{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T23:05:06Z","timestamp":1789254306249,"version":"build-2803163510"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032369680","type":"print"},{"value":"9783032369697","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-36969-7_8","type":"book-chapter","created":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T22:21:26Z","timestamp":1789251686000},"page":"134-151","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Closing the\u00a0Capacity\u2013Convergence Gap: Globally Optimal Configuration of\u00a0Implicit Neural Representations"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5576-6499","authenticated-orcid":false,"given":"Sipeng","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9778-2620","authenticated-orcid":false,"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1076-282X","authenticated-orcid":false,"given":"Shibo","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,13]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R.: NTIRE 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 126\u2013135 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"8_CR2","unstructured":"Balandat, M., et al.: BoTorch: a framework for efficient Monte-Carlo Bayesian optimization. In: Advances in Neural Information Processing Systems, vol. 33 (2020). http:\/\/arxiv.org\/abs\/1910.06403. Accessed 30 June 2026"},{"issue":"1","key":"8_CR3","doi-asserted-by":"publisher","first-page":"2914","DOI":"10.1038\/s41467-021-23103-1","volume":"12","author":"A Canatar","year":"2021","unstructured":"Canatar, A., Bordelon, B., Pehlevan, C.: Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks. Nat. Commun. 12(1), 2914 (2021)","journal-title":"Nat. Commun."},{"issue":"4","key":"8_CR4","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/BF02551274","volume":"2","author":"G Cybenko","year":"1989","unstructured":"Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control Sig. Syst. 2(4), 303\u2013314 (1989)","journal-title":"Math. Control Sig. Syst."},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Daulton, S., Wan, X., Eriksson, D., Balandat, M., Osborne, M.A., Bakshy, E.: Bayesian optimization over discrete and mixed spaces via probabilistic reparameterization. In: Advances in Neural Information Processing Systems, vol. 35, pp. 12760\u201312774 (2022)","DOI":"10.52202\/068431-0927"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Daxberger, E., Makarova, A., Turchetta, M., Krause, A.: Mixed-variable Bayesian optimization (2019). arXiv preprint arXiv:1907.01329","DOI":"10.24963\/ijcai.2020\/365"},{"key":"8_CR7","unstructured":"Dupont, E., Goli\u0144ski, A., Alizadeh, M., Teh, Y.W., Doucet, A.: COIN: COmpression with implicit neural representations (2021). arXiv:2103.03123 arXiv preprint"},{"issue":"55","key":"8_CR8","first-page":"1","volume":"20","author":"T Elsken","year":"2019","unstructured":"Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: a survey. J. Mach. Learn. Res. 20(55), 1\u201321 (2019)","journal-title":"J. Mach. Learn. Res."},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Feurer, M., Hutter, F.: Hyperparameter optimization. In: Automated Machine Learning: Methods, Systems, Challenges, pp. 3\u201333. Springer International Publishing, Cham (2019)","DOI":"10.1007\/978-3-030-05318-5_1"},{"key":"8_CR10","unstructured":"Franzen, R.: Kodak lossless true color image suite (1999)"},{"issue":"5","key":"8_CR11","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","volume":"2","author":"K Hornik","year":"1989","unstructured":"Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359\u2013366 (1989)","journal-title":"Neural Netw."},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Jayasundara, D., Zhao, H., Labate, D., Patel, V.M.: MIRE: matched implicit neural representations. In: 2025 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8279\u20138288 (2025)","DOI":"10.1109\/CVPR52734.2025.00775"},{"issue":"4","key":"8_CR13","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1023\/A:1008306431147","volume":"13","author":"DR Jones","year":"1998","unstructured":"Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4), 455\u2013492 (1998)","journal-title":"J. Global Optim."},{"key":"8_CR14","unstructured":"Kania, A., Mihajlovic, M., Prokudin, S., Tabor, J., Spurek, P.: FreSh: frequency shifting for accelerated neural representation learning (2024). arXiv:2410.05050 arXiv preprint"},{"key":"8_CR15","unstructured":"Konakovic Lukovic, M., Tian, Y., Matusik, W.: Diversity-guided multi-objective Bayesian optimization with batch evaluations. In: Advances in Neural Information Processing Systems, vol. 33 (2020)"},{"key":"8_CR16","unstructured":"Levoy, M., et\u00a0al.: The Stanford 3D scanning repository (2000). http:\/\/graphics.stanford.edu\/data\/3Dscanrep. Accessed 30 June 2026"},{"key":"8_CR17","unstructured":"Li, Y., Mustikovela, S., Tewari, A., Thies, J., Wu, T., Zollhofer, M.: Neural fields in visual computing and beyond. In: ACM SIGGRAPH 2021 Courses, pp. 1\u2013100 (2021)"},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: FINER: flexible spectral-bias tuning in implicit neural representation by variable-periodic activation functions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2713\u20132722 (2024)","DOI":"10.1109\/CVPR52733.2024.00262"},{"key":"8_CR19","unstructured":"Liu, Z., et al.: KAN: Kolmogorov-Arnold networks. In: International Conference on Learning Representations, vol. 2025, pp. 70367\u201370413 (2025)"},{"key":"8_CR20","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization (2017). arXiv:1711.05101 arXiv preprint"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3D reconstruction in function space. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4460\u20134470 (2019)","DOI":"10.1109\/CVPR.2019.00459"},{"issue":"1","key":"8_CR22","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1145\/3503250","volume":"65","author":"B Mildenhall","year":"2021","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65(1), 99\u2013106 (2021)","journal-title":"Commun. ACM"},{"key":"8_CR23","doi-asserted-by":"crossref","unstructured":"Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 165\u2013174 (2019)","DOI":"10.1109\/CVPR.2019.00025"},{"key":"8_CR24","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"8_CR25","unstructured":"Rahaman, N., et al.: On the spectral bias of neural networks. In: International Conference on Machine Learning, pp. 5301\u20135310. PMLR (2019)"},{"key":"8_CR26","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","volume":"378","author":"M Raissi","year":"2019","unstructured":"Raissi, M., Perdikaris, P., Karniadakis, G.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686\u2013707 (2019)","journal-title":"J. Comput. Phys."},{"key":"8_CR27","doi-asserted-by":"crossref","unstructured":"Ramasinghe, S., Lucey, S.: Beyond periodicity: towards a unifying framework for activations in coordinate-MLPs. In: European Conference on Computer Vision, pp. 142\u2013158. Springer (2022)","DOI":"10.1007\/978-3-031-19827-4_9"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Saragadam, V., LeJeune, D., Tan, J., Balakrishnan, G., Veeraraghavan, A., Baraniuk, R.G.: WIRE: wavelet implicit neural representations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18507\u201318516 (2023)","DOI":"10.1109\/CVPR52729.2023.01775"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Seeger, M.: Gaussian Processes for Machine Learning, vol. 14. World Scientific (2004)","DOI":"10.1142\/S0129065704001899"},{"key":"8_CR30","unstructured":"Shah, K., Sitawarin, C.: SPDER: semiperiodic damping-enabled object representation (2023). arXiv:2306.15242 arXiv preprint"},{"issue":"1","key":"8_CR31","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/JPROC.2015.2494218","volume":"104","author":"B Shahriari","year":"2015","unstructured":"Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., De Freitas, N.: Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104(1), 148\u2013175 (2015)","journal-title":"Proc. IEEE"},{"issue":"11","key":"8_CR32","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s00158-022-03382-y","volume":"65","author":"HM Sheikh","year":"2022","unstructured":"Sheikh, H.M., Marcus, P.S.: Bayesian optimization for mixed-variable, multi-objective problems. Struct. Multidiscip. Optim. 65(11), 331 (2022). https:\/\/doi.org\/10.1007\/s00158-022-03382-y","journal-title":"Struct. Multidiscip. Optim."},{"key":"8_CR33","unstructured":"Shi, K., Chen, H., Zhang, L., Gu, S.: Inductive gradient adjustment for spectral bias in implicit neural representations (2024). arXiv:2410.13271 arXiv preprint"},{"key":"8_CR34","doi-asserted-by":"crossref","unstructured":"Shi, K., Zhou, X., Gu, S.: Improved implicit neural representation with Fourier reparameterized training. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 25985\u201325994 (2024)","DOI":"10.1109\/CVPR52733.2024.02455"},{"key":"8_CR35","unstructured":"Sitzmann, V., Chan, E., Tucker, R., Snavely, N., Wetzstein, G.: MetaSDF: meta-learning signed distance functions. In: Advances in Neural Information Processing Systems, vol. 33, pp. 10136\u201310147 (2020)"},{"key":"8_CR36","unstructured":"Sitzmann, V., Martel, J., Bergman, A., Lindell, D., Wetzstein, G.: Implicit neural representations with periodic activation functions. In: Advances in Neural Information Processing Systems, vol. 33, pp. 7462\u20137473 (2020)"},{"key":"8_CR37","unstructured":"Sitzmann, V., Zollh\u00f6fer, M., Wetzstein, G.: Scene representation networks: continuous 3D-structure-aware neural scene representations. In: Advances in Neural Information Processing Systems, p. 32 (2019)"},{"key":"8_CR38","unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems, p. 25 (2012)"},{"key":"8_CR39","doi-asserted-by":"crossref","unstructured":"Tancik, M., et al.: Learned initializations for optimizing coordinate-based neural representations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2846\u20132855 (2021)","DOI":"10.1109\/CVPR46437.2021.00287"},{"key":"8_CR40","unstructured":"Tancik, M., et al.: Fourier features let networks learn high frequency functions in low dimensional domains. In: Advances in Neural Information Processing Systems, vol. 33, pp. 7537\u20137547 (2020)"},{"key":"8_CR41","unstructured":"Waczynska, J., Szczepanik, T., Borycki, P., Tadeja, S., Bohn\u00e9, T., Spurek, P.: MiraGe: editable 2D images using gaussian splatting. In: Forty-Second International Conference on Machine Learning (2025). https:\/\/openreview.net\/forum?id=PQYJMq39gI. Accessed 30 June 2026"},{"key":"8_CR42","unstructured":"Wilson, J., Hutter, F., Deisenroth, M.: Maximizing acquisition functions for Bayesian optimization. In: Advances in Neural Information Processing Systems, p. 31 (2018)"},{"key":"8_CR43","unstructured":"Xie, Y., et\u00a0al.: Neural fields for visual computing. In: ACM SIGGRAPH 2022 Courses, pp. 1\u2013100 (2022)"},{"key":"8_CR44","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: GaussianImage: 1000 fps image representation and compression by 2D Gaussian splatting. In: European Conference on Computer Vision, pp. 327\u2013345. Springer (2024)","DOI":"10.1007\/978-3-031-72673-6_18"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-36969-7_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T22:21:31Z","timestamp":1789251691000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-36969-7_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032369680","9783032369697"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-36969-7_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"13 September 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malm\u00f6","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}