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Keyword: mixed-integer linear programming ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Lossless Compression of Deep Neural Networks

   Serra, Kumar and Ramalingam · Lecture notes in computer science · 2020

   The paper introduces LEO, an algorithm that removes units and layers from a neural network without changing its output, and uses L1 regularization in training so that a larger network can be shrunk for deployment.

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   1. UncheckedThe LEO algorithm uses mixed-integer linear programming to find ReLU units that behave linearly across the network's input domain.“This algorithm, which we denote as LEO (Lossless Expressiveness Optimization), relies on Mixed-Integer Linear Programming (MILP) to identify Rectified Linear Units (ReLUs) with linear behavior over the input domain.”
   2. UncheckedUsing L1 regularization to make some units behave linearly lets a network be trained large but deployed smaller, according to the paper.“By using L1 regularization to induce such behavior, we can benefit from training over a larger architecture than we would later use in the environment where the trained neural network is deployed.”