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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 91–100 of 158 papers

TitleStatusHype
Less Data, More Knowledge: Building Next Generation Semantic Communication Networks—0
DreamArtist++: Controllable One-Shot Text-to-Image Generation via Positive-Negative Adapter—0
Analogical Concept Memory for Architectures Implementing the Common Model of Cognition—0
Memorizing Complementation Network for Few-Shot Class-Incremental Learning—0
Diagnosing and Remedying Shot Sensitivity with Cosine Few-Shot Learners—0
Malware Detection and Prevention using Artificial Intelligence Techniques—0
Discovering Latent Concepts Learned in BERT—0
A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches—0
Reaction Network Analysis of Metabolic Insulin Signaling—0
Rockafellian Relaxation and Stochastic Optimization under Perturbations—0
Show:102550
← PrevPage 10 of 16Next →

No leaderboard results yet.