0. Introduction After reading many blog posts and tutorials about RNN online, I felt they were all the same, providing a vague understanding but failing to explain it clearly. RNN is the foundation of many complex models, and even in transformers, you can see the influence of RNN, so it is essential to have a … Read more
↑↑↑ 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
Canonical transformations are classical methods used by physicists, mechanical engineers, and astronomers to handle Hamiltonian systems. By finding suitable variable substitutions, canonical transformations can simplify, or even completely solve the dynamics of Hamiltonian systems. For instance, in the 19th century, French scientist Charles Delaunay published approximately 1800 pages of analytical derivations attempting to simplify the … Read more
Neuromorphic computing is a brain-like computing paradigm, generally referring to running Spiking Neural Networks (SNN) on neuromorphic chips. Essentially, neuromorphic computing is a design paradigm driven by algorithms. With its low-power advantages, neuromorphic computing is also considered a “potential substitute” for traditional AI. Understanding neuromorphic computing should be approached from a system level, rather than … Read more
Compiled by Yu Yang | QbitAI Official Account Want to adjust your neural network to complete specific tasks? It’s not as simple as it seems. Deep Neural Networks (DNNs) are great building blocks, but moving them can be very costly in terms of computational resources and time. Now, Google AI has released MorphNet. After testing … Read more
Renormalization group is a fundamental concept in physics research. It is not only a powerful tool for studying phase transitions and critical phenomena, as well as strong coupling problems, but it also shapes physicists’ worldview: physics is an effective theory about the emergence of phenomena at different scales and energy levels. In the practical applications … Read more
Machine Heart reported Machine Heart Editorial Department Can neural networks complete various tasks without learning weights? Are the image features learned by CNN just what we think they are? Are neural networks merely combinations of functions with no other meaning? From this paper, the answers to these questions seem to be affirmative. Yesterday, a paper … Read more
5.4 The Intellectual The Intellectual Image Source: Freepik ● ● ● Written by｜Zhang Tianrong As physicist and Manhattan Project leader Oppenheimer said, “We are not just scientists; we are also human.” Where there are humans, there is a community, and the scientific world is no exception. People often say that “science knows no borders, but … Read more
Report by New Intelligence Source: aiweirdness, gizmodo Translated by: Xiao Qin [New Intelligence Overview]One of the specialties of neural networks is image recognition. Tech giants like Google, Microsoft, IBM, and Facebook all have their own photo tagging algorithms. However, even the top image recognition algorithms can make very strange mistakes, they only see what they … Read more
Baidu NLP Column Author: Baidu NLP 1. Introduction Text matching is an important foundational problem in natural language processing. Many tasks in natural language processing can be abstracted as text matching tasks. For example, web search can be abstracted as a relevance matching problem between web pages and user search queries, automatic question answering can … Read more