high accuracy, demonstrating a scalable spintronic platform for energy‐efficient edge AI.
Aijaz H. Lone, Meng Tang, Camelia Florica, Bin He, Jingkai Xu, Selma Amara, Georgian Melinte, Xixiang Zhang, Gianluca Setti   +8 more
wiley   +1 more source

Graph neural network for audio representation learning [PDF]

Learning audio representations is an important task with many potential applications. Whether it takes the shape of speech, music, or ambient sounds, audio is a common form of data that may communicate rich information.
Shirian, Amir
core  

Near‐Infrared Light‐Driven Zn/Au Janus Micromotors for Multiplex SERS Detection of Anticancer Drugs

Advanced Functional Materials, EarlyView.
Zn/Au Janus micromotors, propelled by thermophoretic effects under NIR light, function as active SERS platforms for single and multiplex detection of anticancer drugs. Their dynamic motion enhances analyte exchange at the Au interface, reducing saturation and competitive adsorption, thereby improving sensitivity and extending the linear detection range.
Tijana Maric, Gohar Soufi, Ziqiao Li, Erick Chica, Amran Abdirasheed Ali Jinow, Nada Al‐Roubaï, Noor Ahmed, Yaman Göksel, Anja Boisen   +8 more
wiley   +1 more source