Literature Analysis

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Because my Project/Research focuses more on the bridge of how AI and Software come together to form an interesting blend in a 3D space in Blender and Houdini.

Below are some references which helped me understand how Machine Learning and Artificial Intelligence go hand in hand with the help of different methods.

Primary Sources means sources which are directly affecting my process in the Research and Secondary are peer review and case studies of how they understand the concepts of AI.

Primary Sources

1. Bismuth Consultancy BV. (n.d.). MLOPs [GitHub repository]. GitHub. https://github.com/Bismuth-Consultancy-BV/MLOPs

 

2. OpenAI. (n.d.). Text generation. https://platform.openai.com/docs/guides/text-generation

 

3. Side Effects Software. (n.d.). SideFXLabs [GitHub repository]. GitHub. https://github.com/sideeffects/SideFXLabs

 

4. Hugging Face. (n.d.). Models. https://huggingface.co/models?pipeline_tag=text-to-image&sort=downloads

 

5. Side Effects Software. (n.d.). Introducing MLOPs: Machine learning operators. https://www.sidefx.com/tutorials/introducing-mlops-machine-learning-operators/

 

Secondary Sources

1. Radford, A., et al. (2021). Learning Transferable Visual Models From Natural Language Supervision. Retrieved from https://arxiv.org/abs/2103.00020

 

2. Bommasani, R., Hudson, D., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Liang, P. (2021). On the opportunities and risks of foundation models. https://doi.org/10.48550/arXiv.2108.07258

 

3. Bayram, F., & Ahmed, B. S. (2024). Towards Trustworthy Machine Learning in production: An Overview of the Robustness in MLOPs approach. ACM Computing Surveys. https://doi.org/10.1145/3708497

 

4. Matsui, B. M. A., & Goya, D. H. (2022). MLOps. MLOps: A Guide to Its Adoption in the Context of Responsible AI. https://doi.org/10.1145/3526073.3527591

 

5. Shaker, N., Togelius, J., & Nelson, M. J. (Eds.). (2016). Procedural content generation in games: A textbook and an overview of current research. Springer.

 

6. Paleyes, A., Urma, R., & Lawrence, N. D. (2022). Challenges in Deploying Machine Learning: A survey of case studies. ACM Computing Surveys, 55(6), 1–29. https://doi.org/10.1145/3533378


7. CollobertRonan, WestonJason, BottouLéon, KarlenMichael, KavukcuogluKoray, & KuksaPavel. (2011). Natural Language Processing (Almost) from Scratch. Journal of Machine Learning Research. https://dl.acm.org/doi/10.5555/1953048.2078186  

 

8. Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261–266. https://doi.org/10.1126/science.aaa8685

 

9. Zhang, J., Li, X., Wan, Z., Wang, C., & Liao, J. (2023, May 19). Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields. arXiv.org. https://arxiv.org/abs/2305.11588

 

10. Tang, C., Guerin, F., & Lin, C. (2022, March 6). Recent Advances in Neural text Generation: A Task-Agnostic survey. arXiv.org. https://arxiv.org/abs/2203.03047

 

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