{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/generalization-over-different-cellular","title":"Generalization over different cellular automata rules learned by a deep feed-forward neural network","arxiv_id":"2103.14886","date":"2021-03-27","proceeding":null,"authors":["Marcel Aach","Jens Henrik Goebbert","Jenia Jitsev"],"abstract":"To test generalization ability of a class of deep neural networks, we randomly generate a large number of different rule sets for 2-D cellular automata (CA), based on John Conway's Game of Life. Using these rules, we compute several trajectories for each CA instance. A deep convolutional encoder-decoder network with short and long range skip connections is trained on various generated CA trajectories to predict the next CA state given its previous states. Results show that the network is able to learn the rules of various, complex cellular automata and generalize to unseen configurations. To some extent, the network shows generalization to rule sets and neighborhood sizes that were not seen during the training at all. Code to reproduce the experiments is publicly available at: https://github.com/SLAMPAI/generalization-cellular-automata","url_abs":"https://arxiv.org/abs/2103.14886v2","url_pdf":"https://arxiv.org/pdf/2103.14886v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"generalization-over-different-cellular","repo_url":"https://github.com/SLAMPAI/generalization-cellular-automata","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2103.14886","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.14886"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/SLAMPAI/generalization-cellular-automata","reach":null}],"summary":{"ran_honours":1,"unverified":2},"by_repo_kind":{"official":{"samples":3,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"891eebb294b5bc52","entry":"make_environment","repo":"SLAMPAI/generalization-cellular-automata","repo_kind":"official","path":"data_generator.py","file_url":"https://github.com/SLAMPAI/generalization-cellular-automata/blob/HEAD/data_generator.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"891eebb294b5bc52"}},{"code_sha256_prefix":"cb58811668e4515c","entry":"compute_trajectory","repo":"SLAMPAI/generalization-cellular-automata","repo_kind":"official","path":"data_generator.py","file_url":"https://github.com/SLAMPAI/generalization-cellular-automata/blob/HEAD/data_generator.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"cb58811668e4515c"}},{"code_sha256_prefix":"a6b0bcb8dcbebfc6","entry":"update_environment","repo":"SLAMPAI/generalization-cellular-automata","repo_kind":"official","path":"data_generator.py","file_url":"https://github.com/SLAMPAI/generalization-cellular-automata/blob/HEAD/data_generator.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a6b0bcb8dcbebfc6"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}