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Li et al. [18] studied real data sets from social platforms and proposed a content popularity prediction method based on deep neural networks. Yan et al. [19] solved the content-popularity prediction problem based on the local and global user request states by a machine learning algorithm. ...
Machine Learning All research Accelerating LLM Inference on NVIDIA GPUs with ReDrafter content typehighlight|Published year2024 ARMADA: Augmented Reality for Robot Manipulation and Robot-Free Data Acquisition content typepaper|research areaData Science and Annotation,research areaHuman-Computer Interaction|Pub...
深度学习论文Deep neural networks for acoustic modeling in speech recognition_ The shared views of four research groups_20180118194148.pdf,[Geoffrey Hinton, Li Deng, Dong Yu, George E. Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Van
2.2 Neural networks and deep learning Describing today's groundbreaking AI achievements, we have to realize that the underlying powerful deep learning approaches are based on neural network research conducted for many decades, motivated by the knowledge accumulated on the operation and functions of biolo...
Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition benchmarks, sometimes by a large margin. This article provides an overview of this progress and represents the shared views of ...
This code constructs covariance kernel for the Gaussian process that is equivalent to infinitely wide, fully connected, deep neural networks. To use the code, runrun_experiments.py, which uses NNGP kernel to make full Bayesian prediction on the MNIST dataset. ...
Computation on GRG via graph traversal greatly accelerates genome-wide analysis. Drew DeHaas Ziqing Pan Xinzhu Wei Article05 Dec 2024 Spin-symmetry-enforced solution of the many-body Schrödinger equation with a deep neural network An efficient approach is developed to enforce spin symmetry ...
based on deep separable convolution and channel shuffling,we proposed a lightweight,high-efficiency and low-latency convolutional neural network architecture called MagnetNets.In order to evaluate the performance of the MagnetNets network model,we compared it with MobileNets,ShuffleNet,Xception,and ...
large margin classifier for deep networks, where the margin can be based on anylp-norm (p ≥ 1), and the margin may be defined on any chosen set of layers ofa network. 然后你看公式推导的时候说的都是decision boundary,那么应该讲的都是输入空间的margin,而不是输出空间的margin。 我自己觉得...