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Found 9 papers, 3 papers with code
Representation Learning of Tangled Key-Value Sequence Data for Early Classification
To address this problem, we propose a novel method, i. e., Key-Value sequence Early Co-classification (KVEC), which leverages both inner- and inter-correlations of items in a tangled key-value sequence through key correlation and value correlation to learn a better sequence representation.
High-Dimensional Tail Index Regression: with An Application to Text Analyses of Viral Posts in Social Media
To conduct inference, we propose to debias the regularized estimate, and establish the asymptotic normality of the debiased estimator.
TBDLNet: a network for classifying multidrug-resistant and drug-sensitive tuberculosis
Five indexes are selected in this paper, which are accuracy, sensitivity, precision, F1-score, and specificity.
Multi-Action Dialog Policy Learning from Logged User Feedback
To cope with this problem, we explore to improve multi-action dialog policy learning with explicit and implicit turn-level user feedback received for historical predictions (i. e., logged user feedback) that are cost-efficient to collect and faithful to real-world scenarios.
Federated Learning over Coupled Graphs
In this work, we propose a novel FL framework for graph data, FedCog, to efficiently handle coupled graphs that are a kind of distributed graph data, but widely exist in a variety of real-world applications such as mobile carriers' communication networks and banks' transaction networks.
Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data
This paper proposes a doubly robust two-stage semiparametric difference-in-difference estimator for estimating heterogeneous treatment effects with high-dimensional data.
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding
We study the problem of node classification on graphs with few-shot novel labels, which has two distinctive properties: (1) There are novel labels to emerge in the graph; (2) The novel labels have only a few representative nodes for training a classifier.
MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions
To resolve these problems, we present MR-GNN, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (L-STMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs.
Video Rain Streak Removal by Multiscale Convolutional Sparse Coding
Based on such understanding, we specifically formulate both characteristics into a multiscale convolutional sparse coding (MS-CSC) model for the video rain streak removal task.
