Current - Issue

1. Abrahão, F. S., Wehmuth, K., & Ziviani, A. (2017). Algorithmic networks: Central time to trigger expected emergent open-endedness. arXiv:1708.09149.
2. Alakuijala, J., & Szabadka, Z. (2016). Brotli compressed data format (RFC 7932). IETF.
3. Ballé, J., Johnston, N., & Minnen, D. (2019). Integer networks for data compression with latent-variable models. ICLR.
4. Bellard, F. (2019). Lossless data compression with neural networks.
5. Bengio, Y., Léonard, N., & Courville, A. (2013). Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv:1308.3432.
6. Bennett, C. H. (1973). Logical reversibility of computation. IBM Journal of Research and Development, 17(6), 525–532.
7. Bennett, C. H. (1988). Logical depth and physical complexity. In R. Herken (Ed.), The universal turing machine: A half-century survey (pp. 227–257). Oxford University Press.
8. Bernstein, D. J. (2008). ChaCha, a variant of Salsa20. Workshop Record of SASC 2008: The State of the Art of Stream Ciphers.
9. Harris, C. R., Millman, K. J., Walt, S. J. van der, et al. (2020). Array programming with NumPy. Nature, 585, 357–362.
10. Hartmanis, J. (1983). Generalized Kolmogorov complexity and the structure of feasible computations. 24th Annual Symposium on Foundations of Computer Science (FOCS), 439–445.
11. Hellman, M. E. (1980). A cryptanalytic time-memory trade-off. IEEE Transactions on Information Theory, 26(4), 401–406.
12. Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., & Kalenichenko, D. (2018). Quantization and training of neural networks for efficient integer-arithmetic-only inference. CVPR.
13. Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. ICLR.
14. Knoll, B. (2014). Cmix.
15. Kolmogorov, A. N. (1965). Three approaches to the quantitative definition of information. Problems of Information Transmission, 1(1), 1–7.
16. Landauer, R. (1961). Irreversibility and heat generation in the computing process. IBM Journal of Research and Development, 5(3), 183–191.
17. Levin, L. A. (1973). Universal sequential search problems. Problems of Information Transmission, 9(3), 265–266.
18. Li, M., & Vitányi, P. M. B. (2008). An introduction to kolmogorov complexity and its applications (3rd ed.). Springer.
19. Lorenz, E. N. (1963). Deterministic nonperiodic flow. Journal of the Atmospheric Sciences, 20(2), 130–141.
20. Martin, G. N. N. (1979). Range encoding: An algorithm for removing redundancy from a digitised message. Video & Data Recording Conference.
21. Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020). NeRF: Representing scenes as neural radiance fields for view synthesis. ECCV.
22. Muthitacharoen, A., Chen, B., & Mazières, D. (2001). A low-bandwidth network file system. Proceedings of the 18th ACM Symposium on Operating Systems Principles (SOSP), 174–187.
23. Park, J. J., Florence, P., Straub, J., Newcombe, R., & Lovegrove, S. (2019). DeepSDF: Learning continuous signed distance functions for shape representation. CVPR.
24. Patil, M. K. (2026). EmbryonicAL AI model: Seed-driven neural network growth for regenerative AI. Syncaissa Systems Inc. Technical Report.
25. Perlin, K. (1985). An image synthesizer. ACM SIGGRAPH Computer Graphics, 19(3), 287–296.
26. Project Gutenberg. (2026). Project gutenberg literary archive.
27. Ranzato, M., Chopra, S., Auli, M., & Zaremba, W. (2016). Sequence level training with recurrent neural networks. ICLR.
28. Ratcliff, J. W., & Metzener, D. E. (1988). Pattern matching: The gestalt approach. Dr. Dobb’s Journal, 13(7), 46.
29. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423, 623–656.
30. Shannon, C. E. (1951). Prediction and entropy of printed English. Bell System Technical Journal, 30(1), 50–64.
31. Shkarin, D. (2002). PPM: One step to practicality. Data Compression Conference (DCC), 202–211.
32. Sitzmann, V., Martel, J. N. P., Bergman, A. W., Lindell, D. B., & Wetzstein, G. (2020). Implicit neural representations with periodic activation functions. NeurIPS, 33.
33. Solomonoff, R. J. (1964). A formal theory of inductive inference. Part I. Information and Control, 7(1), 1–22.
34. Steele, G. L., Lea, D., & Flood, C. H. (2014). Fast splittable pseudorandom number generators. OOPSLA, 453–472.
35. Chaitin, G. J. (1966). On the length of programs for computing finite binary sequences. Journal of the ACM, 13(4), 547–569.
36. Chen, T., Xu, B., Zhang, C., & Guestrin, C. (2016). Training deep nets with sublinear memory cost. arXiv:1604.06174.
37. Cleary, J. G., & Witten, I. H. (1984). Data compression using adaptive coding and partial string matching. IEEE Transactions on Communications, 32(4), 396–402.
38. Collet, Y., & Kucherawy, M. (2021). Zstandard compression and the application/zstd media type. IETF RFC 8878.
39. Cover, T. M. (1965). Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Transactions on Electronic Computers, EC-14(3), 326–334.
40. Delétang, G., Ruoss, A., Duquenne, P.-A., Catt, E., Genewein, T., Mattern, C., Grau-Moya, J., Wenliang, L. K., Aitchison, M., Orseau, L., Hutter, M., & Veness, J. (2023). Language modeling is compression. arXiv:2309.10668.
41. Dupont, E., Goliński, A., Alizadeh, M., Teh, Y. W., & Doucet, A. (2021). COIN: COmpression with implicit neural representations. arXiv:2103.03123.
42. Subbotin, D. (1999). Carryless rangecoder.
43. Tridgell, A., & Mackerras, P. (1996). The rsync algorithm (TR-CS-96-05). Australian National University.
44. Williams, R. J., & Zipser, D. (1989). A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1(2), 270–280.
45. Witten, I. H., Neal, R. M., & Cleary, J. G. (1987). Arithmetic coding for data compression. Communications of the ACM, 30(6), 520–540.
46. Xia, W., Zhou, Y., Jiang, H., Feng, D., Hua, Y., Liu, Y., & Zhang, Y. (2016). FastCDC: A fast and efficient content-defined chunking approach for data deduplication. USENIX Annual Technical Conference, 101–114.