Source: Algorithm Advancement This article is about 4000 words long and is recommended for an 8-minute read. This article deeply explores the potential connections between Transformer, Recurrent Neural Networks (RNN), and State Space Models (SSM). By exploring the potential connections between seemingly unrelated Large Language Model (LLM) architectures, we may open up new avenues for … Read more
From | DataWhale Training techniques are very important for deep learning. As a highly experimental science, even the same network architecture trained with different methods can yield significantly different results. Here, I summarize my experiences from the past year and share them with everyone. I also welcome additions and corrections. Parameter Initialization Any of the … Read more
Click on the “Datawhalee” above to select the “Starred“ public account Get valuable content at the first time Training techniques are very important for deep learning. As a highly experimental science, using different training methods on the same network structure can yield significantly different results. Here, I summarize my experiences from the past year and … Read more
Introduction: Deep learning can be applied in various fields, and the shapes of deep neural networks vary according to different application scenarios. The common deep learning models mainly include Fully Connected (FC), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). Each of these has its own characteristics and plays an important role in different … Read more
This article is a translated version of the notes from Stanford University’s CS224d course, authorized by Professor Richard Socher of Stanford University. Unauthorized reproduction is prohibited; for specific reproduction requirements, please see the end of the article. Translation: Hu Yang & Xu Ke Proofreading: Han Xiaoyang & Long Xincheng Editor’s Note: This article is the … Read more
New Intelligence Report Source: Reddit, Science Editors: Daming, Pengfei [New Intelligence Guide]Recently, Stanford University Chinese Professor Shanhui Fan’s team published an article in a sub-journal of Science, pointing out that whether it is sound waves, light waves, or other forms of waves, their descriptive equations can be equivalent to Recurrent Neural Networks (RNNs). This discovery … Read more
Source: Panchuang AI, Author: VK Panchuang AI Share Author | Renu Khandelwal Compiler | VK Source | Medium We start with the following questions: Recurrent Neural Networks can solve the problems present in Artificial Neural Networks and Convolutional Neural Networks. Where can RNNs be used? What is RNN and how does it work? Challenges of … Read more
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
Selected from Medium Author: Eugenio Culurciello Translation by Machine Heart Contributors: Liu Xiaokun, Siyuan The author states: We have been trapped in the pit of RNNs, LSTMs, and their variants for many years; it is time to abandon them! In 2014, RNNs and LSTMs were revived. We all read Colah’s blog “Understanding LSTM Networks” and … Read more
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