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 111120 of 158 papers

TitleStatusHype
DER: Dynamically Expandable Representation for Class Incremental LearningCode1
A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classificationCode0
Linguistically Routing Capsule Network for Out-of-Distribution Visual Question Answering0
Enhancing Balanced Graph Edge Partition with Effective Local Search0
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and ReasoningCode1
SketchEmbedNet: Learning Novel Concepts by Imitating DrawingsCode0
Dialog Policy Learning for Joint Clarification and Active Learning Queries0
Characterizing an Analogical Concept Memory for Architectures Implementing the Common Model of Cognition0
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot LearningCode1
Revisit Systematic Generalization via Meaningful LearningCode0
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