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Found 4 papers, 1 papers with code
The Viability of Best-worst Scaling and Categorical Data Label Annotation Tasks in Detecting Implicit Bias
We identify several shortcomings of BWS relative to traditional categorical annotation: (1) When compared to categorical annotation, we estimate BWS takes approximately 4. 5x longer to complete; (2) BWS does not scale well to large annotation tasks with sparse target phenomena; (3) The high correlation between BWS and the traditional task shows that the benefits of BWS can be recovered from a simple categorically annotated, non-aggregated dataset.
TalkUp: Paving the Way for Understanding Empowering Language
Empowering language is important in many real-world contexts, from education to workplace dynamics to healthcare.
Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourages convex latent distributions
We present a neural network architecture based upon the Autoencoder (AE) and Generative Adversarial Network (GAN) that promotes a convex latent distribution by training adversarially on latent space interpolations.
Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourage convex latent distributions
We present a neural network architecture based upon the Autoencoder (AE) and Generative Adversarial Network (GAN) that promotes a convex latent distribution by training adversarially on latent space interpolations.
