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 111–120 of 158 papers

TitleStatusHype
Statistical QoS Provisioning Analysis and Performance Optimization in xURLLC-enabled Massive MU-MIMO Networks: A Stochastic Network Calculus Perspective—0
Strategy Synthesis in Markov Decision Processes Under Limited Sampling Access—0
Structure Matters: Towards Generating Transferable Adversarial Images—0
Surprisal Driven k-NN for Robust and Interpretable Nonparametric Learning—0
Theoretical and Empirical Analysis of Adaptive Entry Point Selection for Graph-based Approximate Nearest Neighbor Search—0
The Price of Cognition and Replicator Equations in Parallel Neural Networks—0
Tinkering Under the Hood: Interactive Zero-Shot Learning with Net Surgery—0
Toward enriched Cognitive Learning with XAI—0
Towards A Litmus Test for Common Sense—0
Weakly Supervised Open-Vocabulary Object Detection—0
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