Papers Program induction
“Program induction” 태그가 달린 논문 67편 · 필터 해제
Map Induction: Compositional spatial submap learning for efficient exploration in novel environments
Humans are expert explorers. Understanding the computational cognitive mechanisms that support this efficiency can advance the study of the human mind and enable more efficient exploration algorithms. We hypothesize that…
Efficient ExplorationProgram inductionProgram Transfer for Answering Complex Questions over Knowledge Bases
Program induction for answering complex questions over knowledge bases (KBs) aims to decompose a question into a multi-step program, whose execution against the KB produces the final answer. Learning to induce programs r…
Program inductionSemantic ParsingUnsupervised Visual Program Induction with Function Modularization
Program induction serves as one way to analog the ability of human thinking. However, existing methods could only tackle the task under simple scenarios (Fig~\ref{fig:task_examples}(a),(b)). When it comes to complex scen…
Program inductionFlexible Compositional Learning of Structured Visual Concepts
Humans are highly efficient learners, with the ability to grasp the meaning of a new concept from just a few examples. Unlike popular computer vision systems, humans can flexibly leverage the compositional structure of t…
Program inductionFast and flexible: Human program induction in abstract reasoning tasks
The Abstraction and Reasoning Corpus (ARC) is a challenging program induction dataset that was recently proposed by Chollet (2019). Here, we report the first set of results collected from a behavioral study of humans sol…
ARCProgram inductionAbstraction and Analogy-Making in Artificial Intelligence
Conceptual abstraction and analogy-making are key abilities underlying humans' abilities to learn, reason, and robustly adapt their knowledge to new domains. Despite of a long history of research on constructing AI syste…
Program inductionLearning a Deep Generative Model like a Program: the Free Category Prior
Humans surpass the cognitive abilities of most other animals in our ability to "chunk" concepts into words, and then combine the words to combine the concepts. In this process, we make "infinite use of finite means", ena…
Program inductionMulti-Plane Program Induction with 3D Box Priors
We consider two important aspects in understanding and editing images: modeling regular, program-like texture or patterns in 2D planes, and 3D posing of these planes in the scene. Unlike prior work on image-based program…
Program inductionProgram SynthesisFew-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning
Complex question-answering (CQA) involves answering complex natural-language questions on a knowledge base (KB). However, the conventional neural program induction (NPI) approach exhibits uneven performance when the ques…
Knowledge Base Question AnsweringMeta Reinforcement LearningProgram inductionQuestion Answering+2Measuring few-shot extrapolation with program induction
Neural networks are capable of learning complex functions, but still have problems generalizing from few examples and beyond their training distribution. Meta-learning provides a paradigm to train networks to learn from …
Meta-LearningProgram inductionLearning abstract structure for drawing by efficient motor program induction
Humans flexibly solve new problems that differ qualitatively from those they were trained on. This ability to generalize is supported by learned concepts that capture structure common across different problems. Here we d…
Program inductionStrong Generalization and Efficiency in Neural Programs
We study the problem of learning efficient algorithms that strongly generalize in the framework of neural program induction. By carefully designing the input / output interfaces of the neural model and through imitation,…
Program inductionLearning to learn generative programs with Memoised Wake-Sleep
We study a class of neuro-symbolic generative models in which neural networks are used both for inference and as priors over symbolic, data-generating programs. As generative models, these programs capture compositional …
Explainable ModelsFew-Shot LearningProgram inductionPerspective Plane Program Induction from a Single Image
We study the inverse graphics problem of inferring a holistic representation for natural images. Given an input image, our goal is to induce a neuro-symbolic, program-like representation that jointly models camera poses,…
Camera Pose EstimationImage ManipulationPose EstimationProgram induction+1DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning
Expert problem-solving is driven by powerful languages for thinking about problems and their solutions. Acquiring expertise means learning these languages -- systems of concepts, alongside the skills to use them. We pres…
Drawing PicturesProgram inductionProgram SynthesisAutomatic Discovery of Interpretable Planning Strategies
When making decisions, people often overlook critical information or are overly swayed by irrelevant information. A common approach to mitigate these biases is to provide decision-makers, especially professionals such as…
ClusteringDecision MakingImitation LearningProgram induction+2Knowledge Refactoring for Inductive Program Synthesis
Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introduce the \textit{knowledge refactoring} p…
Inductive logic programmingProgram inductionProgram SynthesisForgetting to learn logic programs
Most program induction approaches require predefined, often hand-engineered, background knowledge (BK). To overcome this limitation, we explore methods to automatically acquire BK through multi-task learning. In this app…
Inductive logic programmingMulti-Task LearningProgram inductionAttention on Abstract Visual Reasoning
Attention mechanisms have been boosting the performance of deep learning models on a wide range of applications, ranging from speech understanding to program induction. However, despite experiments from psychology which …
Program inductionRelationRelational ReasoningRelation Network+1Neural Probabilistic Logic Programming in DeepProbLog
We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques of the underlying probabil…
Deep LearningProgram induction