{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T12:56:08Z","timestamp":1761396968795,"version":"3.28.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"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":["J Supercomput"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s11227-024-06701-w","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T12:57:08Z","timestamp":1732539428000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Learning to learn: a lightweight meta-learning approach with indispensable connections"],"prefix":"10.1007","volume":"81","author":[{"given":"Sambhavi","family":"Tiwari","sequence":"first","affiliation":[]},{"given":"Manas","family":"Gogoi","sequence":"additional","affiliation":[]},{"given":"Shekhar","family":"Verma","sequence":"additional","affiliation":[]},{"given":"Krishna Pratap","family":"Singh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,25]]},"reference":[{"key":"6701_CR1","unstructured":"Koch G, Zemel R, Salakhutdinov R, et\u00a0al (2015) Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol. 2. Lille"},{"key":"6701_CR2","doi-asserted-by":"crossref","unstructured":"Sung F, Yang Y, Zhang L, Xiang T, Torr PH, Hospedales TM (2018) Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199\u20131208","DOI":"10.1109\/CVPR.2018.00131"},{"key":"6701_CR3","unstructured":"Vinyals O, Blundell C, Lillicrap T, Wierstra D, et al (2016) Matching networks for one shot learning. Advances in neural information processing systems 29"},{"key":"6701_CR4","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. Advances in neural information processing systems 30"},{"key":"6701_CR5","unstructured":"Gogoi M, Tiwari S, Verma S (2012) Adaptive prototypical networks. arXiv preprint arXiv:2211.12479"},{"key":"6701_CR6","unstructured":"Santoro A, Bartunov S, Botvinick M, Wierstra D, Lillicrap T (2016) Meta-learning with memory-augmented neural networks. In: International Conference on Machine Learning, pp. 1842\u20131850. PMLR"},{"key":"6701_CR7","unstructured":"Munkhdalai T, Yu H (2017) Meta networks. In: International Conference on Machine Learning, pp. 2554\u20132563. PMLR"},{"key":"6701_CR8","doi-asserted-by":"crossref","unstructured":"Tiwari S, Gogoi M, Verma S, Singh KP (2022) Meta-learning with hopfield neural network. In: 2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), pp. 1\u20135. IEEE","DOI":"10.1109\/UPCON56432.2022.9986399"},{"key":"6701_CR9","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126\u20131135. PMLR"},{"key":"6701_CR10","unstructured":"Nichol A, Achiam J, Schulman J (2018) On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999"},{"key":"6701_CR11","unstructured":"Raghu A, Raghu M, Bengio S, Vinyals O (2019) Rapid learning or feature reuse? towards understanding the effectiveness of maml. arXiv preprint arXiv:1909.09157"},{"key":"6701_CR12","doi-asserted-by":"publisher","unstructured":"Tiwari S, Gogoi M, Verma S, Singh KP (2022) Meta-learning with hopfield neural network. In: 2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), pp. 1\u20135. https:\/\/doi.org\/10.1109\/UPCON56432.2022.9986399","DOI":"10.1109\/UPCON56432.2022.9986399"},{"issue":"10s","key":"6701_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3502287","volume":"54","author":"MA Bansal","year":"2022","unstructured":"Bansal MA, Sharma DR, Kathuria DM (2022) A systematic review on data scarcity problem in deep learning: solution and applications. ACM Computing Surveys (Csur) 54(10s):1\u201329","journal-title":"ACM Computing Surveys (Csur)"},{"key":"6701_CR14","unstructured":"Ravi S, Larochelle H (2017) Optimization as a model for few-shot learning. 5th int. In: Conf. Learn. Represent. ICLR 2017-Conf. Track Proc. 1\u201311"},{"key":"6701_CR15","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.neucom.2023.02.051","volume":"533","author":"I Rehman","year":"2023","unstructured":"Rehman I, Ali W, Jan Z, Ali Z, Xu H, Shao J (2023) Caml: Contextual augmented meta-learning for cold-start recommendation. Neurocomputing 533:178\u2013190","journal-title":"Neurocomputing"},{"key":"6701_CR16","unstructured":"Antoniou A, Edwards H, Storkey A (2018) How to train your maml. arXiv preprint arXiv:1810.09502"},{"issue":"3","key":"6701_CR17","first-page":"4","volume":"2","author":"A Nichol","year":"2018","unstructured":"Nichol A, Schulman J (2018) Reptile: a scalable metalearning algorithm. 2(3):4 arXiv:1803.02999","journal-title":"Reptile: a scalable metalearning algorithm."},{"key":"6701_CR18","doi-asserted-by":"crossref","unstructured":"Elsken T, Staffler B, Metzen JH, Hutter F (2020) Meta-learning of neural architectures for few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12365\u201312375","DOI":"10.1109\/CVPR42600.2020.01238"},{"key":"6701_CR19","unstructured":"Oh J, Yoo H, Kim C, Yun S-Y (2020) Boil: Towards representation change for few-shot learning. arXiv preprint arXiv:2008.08882"},{"key":"6701_CR20","unstructured":"Hou L, Kwok JT (2018) Loss-aware weight quantization of deep networks. arXiv preprint arXiv:1802.08635"},{"key":"6701_CR21","doi-asserted-by":"crossref","unstructured":"Zhuang B, Shen C, Tan M, Liu L, Reid I (2019) Structured binary neural networks for accurate image classification and semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 413\u2013422","DOI":"10.1109\/CVPR.2019.00050"},{"key":"6701_CR22","doi-asserted-by":"crossref","unstructured":"Zhou A, Yao A, Wang K, Chen Y (2018) Explicit loss-error-aware quantization for low-bit deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9426\u20139435","DOI":"10.1109\/CVPR.2018.00982"},{"key":"6701_CR23","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"6701_CR24","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848\u20136856","DOI":"10.1109\/CVPR.2018.00716"},{"key":"6701_CR25","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and $$<0.5$$ mb model size. arXiv preprint arXiv:1602.07360"},{"key":"6701_CR26","doi-asserted-by":"crossref","unstructured":"He Y, Zhang X, Sun J (2017) Channel pruning for accelerating very deep neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1389\u20131397","DOI":"10.1109\/ICCV.2017.155"},{"key":"6701_CR27","unstructured":"Han S, Mao H, Dally WJ (2015) Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149"},{"key":"6701_CR28","doi-asserted-by":"crossref","unstructured":"Tian H, Liu B, Yuan X-T, Liu Q (2020) Meta-learning with network pruning. In: European Conference on Computer Vision, pp. 675\u2013700. Springer","DOI":"10.1007\/978-3-030-58529-7_40"},{"key":"6701_CR29","unstructured":"Han S, Pool J, Narang S, Mao H, Gong E, Tang S, Elsen E, Vajda P, Paluri M, Tran J, et al (2016) Dsd: Dense-sparse-dense training for deep neural networks. arXiv preprint arXiv:1607.04381"},{"key":"6701_CR30","unstructured":"Jin X, Yuan X, Feng J, Yan S (2016) Training skinny deep neural networks with iterative hard thresholding methods. arXiv preprint arXiv:1607.05423"},{"key":"6701_CR31","doi-asserted-by":"crossref","unstructured":"Liu Z, Mu H, Zhang X, Guo Z, Yang X, Cheng K-T, Sun J (2019) Metapruning: Meta learning for automatic neural network channel pruning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3296\u20133305","DOI":"10.1109\/ICCV.2019.00339"},{"key":"6701_CR32","unstructured":"Dong X, Chen S, Pan S (2017) Learning to prune deep neural networks via layer-wise optimal brain surgeon. Advances in Neural Information Processing Systems 30"},{"issue":"6266","key":"6701_CR33","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1126\/science.aab3050","volume":"350","author":"BM Lake","year":"2015","unstructured":"Lake BM, Salakhutdinov R, Tenenbaum JB (2015) Human-level concept learning through probabilistic program induction. Science 350(6266):1332\u20131338","journal-title":"Science"},{"key":"6701_CR34","unstructured":"Oreshkin B, Rodr\u00edguez\u00a0L\u00f3pez P, Lacoste A (2018) Tadam: Task dependent adaptive metric for improved few-shot learning. Advances in neural information processing systems 31"},{"key":"6701_CR35","unstructured":"Krizhevsky A, Hinton G, et al (2009) Learning multiple layers of features from tiny images"},{"key":"6701_CR36","unstructured":"Deleu T, W\u00fcrfl T, Samiei M, Cohen JP, Bengio Y (2019) Torchmeta: A meta-learning library for pytorch. arXiv preprint arXiv:1909.06576"},{"key":"6701_CR37","unstructured":"Mishra N, Rohaninejad M, Chen X, Abbeel P (2017) A simple neural attentive meta-learner. arXiv preprint arXiv:1707.03141"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06701-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06701-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06701-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T13:07:52Z","timestamp":1732540072000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06701-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,25]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["6701"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06701-w","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,25]]},"assertion":[{"value":"7 November 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"207"}}