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 4150 of 158 papers

TitleStatusHype
Neural Concept Formation in Knowledge GraphsCode0
Can Vision Language Models Learn from Visual Demonstrations of Ambiguous Spatial Reasoning?Code0
Multi-level Semantic Feature Augmentation for One-shot LearningCode0
NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional GeneralizationCode0
Knowledge Graph Transfer Network for Few-Shot RecognitionCode0
L3DMC: Lifelong Learning using Distillation via Mixed-Curvature SpaceCode0
A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classificationCode0
BOWLL: A Deceptively Simple Open World Lifelong LearnerCode0
Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of ImagesCode0
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training DataCode0
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