Training Quantized Neural Networks to Global Optimality via Semidefinite Programming

4 May 2021

Papers citing "Training Quantized Neural Networks to Global Optimality via Semidefinite Programming"

5 / 5 papers shown

Title
MixQuant: Mixed Precision Quantization with a Bit-width Optimization Search Yichen Xie Wei Le MQ 16 4 0 29 Sep 2023
From Complexity to Clarity: Analytical Expressions of Deep Neural Network Weights via Clifford's Geometric Algebra and Convexity Mert Pilanci 34 2 0 28 Sep 2023
Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation Pedro J. Freire A. Napoli D. A. Ron B. Spinnler M. Anderson W. Schairer T. Bex N. Costa S. Turitsyn Jaroslaw E. Prilepsky 27 28 0 26 Aug 2022
Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers Arda Sahiner Tolga Ergen Batu Mehmet Ozturkler John M. Pauly Morteza Mardani Mert Pilanci 26 33 0 17 May 2022
The Convex Geometry of Backpropagation: Neural Network Gradient Flows Converge to Extreme Points of the Dual Convex Program Yifei Wang Mert Pilanci MLT MDE 47 11 0 13 Oct 2021