Neural NetworksJ. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks 61 (2015) 85 - 117.Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61:85-117.Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural...
Draft: Deep Learning in Neural Networks: An Overview Technical Report IDSIA-03-14 / arXiv:1404.7828 (v1.5) [cs.NE] J¨ urgen Schmidhuber The Swiss AI Lab IDSIA Istituto Dalle Molle di Studi sull’Intelligenza Arti?ciale University of Lugano & SUPSI Galleria 2, 6928 Manno-Lugano Switzerland...
Title Deep Learning in Neural Networks: An Overview Author(s) Juergen Schmidhuber Publisher: arxiv.org (October 2014) and University of Lugano License(s): Non-exclusive License to Distribute Hardcover N/A eBook PDF (206 pages) Language: English ISBN-10: N/A ISBN-13: N/A Share This: ...
Deep Learning- (Review Article in Nature, May 2015) 三大神 Yann LeCun, Yoshua Bengio, and Geoffrey Hinton的文章,不解释。 Growing Pains in Deep Learning Deep Learning in Neural Networks- This technical report provides an overview of deep learning and related techniques with a special focus on d...
An Overview of Multi-Task Learning in Deep Neural Networks 摘要 1. 引言 2. 动机 3. 两种深度学习多任务学习方法 3.1. 硬参数共享 3.2. 软参数共享 4. 为什么多任务学习有效? 4.1. 隐含的数据增强 4.2. 注意力聚焦 4.3. 窃听 4.4. 表示偏见 ...
Artificial neural networks are not new; they have been around for about 50 years and got some practical recognition after the mid-1980s with the introduction of a method (backpropagation) that allowed for the training of multiple-layer neural networks. However, the true birth of deep learning ...
概括来讲,一旦发现正在优化多于一个的目标函数,你就可以通过多任务学习来有效求解(Generally, as soon as you find yourself optimizing more than one loss function, you are effectively doing multi-task learning (in contrast to single-task learning))。在那种场景中,这样做有利于想清楚我们真正要做的是什么...
【多任务学习】An Overview of Multi-Task Learning in Deep Neural Networks,译自:http://sebastianruder.com/multi-task/1.前言在机器学习中,我们通常关心优化某一特定指标,不管这个指标是一个标准值,还是企业KPI。为了达到这个目标,我们训练单一模型或多个模型集合
Additional material:Deep Learning in Neural Networks: An Overview Perceptron A perceptron contains only a single linear or nonlinearunit. Geometrically, a perceptron with a nonlinear unit trained with the delta rule can find the nonlinear plane separating data points of two different classes (if the...
Here we need to have an overview of the DRL algorithm taxonomy. As we can see, DRL is a fast-developing field, and it is not easy to draw the taxonomy accurately. We can have some traditional perspectives for this, which ignore the most advanced area of DRL (such as meta-learning, tr...