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 76–100 of 158 papers

TitleStatusHype
CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual LearningCode1
DreamArtist++: Controllable One-Shot Text-to-Image Generation via Positive-Negative Adapter—0
Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot LearningCode1
Analogical Concept Memory for Architectures Implementing the Common Model of Cognition—0
Memorizing Complementation Network for Few-Shot Class-Incremental Learning—0
XCon: Learning with Experts for Fine-grained Category DiscoveryCode1
Diagnosing and Remedying Shot Sensitivity with Cosine Few-Shot Learners—0
ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference TimeCode1
Malware Detection and Prevention using Artificial Intelligence Techniques—0
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object InteractionsCode1
EDIN: An End-to-end Benchmark and Pipeline for Unknown Entity Discovery and IndexingCode1
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
PaLM: Scaling Language Modeling with PathwaysCode2
A Closer Look at Rehearsal-Free Continual Learning—0
FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations—0
Training Compute-Optimal Large Language ModelsCode6
Emergence of hierarchical reference systems in multi-agent communicationCode0
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications—0
Scaling Language Models: Methods, Analysis & Insights from Training GopherCode2
Learning Instance and Task-Aware Dynamic Kernels for Few Shot LearningCode1
Extract Free Dense Labels from CLIPCode1
Generative Pre-Trained Transformer for Design Concept Generation: An Exploration—0
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