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Limits on representing Boolean functions by linear combinations of
  simple functions: thresholds, ReLUs, and low-degree polynomials

Limits on representing Boolean functions by linear combinations of simple functions: thresholds, ReLUs, and low-degree polynomials

Cybersecurity and Cyberforensics Conference (CC), 2018
26 February 2018
Richard Ryan Williams
ArXiv (abs)PDFHTML

Papers citing "Limits on representing Boolean functions by linear combinations of simple functions: thresholds, ReLUs, and low-degree polynomials"

4 / 4 papers shown
Better Neural Network Expressivity: Subdividing the Simplex
Better Neural Network Expressivity: Subdividing the Simplex
Egor Bakaev
Florestan Brunck
Christoph Hertrich
Jack Stade
Amir Yehudayoff
370
8
0
20 May 2025
On the Depth of Monotone ReLU Neural Networks and ICNNs
On the Depth of Monotone ReLU Neural Networks and ICNNs
Egor Bakaev
Florestan Brunck
Christoph Hertrich
Daniel Reichman
Amir Yehudayoff
241
7
0
09 May 2025
Neural Sculpting: Uncovering hierarchically modular task structure in
  neural networks through pruning and network analysis
Neural Sculpting: Uncovering hierarchically modular task structure in neural networks through pruning and network analysisNeural Information Processing Systems (NeurIPS), 2023
S. M. Patil
Loizos Michael
C. Dovrolis
230
0
0
28 May 2023
Size and Depth Separation in Approximating Benign Functions with Neural
  Networks
Size and Depth Separation in Approximating Benign Functions with Neural NetworksAnnual Conference Computational Learning Theory (COLT), 2021
Gal Vardi
Daniel Reichman
T. Pitassi
Ohad Shamir
296
7
0
30 Jan 2021
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