论文翻译Neural Networks With Few Multiplications 众所周知,对于大多数深度学习算法而言,训练是非常耗时的. 由于训练神经网络中的大多数计算通常用于浮点数乘法,我们研究了一种可以减少或消除这些乘法的训练方式. 我们的方式由两部分组成:首先我们随机地二值化权重,以转换计算隐藏状态涉及符号变化的乘法. 其次,
BinaryConnect: Training Deep Neural Networks with binary weights during propagations. You may want to checkout our subsequent work: Neural Networks with Few Multiplications BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 Requirements Python, Numpy, Scipy ...
Lin, Z., Courbariaux, M., Memisevic, R., Bengio, Y.: Neural networks with few multiplications. arXiv preprint arXiv:1510.03009 (2015) Courbariaux, M., Bengio, Y., David, J.P.: Training deep neural networks with low precision multiplications. arXiv preprint arXiv:1412.7024 (2014) Soud...
Recent years have witnessed a surge of interest in learning representations of graph-structured data, with applications from social networks to drug discovery. However, graph neural networks, the machine learning models for handling graph-structured data, face significant challenges when running on conven...
c Our multilayer accelerator implements three optoelectronic matrix-vector multiplications (MVM) each followed by a ReLU. This implementation can transfer over weights from pre-trained neural networks (partially reproduced from ref. 53). d Number of compute operations performed per data read-in ...
Neural Networks with Few Multiplications. In Proceedings of the 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, 2–4 May 2016. [Google Scholar] Ni, R.; Chu, H.; Castañeda, O.; Chiang, P.; Studer, C.; Goldstein, T. WrapNet: Neural Net ...
In particular, it's not possible to sum up the design process for the hidden layers with a few simple rules of thumb. Instead, neural networks researchers have developed many design heuristics for the hidden layers, which help people get the behaviour they want out of their nets. For ...
Such insights are key motivators of this study, as we show that neural networks can be used to produce, and operate directly with interpretable logical expressions. The main contributions of this paper are the following: We propose HorNets (Horn Networks), a neural network architecture capable of...
Even in the late 1980s people ran up against limits, especially when attempting to use backpropagation to train deep neural networks, i.e., networks with many hidden layers. Later in the book we'll see how modern computers and some clever new ideas now make it possible to use back...
【论文笔记1】RNN在图像压缩领域的运用——Variable Rate Image Compression with Recurrent Neural Networks 一、引言 随着互联网的发展,网络图片的数量越来越多,而用户对网页加载的速度要求越来越高。为了满足用户对网页加载快速性、舒适性的服务需求,如何将图像以更低的字节数保存(存储空间的节省意味着更快的传输...