A Metalearned Neural Circuit for Nonparametric Bayesian Inference (2311.14601v1)

Abstract: Most applications of machine learning to classification assume a closed set of balanced classes. This is at odds with the real world, where class occurrence statistics often follow a long-tailed power-law distribution and it is unlikely that all classes are seen in a single sample. Nonparametric Bayesian models naturally capture this phenomenon, but have significant practical barriers to widespread adoption, namely implementation complexity and computational inefficiency. To address this, we present a method for extracting the inductive bias from a nonparametric Bayesian model and transferring it to an artificial neural network. By simulating data with a nonparametric Bayesian prior, we can metalearn a sequence model that performs inference over an unlimited set of classes. After training, this "neural circuit" has distilled the corresponding inductive bias and can successfully perform sequential inference over an open set of classes. Our experimental results show that the metalearned neural circuit achieves comparable or better performance than particle filter-based methods for inference in these models while being faster and simpler to use than methods that explicitly incorporate Bayesian nonparametric inference.

• The paper introduces a metalearned neural circuit that transfers nonparametric Bayesian inference into a recurrent neural network for flexible sequential classification.
• By training on simulated data, the approach efficiently infers an open-ended number of classes, outperforming traditional particle filter methods in both speed and accuracy.
• Its integration of Bayesian modeling with deep learning offers a scalable and practical solution for complex, evolving classification tasks.

Introduction

The real world frequently presents situations where we need to classify objects into categories, but existing machine learning models often assume that all possible categories are known in advance. This assumption is usually unrealistic since new categories can emerge over time. Nonparametric Bayesian models offer a solution to this issue by allowing the number of categories to grow as necessary. However, these models are complex to implement and computationally demanding.

Nonparametric Bayesian Models and Challenges

In practical scenarios such as classifying images, the categories can follow a long-tail distribution, meaning some categories are very common, while others are rare. Nonparametric Bayesian models like the Dirichlet Process Mixture Model (DPMM) capture this reality because they can infer an unbounded set of classes. Despite the DPMM's conceptual elegance, the complexity of their implementation and computational inefficiency have limited their adoption, particularly when dealing with large-scale data or complex objects.

A Neural Circuit for Bayesian Inference

This paper introduces a new method that transfers the inductive reasoning capability of a nonparametric Bayesian model into an artificial neural network. The approach involves training a recurrent neural network (RNN) on simulated data, enabling the network, referred to as a "neural circuit," to predict class membership for sequential inputs. Experimental results show that this neural circuit can perform on par with or better than existing particle filter methods while being significantly faster and simpler.

Benefits and Results

The neural circuit technique combines the adaptability and power of nonparametric Bayesian modeling with the scalability of deep learning. Unlike traditional Bayesian inference methods, neural circuits can classify complex inputs like images more efficiently. With its ability to perform sequential predictions for an open-ended number of classes, the neural circuit significantly outperformed traditional particle filter-based models in both predictive performance and computation time in tests involving synthetic data and challenging image classification tasks.