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 101–110 of 158 papers

TitleStatusHype
Resonant Machine Learning Based on Complex Growth Transform Dynamical Systems—0
A Robust, Efficient Predictive Safety Filter—0
Rockafellian Relaxation and Stochastic Optimization under Perturbations—0
Schema Independent Relational Learning—0
SECURE: Semantics-aware Embodied Conversation under Unawareness for Lifelong Robot Learning—0
Sequential Local Learning for Latent Graphical Models—0
Situational Grounding within Multimodal Simulations—0
Sketch-Plan-Generalize: Learning and Planning with Neuro-Symbolic Programmatic Representations for Inductive Spatial Concepts—0
Smoothed Bernstein Online Aggregation for Day-Ahead Electricity Demand Forecasting—0
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications—0
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