Bi-RNN

Stanford Study: Waves and RNNs

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Stanford Study: Waves and RNNs

Selected from Reddit Author: Ian Williamson Translated by Machine Heart Contributors: Wang Zhi Jia, Mo Wang A study from Stanford University found a correspondence between waves in physics and computations in RNNs. Paper link:https://advances.sciencemag.org/content/5/12/eaay6946 GitHub link:https://github.com/fancompute/wavetorch Recently, there has been a lot of exciting interaction between machine learning and some fields of physics and numerical … Read more

Comparison of Mamba, RNN, and Transformer Architectures

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Comparison of Mamba, RNN, and Transformer Architectures

The Transformer architecture has become a major component of the success of large language models (LLMs). To further improve LLMs, new architectures that may outperform the Transformer architecture are being developed. One such approach is Mamba, a state space model. The paper “Mamba: Linear-Time Sequence Modeling with Selective State Spaces” introduces Mamba, which we have … Read more

Principles and Differences of CNN and RNN in Artificial Intelligence

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Principles and Differences of CNN and RNN in Artificial Intelligence

Convolutional Neural Networks and Recurrent Neural Networks are widely used in machine learning today. However, they are typically used for completely different use cases. What are the principles and differences of CNN and RNN in artificial intelligence? In machine learning, each type of artificial neural network is tailored for specific tasks. Below, we will introduce … Read more

Understanding RNN Parameter Calculation

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Understanding RNN Parameter Calculation

Regarding the calculation of RNN parameters, the PPT does not explain it very clearly, as it only contains images without text. At the same time, the textbook version by Qizhi Yao does not provide any exercises related to RNN parameter calculation, and the final exam may only test based on the descriptions in the images, … Read more

Overview of Dropout Applications in RNNs

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Overview of Dropout Applications in RNNs

【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 Compiler｜Zhuanzhi (No secondary reproduction), Xiaoshi Organizer｜Yingying Dropout Inspired by the role of gender in evolution, Hinton et al. first proposed Dropout, which temporarily removes units … Read more

Step-by-Step Guide to Using RNN for Stock Price Prediction

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Step-by-Step Guide to Using RNN for Stock Price Prediction

RNN is a popular model for processing time series data, demonstrating significant effectiveness in fields such as NLP and time series forecasting.As this article focuses on the practical application of RNN rather than theoretical knowledge, interested readers are encouraged to study RNN systematically. The following example is implemented using TensorFlow.Using TensorFlow to implement RNN or … Read more

Summary of RNN, LSTM, GRU, ConvLSTM, ConvGRU, and ST-LSTM

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Summary of RNN, LSTM, GRU, ConvLSTM, ConvGRU, and ST-LSTM

Introduction I rarely write summary articles, but I feel it’s necessary to periodically summarize some interconnected knowledge points, so I’ve written this one. Since my content mainly focuses on time series and spatio-temporal prediction, I will primarily discuss RNN, LSTM, GRU, ConvLSTM, ConvGRU, and ST-LSTM. 1. RNN The most primitive recurrent neural network, essentially a … Read more

Manual for Recurrent Neural Networks (RNN)

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Manual for Recurrent Neural Networks (RNN)

Recently, the Google Translate that has been spreading like wildfire among friends has achieved stunning performance. The core technology here is RNN – the so-called Recurrent Neural Network. RNN can be regarded as one of the most promising tools in deep learning’s future. Do you want to understand the source of its power? Do you … Read more

Exploring RNN Interpretability Methods Proposed by Zhou Zhihua et al.

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Exploring RNN Interpretability Methods Proposed by Zhou Zhihua et al.

Selected from ArXiv Authors: Bo-Jian Hou, Zhi-Hua Zhou Contributors: Si Yuan, Xiao Kun This article is authorized for reproduction by Almost Human (almosthuman2014) Reproduction is prohibited Apart from numerical calculations, do you really know what neural networks are doing internally? We have always understood deep models based on their computational flow, but we are still … Read more

Can We Use RNNs to Write Strategies?

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Can We Use RNNs to Write Strategies?

Editor: We have a user who enjoys using machine learning to experiment with strategies. His descriptions of several models are quite vivid, and he has written a demo strategy using PonderLSTM, which we are sharing today~ The ACT model simulates the thinking process of complex problems by performing multiple computations at each time step (time … Read more