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 61–70 of 158 papers

TitleStatusHype
L3DMC: Lifelong Learning using Distillation via Mixed-Curvature SpaceCode0
Continual Zero-Shot Learning through Semantically Guided Generative Random WalksCode0
How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue CorpusCode0
A Clustering-based Framework for Classifying Data StreamsCode0
A Robust, Efficient Predictive Safety FilterCode0
FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations—0
Exploring Partial Intrinsic and Extrinsic Symmetry in 3D Medical Imaging—0
Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review—0
Exploring internal representation of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects—0
Explaining deep neural network models for electricity price forecasting with XAI—0
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