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
Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review—0
Contextual Blocking Bandits—0
Contrastive Visual Data Augmentation—0
CoSe-Co: Sentence Conditioned Generative CommonSense Contextualizer for Language Models—0
Decision Making Using Rough Set based Spanning Sets for a Decision System—0
Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models—0
Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods—0
Diagnosing and Remedying Shot Sensitivity with Cosine Few-Shot Learners—0
Dialog Policy Learning for Joint Clarification and Active Learning Queries—0
Direct and indirect transactions and requirements—0
Discovering Latent Concepts Learned in BERT—0
DreamArtist++: Controllable One-Shot Text-to-Image Generation via Positive-Negative Adapter—0
DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning—0
Dual-View Data Hallucination with Semantic Relation Guidance for Few-Shot Image Recognition—0
Efficient Transmission of Radiomaps via Physics-Enhanced Semantic Communications—0
Enhancing Balanced Graph Edge Partition with Effective Local Search—0
Error Analysis of Shapley Value-Based Model Explanations: An Informative Perspective—0
Experiencing Urban Air Mobility: How Passengers evaluate a simulated flight with an Air Taxi—0
Experimental Contexts Can Facilitate Robust Semantic Property Inference in Language Models, but Inconsistently—0
Explaining deep neural network models for electricity price forecasting with XAI—0
Exploring internal representation of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects—0
Open-Set Representation Learning through Combinatorial Embedding—0
Open-vocabulary object 6D pose estimation—0
Privacy-Enabled Biometric Search—0
Prototype Recalls for Continual Learning—0
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