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Found 6 papers, 2 papers with code
Why Do Neural Language Models Still Need Commonsense Knowledge to Handle Semantic Variations in Question Answering?
We exemplify the possibility to overcome the limitations of the MNLM-based RC models by enriching text with the required knowledge from an external commonsense knowledge repository in controlled experiments.
Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images
In this work, we propose a new evaluation metric, called `rarity score', to measure the individual rarity of each image synthesized by generative models.
An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks
Despite significant improvements on the image generation performance of Generative Adversarial Networks (GANs), generations with low visual fidelity still have been observed.
Automatic Correction of Internal Units in Generative Neural Networks
Generative Adversarial Networks (GANs) have shown satisfactory performance in synthetic image generation by devising complex network structure and adversarial training scheme.
Why Do Masked Neural Language Models Still Need Common Sense Knowledge?
From the test, we observed that MNLMs partially understand various types of common sense knowledge but do not accurately understand the semantic meaning of relations.
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes
We also provide conditions under which CBOCPD provides the lower prediction error compared to BOCPD.
