Deep Learning

New Architecture Introduced for Spiking Neural Networks

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New Architecture Introduced for Spiking Neural Networks

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

The Rise and Fall of Neural Networks in AI

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The Rise and Fall of Neural Networks in AI

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

Weight Agnostic Neural Networks: A Revolutionary Approach

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Weight Agnostic Neural Networks: A Revolutionary Approach

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

Baidu NLP | Neural Network Semantic Matching Technology

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Baidu NLP | Neural Network Semantic Matching Technology

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

Guide to Fooling Neural Networks: How to Trick Deep Learning Models

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Guide to Fooling Neural Networks: How to Trick Deep Learning Models

This is a work by Big Data Digest, please refer to the end of the article for reproduction requirements. Original Author | Adam Geitgey Translation | Wu Shuang, Da Li, Da Jieqiong, Aileen To know oneself and one’s enemy, whether you want to become a hacker (which is not recommended!) or prevent future hacking intrusions, … Read more

MIT Research Unveils Insights into Neural Network Processes

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MIT Research Unveils Insights into Neural Network Processes

Translated by AI Source: news.mit.edu Translator: Wen Qiang [Introduction by AI]MIT’s new research takes a significant step toward unraveling the black box of deep neural networks: at this year’s CVPR, researchers submitted a new study that fully automates the analysis of ResNet, VGG-16, GoogLeNet, and AlexNet performing over 20 tasks. Their proposed Network Dissection can … Read more

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

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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

In-Depth Explanation of Convolutional Neural Networks

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In-Depth Explanation of Convolutional Neural Networks

Selected from Medium Author: Harsh Pokharna Translated by: Machine Heart Contributors: Duxiade This is one of the articles in the author’s series on neural networks introduced on Medium, where he provides a detailed explanation of convolutional neural networks. Convolutional neural networks have wide applications in image recognition, video recognition, recommendation systems, and natural language processing. … Read more

The Rise and Fall of Neural Networks in the 1990s

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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

What Are Artificial Neural Networks?

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What Are Artificial Neural Networks?

*This article is from the 22nd issue of “Banyue Tan” in 2024 The 2024 Nobel Prize in Physics has unexpectedly honored the achievement of “fundamental discoveries and inventions to promote the use of artificial neural networks for machine learning.” What exactly are artificial neural networks? Can their potential really be compared to fundamental physical sciences? … Read more