SOTAVerified

Novel Concepts

Measures the ability of models to uncover an underlying concept that unites several ostensibly disparate entities, which hopefully would not co-occur frequently. This provides a limited test of a model's ability to creatively construct the necessary abstraction to make sense of a situation that it cannot have memorized in training.

Source: BIG-bench

Papers

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Showing 101–110 of 158 papers

TitleStatusHype
Meta-Learning by Hallucinating Useful Examples—0
Meta-Learning to Detect Rare Objects—0
Towards A Litmus Test for Common Sense—0
A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches—0
A Robust Framework for Classifying Evolving Document Streams in an Expert-Machine-Crowd Setting—0
Neuro-Symbolic Concepts—0
Characterizing an Analogical Concept Memory for Architectures Implementing the Common Model of Cognition—0
Open Ad-hoc Categorization with Contextualized Feature Learning—0
Open-Set Representation Learning through Combinatorial Embedding—0
Open-vocabulary object 6D pose estimation—0
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