Provable Bounds for Learning Some Deep Representations

Type: Preprint

Publication Date: 2013-01-01

Citations: 236

DOI: https://doi.org/10.48550/arxiv.1310.6343

Locations

  • arXiv (Cornell University) - View - PDF
  • DataCite API - View

Similar Works

Action Title Year Authors
+ Provable Bounds for Learning Some Deep Representations 2013 Sanjeev Arora
Aditya Bhaskara
Rong Ge
Tengyu Ma
+ Provable approximation properties for deep neural networks 2016 Uri Shaham
Alexander Cloninger
Ronald R. Coifman
+ Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy 2023 Amit Daniely
Nathan Srebro
Gal Vardi
+ A Provably Correct Algorithm for Deep Learning that Actually Works 2018 Eran Malach
Shai Shalev‐Shwartz
+ Provable Methods for Training Neural Networks with Sparse Connectivity 2014 Hanie Sedghi
Anima Anandkumar
+ Provable Methods for Training Neural Networks with Sparse Connectivity 2014 Hanie Sedghi
Anima Anandkumar
+ Recovering the Lowest Layer of Deep Networks with High Threshold Activations 2019 Surbhi Goel
Rina Panigrahy‎
+ PDF Chat Generalization in Deep Learning 2022 Kenji Kawaguchi
Yoshua Bengio
Leslie Pack Kaelbling
+ Generalization in Deep Learning 2017 Kenji Kawaguchi
Leslie Pack Kaelbling
Yoshua Bengio
+ On the Learnability of Deep Random Networks 2019 Abhimanyu Das
Sreenivas Gollapudi
Ravi Kumar
Rina Panigrahy‎
+ On the Learnability of Deep Random Networks 2019 Abhimanyu Das
Sreenivas Gollapudi
Ravi Kumar
Rina Panigrahy‎
+ PDF Chat A gentle introduction to deep learning for graphs 2020 Davide Bacciu
Federico Errica
Alessio Micheli
Marco Podda
+ Width Provably Matters in Optimization for Deep Linear Neural Networks 2019 Simon S. Du
Wei Hu
+ PDF Chat Graph neural network outputs are almost surely asymptotically constant 2024 Sam Adam-Day
Michael Benedikt
Ä°smail Ä°lkan Ceylan
Ben Finkelshtein
+ Most Neural Networks Are Almost Learnable 2023 Amit Daniely
Nathan Srebro
Gal Vardi
+ PDF Chat Robust and Provable Guarantees for Sparse Random Embeddings 2022 Maciej SkĂłrski
Alessandro Temperoni
Martin Theobald
+ PDF Chat Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis 2024 Hongkang Li
Meng Wang
Shuai Zhang
Sijia Liu
Pin‐Yu Chen
+ PDF Chat Deep Graph Neural Networks with Shallow Subgraph Samplers 2020 Hanqing Zeng
Muhan Zhang
Yinglong Xia
Ajitesh Srivastava
Andrey Malevich
Rajgopal Kannan
Viktor K. Prasanna
Long Jin
Ren Chen
+ Deep Graph Neural Networks with Shallow Subgraph Samplers 2020 Hanqing Zeng
Muhan Zhang
Yinglong Xia
Ajitesh Srivastava
Rajgopal Kannan
Viktor K. Prasanna
Long Jin
Andrey Malevich
Chen Ren
+ Random Features Strengthen Graph Neural Networks 2021 Ryoma Sato
Makoto Yamada
Hisashi Kashima