{"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/segen-sample-ensemble-genetic-evolutional","title":"SEGEN: Sample-Ensemble Genetic Evolutional Network Model","arxiv_id":"1803.08631","date":"2018-03-23","proceeding":null,"authors":["Jiawei Zhang","Limeng Cui","Fisher B. Gouza"],"abstract":"Deep learning, a rebranding of deep neural network research works, has\nachieved a remarkable success in recent years. With multiple hidden layers,\ndeep learning models aim at computing the hierarchical feature representations\nof the observational data. Meanwhile, due to its severe disadvantages in data\nconsumption, computational resources, parameter tuning costs and the lack of\nresult explainability, deep learning has also suffered from lots of criticism.\nIn this paper, we will introduce a new representation learning model, namely\n\"Sample-Ensemble Genetic Evolutionary Network\" (SEGEN), which can serve as an\nalternative approach to deep learning models. Instead of building one single\ndeep model, based on a set of sampled sub-instances, SEGEN adopts a\ngenetic-evolutionary learning strategy to build a group of unit models\ngenerations by generations. The unit models incorporated in SEGEN can be either\ntraditional machine learning models or the recent deep learning models with a\nmuch \"narrower\" and \"shallower\" architecture. The learning results of each\ninstance at the final generation will be effectively combined from each unit\nmodel via diffusive propagation and ensemble learning strategies. From the\ncomputational perspective, SEGEN requires far less data, fewer computational\nresources and parameter tuning efforts, but has sound theoretic\ninterpretability of the learning process and results. Extensive experiments\nhave been done on several different real-world benchmark datasets, and the\nexperimental results obtained by SEGEN have demonstrated its advantages over\nthe state-of-the-art representation learning models.","url_abs":"http://arxiv.org/abs/1803.08631v2","url_pdf":"http://arxiv.org/pdf/1803.08631v2.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":"segen-sample-ensemble-genetic-evolutional","repo_url":"https://github.com/jwzhanggy/Graph-Bert","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}