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Neural computation , Vol. 15 , 6 ( 2003 ), 1373--1396. Mikhail Belkin and Partha Niyogi. 2003. Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation, Vol. 15, 6 (2003), 1373--1396."},{"key":"e_1_3_2_1_4_1","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell etal 2020. Language models are few-shot learners. Advances in neural information processing systems Vol. 33 (2020) 1877--1901.  Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems Vol. 33 (2020) 1877--1901."},{"key":"e_1_3_2_1_5_1","volume-title":"Multidimensional scaling. Measurement, judgment and decision making","author":"Douglas Carroll J","year":"1998","unstructured":"J Douglas Carroll and Phipps Arabie . 1998. Multidimensional scaling. Measurement, judgment and decision making ( 1998 ), 179--250. J Douglas Carroll and Phipps Arabie. 1998. Multidimensional scaling. Measurement, judgment and decision making (1998), 179--250."},{"key":"e_1_3_2_1_6_1","volume-title":"Deep manifold learning combined with convolutional neural networks for action recognition","author":"Chen Xin","year":"2017","unstructured":"Xin Chen , Jian Weng , Wei Lu , Jiaming Xu , and Jiasi Weng . 2017. Deep manifold learning combined with convolutional neural networks for action recognition . IEEE transactions on neural networks and learning systems, Vol. 29 , 9 ( 2017 ), 3938--3952. Xin Chen, Jian Weng, Wei Lu, Jiaming Xu, and Jiasi Weng. 2017. Deep manifold learning combined with convolutional neural networks for action recognition. IEEE transactions on neural networks and learning systems, Vol. 29, 9 (2017), 3938--3952."},{"key":"e_1_3_2_1_7_1","volume-title":"Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. Advances in neural information processing systems","author":"Dauphin Yann N","year":"2014","unstructured":"Yann N Dauphin , Razvan Pascanu , Caglar Gulcehre , Kyunghyun Cho , Surya Ganguli , and Yoshua Bengio . 2014. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. Advances in neural information processing systems , Vol. 27 ( 2014 ). Yann N Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio. 2014. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. Advances in neural information processing systems, Vol. 27 (2014)."},{"key":"e_1_3_2_1_8_1","unstructured":"Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly etal 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).  Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly et al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)."},{"key":"e_1_3_2_1_9_1","volume-title":"International conference on machine learning. PMLR, 1309--1318","author":"Draxler Felix","year":"2018","unstructured":"Felix Draxler , Kambis Veschgini , Manfred Salmhofer , and Fred Hamprecht . 2018 . Essentially no barriers in neural network energy landscape . In International conference on machine learning. PMLR, 1309--1318 . Felix Draxler, Kambis Veschgini, Manfred Salmhofer, and Fred Hamprecht. 2018. Essentially no barriers in neural network energy landscape. In International conference on machine learning. PMLR, 1309--1318."},{"key":"e_1_3_2_1_10_1","volume-title":"Proceedings of the 19th ACM international conference on Information and knowledge management. 419--428","author":"Sakellaridi Sophia","year":"2010","unstructured":"Haw-ren Fang, Sophia Sakellaridi , and Yousef Saad . 2010 . Multilevel manifold learning with application to spectral clustering . In Proceedings of the 19th ACM international conference on Information and knowledge management. 419--428 . Haw-ren Fang, Sophia Sakellaridi, and Yousef Saad. 2010. Multilevel manifold learning with application to spectral clustering. In Proceedings of the 19th ACM international conference on Information and knowledge management. 419--428."},{"key":"e_1_3_2_1_11_1","volume-title":"Topology and geometry of half-rectified network optimization. arXiv preprint arXiv:1611.01540","author":"Daniel Freeman C","year":"2016","unstructured":"C Daniel Freeman and Joan Bruna . 2016. Topology and geometry of half-rectified network optimization. arXiv preprint arXiv:1611.01540 ( 2016 ). C Daniel Freeman and Joan Bruna. 2016. Topology and geometry of half-rectified network optimization. arXiv preprint arXiv:1611.01540 (2016)."},{"key":"e_1_3_2_1_12_1","volume-title":"Qualitatively characterizing neural network optimization problems. arXiv preprint arXiv:1412.6544","author":"Goodfellow Ian J","year":"2014","unstructured":"Ian J Goodfellow , Oriol Vinyals , and Andrew M Saxe . 2014. Qualitatively characterizing neural network optimization problems. arXiv preprint arXiv:1412.6544 ( 2014 ). Ian J Goodfellow, Oriol Vinyals, and Andrew M Saxe. 2014. Qualitatively characterizing neural network optimization problems. arXiv preprint arXiv:1412.6544 (2014)."},{"key":"e_1_3_2_1_13_1","first-page":"12140","article-title":"Improving neural network training in low dimensional random bases","volume":"33","author":"Gressmann Frithjof","year":"2020","unstructured":"Frithjof Gressmann , Zach Eaton-Rosen , and Carlo Luschi . 2020 . Improving neural network training in low dimensional random bases . Advances in Neural Information Processing Systems , Vol. 33 (2020), 12140 -- 12150 . Frithjof Gressmann, Zach Eaton-Rosen, and Carlo Luschi. 2020. Improving neural network training in low dimensional random bases. Advances in Neural Information Processing Systems, Vol. 33 (2020), 12140--12150.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_14_1","volume-title":"Gradient descent happens in a tiny subspace. arXiv preprint arXiv:1812.04754","author":"Gur-Ari Guy","year":"2018","unstructured":"Guy Gur-Ari , Daniel A Roberts , and Ethan Dyer . 2018. Gradient descent happens in a tiny subspace. arXiv preprint arXiv:1812.04754 ( 2018 ). Guy Gur-Ari, Daniel A Roberts, and Ethan Dyer. 2018. Gradient descent happens in a tiny subspace. arXiv preprint arXiv:1812.04754 (2018)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_48"},{"key":"e_1_3_2_1_17_1","volume-title":"Multimodal face-pose estimation with multitask manifold deep learning","author":"Hong Chaoqun","year":"2018","unstructured":"Chaoqun Hong , Jun Yu , Jian Zhang , Xiongnan Jin , and Kyong-Ho Lee . 2018. Multimodal face-pose estimation with multitask manifold deep learning . IEEE transactions on industrial informatics, Vol. 15 , 7 ( 2018 ), 3952--3961. Chaoqun Hong, Jun Yu, Jian Zhang, Xiongnan Jin, and Kyong-Ho Lee. 2018. Multimodal face-pose estimation with multitask manifold deep learning. IEEE transactions on industrial informatics, Vol. 15, 7 (2018), 3952--3961."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_2_1_19_1","unstructured":"Alex Krizhevsky Geoffrey Hinton etal 2009. Learning multiple layers of features from tiny images. (2009).  Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_20_1","volume-title":"International Conference on Machine Learning. PMLR, 12282--12351","author":"Lee Jongmin","year":"2022","unstructured":"Jongmin Lee , Joo Young Choi , Ernest K Ryu , and Albert No . 2022 . Neural tangent kernel analysis of deep narrow neural networks . In International Conference on Machine Learning. PMLR, 12282--12351 . Jongmin Lee, Joo Young Choi, Ernest K Ryu, and Albert No. 2022. Neural tangent kernel analysis of deep narrow neural networks. In International Conference on Machine Learning. 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Train big, then compress: Rethinking model size for efficient training and inference of transformers . In International Conference on machine learning. PMLR, 5958--5968 . Zhuohan Li, Eric Wallace, Sheng Shen, Kevin Lin, Kurt Keutzer, Dan Klein, and Joey Gonzalez. 2020. Train big, then compress: Rethinking model size for efficient training and inference of transformers. In International Conference on machine learning. PMLR, 5958--5968."},{"key":"e_1_3_2_1_23_1","volume-title":"On the limited memory BFGS method for large scale optimization. Mathematical programming","author":"Liu Dong C","year":"1989","unstructured":"Dong C Liu and Jorge Nocedal . 1989. On the limited memory BFGS method for large scale optimization. Mathematical programming , Vol. 45 , 1--3 ( 1989 ), 503--528. Dong C Liu and Jorge Nocedal. 1989. On the limited memory BFGS method for large scale optimization. Mathematical programming, Vol. 45, 1--3 (1989), 503--528."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557597"},{"key":"e_1_3_2_1_25_1","volume-title":"International Conference on Machine Learning. PMLR, 6426--6436","author":"Lu Yiping","year":"2020","unstructured":"Yiping Lu , Chao Ma , Yulong Lu , Jianfeng Lu , and Lexing Ying . 2020 . A mean field analysis of deep resnet and beyond: Towards provably optimization via overparameterization from depth . In International Conference on Machine Learning. PMLR, 6426--6436 . Yiping Lu, Chao Ma, Yulong Lu, Jianfeng Lu, and Lexing Ying. 2020. A mean field analysis of deep resnet and beyond: Towards provably optimization via overparameterization from depth. In International Conference on Machine Learning. PMLR, 6426--6436."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2696365"},{"key":"e_1_3_2_1_27_1","unstructured":"H. Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Artificial intelligence and statistics. 1273--1282.  H. Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Artificial intelligence and statistics. 1273--1282."},{"key":"e_1_3_2_1_28_1","volume-title":"International Conference on Machine Learning. PMLR, 7588--7598","author":"Mellor Joe","year":"2021","unstructured":"Joe Mellor , Jack Turner , Amos Storkey , and Elliot J Crowley . 2021 . Neural architecture search without training . In International Conference on Machine Learning. PMLR, 7588--7598 . Joe Mellor, Jack Turner, Amos Storkey, and Elliot J Crowley. 2021. Neural architecture search without training. In International Conference on Machine Learning. PMLR, 7588--7598."},{"key":"e_1_3_2_1_29_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , 2019 . Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems , Vol. 32 (2019). Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_30_1","volume-title":"Multimedia big data analytics: A survey. ACM computing surveys (CSUR)","author":"Pouyanfar Samira","year":"2018","unstructured":"Samira Pouyanfar , Yimin Yang , Shu-Ching Chen , Mei-Ling Shyu , and SS Iyengar . 2018. Multimedia big data analytics: A survey. ACM computing surveys (CSUR) , Vol. 51 , 1 ( 2018 ), 1--34. Samira Pouyanfar, Yimin Yang, Shu-Ching Chen, Mei-Ling Shyu, and SS Iyengar. 2018. Multimedia big data analytics: A survey. ACM computing surveys (CSUR), Vol. 51, 1 (2018), 1--34."},{"key":"e_1_3_2_1_31_1","volume-title":"Nonlinear dimensionality reduction by locally linear embedding. science","author":"Roweis Sam T","year":"2000","unstructured":"Sam T Roweis and Lawrence K Saul . 2000. Nonlinear dimensionality reduction by locally linear embedding. science , Vol. 290 , 5500 ( 2000 ), 2323--2326. Sam T Roweis and Lawrence K Saul. 2000. Nonlinear dimensionality reduction by locally linear embedding. science, Vol. 290, 5500 (2000), 2323--2326."},{"key":"e_1_3_2_1_32_1","volume-title":"An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747","author":"Ruder Sebastian","year":"2016","unstructured":"Sebastian Ruder . 2016. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 ( 2016 ). Sebastian Ruder. 2016. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)."},{"key":"e_1_3_2_1_33_1","volume-title":"International Conference on Machine Learning. PMLR, 774--782","author":"Safran Itay","year":"2016","unstructured":"Itay Safran and Ohad Shamir . 2016 . On the quality of the initial basin in overspecified neural networks . In International Conference on Machine Learning. PMLR, 774--782 . Itay Safran and Ohad Shamir. 2016. On the quality of the initial basin in overspecified neural networks. In International Conference on Machine Learning. PMLR, 774--782."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2008.4587670"},{"key":"e_1_3_2_1_35_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N Gomez , \u0141ukasz Kaiser , and Illia Polosukhin . 2017. Attention is all you need. Advances in neural information processing systems , Vol. 30 ( 2017 ). Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_36_1","volume-title":"Nature","volume":"575","author":"Vinyals Oriol","year":"2019","unstructured":"Oriol Vinyals , Igor Babuschkin , Wojciech M Czarnecki , Micha\u00ebl Mathieu , Andrew Dudzik , Junyoung Chung , David H Choi , Richard Powell , Timo Ewalds , Petko Georgiev , 2019 . Grandmaster level in StarCraft II using multi-agent reinforcement learning . Nature , Vol. 575 , 7782 (2019), 350--354. Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Micha\u00ebl Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al. 2019. Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, Vol. 575, 7782 (2019), 350--354."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358091"},{"key":"e_1_3_2_1_38_1","volume-title":"Data mining with big data","author":"Wu Xindong","year":"2013","unstructured":"Xindong Wu , Xingquan Zhu , Gong-Qing Wu , and Wei Ding . 2013. Data mining with big data . IEEE transactions on knowledge and data engineering, Vol. 26 , 1 ( 2013 ), 97--107. Xindong Wu, Xingquan Zhu, Gong-Qing Wu, and Wei Ding. 2013. Data mining with big data. IEEE transactions on knowledge and data engineering, Vol. 26, 1 (2013), 97--107."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00748"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412740"},{"key":"e_1_3_2_1_41_1","volume-title":"Hyper-parameter optimization: A review of algorithms and applications. arXiv preprint arXiv:2003.05689","author":"Yu Tong","year":"2020","unstructured":"Tong Yu and Hong Zhu . 2020. Hyper-parameter optimization: A review of algorithms and applications. arXiv preprint arXiv:2003.05689 ( 2020 ). Tong Yu and Hong Zhu. 2020. Hyper-parameter optimization: A review of algorithms and applications. arXiv preprint arXiv:2003.05689 (2020)."},{"key":"e_1_3_2_1_42_1","volume-title":"Dialogpt: Large-scale generative pre-training for conversational response generation. arXiv preprint arXiv:1911.00536","author":"Zhang Yizhe","year":"2019","unstructured":"Yizhe Zhang , Siqi Sun , Michel Galley , Yen-Chun Chen , Chris Brockett , Xiang Gao , Jianfeng Gao , Jingjing Liu , and Bill Dolan . 2019 . Dialogpt: Large-scale generative pre-training for conversational response generation. arXiv preprint arXiv:1911.00536 (2019). Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, and Bill Dolan. 2019. Dialogpt: Large-scale generative pre-training for conversational response generation. arXiv preprint arXiv:1911.00536 (2019)."},{"key":"e_1_3_2_1_43_1","series-title":"SIAM journal on scientific computing","volume-title":"Principal manifolds and nonlinear dimensionality reduction via tangent space alignment","author":"Zhang Zhenyue","year":"2004","unstructured":"Zhenyue Zhang and Hongyuan Zha . 2004. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment . SIAM journal on scientific computing , Vol. 26 , 1 ( 2004 ), 313--338. Zhenyue Zhang and Hongyuan Zha. 2004. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. 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