Results 111 to 120 of about 250,191 (314)

Convexified Convolutional Neural Networks

CoRR, 2016
We describe the class of convexified convolutional neural networks (CCNNs), which capture the parameter sharing of convolutional neural networks in a convex manner. By representing the nonlinear convolutional filters as vectors in a reproducing kernel Hilbert space, the CNN parameters can be represented as a low-rank matrix, which can be relaxed to ...
Yuchen Zhang 0002, Percy Liang, Martin J. Wainwright   +2 more
openaire   +3 more sources

Ferroelectric Quantum Dots for Retinomorphic In‐Sensor Computing

Advanced Materials, EarlyView.
This work has provided a protocol for fabricating retinomorphic phototransistors by integrating ferroelectric ligands with quantum dots. The resulting device combines ferroelectricity, optical responsiveness, and low‐power operation to enable adaptive signal amplification and high recognition accuracy under low‐light conditions, while supporting ...
Tingyu Long, Huanyu Zhou, Jaewan Ko, Hongwei Tan, Jaemin Lim, Yanfei Zhao, Daehan Kang, Eojin Yoon, Gyeong‐Tak Go, Somin Kim, Seung‐Woo Lee, Chan‐Yul Park, Hyojun Choi, Hyeran Kim, Hyung Joong Yun, Sung Hyuk Park, Kwan Sik Park, Jeong Woo Park, Mungeun Kim, Yong Soo Cho, Ho Won Jang, Wenqiang Yang, Min Hyuk Park, Wan Ki Bae, Sebastiaan van Dijken, Joona Bang, Tae‐Woo Lee   +26 more
wiley   +1 more source

Development and Application of Convolutional Neural Network Model

Jisuanji kexue yu tansuo, 2021
Deep learning is the latest trend of machine learning and artificial intelligence research. As a new field of rapid development in the past ten years, more and more researchers pay attention to it.
YAN Chunman ,WANG Cheng
doaj   +1 more source

Bioinspired Adaptive Sensors: A Review on Current Developments in Theory and Application

Advanced Materials, EarlyView.
This review comprehensively summarizes the recent progress in the design and fabrication of sensory‐adaptation‐inspired devices and highlights their valuable applications in electronic skin, wearable electronics, and machine vision. The existing challenges and future directions are addressed in aspects such as device performance optimization ...
Guodong Gong, You Zhou, Qingxiu Li, Wenyu Zhao, Sunyingyue Geng, Hangfei Li, Yan‐Bing Leng, Shirui Zhu, Guanglong Ding, Yongbiao Zhai, Ziyu Lv, Ye Zhou, Su‐Ting Han   +12 more
wiley   +1 more source

Enhanced image classification with a fast-learning shallow convolutional neural network [PDF]

, 2015
We present a neural network architecture and training method designed to enable very rapid training and low implementation complexity. Due to its training speed and the absence of iteratively-tuned parameters, the method has strong potential for ...
Vladusich, T., Mark D. McDonnell, Tony Vladusich, McDonnell, M.   +3 more
core   +1 more source

Artificial Intelligence‐Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling

Advanced Materials, EarlyView.
AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll, Ivan Pinto‐Huguet, Enzo Rotunno, Thomas Galvani, Catalina Coll, Payam Habibzadeh Kavkani, Maria Chiara Spadaro, Yann‐Michel Niquet, Martin Børstad Eriksen, Sara Martí‐Sánchez, Georgios Katsaros, Giordano Scappucci, Peter Krogstrup, Giovanni Isella, Andreu Cabot, Gonzalo Merino, Pablo Ordejón, Stephan Roche, Vincenzo Grillo, Jordi Arbiol   +19 more
wiley   +1 more source

Learning shape correspondence with anisotropic convolutional neural networks [PDF]

, 2016
Convolutional neural networks have achieved extraordinary results in many computer vision and pattern recognition applications; however, their adoption in the computer graphics and geometry processing communities is limited due to the non-Euclidean ...
Rodolà, Emanuele, Bronstein, Michael, Masci, Jonathan, Boscaini, Davide, Bronstein, Michael M.   +4 more
core  

A classification method of underwater target radiated noise signals based on enhanced images and convolutional neural networks

Frontiers in Physics
As the economy and society continue to develop, the range of underwater vehicles is expanding and technology is constantly being upgraded. Consequently, it is becoming increasingly difficult to classify and identify them, and the traditional ...
Yuan Muye, Lei Zhufeng, Dang Yinuo, Shi Zanrong   +3 more
doaj   +1 more source

A Frequency-Domain Convolutional Neural Network Architecture Based on the Frequency-Domain Randomized Offset Rectified Linear Unit and Frequency-Domain Chunk Max Pooling Method

IEEE Access, 2020
It is of great importance to construct a convolutional neural network architecture in the frequency domain to explore the theory of deep learning in the frequency domain.
Jinhua Lin, Lin Ma, Jingxia Cui
doaj   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source