150 research outputs found
One-Shot Learning with a Hierarchical Nonparametric Bayesian Model
We develop a hierarchical Bayesian model that learns to learn categories from single training examples. The model transfers acquired knowledge from previously learned categories to a novel category, in the form of a prior over category means and variances. The model discovers how to group categories into meaningful super-categories that express different priors for new classes. Given a single example of a novel category, we can efficiently infer which super-category the novel category belongs to, and thereby estimate not only the new category's mean but also an appropriate similarity metric based on parameters inherited from the super-category. On MNIST and MSR Cambridge image datasets the model learns useful representations of novel categories based on just a single training example, and performs significantly better than simpler hierarchical Bayesian approaches. It can also discover new categories in a completely unsupervised fashion, given just one or a few examples
Few-Shot Bayesian Imitation Learning with Logical Program Policies
Humans can learn many novel tasks from a very small number (1--5) of
demonstrations, in stark contrast to the data requirements of nearly tabula
rasa deep learning methods. We propose an expressive class of policies, a
strong but general prior, and a learning algorithm that, together, can learn
interesting policies from very few examples. We represent policies as logical
combinations of programs drawn from a domain-specific language (DSL), define a
prior over policies with a probabilistic grammar, and derive an approximate
Bayesian inference algorithm to learn policies from demonstrations. In
experiments, we study five strategy games played on a 2D grid with one shared
DSL. After a few demonstrations of each game, the inferred policies generalize
to new game instances that differ substantially from the demonstrations. Our
policy learning is 20--1,000x more data efficient than convolutional and fully
convolutional policy learning and many orders of magnitude more computationally
efficient than vanilla program induction. We argue that the proposed method is
an apt choice for tasks that have scarce training data and feature significant,
structured variation between task instances.Comment: AAAI 202
Inferring the Future by Imagining the Past
A single panel of a comic book can say a lot: it shows not only where
characters currently are, but also where they came from, what their motivations
are, and what might happen next. More generally, humans can often infer a
complex sequence of past and future events from a *single snapshot image* of an
intelligent agent.
Building on recent work in cognitive science, we offer a Monte Carlo
algorithm for making such inferences. Drawing a connection to Monte Carlo path
tracing in computer graphics, we borrow ideas that help us dramatically improve
upon prior work in sample efficiency. This allows us to scale to a wide variety
of challenging inference problems with only a handful of samples. It also
suggests some degree of cognitive plausibility, and indeed we present human
subject studies showing that our algorithm matches human intuitions in a
variety of domains that previous methods could not scale to
Learning a Hierarchical Planner from Humans in Multiple Generations
A typical way in which a machine acquires knowledge from humans is by
programming. Compared to learning from demonstrations or experiences,
programmatic learning allows the machine to acquire a novel skill as soon as
the program is written, and, by building a library of programs, a machine can
quickly learn how to perform complex tasks. However, as programs often take
their execution contexts for granted, they are brittle when the contexts
change, making it difficult to adapt complex programs to new contexts. We
present natural programming, a library learning system that combines
programmatic learning with a hierarchical planner. Natural programming
maintains a library of decompositions, consisting of a goal, a linguistic
description of how this goal decompose into sub-goals, and a concrete instance
of its decomposition into sub-goals. A user teaches the system via curriculum
building, by identifying a challenging yet not impossible goal along with
linguistic hints on how this goal may be decomposed into sub-goals. The system
solves for the goal via hierarchical planning, using the linguistic hints to
guide its probability distribution in proposing the right plans. The system
learns from this interaction by adding newly found decompositions in the
successful search into its library. Simulated studies and a human experiment
(n=360) on a controlled environment demonstrate that natural programming can
robustly compose programs learned from different users and contexts, adapting
faster and solving more complex tasks when compared to programmatic baselines.Comment: First two authors contributed equall
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