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

Showing 141150 of 158 papers

TitleStatusHype
Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs0
From Known to the Unknown: Transferring Knowledge to Answer Questions about Novel Visual and Semantic Concepts0
Characterizing the Influence of Features on Reading Difficulty Estimation for Non-native Readers0
Can Machines Design? An Artificial General Intelligence Approach0
Multi-level Semantic Feature Augmentation for One-shot LearningCode0
Decoupled Novel Object CaptionerCode0
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel ConceptsCode0
Privacy-Enabled Biometric Search0
Zero-Shot Learning by Generating Pseudo Feature Representations0
Sequential Local Learning for Latent Graphical Models0
Show:102550
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