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目录 Abstract 1 Introduction 1.1 Binary weight networks and model compression 2 Ternary weight networks 2.1 Problem formulation 2.2 Approximated solution with threshold-based ternary function 2.3 Training with stochastic gradient descent method 2.4 Mod…
Introduction 这两天看了一下这篇文章,我就这里分享一下,不过我还是只记录一下跟别人blog上没有,或者自己的想法(ps: 因为有时候翻blog时候发现每篇都一样还是挺烦的= =) .为了不重复前人的工作,我post一个不小心翻到的博客权值简化(1):三值神经网络(Ternary Weight Networks),整个论文内容及实现都讲的很全面了,可以翻阅一下,我也借鉴一下. 文中主要工作的点在三个方面: 增加了网络的表达力(expressive ability).在{1,0,1}基础…
Survey Recent Advances in Efficient Computation of Deep Convolutional Neural Networks, [arxiv '18] A Survey of Model Compression and Acceleration for Deep Neural Networks [arXiv '17] Quantization The ZipML Framework for Training Models with End-to-En…
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Understanding the Effective Receptive Field in Deep Convolutional Neural Networks 理解深度卷积神经网络中的有效感受野 Abstract摘要 We study characteristics of receptive fields of units in deep convolutional networks. The receptive field size is a crucial issue in many vis…
About this Course This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applica…
课程主页:http://cs231n.stanford.edu/   Introduction to neural networks -Training Neural Network ______________________________________________________________________________________________________________________________________________________________…
原文:written by Sebastian Raschka on March 14, 2015 中文版译文:伯乐在线 - atmanic 翻译,toolate 校稿 This article offers a brief glimpse of the history and basic concepts of machine learning. We will take a look at the first algorithmically described neural network…
别看本文没有几页纸,本着把经典的文多读几遍的想法,把它彩印出来看,没想到效果很好,比在屏幕上看着舒服.若用蓝色的笔圈出重点,这篇文章中几乎要全蓝.字字珠玑. Reducing the Dimensionality of Data with Neural Networks G.E. Hinton and R.R. Salakhutdinov  摘要 训练一个带有很小的中间层的多层神经网络,可以重构高维空间的输入向量,实现从高维数据到低维编码的效果.(原文为high-dimensional data…