BLSTM

Enhancing RNN with Adaptive Computation Time for Multi-Factor Stock Selection

by
Enhancing RNN with Adaptive Computation Time for Multi-Factor Stock Selection

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

Understanding Recurrent Neural Networks (RNNs)

by
Understanding Recurrent Neural Networks (RNNs)

↑↑↑ Follow “Star Mark” Datawhale Daily Insights & Monthly Study Groups, Don’t Miss Out Datawhale Insights Focus: Neural Networks, Source: Artificial Intelligence and Algorithm Learning Neural networks are the carriers of deep learning, and among neural network models, the most classic non-RNN model belongs here. Although it is not perfect, it possesses the ability to … Read more

Using Neural Networks to Continue Writing Game of Thrones

by
Using Neural Networks to Continue Writing Game of Thrones

Author: Sam Hill Translators: Tian Ao, Song Qingbo, Aileen, Long Muxue Reply “novel” in the background to download the complete new novel created by the neural network～ Winter is coming…… Season 7 of “Game of Thrones” has ended, but it is said that the last six episodes of this series will not air until spring … Read more

The Father of Recurrent Neural Networks: Building Unsupervised General Neural Network AI

by
The Father of Recurrent Neural Networks: Building Unsupervised General Neural Network AI

Recommended by New Intelligence Source: Authorized Reprint from InfoQ Translator: He Wuyu [New Intelligence Overview] Jürgen Schmidhuber, the scientific affairs director at the Swiss AI lab IDSIA, led a team in 1997 to propose the Long Short-Term Memory Recurrent Neural Network (LSTM RNN), which simplifies time-dependent recurrent neural networks, thus earning him the title of … Read more

The Rise and Fall of Neural Networks in the 1990s

by
The Rise and Fall of Neural Networks in the 1990s

Excerpt from andreykurenkov Author: Andrey Kurenkov Translated by Machine Heart Contributors: salmoner, Electronic Sheep, Sister Niu Niu, Ben, Slightly Chubby This is part three of the History of Neural Networks and Deep Learning (see Part One, Part Two). In this section, we will continue to explore the rapid development of research in the 1990s and … Read more

Implementing Single-Head and Multi-Head Attention Mechanisms in One Line

by
Implementing Single-Head and Multi-Head Attention Mechanisms in One Line

Click the blue text above to follow us In recent years, the attention mechanism has become very popular due to its effectiveness, and the combination of attention with various networks is increasingly common. MATLAB 2023 has added the Attention layer, making the implementation of the attention mechanism extremely simple. The detailed usage can be found … Read more

Latest Review on Attention Mechanism and Related Source Code

by
Latest Review on Attention Mechanism and Related Source Code

Introduction The left side of the figure below shows the traditional Seq2Seq model (which encodes a sequence and then decodes it back into a sequence). This is a conventional LSTM-based model, where the hidden state at a given timestamp in the Decoder only depends on the current timestamp’s hidden state and the output from the … Read more

Understanding Attention Mechanism and Its PyTorch Implementation

by
Understanding Attention Mechanism and Its PyTorch Implementation

Click the “MLNLP” above to select the “Star” public account. Heavyweight content delivered to you first. From | Zhihu Author | Lucas Address | https://zhuanlan.zhihu.com/p/88376673 Column | Deep Learning and Sentiment Analysis Editor | Machine Learning Algorithms and Natural Language Processing Understanding Attention: The Attention Mechanism and Its PyTorch Implementation Biomimetic Brain Attention Model -> … Read more

Enhancing Online Speech Recognition Efficiency with Upgraded Algorithms

by
Enhancing Online Speech Recognition Efficiency with Upgraded Algorithms

Recently, Alibaba algorithm expert Kun Cheng participated in the ICASSP 2017 conference with the paper titled Improving Latency-Controlled BLSTM Acoustic Models for Online Speech Recognition. Author Kun Cheng communicating with attendees The research of this paper is based on the premise that to achieve better speech recognition accuracy, the Latency-controlled BLSTM model was used in … Read more