{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T12:04:57Z","timestamp":1784203497816,"version":"3.55.0"},"reference-count":68,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["93K172024K24"],"award-info":[{"award-number":["93K172024K24"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62576116"],"award-info":[{"award-number":["62576116"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["92570116"],"award-info":[{"award-number":["92570116"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276103"],"award-info":[{"award-number":["62276103"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2025YFC3410000"],"award-info":[{"award-number":["2025YFC3410000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2026A1515011836"],"award-info":[{"award-number":["2026A1515011836"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2023B1515120020"],"award-info":[{"award-number":["2023B1515120020"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neural Networks"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.neunet.2026.109041","type":"journal-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T06:33:44Z","timestamp":1777271624000},"page":"109041","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Contrastive diffusion model for exploring mathematical expressions from data"],"prefix":"10.1016","volume":"202","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5312-1609","authenticated-orcid":false,"given":"Canmiao","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueming","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhifeng","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunguo","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neunet.2026.109041_bib0001","unstructured":"Arnold, C., & K\u00fcpfer, A. (2024). How alignment helps make the most of multimodal data. arXiv: 2405.08454."},{"key":"10.1016\/j.neunet.2026.109041_bib0002","first-page":"17981","article-title":"Structured denoising diffusion models in discrete state-spaces","volume":"34","author":"Austin","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109041_bib0003","unstructured":"Bastiani, Z., Kirby, R. M., Hochhalter, J., & Zhe, S. (2025). Diffusion-based symbolic regression. arXiv preprint arXiv: 2505.24776."},{"key":"10.1016\/j.neunet.2026.109041_bib0004","series-title":"Proceedings of the 38th international conference on machine learning","first-page":"936","article-title":"Neural symbolic regression that scales","volume":"vol. 139","author":"Biggio","year":"2021"},{"key":"10.1016\/j.neunet.2026.109041_bib0005","series-title":"Proceedings of the 2020 genetic and evolutionary computation conference companion","first-page":"1562-1570","article-title":"Operon c++: An efficient genetic programming framework for symbolic regression","author":"Burlacu","year":"2020"},{"issue":"3","key":"10.1016\/j.neunet.2026.109041_bib0006","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1007\/s11047-022-09934-x","article-title":"Population diversity and inheritance in genetic programming for symbolic regression","volume":"23","author":"Burlacu","year":"2023","journal-title":"Natural Computing"},{"key":"10.1016\/j.neunet.2026.109041_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107646","article-title":"Feature-decorrelation adaptive contrastive learning for knowledge-aware recommendation","volume":"190","author":"Cai","year":"2025","journal-title":"Neural Networks"},{"issue":"7","key":"10.1016\/j.neunet.2026.109041_bib0008","doi-asserted-by":"crossref","first-page":"2814","DOI":"10.1109\/TKDE.2024.3361474","article-title":"A survey on generative diffusion models","volume":"36","author":"Cao","year":"2024","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.neunet.2026.109041_bib0009","series-title":"The twelfth international conference on learning representations","article-title":"SYMBOL: Generating flexible black-box optimizers through symbolic equation learning","author":"Chen","year":"2024"},{"key":"10.1016\/j.neunet.2026.109041_bib0010","unstructured":"Chen, J., Yu, J., Ge, C., Yao, L., Xie, E., Wu, Y., Wang, Z., Kwok, J., Luo, P., Lu, H. et al. (2023). Pixart-alpha: Fast training of diffusion transformer for photorealistic text-to-image synthesis. arXiv: 2310.00426."},{"key":"10.1016\/j.neunet.2026.109041_bib0011","series-title":"Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: Human language technologies, volume 1 (long and short papers)","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2019"},{"issue":"4","key":"10.1016\/j.neunet.2026.109041_bib0012","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1109\/TEVC.2024.3423681","article-title":"Srbench++: Principled benchmarking of symbolic regression with domain-expert interpretation","volume":"29","author":"de Franca","year":"2025","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"10.1016\/j.neunet.2026.109041_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.cosrev.2021.100379","article-title":"A survey on deep learning and its applications","volume":"40","author":"Dong","year":"2021","journal-title":"Computer Science Review"},{"key":"10.1016\/j.neunet.2026.109041_bib0014","series-title":"Practical methods of optimization","author":"Fletcher","year":"2000"},{"key":"10.1016\/j.neunet.2026.109041_bib0015","series-title":"The eleventh international conference on learning representations","article-title":"Diffuseq: Sequence to sequence text generation with diffusion models","author":"Gong","year":"2023"},{"key":"10.1016\/j.neunet.2026.109041_bib0016","series-title":"Findings of the association for computational linguistics: EMNLP 2023","first-page":"9868","article-title":"DiffuSeq-v2: Bridging discrete and continuous text spaces for accelerated Seq2Seq diffusion models","author":"Gong","year":"2023"},{"key":"10.1016\/j.neunet.2026.109041_bib0017","article-title":"Smooth diffusion model for multimodal recommendation","volume":"331","author":"He","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.neunet.2026.109041_bib0018","series-title":"Proceedings of the 34th international conference on neural information processing systems","article-title":"Denoising diffusion probabilistic models","author":"Ho","year":"2020"},{"key":"10.1016\/j.neunet.2026.109041_bib0019","unstructured":"Jin, Y., Fu, W., Kang, J., Guo, J., & Guo, J. (2019). Bayesian symbolic regression. arXiv: 1910.08892."},{"key":"10.1016\/j.neunet.2026.109041_bib0020","series-title":"Advances in neural information processing systems","first-page":"10269","article-title":"End-to-end symbolic regression with transformers","volume":"vol. 35","author":"Kamienny","year":"2022"},{"key":"10.1016\/j.neunet.2026.109041_bib0021","series-title":"Proceedings of the 6th european conference on genetic programming","first-page":"70-82","article-title":"Improving symbolic regression with interval arithmetic and linear scaling","author":"Keijzer","year":"2003"},{"issue":"1","key":"10.1016\/j.neunet.2026.109041_bib0022","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1038\/s41598-023-28328-2","article-title":"A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge","volume":"13","author":"Keren","year":"2023","journal-title":"Scientific Reports"},{"issue":"9","key":"10.1016\/j.neunet.2026.109041_bib0023","doi-asserted-by":"crossref","first-page":"4166","DOI":"10.1109\/TNNLS.2020.3017010","article-title":"Integration of neural network-based symbolic regression in deep learning for scientific discovery","volume":"32","author":"Kim","year":"2021","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.neunet.2026.109041_bib0024","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/BF00175355","article-title":"Genetic programming as a means for programming computers by natural selection","volume":"4","author":"Koza","year":"1994","journal-title":"Statistics and Computing"},{"key":"10.1016\/j.neunet.2026.109041_bib0025","series-title":"Proceedings of the 15th annual conference on genetic and evolutionary computation","first-page":"941-948","article-title":"Approximating geometric crossover by semantic backpropagation","author":"Krawiec","year":"2013"},{"key":"10.1016\/j.neunet.2026.109041_bib0026","series-title":"Proceedings of the human language technology conference of the north American chapter of the association for computational linguistics: HLT-NAACL 2004","first-page":"169","article-title":"Minimum Bayes-risk decoding for statistical machine translation","author":"Kumar","year":"2004"},{"key":"10.1016\/j.neunet.2026.109041_bib0027","series-title":"Thirty-fifth conference on neural information processing systems datasets and benchmarks track (round 1)","article-title":"Contemporary symbolic regression methods and their relative performance","author":"La Cava","year":"2021"},{"key":"10.1016\/j.neunet.2026.109041_bib0028","series-title":"International conference on learning representations","article-title":"Deep learning for symbolic mathematics","author":"Lample","year":"2020"},{"key":"10.1016\/j.neunet.2026.109041_bib0029","series-title":"Advances in neural information processing systems","article-title":"A unified framework for deep symbolic regression","author":"Landajuela","year":"2022"},{"key":"10.1016\/j.neunet.2026.109041_bib0030","series-title":"Proceedings of the 38th international conference on machine learning","first-page":"5979","article-title":"Discovering symbolic policies with deep reinforcement learning","volume":"vol. 139","author":"Landajuela","year":"2021"},{"issue":"1","key":"10.1016\/j.neunet.2026.109041_bib0031","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1007\/s10115-025-02639-4","article-title":"Leveraging hierarchy-aware diffusion model and knowledge-enhanced contrastive learning for recommendation","volume":"68","author":"Li","year":"2026","journal-title":"Knowledge and Information Systems"},{"issue":"9","key":"10.1016\/j.neunet.2026.109041_bib0032","doi-asserted-by":"crossref","first-page":"5407","DOI":"10.1109\/TKDE.2025.3582767","article-title":"Mask diffusion-based contrastive learning for knowledge-aware recommendation","volume":"37","author":"Li","year":"2025","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.neunet.2026.109041_bib0033","series-title":"Proceedings of the 41st international conference on machine learning","first-page":"28222","article-title":"A neural-guided dynamic symbolic network for exploring mathematical expressions from data","volume":"vol. 235","author":"Li","year":"2024"},{"key":"10.1016\/j.neunet.2026.109041_bib0034","series-title":"Proceedings of the 36th international conference on neural information processing systems","article-title":"Diffusion-LM improves controllable text generation","author":"Li","year":"2024"},{"key":"10.1016\/j.neunet.2026.109041_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102681","article-title":"Mmsr: Symbolic regression is a multi-modal information fusion task","volume":"114","author":"Li","year":"2025","journal-title":"Information Fusion"},{"key":"10.1016\/j.neunet.2026.109041_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107405","article-title":"Mathematical expression exploration with graph representation and generative graph neural network","volume":"187","author":"Liu","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109041_bib0037","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1016\/j.neunet.2023.06.046","article-title":"Snr: Symbolic network-based rectifiable learning framework for symbolic regression","volume":"165","author":"Liu","year":"2023","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109041_bib0038","series-title":"Advances in neural information processing systems","first-page":"5775","article-title":"Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps","volume":"vol. 35","author":"Lu","year":"2022"},{"key":"10.1016\/j.neunet.2026.109041_bib0039","unstructured":"Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., & Zhu, J. (2022b). Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models. arXiv: 2211.01095."},{"key":"10.1016\/j.neunet.2026.109041_bib0040","series-title":"International conference on learning representations","article-title":"Rethinking network design and local geometry in point cloud: A simple residual MLP framework","author":"Ma","year":"2022"},{"key":"10.1016\/j.neunet.2026.109041_bib0041","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.103","article-title":"Sympy: Symbolic computing in python","volume":"3","author":"Meurer","year":"2017","journal-title":"PeerJ Computer Science"},{"key":"10.1016\/j.neunet.2026.109041_bib0042","series-title":"Advances in neural information processing systems","first-page":"24912","article-title":"Symbolic regression via deep reinforcement learning enhanced genetic programming seeding","volume":"vol. 34","author":"Mundhenk","year":"2021"},{"key":"10.1016\/j.neunet.2026.109041_bib0043","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"4195","article-title":"Scalable diffusion models with transformers","author":"Peebles","year":"2023"},{"key":"10.1016\/j.neunet.2026.109041_bib0044","series-title":"International conference on learning representations","article-title":"Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients","author":"Petersen","year":"2021"},{"key":"10.1016\/j.neunet.2026.109041_bib0045","series-title":"International conference on machine learning","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"issue":"5923","key":"10.1016\/j.neunet.2026.109041_bib0046","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1126\/science.1165893","article-title":"Distilling free-form natural laws from experimental data","volume":"324","author":"Schmidt","year":"2009","journal-title":"Science"},{"key":"10.1016\/j.neunet.2026.109041_bib0047","series-title":"The thirteenth international conference on learning representations","article-title":"LLM-SR: Scientific equation discovery via programming with large language models","author":"Shojaee","year":"2025"},{"key":"10.1016\/j.neunet.2026.109041_bib0048","unstructured":"Shojaee, P., Nguyen, N.-H., Meidani, K., Farimani, A. B., Doan, K. D., & Reddy, C. K. (2025b). Llm-srbench: A new benchmark for scientific equation discovery with large language models. arXiv:abs\/2504.10415."},{"key":"10.1016\/j.neunet.2026.109041_bib0049","series-title":"Proceedings of the 32nd international conference on machine learning","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume":"vol. 37","author":"Sohl-Dickstein","year":"2015"},{"key":"10.1016\/j.neunet.2026.109041_bib0050","unstructured":"Song, J., Lu, Q., Tian, B., Zhang, J., Luo, J., & Wang, Z. (2024). Prove symbolic regression is NP-hard by symbol graph. arXiv: 2404.13820."},{"issue":"1","key":"10.1016\/j.neunet.2026.109041_bib0051","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/TPAMI.2022.3148210","article-title":"From show to tell: A survey on deep learning-based image captioning","volume":"45","author":"Stefanini","year":"2023","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neunet.2026.109041_bib0052","series-title":"The thirty-eighth annual conference on neural information processing systems","article-title":"Visual autoregressive modeling: Scalable image generation via next-scale prediction","author":"Tian","year":"2024"},{"key":"10.1016\/j.neunet.2026.109041_bib0053","unstructured":"Tymkow, R. T., Schnapp, B. D., Valipour, M., & Ghodshi, A. (2025). Symbolic-diffusion: Deep learning based symbolic regression with d3PM discrete token diffusion. arXiv preprint arXiv: 2510.07570."},{"key":"10.1016\/j.neunet.2026.109041_bib0054","series-title":"Proceedings of the 34th international conference on neural information processing systems","article-title":"Ai feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity","author":"Udrescu","year":"2020"},{"issue":"16","key":"10.1016\/j.neunet.2026.109041_bib0055","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.aay2631","article-title":"Ai feynman: A physics-inspired method for symbolic regression","volume":"6","author":"Udrescu","year":"2020","journal-title":"Science Advances"},{"key":"10.1016\/j.neunet.2026.109041_bib0056","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s10710-010-9121-2","article-title":"Semantically-based crossover in genetic programming: application to real-valued symbolic regression","volume":"12","author":"Uy","year":"2011","journal-title":"Genetic Programming and Evolvable Machines"},{"key":"10.1016\/j.neunet.2026.109041_bib0057","unstructured":"Valipour, M., You, B., Panju, M., & Ghodsi, A. (2021). Symbolicgpt: A generative transformer model for symbolic regression. arXiv: 2106.14131."},{"key":"10.1016\/j.neunet.2026.109041_bib0058","series-title":"Advances in neural information processing systems","article-title":"Attention is all you need","volume":"vol. 30","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.neunet.2026.109041_bib0059","article-title":"Symbolic regression is NP-hard","volume":"2022","author":"Virgolin","year":"2022","journal-title":"IEEE Transactions on Machine Learning Research"},{"issue":"1","key":"10.1016\/j.neunet.2026.109041_bib0060","doi-asserted-by":"crossref","first-page":"1356","DOI":"10.1109\/TNNLS.2023.3332400","article-title":"Discovering mathematical expressions through deepsymnet: A classification-based symbolic regression framework","volume":"36","author":"Wu","year":"2025","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"10","key":"10.1016\/j.neunet.2026.109041_bib0061","doi-asserted-by":"crossref","first-page":"11407","DOI":"10.1109\/TPAMI.2023.3277122","article-title":"A survey on non-autoregressive generation for neural machine translation and beyond","volume":"45","author":"Xiao","year":"2023","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"4","key":"10.1016\/j.neunet.2026.109041_bib0062","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3626235","article-title":"Diffusion models: A comprehensive survey of methods and applications","volume":"56","author":"Yang","year":"2023","journal-title":"ACM Computing Surveys"},{"key":"10.1016\/j.neunet.2026.109041_bib0063","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.1905","article-title":"Diffusion models in text generation: A survey","volume":"10","author":"Yi","year":"2024","journal-title":"PeerJ Computer Science"},{"key":"10.1016\/j.neunet.2026.109041_bib0064","series-title":"The thirteenth international conference on learning representations","article-title":"Symbolic regression via MDLformer-guided search: from minimizing prediction error to minimizing description length","author":"Yu","year":"2025"},{"issue":"4","key":"10.1016\/j.neunet.2026.109041_bib0065","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1109\/TCYB.2020.3024849","article-title":"Evolving scheduling heuristics via genetic programming with feature selection in dynamic flexible job-shop scheduling","volume":"51","author":"Zhang","year":"2021","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"5","key":"10.1016\/j.neunet.2026.109041_bib0066","doi-asserted-by":"crossref","first-page":"2440","DOI":"10.1109\/TNNLS.2021.3106648","article-title":"Modeling and control of robotic manipulators based on symbolic regression","volume":"34","author":"Zhang","year":"2023","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"11","key":"10.1016\/j.neunet.2026.109041_bib0067","doi-asserted-by":"crossref","first-page":"4492","DOI":"10.1109\/TSMC.2018.2853719","article-title":"Multifactorial genetic programming for symbolic regression problems","volume":"50","author":"Zhong","year":"2020","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"10.1016\/j.neunet.2026.109041_bib0068","series-title":"Proceedings of the 2025 conference on empirical methods in natural language processing","first-page":"13148","article-title":"Syntax-aware retrieval augmentation for neural symbolic regression","author":"Zhou","year":"2025"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026005010?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026005010?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T11:20:34Z","timestamp":1784200834000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026005010"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":68,"alternative-id":["S0893608026005010"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109041","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Contrastive diffusion model for exploring mathematical expressions from data","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109041","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109041"}}