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中国科学院半导体研究所高速电路与神经网络实验室刘婧逸博士一篇论文被人工智能顶刊Neural Networks录用（图）
中国科学院 高速电路 神经网络 刘婧逸 人工智能 Neural Networks
2023/11/28
近日，实验室2021级博士研究生刘婧逸的学术论文《SNR: Symbolic Network-based Rectifiable Learning Framework for Symbolic Regression》被人工智能顶级期刊Neural Networks录用。《Neural Networks》是世界上三个最古老的神经建模学会的档案期刊:国际神经网络学会(INNS)、欧洲神经网络学会(EN...
Cornell researchers have developed an optical neural network (ONN) that can filter relevant information from a scene before the visual image is detected by a camera, a method that may make it possible...
In the vast, expansive skies where birds once ruled supreme, a new crop of aviators is taking flight. These pioneers of the air are not living creatures, but rather a product of deliberate innovation:...
A method for designing neural networks optimally suited for certain tasks（图）
神经网络 机器学习模型 计算机网络
2023/6/20
Neural networks, a type of machine-learning model, are being used to help humans complete a wide variety of tasks, from predicting if someone’s credit score is high enough to qualify for a loan to dia...
Many of us easily recognize emotions expressed in others’ faces. A smile may mean happiness, while a frown may indicate anger. Autistic people often have a more difficult time with this task. It’s unc...
Artificial intelligence and machine learning have become crucial tools for medical imaging, autonomous vehicles and many other applications, but engineers warn that the exponential increase in compute...
Stanford researchers create a high-performance,low-energy artificial synapse for neural network computing
Stanford researchers high-performance low-energy artificial synapse neural network computing
2017/3/14
For all the improvements in computer technology over the years, we still struggle to recreate the low-energy, elegant processing of the human brain. Now, researchers at Stanford University and Sandia ...
February 23, 2007，For the first time, MIT scientists have applied a computer model of how the brain processes visual information to a complex, real world task: recognizing the objects in a busy street...