A Pilot Study on the Use of Generative Adversarial Networks for Data Augmentation of Time Series

Morizet, Nicolas; Rizzato, Matteo; Grimbert, David; Luta, George

doi:10.3390/ai3040047

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…As Breiman (2001) spelled out the useful marriage between statistics and ML, see, e.g. Morizet (2020), Shalev-Shwartz and Ben-David (2014), Wasserstein metrics are used also in ML, e.g. in WGAN.…”

Section: Typical Positions In Frechet's Programmentioning

confidence: 99%

A prelude to statistics in Wasserstein metric spaces

Le,

Pham

2023

AJEB

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PurposeThis paper aims mainly at introducing applied statisticians and econometricians to the current research methodology with non-Euclidean data sets. Specifically, it provides the basis and rationale for statistics in Wasserstein space, where the metric on probability measures is taken as a Wasserstein metric arising from optimal transport theory.Design/methodology/approachThe authors spell out the basis and rationale for using Wasserstein metrics on the data space of (random) probability measures.FindingsIn elaborating the new statistical analysis of non-Euclidean data sets, the paper illustrates the generalization of traditional aspects of statistical inference following Frechet's program.Originality/valueBesides the elaboration of research methodology for a new data analysis, the paper discusses the applications of Wasserstein metrics to the robustness of financial risk measures.

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Section: Typical Positions In Frechet's Programmentioning

confidence: 99%

A prelude to statistics in Wasserstein metric spaces

Le,

Pham

2023

AJEB

View full text Add to dashboard Cite

show abstract

“…They are crucial in advancing AI technology and revolutionizing human-computer interactions [11]. LLMs utilize a transformer architecture model, serving as a foundation for generative AI applications, along with generative adversarial networks (GANs) [26] and variational autoencoders (VAEs) [30].…”

Section: Introductionmentioning

confidence: 99%

A Case Study on the Generative AI Project Life Cycle Using Large Language Models

Bandi,

Kagitha

EPiC Series in Computing

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Large Language Models represent a disruptive technology set to revolutionize the fu- ture of artificial intelligence. While numerous literature reviews and survey articles discuss their benefits and address security and compliance concerns, there remains a shortage of research exploring the implementation life cycle of generative AI systems. This paper addresses this gap by presenting the various phases of the generative AI life cycle and detailing the development of a chatbot designed to address inquiries from prospective stu- dents. Utilizing Google Flan LLM and a question-answering pipeline, we processed user prompts. In addition, we compiled an input file containing domain knowledge of the edu- cation program, which was preprocessed and condensed into vector embeddings using the HuggingFace library. Furthermore, we designed a chat interface for user interaction using Streamlit. The responses generated by the chatbot are both descriptive and contextu- ally pertinent to the prompts, with their quality improving in response to more detailed prompts. However, a significant constraint is the size limit of the input file, given the processing power limitations of CPUs.

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A Pilot Study on the Use of Generative Adversarial Networks for Data Augmentation of Time Series

Cited by 2 publications

References 10 publications

A prelude to statistics in Wasserstein metric spaces

A prelude to statistics in Wasserstein metric spaces

A Case Study on the Generative AI Project Life Cycle Using Large Language Models

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