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On the Learnability of Deep Random Networks

On the Learnability of Deep Random Networks

8 April 2019
Abhimanyu Das
Sreenivas Gollapudi
Ravi Kumar
Rina Panigrahy
ArXiv (abs)PDFHTML

Papers citing "On the Learnability of Deep Random Networks"

7 / 7 papers shown
Most Neural Networks Are Almost Learnable
Most Neural Networks Are Almost LearnableNeural Information Processing Systems (NeurIPS), 2023
Amit Daniely
Nathan Srebro
Gal Vardi
228
0
0
25 May 2023
Learning to Reason with Neural Networks: Generalization, Unseen Data and
  Boolean Measures
Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean MeasuresNeural Information Processing Systems (NeurIPS), 2022
Emmanuel Abbe
Samy Bengio
Elisabetta Cornacchia
Jon M. Kleinberg
Aryo Lotfi
M. Raghu
Chiyuan Zhang
MLT
223
14
0
26 May 2022
From Local Pseudorandom Generators to Hardness of Learning
From Local Pseudorandom Generators to Hardness of LearningAnnual Conference Computational Learning Theory (COLT), 2021
Amit Daniely
Gal Vardi
271
37
0
20 Jan 2021
Hardness of Learning Neural Networks with Natural Weights
Hardness of Learning Neural Networks with Natural Weights
Amit Daniely
Gal Vardi
216
21
0
05 Jun 2020
A Deep Conditioning Treatment of Neural Networks
A Deep Conditioning Treatment of Neural NetworksInternational Conference on Algorithmic Learning Theory (ALT), 2020
Naman Agarwal
Pranjal Awasthi
Satyen Kale
AI4CE
348
18
0
04 Feb 2020
Learning Boolean Circuits with Neural Networks
Learning Boolean Circuits with Neural Networks
Eran Malach
Shai Shalev-Shwartz
171
4
0
25 Oct 2019
High Accuracy and High Fidelity Extraction of Neural Networks
High Accuracy and High Fidelity Extraction of Neural NetworksUSENIX Security Symposium (USENIX Security), 2019
Matthew Jagielski
Nicholas Carlini
David Berthelot
Alexey Kurakin
Nicolas Papernot
MLAUMIACV
337
424
0
03 Sep 2019
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