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.

从非参数贝叶斯模型中提取归纳偏好并将其转移到人工神经网络，使得神经电路能够在开放类集合上成功进行顺序推理，实现了与基于粒子滤波器方法相当甚至更好的性能，并且速度更快、使用更简单。

非参数贝叶斯推断的元学习神经回路

A Metalearned Neural Circuit for Nonparametric Bayesian Inference

Most human behaviors consist of multiple parts, steps, or subtasks. These
structures guide our action planning and execution, but when we observe others,
the latent structure of their actions is typically unobservable, and must be
inferred in order to learn new skills by demonstration, or to assist others in
completing their tasks. For example, an assistant who has learned the subgoal
structure of a colleague's task can more rapidly recognize and support their
actions as they unfold. Here we model how humans infer subgoals from
observations of complex action sequences using a nonparametric Bayesian model,
which assumes that observed actions are generated by approximately rational
planning over unknown subgoal sequences. We test this model with a behavioral
experiment in which humans observed different series of goal-directed actions,
and inferred both the number and composition of the subgoal sequences
associated with each goal. The Bayesian model predicts human subgoal inferences
with high accuracy, and significantly better than several alternative models
and straightforward heuristics. Motivated by this result, we simulate how
learning and inference of subgoals can improve performance in an artificial
user assistance task. The Bayesian model learns the correct subgoals from fewer
observations, and better assists users by more rapidly and accurately inferring
the goal of their actions than alternative approaches.

本文模拟了人类如何通过观察复杂动作序列来推断子目标，结合非参数贝叶斯模型进行了行为实验，证明了该模型在推断人类子目标方面的高准确性和较好的效果，同时还模拟了使用子目标学习和推断在人工用户辅助任务中如何提高性能。

使用贝叶斯非参数子目标模型建模人类对复杂故意行为的理解

Modeling Human Understanding of Complex Intentional Action with a  Bayesian Nonparametric Subgoal Model

Multitask learning algorithms are typically designed assuming some fixed, a
priori known latent structure shared by all the tasks. However, it is usually
unclear what type of latent task structure is the most appropriate for a given
multitask learning problem. Ideally, the "right" latent task structure should
be learned in a data-driven manner. We present a flexible, nonparametric
Bayesian model that posits a mixture of factor analyzers structure on the
tasks. The nonparametric aspect makes the model expressive enough to subsume
many existing models of latent task structures (e.g, mean-regularized tasks,
clustered tasks, low-rank or linear/non-linear subspace assumption on tasks,
etc.). Moreover, it can also learn more general task structures, addressing the
shortcomings of such models. We present a variational inference algorithm for
our model. Experimental results on synthetic and real-world datasets, on both
regression and classification problems, demonstrate the effectiveness of the
proposed method.

使用柔性的、非参数的贝叶斯模型在多任务学习中建立混合的因子分析器结构，以解决什么样的隐含任务结构最适合于一个给定的多任务学习问题通常不清楚的问题，并给出了变分推断算法用于这个模型。