Learning in Gated Neural Networks.Ashok Vardhan MakkuvaSewoong OhSreeram KannanPramod ViswanathPMLRInternational Conference on Artificial Intelligence and Statistics
论文地址:ENet:A Deep Neural Network Architecture for Real-Time Semantic Segmentation 背景 近年深度神经网络在计算机视觉领域发展迅猛,特别是图像分类等领域。但是大多数神经网络仍受限于计算量、存储空间、运算速度等因素...猜你喜欢Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification ...
scNET combines single-cell gene expression information with protein–protein interaction networks using a dual-view architecture based on graph neural networks to better characterize changes in cellular pathways and complexes across cellular conditions. ...
For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an...
Behavioural feedback is critical for learning in the cerebral cortex. However, such feedback is often not readily available. How the cerebral cortex learns efficiently despite the sparse nature of feedback remains unclear. Inspired by recent deep learnin
Name:Graph-to-Sequence Learning using Gated Graph Neural Networks Link:arxiv.org/pdf/1806.0983 Conference:ACL2018 0 Abstract 许多的NLP应用都可以看做是Graph2Seq的学习问题,之前的工作都是与grammar-based的方法相比较,依旧依赖于线性搜索或者循环搜索的方式来实现较好的效果,在这篇工作,我们提出一种新的model...
RNN 在每个时间步骤(time step)产生输出。每个时间步骤的隐藏神经元是根据当前输入数据和前一个时间步骤的隐藏神经元计算的。长短期记忆(Long Short-Term Memory, LSTM)和具有可控门的门控循环单元(Gated Recurrent Unit , GRU)旨在避免 RNN 在长期依赖中的梯度消失/爆炸。
[87], enabling an entire speech recognition system that goes all the way from acoustics to the sequence of characters in the transcription. LSTM networks or related forms of gated units are also currently used for the encoder and decoder networks that perform so well at machine translation [17...
3.2. Gated convolutional neural networks A GCNN is a non-recurrent network alternative to capture long-term dependencies while avoiding sequential operations for better parallelizability. Thus, recurrent connections typically applied in RNNs are replaced by gated temporal convolutions. In general, convol...
are “data-free” NNs, i.e. they enforce the initial/boundary conditions (hard BC) via a custom NN architecture while embedding the PDE in the training loss. This soft form technique is described in Raissi et al [146], where the term “physics-informed neural networks” was coined (PINNs...