Search Results for author:
Found 32 papers, 7 papers with code
One-shot learning by inverting a compositional causal process
People can learn a new visual class from just one example, yet machine learning algorithms typically require hundreds or thousands of examples to tackle the same problems.
Softstar: Heuristic-Guided Probabilistic Inference
Recent machine learning methods for sequential behavior prediction estimate the motives of behavior rather than the behavior itself.
Unsupervised Learning by Program Synthesis
We introduce an unsupervised learning algorithmthat combines probabilistic modeling with solver-based techniques for program synthesis. We apply our techniques to both a visual learning domain and a language learning problem, showing that our algorithm can learn many visual concepts from only a few examplesand that it can recover some English inflectional morphology. Taken together, these results give both a new approach to unsupervised learning of symbolic compositional structures, and a technique for applying program synthesis tools to noisy data.
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning
Humans demonstrate remarkable abilities to predict physical events in dynamic scenes, and to infer the physical properties of objects from static images.
Sampling for Bayesian Program Learning
Towards learning programs from data, we introduce the problem of sampling programs from posterior distributions conditioned on that data.
Probing the Compositionality of Intuitive Functions
How do people learn about complex functional structure?
Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints
In Experiment~2, we asked participants online to judge whether they think the person in the lab used one or two hands.
Learning to See Physics via Visual De-animation
At the core of our system is a physical world representation that is first recovered by a perception module and then utilized by physics and graphics engines.
Shape and Material from Sound
Hearing an object falling onto the ground, humans can recover rich information including its rough shape, material, and falling height.
A Computational Model of Commonsense Moral Decision Making
We introduce a new computational model of moral decision making, drawing on a recent theory of commonsense moral learning via social dynamics.
Learning to Share and Hide Intentions using Information Regularization
We show how to optimize these regularizers in a way that is easy to integrate with policy gradient reinforcement learning.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Logical Rule Induction and Theory Learning Using Neural Theorem Proving
Our approach is neuro-symbolic in the sense that the rule pred- icates and core facts are given dense vector representations.
At Human Speed: Deep Reinforcement Learning with Action Delay
There has been a recent explosion in the capabilities of game-playing artificial intelligence.
End-to-End Differentiable Physics for Learning and Control
We present a differentiable physics engine that can be integrated as a module in deep neural networks for end-to-end learning.
Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction
Successful approaches to program induction require a hand-engineered domain-specific language (DSL), constraining the space of allowed programs and imparting prior knowledge of the domain.
Visual Object Networks: Image Generation with Disentangled 3D Representations
The VON not only generates images that are more realistic than the state-of-the-art 2D image synthesis methods but also enables many 3D operations such as changing the viewpoint of a generated image, shape and texture editing, linear interpolation in texture and shape space, and transferring appearance across different objects and viewpoints.
Learning to Exploit Stability for 3D Scene Parsing
We first show that applying physics supervision to an existing scene understanding model increases performance, produces more stable predictions, and allows training to an equivalent performance level with fewer annotated training examples.
Residual Policy Learning
In these tasks, reinforcement learning from scratch remains data-inefficient or intractable, but learning a residual on top of the initial controller can yield substantial improvements.
Few-Shot Bayesian Imitation Learning with Logical Program Policies
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.
Write, Execute, Assess: Program Synthesis with a REPL
We present a neural program synthesis approach integrating components which write, execute, and assess code to navigate the search space of possible programs.
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations
We also present a new test set for measuring violations of physical expectations, using a range of scenarios derived from developmental psychology.
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Although we focus on object recognition here, data with controls can be gathered at scale using automated tools throughout machine learning to generate datasets that exercise models in new ways thus providing valuable feedback to researchers.
Ranked #51 on
Image Classification
on ObjectNet
(using extra training data)
CZ-GEM: A FRAMEWORK FOR DISENTANGLED REPRESENTATION LEARNING
In this work, we tackle a slightly more intricate scenario where the observations are generated from a conditional distribution of some known control variate and some latent noise variate.
Program Synthesis with Pragmatic Communication
Given a specification, we score a candidate program both on its consistency with the specification, and also whether a rational speaker would chose this particular specification to communicate that program.
Online Bayesian Goal Inference for Boundedly Rational Planning Agents
These models are specified as probabilistic programs, allowing us to represent and perform efficient Bayesian inference over an agent's goals and internal planning processes.
Learning Evolved Combinatorial Symbols with a Neuro-symbolic Generative Model
Humans have the ability to rapidly understand rich combinatorial concepts from limited data.
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics
In this paper, we propose a Bayesian-symbolic framework (BSP) for physical reasoning and learning that is close to human-level sample-efficiency and accuracy.
Modeling human intention inference in continuous 3D domains by inverse planning and body kinematics
We describe Generative Body Kinematics model, which predicts human intention inference in this domain using Bayesian inverse planning and inverse body kinematics.
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment
Using a state-of-the-art large language model (LLM) as a basis, we propose a novel moral chain of thought (MORALCOT) prompting strategy that combines the strengths of LLMs with theories of moral reasoning developed in cognitive science to predict human moral judgments.
Learning a Hierarchical Planner from Humans in Multiple Generations
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.
