Adversarial Regularization for Discrete Neural Weight Training
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Solution Overview
Problem
Existing methods for training compact supervised neural networks face challenges in optimizing discrete distributions due to the need for derivative-based methods or moment calculation, which are computationally burdensome and not suitable for heavy-tailed distributions, especially when implementing discrete distributions.
Innovation Solution
Adversarial Probabilistic Regularization (APR) framework using Generative Adversarial Networks (GANs) to learn a natural parameterized regularizer directly from data, rephrasing distribution matching as a distribution learning problem, allowing for principled quantization and sparsification of neural networks without relying on PDFs or moment calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If derivative-based methods or moment calculation are used to optimize discrete distributions, then optimization can be performed, but computational burden increases significantly
Solution Approach 1:
The patent replaces derivative-based optimization methods and moment calculation with a GAN-based adversarial learning framework. Instead of using calculus-based approaches to match distributions, the invention uses a generator-discriminator neural network system where the generator learns to produce samples matching the target distribution through adversarial training, eliminating the need for analytical derivatives or moment computations.
Solution Approach 2:
The patent introduces a discriminator network as an intermediary that mediates between the generator and the target distribution. The discriminator learns to distinguish between samples from the target distribution and samples generated by the generator, providing a differentiable surrogate objective that enables optimization without directly computing derivatives or moments of the discrete distribution.
2Productivity
If discrete distributions are implemented in neural networks, then network compactness and hardware efficiency improve, but training optimization becomes difficult
Solution Approach 1:
The patent replaces traditional gradient-based optimization methods with adversarial learning using GANs. This substitution allows discrete distributions to be learned through the generator-discriminator framework, where the discriminator provides gradient signals that guide the generator to produce discrete-valued weights without requiring direct optimization of discrete variables.
Solution Approach 2:
The patent changes the optimization parameters from direct weight values to the parameters of the generator network. By optimizing the generator's parameters rather than the discrete weights directly, the method enables efficient training of compact networks with discrete distributions through continuous parameter optimization in the generator.
3Reliability
If heavy-tailed distributions are used for network weights, then robustness to adversarial examples improves, but moment calculation becomes unsuitable
Solution Approach 1:
The patent replaces moment calculation-based optimization with adversarial learning using GANs. Since the GAN framework does not rely on computing moments (which are problematic for heavy-tailed distributions), it enables the use of heavy-tailed distributions for weight initialization and regularization while maintaining optimization feasibility through the discriminator-based surrogate objective.
Data Source
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AI summary
A method of training a supervised neural network to solve an optimization problem that involves minimizing an error function f(θ) where θ is a vector of independent and identically distributed (i.i.d.) samples of a target distribution £ t is proposed. The method includes generating an adversarial probabilistic regularizer (APR) ϕ £t (θ) using a discriminator of a generative adversarial network. The discriminator receives samples from θ and samples from a regularizer distribution p r as inputs. The APR ϕ £t (θ) is then added to the error function f(θ) for each training iteration of the supervised neural network.