THE POINT OF OBLIVION THEOREM :  INFORMATION LOSS IN ITERATIVE SYSTEMS

This paper explores the concept of the Point of Oblivion Theorem within the context of computer science. The theorem posits that in certain computational systems, there exists a point beyond which the system’s ability to retain information about its initial state or previous iterations diminishes to a negligible level. This paper investigates the implications of this theorem for analyzing and designing algorithms that operate under iterative processes.

Content

Author and article information

Journal

Title: ScienceOpen Preprints

Publisher: ScienceOpen

Publication date (Electronic preprint): 13 June 2024

Affiliations

[1 ] Innobate;

Author notes

[* ]Email: sdat001@ 123456aucklanduni.ac.nz .

Author information

Shelvin Datt https://orcid.org/0009-0006-6596-7070

Article

DOI: 10.14293/PR2199.000921.v1

SO-VID: aa8405fb-a1c7-476b-bb38-37a09d5590e3

License:

This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

History

Date received : 13 June 2024

References

Tishby Naftali, Zaslavsky Noga. Deep learning and the information bottleneck principle. 2015 IEEE Information Theory Workshop (ITW). 2015. IEEE. [Cross Ref]
Datt Shelvin. The Point of Oblivion Theorem. ScienceOpen. [Cross Ref]
Chen Guanglin, Zhang Zhiyong. IDRWalker: A Random Walk based Modeling Tool for Disordered Regions in Proteins. Cold Spring Harbor Laboratory. [Cross Ref]
KREMER ILAN, SCHREIBER AMNON, SKRZYPACZ ANDRZEJ. Disclosing a Random Walk. The Journal of Finance. Vol. 79(2):1123–1146. 2024. Wiley. [Cross Ref]
Luu Hoang Phuc Hau, Le Hoai Minh, Le Thi Hoai An. Markov chain stochastic DCA and applications in deep learning with PDEs regularization. Neural Networks. Vol. 170:149–166. 2024. Elsevier BV. [Cross Ref]
Matin Sakib, Allen Alice E. A., Smith Justin, Lubbers Nicholas, Jadrich Ryan B., Messerly Richard, Nebgen Benjamin, Li Ying Wai, Tretiak Sergei, Barros Kipton. Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment. Journal of Chemical Theory and Computation. Vol. 20(3):1274–1281. 2024. American Chemical Society (ACS). [Cross Ref]
Oluwarotimi Israel Oluwafemi, Emmanuel Olamigoke Famakinwa, Ometere Deborah Balogun. Random walk theory and application. World Journal of Advanced Engineering Technology and Sciences. Vol. 11(2):346–367. 2024. GSC Online Press. [Cross Ref]
SOORI Mohsen, DASTRES Roza, AREZOO Behrooz, Karimi Ghaleh JOUGH Fooad. Intelligent robotic systems in Industry 4.0: A review. Journal of Advanced Manufacturing Science and Technology. Vol. 4(3)2024. Huatuo Culture Media Co., Limited. [Cross Ref]
Wang Junli, Furube Akihiro. Monte Carlo random walk simulation of transient absorption kinetics using reflectance and absorption of electrons at Au/TiO<sub>2</sub> nanoparticle boundaries. International Journal of Modern Physics B. Vol. 38(12n13)2024. World Scientific Pub Co Pte Ltd. [Cross Ref]
Zhang Kexin, Wen Qingsong, Zhang Chaoli, Cai Rongyao, Jin Ming, Liu Yong, Zhang James Y., Liang Yuxuan, Pang Guansong, Song Dongjin, Pan Shirui. Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1–20. 2024. Institute of Electrical and Electronics Engineers (IEEE). [Cross Ref]

Comments

Comment on this article

[1] Tishby Naftali, Zaslavsky Noga. Deep learning and the information bottleneck principle. 2015 IEEE Information Theory Workshop (ITW). 2015. IEEE. [Cross Ref]

[2] Datt Shelvin. The Point of Oblivion Theorem. ScienceOpen. [Cross Ref]

[3] Chen Guanglin, Zhang Zhiyong. IDRWalker: A Random Walk based Modeling Tool for Disordered Regions in Proteins. Cold Spring Harbor Laboratory. [Cross Ref]

[4] KREMER ILAN, SCHREIBER AMNON, SKRZYPACZ ANDRZEJ. Disclosing a Random Walk. The Journal of Finance. Vol. 79(2):1123–1146. 2024. Wiley. [Cross Ref]

[5] Luu Hoang Phuc Hau, Le Hoai Minh, Le Thi Hoai An. Markov chain stochastic DCA and applications in deep learning with PDEs regularization. Neural Networks. Vol. 170:149–166. 2024. Elsevier BV. [Cross Ref]

[6] Matin Sakib, Allen Alice E. A., Smith Justin, Lubbers Nicholas, Jadrich Ryan B., Messerly Richard, Nebgen Benjamin, Li Ying Wai, Tretiak Sergei, Barros Kipton. Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment. Journal of Chemical Theory and Computation. Vol. 20(3):1274–1281. 2024. American Chemical Society (ACS). [Cross Ref]

[7] Oluwarotimi Israel Oluwafemi, Emmanuel Olamigoke Famakinwa, Ometere Deborah Balogun. Random walk theory and application. World Journal of Advanced Engineering Technology and Sciences. Vol. 11(2):346–367. 2024. GSC Online Press. [Cross Ref]

[8] SOORI Mohsen, DASTRES Roza, AREZOO Behrooz, Karimi Ghaleh JOUGH Fooad. Intelligent robotic systems in Industry 4.0: A review. Journal of Advanced Manufacturing Science and Technology. Vol. 4(3)2024. Huatuo Culture Media Co., Limited. [Cross Ref]

[9] Wang Junli, Furube Akihiro. Monte Carlo random walk simulation of transient absorption kinetics using reflectance and absorption of electrons at Au/TiO<sub>2</sub> nanoparticle boundaries. International Journal of Modern Physics B. Vol. 38(12n13)2024. World Scientific Pub Co Pte Ltd. [Cross Ref]

[10] Zhang Kexin, Wen Qingsong, Zhang Chaoli, Cai Rongyao, Jin Ming, Liu Yong, Zhang James Y., Liang Yuxuan, Pang Guansong, Song Dongjin, Pan Shirui. Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1–20. 2024. Institute of Electrical and Electronics Engineers (IEEE). [Cross Ref]

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THE POINT OF OBLIVION THEOREM : INFORMATION LOSS IN ITERATIVE SYSTEMS