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04/29/2023
When Deep Learning Meets Polyhedral Theory: A Survey
In the past decade, deep learning became the prevalent methodology for p...
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06/07/2022
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm
Pruning techniques have been successfully used in neural networks to tra...
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05/28/2022
Optimal Decision Diagrams for Classification
Decision diagrams for classification have some notable advantages over d...
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03/09/2022
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks
Neural networks tend to achieve better accuracy with training if they ar...
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01/30/2022
Training Thinner and Deeper Neural Networks: Jumpstart Regularization
Neural networks are more expressive when they have multiple layers. In t...
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02/15/2021
Scaling Up Exact Neural Network Compression by ReLU Stability
We can compress a neural network while exactly preserving its underlying...
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01/01/2020
Lossless Compression of Deep Neural Networks
Deep neural networks have been successful in many predictive modeling ta...
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10/05/2019
Template-based Minor Embedding for Adiabatic Quantum Optimization
Quantum Annealing (QA) can be used to quickly obtain near-optimal soluti...
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05/27/2019
Equivalent and Approximate Transformations of Deep Neural Networks
Two networks are equivalent if they produce the same output for any give...
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10/08/2018
Empirical Bounds on Linear Regions of Deep Rectifier Networks
One form of characterizing the expressiveness of a piecewise linear neur...
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09/16/2018
When Lift-and-Project Cuts are Different
In this paper, we present a method to determine if a lift-and-project cu...
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06/17/2018
How Could Polyhedral Theory Harness Deep Learning?
The holy grail of deep learning is to come up with an automatic method t...
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11/06/2017