44 research outputs found
Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning
We explore the benefits of augmenting state-of-the-art model-free deep
reinforcement algorithms with simple object representations. Following the
Frostbite challenge posited by Lake et al. (2017), we identify object
representations as a critical cognitive capacity lacking from current
reinforcement learning agents. We discover that providing the Rainbow model
(Hessel et al.,2018) with simple, feature-engineered object representations
substantially boosts its performance on the Frostbite game from Atari 2600. We
then analyze the relative contributions of the representations of different
types of objects, identify environment states where these representations are
most impactful, and examine how these representations aid in generalizing to
novel situations
People infer recursive visual concepts from just a few examples
Machine learning has made major advances in categorizing objects in images,
yet the best algorithms miss important aspects of how people learn and think
about categories. People can learn richer concepts from fewer examples,
including causal models that explain how members of a category are formed.
Here, we explore the limits of this human ability to infer causal "programs" --
latent generating processes with nontrivial algorithmic properties -- from one,
two, or three visual examples. People were asked to extrapolate the programs in
several ways, for both classifying and generating new examples. As a theory of
these inductive abilities, we present a Bayesian program learning model that
searches the space of programs for the best explanation of the observations.
Although variable, people's judgments are broadly consistent with the model and
inconsistent with several alternatives, including a pre-trained deep neural
network for object recognition, indicating that people can learn and reason
with rich algorithmic abstractions from sparse input data.Comment: In press at "Computational Brain & Behavior
Compositional generalization through meta sequence-to-sequence learning
People can learn a new concept and use it compositionally, understanding how
to "blicket twice" after learning how to "blicket." In contrast, powerful
sequence-to-sequence (seq2seq) neural networks fail such tests of
compositionality, especially when composing new concepts together with existing
concepts. In this paper, I show how memory-augmented neural networks can be
trained to generalize compositionally through meta seq2seq learning. In this
approach, models train on a series of seq2seq problems to acquire the
compositional skills needed to solve new seq2seq problems. Meta se2seq learning
solves several of the SCAN tests for compositional learning and can learn to
apply implicit rules to variables.Comment: This paper appears in the 33rd Conference on Neural Information
Processing Systems (NeurIPS 2019), Vancouver, Canad
Question Asking as Program Generation
A hallmark of human intelligence is the ability to ask rich, creative, and
revealing questions. Here we introduce a cognitive model capable of
constructing human-like questions. Our approach treats questions as formal
programs that, when executed on the state of the world, output an answer. The
model specifies a probability distribution over a complex, compositional space
of programs, favoring concise programs that help the agent learn in the current
context. We evaluate our approach by modeling the types of open-ended questions
generated by humans who were attempting to learn about an ambiguous situation
in a game. We find that our model predicts what questions people will ask, and
can creatively produce novel questions that were not present in the training
set. In addition, we compare a number of model variants, finding that both
question informativeness and complexity are important for producing human-like
questions.Comment: Published in Advances in Neural Information Processing Systems (NIPS)
30, December 201
The Omniglot challenge: a 3-year progress report
Three years ago, we released the Omniglot dataset for one-shot learning,
along with five challenge tasks and a computational model that addresses these
tasks. The model was not meant to be the final word on Omniglot; we hoped that
the community would build on our work and develop new approaches. In the time
since, we have been pleased to see wide adoption of the dataset. There has been
notable progress on one-shot classification, but researchers have adopted new
splits and procedures that make the task easier. There has been less progress
on the other four tasks. We conclude that recent approaches are still far from
human-like concept learning on Omniglot, a challenge that requires performing
many tasks with a single model.Comment: In press at Current Opinion in Behavioral Science
Modeling question asking using neural program generation
People ask questions that are far richer, more informative, and more creative
than current AI systems. We propose a neural program generation framework for
modeling human question asking, which represents questions as formal programs
and generates programs with an encoder-decoder based deep neural network. From
extensive experiments using an information-search game, we show that our method
can ask optimal questions in synthetic settings, and predict which questions
humans are likely to ask in unconstrained settings. We also propose a novel
grammar-based question generation framework trained with reinforcement
learning, which is able to generate creative questions without supervised data
Mutual exclusivity as a challenge for deep neural networks
Strong inductive biases allow children to learn in fast and adaptable ways.
Children use the mutual exclusivity (ME) bias to help disambiguate how words
map to referents, assuming that if an object has one label then it does not
need another. In this paper, we investigate whether or not standard neural
architectures have an ME bias, demonstrating that they lack this learning
assumption. Moreover, we show that their inductive biases are poorly matched to
lifelong learning formulations of classification and translation. We
demonstrate that there is a compelling case for designing neural networks that
reason by mutual exclusivity, which remains an open challenge
Generating new concepts with hybrid neuro-symbolic models
Human conceptual knowledge supports the ability to generate novel yet highly
structured concepts, and the form of this conceptual knowledge is of great
interest to cognitive scientists. One tradition has emphasized structured
knowledge, viewing concepts as embedded in intuitive theories or organized in
complex symbolic knowledge structures. A second tradition has emphasized
statistical knowledge, viewing conceptual knowledge as an emerging from the
rich correlational structure captured by training neural networks and other
statistical models. In this paper, we explore a synthesis of these two
traditions through a novel neuro-symbolic model for generating new concepts.
Using simple visual concepts as a testbed, we bring together neural networks
and symbolic probabilistic programs to learn a generative model of novel
handwritten characters. Two alternative models are explored with more generic
neural network architectures. We compare each of these three models for their
likelihoods on held-out character classes and for the quality of their
productions, finding that our hybrid model learns the most convincing
representation and generalizes further from the training observations.Comment: Published in Proceedings of the 42nd Annual Meeting of the Cognitive
Science Society, July 202
Learning Inductive Biases with Simple Neural Networks
People use rich prior knowledge about the world in order to efficiently learn
new concepts. These priors - also known as "inductive biases" - pertain to the
space of internal models considered by a learner, and they help the learner
make inferences that go beyond the observed data. A recent study found that
deep neural networks optimized for object recognition develop the shape bias
(Ritter et al., 2017), an inductive bias possessed by children that plays an
important role in early word learning. However, these networks use
unrealistically large quantities of training data, and the conditions required
for these biases to develop are not well understood. Moreover, it is unclear
how the learning dynamics of these networks relate to developmental processes
in childhood. We investigate the development and influence of the shape bias in
neural networks using controlled datasets of abstract patterns and synthetic
images, allowing us to systematically vary the quantity and form of the
experience provided to the learning algorithms. We find that simple neural
networks develop a shape bias after seeing as few as 3 examples of 4 object
categories. The development of these biases predicts the onset of vocabulary
acceleration in our networks, consistent with the developmental process in
children.Comment: Published in Proceedings of the 40th Annual Meeting of the Cognitive
Science Society, July 201
Learning a smooth kernel regularizer for convolutional neural networks
Modern deep neural networks require a tremendous amount of data to train,
often needing hundreds or thousands of labeled examples to learn an effective
representation. For these networks to work with less data, more structure must
be built into their architectures or learned from previous experience. The
learned weights of convolutional neural networks (CNNs) trained on large
datasets for object recognition contain a substantial amount of structure.
These representations have parallels to simple cells in the primary visual
cortex, where receptive fields are smooth and contain many regularities.
Incorporating smoothness constraints over the kernel weights of modern CNN
architectures is a promising way to improve their sample complexity. We propose
a smooth kernel regularizer that encourages spatial correlations in convolution
kernel weights. The correlation parameters of this regularizer are learned from
previous experience, yielding a method with a hierarchical Bayesian
interpretation. We show that our correlated regularizer can help constrain
models for visual recognition, improving over an L2 regularization baseline.Comment: Submitted to CogSci 201