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(2025). SR-scientist: Scientific equation discovery with agentic AI. arXiv preprint arXiv: 2510.11661."},{"key":"10.1016\/j.neunet.2026.109017_bib0077","article-title":"Integrating audio\u2013visual text generation with contrastive learning for enhanced multimodal emotion analysis","author":"Xiang","year":"2025","journal-title":"Information Fusion"},{"issue":"10","key":"10.1016\/j.neunet.2026.109017_bib0078","doi-asserted-by":"crossref","first-page":"12113","DOI":"10.1109\/TPAMI.2023.3275156","article-title":"Multimodal learning with transformers: A survey","volume":"45","author":"Xu","year":"2023","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neunet.2026.109017_bib0079","doi-asserted-by":"crossref","unstructured":"Zhang, D., Yu, Y., Dong, J., Li, C., Su, D., Chu, C., & Yu, D. (2024). MM-LLMS: Recent advances in multimodal large language models. arXiv preprint arXiv: 2401.13601.","DOI":"10.18653\/v1\/2024.findings-acl.738"},{"issue":"5","key":"10.1016\/j.neunet.2026.109017_bib0080","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/MIS.2025.3597120","article-title":"A generative random modality dropout framework for robust multimodal emotion recognition","volume":"40","author":"Zhang","year":"2025","journal-title":"IEEE Intelligent Systems"},{"key":"10.1016\/j.neunet.2026.109017_bib0081","article-title":"Tempo: Training-time equilibration of modalities for per-sample optimization in multimodal sentiment","author":"Zhao","year":"2026","journal-title":"IEEE Transactions on Affective Computing"},{"key":"10.1016\/j.neunet.2026.109017_bib0082","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108564","article-title":"A client\u2013server based recognition system: Non-contact single\/multiple emotional and behavioral state assessment methods","volume":"260","author":"Zhu","year":"2025","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"10.1016\/j.neunet.2026.109017_bib0083","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103268","article-title":"RMER-DT: Robust multimodal emotion recognition in conversational contexts based on diffusion and transformers","volume":"123","author":"Zhu","year":"2025","journal-title":"Information Fusion"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026004776?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026004776?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T14:33:06Z","timestamp":1781274786000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026004776"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":83,"alternative-id":["S0893608026004776"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109017","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":"ViSymRe: Vision multimodal symbolic regression","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109017","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. 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