5 accepted papers
Deep learning models for different NLP tasks often rely on pre-trained word embeddings, that is, vector representations of words. Therefore, it is crucial to evaluate pre-trained word embeddings independently of downstream tasks. Such evaluations try to assess whether the geometry induced by a word…
In image segmentation, the classic approach of learning a deterministic segmentation neither accounts for noise and ambiguity in the data nor for expert disagreements about the correct segmentation. This has been addressed by architectures that predict heteroscedastic (input-dependent) segmentation…
Topic models are characterized by a latent class variable that represents the different topics. Traditionally, their observable variables are modeled as discrete variables like, for instance, in the prototypical latent Dirichlet allocation (LDA) topic model. In LDA, words in text documents are enco…
It has long been known that principal component analysis (PCA) is not robust with respect to gross data corruption. This has been addressed by robust principal component analysis (RPCA). The first computationally tractable definition of RPCA decomposes a data matrix into a low-rank and a sparse comp…
Measurement is at the core of scientific discovery. However, some quantities, such as economic behavior or intelligence, do not allow for direct measurement. They represent latent constructs that require surrogate measurements. In other scenarios, non-observed quantities can influence the variables…