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Found 7 papers, 3 papers with code
Slot Abstractors: Toward Scalable Abstract Visual Reasoning
Abstract visual reasoning is a characteristically human ability, allowing the identification of relational patterns that are abstracted away from object features, and the systematic generalization of those patterns to unseen problems.
The Relational Bottleneck as an Inductive Bias for Efficient Abstraction
A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience.
Systematic Visual Reasoning through Object-Centric Relational Abstraction
Human visual reasoning is characterized by an ability to identify abstract patterns from only a small number of examples, and to systematically generalize those patterns to novel inputs.
Zero-shot visual reasoning through probabilistic analogical mapping
Human reasoning is grounded in an ability to identify highly abstract commonalities governing superficially dissimilar visual inputs.
Emergent Symbols through Binding in External Memory
A key aspect of human intelligence is the ability to infer abstract rules directly from high-dimensional sensory data, and to do so given only a limited amount of training experience.
A Memory-Augmented Neural Network Model of Abstract Rule Learning
Further, we introduce the Emergent Symbol Binding Network (ESBN), a recurrent neural network model that learns to use an external memory as a binding mechanism.
Learning Representations that Support Extrapolation
Extrapolation -- the ability to make inferences that go beyond the scope of one's experiences -- is a hallmark of human intelligence.
