mlsanityData to 14 Oct 2026

AI Learning Algorithms: Deep Learning, Hybrid Models, and Large-Scale Model Integration

arXiv 2410.09186 ↗First posted 11 Oct 2024cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

In this paper, we discuss learning algorithms and their importance in different types of applications which includes training to identify important patterns and features in a straightforward, easy-to-understand manner. We will review the main concepts of artificial intelligence (AI), machine learning (ML), deep learning (DL), and hybrid models. Some important subsets of Machine Learning algorithms such as supervised, unsupervised, and reinforcement learning are also discussed in this paper. These techniques can be used for some important tasks like prediction, classification, and segmentation. Convolutional Neural Networks (CNNs) are used for image and video processing and many more applications. We dive into the architecture of CNNs and how to integrate CNNs with ML algorithms to build hybrid models. This paper explores the vulnerability of learning algorithms to noise, leading to misclassification. We further discuss the integration of learning algorithms with Large Language Models (LLM) to generate coherent responses applicable to many domains such as healthcare, marketing, and finance by learning important patterns from large volumes of data. Furthermore, we discuss the next generation of learning algorithms and how we may have an unified Adaptive and Dynamic Network to perform important tasks. Overall, this article provides brief overview of learning algorithms, exploring their current state, applications and future direction.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 111 references that are in the map. First posted on arXiv 11 Oct 2024.

Builds on

28 of 111 references.

References · 28

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  5. 2016Densely Connected Convolutional Networkscited by 6,720
  6. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  7. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  8. 2017Improved Training of Wasserstein GANscited by 2,235
  9. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  10. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,039
  11. 2024Reinforcement learningcited by 247
  12. 2017BEGAN: Boundary Equilibrium Generative Adversarial Networkscited by 121
16 more
  1. 2016Energy-based Generative Adversarial Networkcited by 119
  2. 2024MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmarkcited by 105
  3. 2024LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Modelscited by 70
  4. 2023LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Codingcited by 64
  5. 2020Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imagingcited by 57
  6. 2020k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)cited by 56
  7. 2024Designing Heterogeneous LLM Agents for Financial Sentiment Analysiscited by 48
  8. 2023A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIMEcited by 42
  9. 2017Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networkscited by 40
  10. 2024Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verificationcited by 40
  11. 2024HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversationscited by 31
  12. 2017Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Networkcited by 28
  13. 2024Rambler: Supporting Writing With Speech via LLM-Assisted Gist Manipulationcited by 18
  14. 2024From Questions to Insightful Answers: Building an Informed Chatbot for University Resourcescited by 12
  15. 2020ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Seriescited by 9
  16. 2018Complex Network Classification with Convolutional Neural Networkcited by 6

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024A Decade of Deep Learning: A Survey on The Magnificent Seven
  2. 2024Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Unveiling AI's Potential Through Tools, Techniques, and Applications
  3. 2024Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art
  4. 2025Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning
  5. 2022Statistical Foundation Behind Machine Learning and Its Impact on Computer Vision
  6. 2026Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
  7. 2024Gradient Reduction Convolutional Neural Network Policy for Financial Deep Reinforcement Learning
  8. 2024Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective