In this technical report we compare different deep learning models for prediction of water depth rasters at high spatial resolution.
On those real tasks, optirank performs at least as well as the vanilla logistic regression on classical ranks, while producing sparser solutions.
Fingerprints are key tools in climate change detection and attribution (D&A) that are used to determine whether changes in observations are different from internal climate variability (detection), and whether observed changes can be assigned to specific external drivers (attribution).
The evidence lower bound of the marginal log-likelihood of empirical Bayes decomposes as a sum of local KL divergences between the variational posterior and the true posterior on the query set of each task.
Ranked #11 on Few-Shot Image Classification on CIFAR-FS 5-way (1-shot)
Additionally, we propose a new dataset for knowledge base completion constructed from Wikidata, larger than previous benchmarks by an order of magnitude, as a new reference for evaluating temporal and non-temporal link prediction methods.
Ranked #1 on Link Prediction on YAGO15k
However, as we show in this paper through experiments on standard benchmarks of link prediction in knowledge bases, ComplEx, a variant of CP, achieves similar performances to recent approaches based on Tucker decomposition on all operating points in terms of number of parameters.
In this work, we consider a family of latent variable Gaussian graphical models in which the graph of the joint distribution between observed and unobserved variables is sparse, and the unobserved variables are conditionally independent given the others.
The problem of Knowledge Base Completion can be framed as a 3rd-order binary tensor completion problem.
Ranked #2 on Link Prediction on FB15k
We present a new shape prior formalism for segmentation of rectified facade images.
Based on a new atomic norm, we propose a new convex formulation for sparse matrix factorization problems in which the number of nonzero elements of the factors is assumed fixed and known.
Most natural language processing systems based on machine learning are not robust to domain shift.
The aim of this work is to localize a query photograph by finding other images depicting the same place in a large geotagged image database.
We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespecified set of shapes.