A Decade of Deep Learning: A Survey on The Magnificent Seven

• URL: http://arxiv.org/abs/2412.16188v1
• Date: Fri, 13 Dec 2024 17:55:39 GMT
• Title: A Decade of Deep Learning: A Survey on The Magnificent Seven
• Authors: Dilshod Azizov, Muhammad Arslan Manzoor, Velibor Bojkovic, Yingxu Wang, Zixiao Wang, Zangir Iklassov, Kailong Zhao, Liang Li, Siwei Liu, Yu Zhong, Wei Liu, Shangsong Liang,
• Abstract summary: Deep learning has fundamentally reshaped the landscape of artificial intelligence over the past decade. We present a comprehensive overview of the most influential deep learning algorithms selected through a broad-based survey of the field. Our discussion centers on pivotal architectures, including Residual Networks, Transformers, Generative Adversarial Networks, Variational Autoencoders, Graph Neural Networks, Contrastive Language-Image Pre-training, and Diffusion models.
• Score: 19.444198085817543
• License:
• Abstract: Deep learning has fundamentally reshaped the landscape of artificial intelligence over the past decade, enabling remarkable achievements across diverse domains. At the heart of these developments lie multi-layered neural network architectures that excel at automatic feature extraction, leading to significant improvements in machine learning tasks. To demystify these advances and offer accessible guidance, we present a comprehensive overview of the most influential deep learning algorithms selected through a broad-based survey of the field. Our discussion centers on pivotal architectures, including Residual Networks, Transformers, Generative Adversarial Networks, Variational Autoencoders, Graph Neural Networks, Contrastive Language-Image Pre-training, and Diffusion models. We detail their historical context, highlight their mathematical foundations and algorithmic principles, and examine subsequent variants, extensions, and practical considerations such as training methodologies, normalization techniques, and learning rate schedules. Beyond historical and technical insights, we also address their applications, challenges, and potential research directions. This survey aims to serve as a practical manual for both newcomers seeking an entry point into cutting-edge deep learning methods and experienced researchers transitioning into this rapidly evolving domain.

Related papers

• A Survey on State-of-the-art Deep Learning Applications and Challenges [0.0]
  Building a deep learning model is challenging due to the algorithm's complexity and the dynamic nature of real-world problems. This study aims to comprehensively review the state-of-the-art deep learning models in computer vision, natural language processing, time series analysis and pervasive computing.
  arXiv  Detail & Related papers  (2024-03-26T10:10:53Z)
• A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond [84.95530356322621]
  This survey presents a systematic review of the advancements in code intelligence. It covers over 50 representative models and their variants, more than 20 categories of tasks, and an extensive coverage of over 680 related works. Building on our examination of the developmental trajectories, we further investigate the emerging synergies between code intelligence and broader machine intelligence.
  arXiv  Detail & Related papers  (2024-03-21T08:54:56Z)
• Masked Modeling for Self-supervised Representation Learning on Vision and Beyond [69.64364187449773]
  Masked modeling has emerged as a distinctive approach that involves predicting parts of the original data that are proportionally masked during training. We elaborate on the details of techniques within masked modeling, including diverse masking strategies, recovering targets, network architectures, and more. We conclude by discussing the limitations of current techniques and point out several potential avenues for advancing masked modeling research.
  arXiv  Detail & Related papers  (2023-12-31T12:03:21Z)
• When Meta-Learning Meets Online and Continual Learning: A Survey [39.53836535326121]
  meta-learning is a data-driven approach to optimize the learning algorithm. Continual learning and online learning, both of which involve incrementally updating a model with streaming data. This paper organizes various problem settings using consistent terminology and formal descriptions.
  arXiv  Detail & Related papers  (2023-11-09T09:49:50Z)
• A Survey on Knowledge Editing of Neural Networks [43.813073385305806]
  Even the largest artificial neural networks make mistakes, and once-correct predictions can become invalid as the world progresses in time. Knowledge editing is emerging as a novel area of research that aims to enable reliable, data-efficient, and fast changes to a pre-trained target model. We first introduce the problem of editing neural networks, formalize it in a common framework and differentiate it from more notorious branches of research such as continuous learning.
  arXiv  Detail & Related papers  (2023-10-30T16:29:47Z)
• Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges [3.9675935847246677]
  We discuss the principles and developments that have been made in the process of learning representations. For each framework or model, the key issues and open challenges, as well as the advantages, are examined.
  arXiv  Detail & Related papers  (2022-11-27T05:37:00Z)
• Neural Architecture Search for Dense Prediction Tasks in Computer Vision [74.9839082859151]
  Deep learning has led to a rising demand for neural network architecture engineering. neural architecture search (NAS) aims at automatically designing neural network architectures in a data-driven manner rather than manually. NAS has become applicable to a much wider range of problems in computer vision.
  arXiv  Detail & Related papers  (2022-02-15T08:06:50Z)
• Transferability in Deep Learning: A Survey [80.67296873915176]
  The ability to acquire and reuse knowledge is known as transferability in deep learning. We present this survey to connect different isolated areas in deep learning with their relation to transferability. We implement a benchmark and an open-source library, enabling a fair evaluation of deep learning methods in terms of transferability.
  arXiv  Detail & Related papers  (2022-01-15T15:03:17Z)
• A Comprehensive Survey on Community Detection with Deep Learning [93.40332347374712]
  A community reveals the features and connections of its members that are different from those in other communities in a network. This survey devises and proposes a new taxonomy covering different categories of the state-of-the-art methods. The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders.
  arXiv  Detail & Related papers  (2021-05-26T14:37:07Z)
• AutoML-Zero: Evolving Machine Learning Algorithms From Scratch [76.83052807776276]
  We show that it is possible to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks. We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space. We believe these preliminary successes in discovering machine learning algorithms from scratch indicate a promising new direction in the field.
  arXiv  Detail & Related papers  (2020-03-06T19:00:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.