Provable limitations of deep learning

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Provable limitations of deep learning

16 December 2018

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Papers citing "Provable limitations of deep learning"

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Title
From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency Kaiyue Wen Huaqing Zhang Hongzhou Lin Jingzhao Zhang MoE LRM 175 7 0 07 Oct 2024
Computational Separation Between Convolutional and Fully-Connected Networks Eran Malach Shai Shalev-Shwartz 87 26 0 03 Oct 2020
When Hardness of Approximation Meets Hardness of Learning Eran Malach Shai Shalev-Shwartz 55 9 0 18 Aug 2020
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions Stefano Sarao Mannelli Eric Vanden-Eijnden Lenka Zdeborová AI4CE 79 49 0 27 Jun 2020
Neural Anisotropy Directions Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard 94 16 0 17 Jun 2020
Scalable Backdoor Detection in Neural Networks Haripriya Harikumar Vuong Le Santu Rana Sourangshu Bhattacharya Sunil R. Gupta Svetha Venkatesh 117 24 0 10 Jun 2020
Can Shallow Neural Networks Beat the Curse of Dimensionality? A mean field training perspective Stephan Wojtowytsch E. Weinan MLT 72 51 0 21 May 2020
Deep Learning in Mining Biological Data M. S. M. Mahmud M. S. Kaiser Amir Hussain AI4CE 68 288 0 28 Feb 2020
Poly-time universality and limitations of deep learning Emmanuel Abbe Colin Sandon 62 23 0 07 Jan 2020
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals Surbhi Goel Sushrut Karmalkar Adam R. Klivans 95 53 0 04 Nov 2019
Robustifying deep networks for image segmentation Zheng Liu Jinnian Zhang Varun Jog Po-Ling Loh A. McMillan AAML OOD 50 7 0 01 Aug 2019
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel Xin Qiu Elliot Meyerson Risto Miikkulainen UQCV 90 54 0 03 Jun 2019
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends Saptarshi Sengupta Sanchita Basak P. Saikia Sayak Paul Vasilios Tsalavoutis Frederick Ditliac Atiah V. Ravi R. Peters AI4CE 166 347 0 30 May 2019
Depth Separations in Neural Networks: What is Actually Being Separated? Itay Safran Ronen Eldan Ohad Shamir MDE 65 36 0 15 Apr 2019
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression Nilaksh Das Madhuri Shanbhogue Shang-Tse Chen Fred Hohman Siwei Li Li-Wei Chen Michael E. Kounavis Duen Horng Chau FedML AAML 85 228 0 19 Feb 2018
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 140 1,869 0 20 May 2017