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Found 8 papers, 1 papers with code
Optimizing Curvature Learning for Robust Hyperbolic Deep Learning in Computer Vision
Hyperbolic deep learning has become a growing research direction in computer vision for the unique properties afforded by the alternate embedding space.
Fully Hyperbolic Convolutional Neural Networks for Computer Vision
To address this, we present HCNN, a fully hyperbolic convolutional neural network (CNN) designed for computer vision tasks.
Deep Distributional Sequence Embeddings Based on a Wasserstein Loss
In this paper, we study deep distributional embeddings of sequences, where the embedding of a sequence is given by the distribution of learned deep features across the sequence.
A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements
We study whether a Fisher-SVM with this Fisher kernel and several reference methods are able to identify readers and estimate their level of text comprehension based on eye-tracking data.
A Semiparametric Model for Bayesian Reader Identification
We study the problem of identifying individuals based on their characteristic gaze patterns during reading of arbitrary text.
Varying-coefficient models with isotropic Gaussian process priors
We study learning problems in which the conditional distribution of the output given the input varies as a function of additional task variables.
Active Comparison of Prediction Models
We address the problem of comparing the risks of two given predictive models - for instance, a baseline model and a challenger - as confidently as possible on a fixed labeling budget.
Active Estimation of F-Measures
We address the problem of estimating the F-measure of a given model as accurately as possible on a fixed labeling budget.
