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Found 11 papers, 6 papers with code
Multi-modal Differentiable Unsupervised Feature Selection
Multi-modal high throughput biological data presents a great scientific opportunity and a significant computational challenge.
DiSC: Differential Spectral Clustering of Features
Selecting subsets of features that differentiate between two conditions is a key task in a broad range of scientific domains.
Weakly Supervised Indoor Localization via Manifold Matching
Inferring the location of a mobile device in an indoor setting is an open problem of utmost significance.
Spectral Top-Down Recovery of Latent Tree Models
For large trees, a common approach, termed divide-and-conquer, is to recover the tree structure in two steps.
Spectral neighbor joining for reconstruction of latent tree models
A common assumption in multiple scientific applications is that the distribution of observed data can be modeled by a latent tree graphical model.
The Spectral Underpinning of word2vec
word2vec due to Mikolov \textit{et al.} (2013) is a word embedding method that is widely used in natural language processing.
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
Latent variable models with hidden binary units appear in various applications.
Minimax-optimal semi-supervised regression on unknown manifolds
We consider semi-supervised regression when the predictor variables are drawn from an unknown manifold.
A Deep Learning Approach to Unsupervised Ensemble Learning
We show how deep learning methods can be applied in the context of crowdsourcing and unsupervised ensemble learning.
Unsupervised Ensemble Learning with Dependent Classifiers
In unsupervised ensemble learning, one obtains predictions from multiple sources or classifiers, yet without knowing the reliability and expertise of each source, and with no labeled data to assess it.
Estimating the Accuracies of Multiple Classifiers Without Labeled Data
In various situations one is given only the predictions of multiple classifiers over a large unlabeled test data.
