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Found 14 papers, 4 papers with code
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference
Notably, the optimization techniques, namely black-box VI and natural-gradient VI, can be reinterpreted as specific instances of the proposed Wasserstein gradient descent.
Central-Smoothing Hypergraph Neural Networks for Predicting Drug-Drug Interactions
This problem can be formulated as predicting labels (i. e. side effects) for each pair of nodes in a DDI graph, of which nodes are drugs and edges are interacting drugs with known labels.
Learning subtree pattern importance for Weisfeiler-Lehmanbased graph kernels
Graph is an usual representation of relational data, which are ubiquitous in manydomains such as molecules, biological and social networks.
DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction
More specifically, we sequentially integrate five different data sets, which have not all been combined in earlier bioinformatic methods for predicting drug responses.
Scalable Probabilistic Matrix Factorization with Graph-Based Priors
The identification and removal of contested edges adds no computational complexity to state-of-the-art graph-regularized matrix factorization, remaining linear with respect to the number of non-zeros.
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Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms
Coupled norms have emerged as a convex method to solve coupled tensor completion.
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification
We propose a new label tree-based deep learning model for XMTC, called AttentionXML, with two unique features: 1) a multi-label attention mechanism with raw text as input, which allows to capture the most relevant part of text to each label; and 2) a shallow and wide probabilistic label tree (PLT), which allows to handle millions of labels, especially for "tail labels".
Learning on Hypergraphs with Sparsity
On a hypergraph, as a generalization of graph, one wishes to learn a smooth function with respect to its topology.
Convex Coupled Matrix and Tensor Completion
We propose a set of convex low rank inducing norms for a coupled matrices and tensors (hereafter coupled tensors), which shares information between matrices and tensors through common modes.
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data
However, sophisticated learning models are computationally unfeasible for data with millions of features.
Convex Factorization Machine for Regression
We propose the convex factorization machine (CFM), which is a convex variant of the widely used Factorization Machines (FMs).
Sparse Learning over Infinite Subgraph Features
We present a supervised-learning algorithm from graph data (a set of graphs) for arbitrary twice-differentiable loss functions and sparse linear models over all possible subgraph features.
Manifold-based Similarity Adaptation for Label Propagation
In this approach, edge weights represent both similarity and local reconstruction weight simultaneously, both being reasonable for label propagation.
