{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T09:59:06Z","timestamp":1730195946410,"version":"3.28.0"},"reference-count":55,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T00:00:00Z","timestamp":1664236800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T00:00:00Z","timestamp":1664236800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,27]]},"DOI":"10.1109\/allerton49937.2022.9929375","type":"proceedings-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T21:34:30Z","timestamp":1667597670000},"page":"1-8","source":"Crossref","is-referenced-by-count":0,"title":["A Kernel Analysis of Feature Learning in Deep Neural Networks"],"prefix":"10.1109","author":[{"given":"Abdulkadir","family":"Canatar","sequence":"first","affiliation":[{"name":"Flatiron Institute,Center for Computational Neuroscience,New York City,NY,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cengiz","family":"Pehlevan","sequence":"additional","affiliation":[{"name":"John A. Paulson School of Engineering and Applied Sciences and Center for Brain Science Harvard University,Cambridge,MA,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","article-title":"On the equivalence between canonical correlation analysis and orthonormalized partial least squares","author":"sun","year":"0","journal-title":"Twenty-First International Joint Conference on Artificial Intelligence"},{"key":"ref38","first-page":"795","article-title":"Algorithms for learning kernels based on centered alignment","volume":"13","author":"cortes","year":"2012","journal-title":"The Journal of Machine Learning Research"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/BF02306029"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.2307\/2333955"},{"key":"ref31","volume":"2","author":"williams","year":"2006","journal-title":"Gaussian Processes for Machine Learning"},{"journal-title":"Understanding intermediate layers using linear classifier probes","year":"2016","author":"alain","key":"ref30"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/11564089_7"},{"key":"ref36","first-page":"1","article-title":"Kernel independent component analysis","volume":"3","author":"bach","year":"2002","journal-title":"Journal of Machine Learning Research"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/0003-2670(86)80028-9"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1162\/0899766042321814"},{"journal-title":"Deep learning versus kernel learning an empirical study of loss landscape geometry and the time evolution of the neural tangent kernel","year":"2020","author":"fort","key":"ref28"},{"key":"ref27","article-title":"Neural networks as kernel learners: The silent alignment effect","author":"atanasov","year":"2022","journal-title":"International Conference on Learning Representations"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-33486-6_8"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1201\/b10620"},{"key":"ref20","article-title":"Asymptotics of representation learning in finite bayesian neural networks","volume":"34","author":"zavatone-veth","year":"2021","journal-title":"Advances in neural information processing systems"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.11.031059"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.104.064301"},{"key":"ref24","article-title":"Exact marginal prior distributions of finite bayesian neural networks","volume":"34","author":"zavatone-veth","year":"2021","journal-title":"Advances in neural information processing systems"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1017\/9781009023405"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2015509117"},{"key":"ref25","article-title":"The large learning rate phase of deep learning: the catapult mechanism","author":"lewkowycz","year":"2020","journal-title":"ArXiv Preprint"},{"journal-title":"ffcv","year":"2022","author":"leclerc","key":"ref50"},{"journal-title":"Limitations of the ntk for understanding generalization in deep learning","year":"2022","author":"vyas","key":"ref51"},{"journal-title":"Implicit regularization via neural feature alignment","year":"2020","author":"baratin","key":"ref55"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"journal-title":"Fast finite width neural tangent kernel","year":"2022","author":"novak","key":"ref53"},{"journal-title":"Neural tangents Fast and easy infinite neural networks in python","year":"2019","author":"novak","key":"ref52"},{"journal-title":"Kernel alignment risk estimator Risk prediction from training data","year":"2020","author":"jacot","key":"ref10"},{"journal-title":"Similarity of neural network representations revisited","year":"2019","author":"kornblith","key":"ref40"},{"journal-title":"How Neural Networks Extrapolate From Feedforward to Graph Neural Networks","year":"2020","author":"xu","key":"ref11"},{"journal-title":"Understanding layer-wise contributions in deep neural networks through spectral analysis","year":"2021","author":"dandi","key":"ref12"},{"journal-title":"A theory of the inductive bias and generalization of kernel regression and wide neural networks","year":"2021","author":"simon","key":"ref13"},{"key":"ref14","first-page":"11727","article-title":"Tensor programs iv: Feature learning in infinite-width neural networks","author":"yang","year":"2021","journal-title":"International Conference on Machine Learning"},{"key":"ref15","article-title":"Self-consistent dynamical field theory of kernel evolution in wide neural networks","author":"christmann","year":"2022","journal-title":"Advances in neural information processing systems"},{"key":"ref16","article-title":"Asymptotics of wide networks from feynman diagrams","author":"dyer","year":"2019","journal-title":"International Conference on Learning Representations"},{"key":"ref17","first-page":"165","article-title":"Non-gaussian processes and neural networks at finite widths","author":"yaida","year":"0","journal-title":"Mathematical and Scientific Machine Learning"},{"key":"ref18","first-page":"4542","article-title":"Dynamics of deep neural networks and neural tangent hierarchy","author":"huang","year":"2020","journal-title":"International Conference on Machine Learning"},{"key":"ref19","article-title":"Finite depth and width corrections to the neural tangent kernel","author":"hanin","year":"2019","journal-title":"International Conference on Learning Representations"},{"key":"ref4","article-title":"On exact computation with an infinitely wide neural net","volume":"32","author":"arora","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref3","first-page":"8571","article-title":"Neural tangent kernel: Convergence and generalization in neural networks","author":"jacot","year":"2018","journal-title":"Advances in neural information processing systems"},{"journal-title":"On lazy training in differentiable programming","year":"2018","author":"chizat","key":"ref6"},{"key":"ref5","article-title":"Wide neural networks of any depth evolve as linear models under gradient descent","volume":"32","author":"lee","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref8","article-title":"Spectrum dependent learning curves in kernel regression and wide neural networks","author":"bordelon","year":"0","journal-title":"Proceedings of the 37th International Conference on Machine Learning"},{"key":"ref49","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009","journal-title":"Tech Rep"},{"journal-title":"Towards understanding the spectral bias of deep learning","year":"2019","author":"cao","key":"ref7"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-23103-1"},{"journal-title":"Bandwidth enables generalization in quantum kernel models","year":"2022","author":"canatar","key":"ref46"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.1909.0016"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1142\/S0219530506000838"},{"journal-title":"Insights on representational similarity in neural networks with canonical correlation","year":"2018","author":"morcos","key":"ref42"},{"journal-title":"Grounding representation similarity with statistical testing","year":"2021","author":"ding","key":"ref41"},{"journal-title":"SVCCA Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability","year":"2017","author":"raghu","key":"ref44"},{"journal-title":"Fitnets Hints for thin deep nets","year":"2014","author":"romero","key":"ref43"}],"event":{"name":"2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","start":{"date-parts":[[2022,9,27]]},"location":"Monticello, IL, USA","end":{"date-parts":[[2022,9,30]]}},"container-title":["2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9929313\/9929314\/09929375.pdf?arnumber=9929375","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T20:25:48Z","timestamp":1669667148000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9929375\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,27]]},"references-count":55,"URL":"https:\/\/doi.org\/10.1109\/allerton49937.2022.9929375","relation":{},"subject":[],"published":{"date-parts":[[2022,9,27]]}}}