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On the Expressive Power of Deep Learning: A Tensor Analysis.

, , and . COLT, volume 49 of JMLR Workshop and Conference Proceedings, page 698-728. JMLR.org, (2016)

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On the Expressive Power of Overlapping Architectures of Deep Learning, and . (2017)cite arxiv:1703.02065Comment: Published as a conference paper at ICLR 2018.Bridging Many-Body Quantum Physics and Deep Learning via Tensor Networks, , , and . (2018)cite arxiv:1803.09780.Image Super-resolution Inspired Electron Density Prediction., , , and . CoRR, (2024)Benefits of Depth for Long-Term Memory of Recurrent Networks., , and . ICLR (Workshop), OpenReview.net, (2018)On the Expressive Power of Deep Learning: A Tensor Analysis., , and . COLT, volume 49 of JMLR Workshop and Conference Proceedings, page 698-728. JMLR.org, (2016)On the Expressive Power of Overlapping Operations of Deep Networks., and . CoRR, (2017)The Cost of Training NLP Models: A Concise Overview., , and . CoRR, (2020)On the Expressive Power of Overlapping Architectures of Deep Learning., and . ICLR (Poster), OpenReview.net, (2018)Deep SimNets., , and . CVPR, page 4782-4791. IEEE Computer Society, (2016)Sum-Product-Quotient Networks., and . AISTATS, volume 84 of Proceedings of Machine Learning Research, page 529-537. PMLR, (2018)