mlsanityData to 13 Oct 2026

Pivoting Retail Supply Chain with Deep Generative Techniques: Taxonomy, Survey and Insights

arXiv 2403.00861 ↗First posted 29 Feb 2024cs.AI cs.LGcited by 0

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Abstract (arXiv)

Generative AI applications, such as ChatGPT or DALL-E, have shown the world their impressive capabilities in generating human-like text or image. Diving deeper, the science stakeholder for those AI applications are Deep Generative Models, a.k.a DGMs, which are designed to learn the underlying distribution of the data and generate new data points that are statistically similar to the original dataset. One critical question is raised: how can we leverage DGMs into morden retail supply chain realm? To address this question, this paper expects to provide a comprehensive review of DGMs and discuss their existing and potential usecases in retail supply chain, by (1) providing a taxonomy and overview of state-of-the-art DGMs and their variants, (2) reviewing existing DGM applications in retail supply chain from a end-to-end view of point, and (3) discussing insights and potential directions on how DGMs can be further utilized on solving retail supply chain problems.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 55 of its 136 references that are in the map. First posted on arXiv 29 Feb 2024.

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55 of 136 references.

References · 55

  1. 2017Attention Is All You Needcited by 60,106
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,952
  4. 2023GPT-4 Technical Reportcited by 18,278
  5. 2020Denoising Diffusion Implicit Modelscited by 9,718
  6. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,028
  7. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,213
  8. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,077
  9. 2013Auto-Encoding Variational Bayescited by 4,024
  10. 2022Scaling Instruction-Finetuned Language Modelscited by 3,007
  11. 2014Conditional Generative Adversarial Netscited by 2,157
  12. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,051
38 more
  1. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,413
  2. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,348
  3. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,159
  4. 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,039
  5. 2016Generative Adversarial Text to Image Synthesiscited by 735
  6. 2018Attention, Learn to Solve Routing Problems!cited by 648
  7. 2023A Comprehensive Overview of Large Language Modelscited by 644
  8. 2020WaveGrad: Estimating Gradients for Waveform Generationcited by 621
  9. 2017Learning Combinatorial Optimization Algorithms over Graphscited by 557
  10. 2022Transformers in Time Series: A Surveycited by 493
  11. 2021Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speechcited by 446
  12. 2019Sliced Score Matching: A Scalable Approach to Density and Score Estimationcited by 382
  13. 2022One Embedder, Any Task: Instruction-Finetuned Text Embeddingscited by 330
  14. 2016NIPS 2016 Tutorial: Generative Adversarial Networkscited by 284
  15. 2021How to Train Your Energy-Based Modelscited by 271
  16. 2019An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problemcited by 266
  17. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 241
  18. 2020Transformer Hawkes Processcited by 230
  19. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 192
  20. 2019Gradio: Hassle-Free Sharing and Testing of ML Models in the Wildcited by 177
  21. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  22. 2022M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systemscited by 152
  23. 2016Energy-based Generative Adversarial Networkcited by 119
  24. 2021Neural Temporal Point Processes: A Reviewcited by 113
  25. 2023How to Index Item IDs for Recommendation Foundation Modelscited by 102
  26. 2016Neural Autoregressive Distribution Estimationcited by 94
  27. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 72
  28. 2023Leveraging Large Language Models in Conversational Recommender Systemscited by 63
  29. 2021The Transformer Network for the Traveling Salesman Problemcited by 59
  30. 2022Exploring Generative Neural Temporal Point Processcited by 30
  31. 2023LLMRec: Benchmarking Large Language Models on Recommendation Taskcited by 30
  32. 2019On Learning Paradigms for the Travelling Salesman Problemcited by 24
  33. 2022Deep Inventory Managementcited by 23
  34. 2023Generative Sequential Recommendation with GPTReccited by 21
  35. 2023OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systemscited by 19
  36. 2022Learning to Solve Vehicle Routing Problems: A Surveycited by 16
  37. 2022COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendationcited by 6
  38. 2019Survey of ETA prediction methods in public transport networkscited by 5

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