Author | Bill Vorhies Translator | Gai Lei Editor | Vincent AI Frontline Overview: From some interesting use cases, it seems we can completely combine CNN and RNN/LSTM. Many researchers are currently working on this research. However, the latest research trends in CNN may render this idea outdated. For more quality content, please follow the … Read more
In an era flooded with fragmented reading, fewer people pay attention to the exploration and thinking behind each paper. In this column, you will quickly get the highlights and pain points of selected papers, keeping up with the forefront of AI achievements. Click the “Read Original” at the bottom of this article to join the … Read more
Broadly speaking, NN (or the more elegant DNN) can indeed be considered to encompass specific variants like CNN and RNN. In practical applications, the so-called deep neural network DNN often integrates various known structures, including convolutional layers or LSTM units. However, based on the question posed, the DNN here should specifically refer to a fully … Read more
1 New Intelligence Compilation Source: distill.pub/2016/augmented-rnns Authors: Chris Olah & Shan Carter, Google Brain Translator: Wen Fei Today is September 10, 2016 Countdown to AI WORLD 2016 World Artificial Intelligence Conference: 38 days Countdown for Early Bird Tickets: 9 days [New Intelligence Guide] The Google Brain team, led by Chris Olah & Shan Carter, has … Read more
[Introduction] This article provides the background and overview of Dropout, as well as a parameter analysis of its application in language modeling using LSTM/GRU recurrent neural networks. Author｜Adrian G Compiled by｜Zhuanzhi Organized by｜Yingying Dropout Inspired by the role of gender in evolution, Hinton et al. first proposed Dropout, which temporarily removes units from the neural … Read more
Click the above “MLNLP” to select the “star” public account Essential content delivered promptly Author: Yulin Ling CS224N（1.29）Vanishing Gradients, Fancy RNNs Vanishing Gradient The figure below is a more vivid example. Suppose we need to predict the next word after the sentence “The writer of the books”. Due to the vanishing gradient, the influence of … Read more
Big Data Digest Works Compiled by: Wan Jun, Da Jie Qiong, Qian Tian Pei Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks, which have been incredibly popular, it’s time to abandon them! LSTM and RNN were invented in the 1980s and 1990s, resurrected in 2014. In the following years, they became the go-to … Read more
Author: Su Jianlin Affiliation: Guangzhou Flame Information Technology Co., Ltd. Research Direction: NLP, Neural Networks Personal Homepage: kexue.fm I had originally decided to stop working with RNNs as they actually correspond to numerical methods for ODEs (Ordinary Differential Equations). This realization provided me with insights into something I have always wanted to do—using deep learning … Read more
Excerpt from Medium Author: Camron Godbout Translated by: Machine Heart Contributors: Duxiade What are Recurrent Neural Networks (RNNs) and how do we use them? This article discusses the basics of RNNs, which are increasingly popular deep learning models. The intention of this article is not to delve into the obscure mathematical principles but to provide … Read more
Editorial Department WeChat Official Account KeywordsSearch across the webLatest Ranking 『Quantitative Investment』: Ranked First 『Quantitative』: Ranked First 『Machine Learning』: Ranked Third We will continue to work hard To become a qualityfinancial and technical public account across the web Today we will read an article from Guosen Securities Research Introduction to RNN The biggest feature that … Read more