搜索结果: 1-13 共查到“计算神经网络 Learning”相关记录13条 . 查询时间(0.14 秒)
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Condensation in deep learning
深度学习 凝结 神经网络
2022/12/8
Why do neural networks (NN) that look so complex usually generalize well? To understand this problem, we find some simple implicit regularizations during training NNs. The first is the frequency princ...
Machine learning is everywhere. For example, it’s how Spotify gives you suggestions of what to listen to next or how Siri answers your questions. And it’s used in particle physics too, from theoretica...
Dataset Bridges Human Vision and Machine Learning（图）
Dataset Bridges Human Vision Machine Learning
2019/5/7
Neuroscientists and computer vision scientists say a new dataset of unprecedented size -- comprising functional brain scans of four volunteers who each viewed 5,000 images -- will help researchers bet...
EVALUATION OF DEEP LEARNING BASED STEREO MATCHING METHODS: FROM GROUND TO AERIAL IMAGES
Dense image matching Deep learning Convolutional neural network Aerial stereos Transfer learning
2018/3/30
Dense stereo matching has been extensively studied in photogrammetry and computer vision. In this paper we evaluate the application of deep learning based stereo methods, which were raised from 2016 a...
DEEP LEARNING FOR LOWTEXTURED IMAGE MATCHING
image matching deep convolutional neural networks auto-encoders cultural heritage
2018/3/30
Low-textured objects pose challenges for an automatic 3D model reconstruction. Such objects are common in archeological applications of photogrammetry. Most of the common feature point descriptors fai...
Rice U. scientists slash computations for ‘deep learning’
Rice U. scientists slash computations deep learning
2017/6/1
Rice University computer scientists have adapted a widely used technique for rapid data lookup to slash the amount of computation — and thus energy and time — required for deep learning, a computation...
New tool for virtual and augmented reality uses ‘deep learning’
New tool virtual augmented deep learning
2016/6/22
Future systems that allow people to interact with virtual environments will require computers to interpret the human hand’s nearly endless variety and complexity of changing motions and joint angles.I...
Learning viewpoint invariant perceptual representations from cluttered images
Computational models of vision Neural Nets invariance object recognition
2015/7/31
In order to perform object recognition, it is necessary to form perceptual representations that are sufficiently specific to distinguish between objects, but that are also sufficiently flexible to gen...
The paper at hand describes an approach to automatise the creation of a class taxonomy. Information about objects, e.g. "a tank is armored and moves by track", but no prior knowledge about taxonomy st...
Empirical learning aided by weak domain knowledge in the form of feature importance
neural network domain knowledge prior knowledge feature importance
2015/7/15
Standard hybrid learners that use domain knowledge require stronger knowledge that is hard and expensive to acquire. However, weaker domain knowledge can benefit from prior knowledge while being cost ...
Hybrid Neural Network Architecture for On-Line Learning
Neural Networks Instantaneously Trained Networks Back-Propagation On-Line Learning
2010/5/7
Approaches to machine intelligence based on brain models use neural networks for generalization but they do so as signal processing black boxes. In reality, the brain consists of many modules that ope...
Accessing e-Learners’ Knowledge for Personalization in e-Learning Environment
e-Learning Data Mining Web Mining Neural Network
2008/4/15
e-Learning has become a trend in the world nowadays. However, most researches neglect a fundamental issue – the e-Learner’s prior knowledge on which the useful intelligent systems are based. This rese...
An Intelligent Offline Handwriting Recognition System Using Evolutionary Neural Learning Algorithm and Rule Based Over Segmented Data Points
An Intelligent Offline Handwriting Recognition System Evolutionary Neural Learning Algorithm Rule Based Over Segmented Data Points
2003/8/25
In this paper we propose a novel technique of using a hybrid evolutionary method, which uses a combination of genetic algorithm and matrix based solution methods such as QR factorization.The training ...