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Found 10 papers, 2 papers with code
Recursive Least Squares Advantage Actor-Critic Algorithms
RLSNA2C uses the Kronecker-factored approximation, the RLS method and the natural policy gradient to learn the compatible parameter and the policy parameter.
Recursive Least Squares for Training and Pruning Convolutional Neural Networks
Convolutional neural networks (CNNs) have succeeded in many practical applications.
Distributionally Robust Semi-Supervised Learning Over Graphs
The data distribution is considered unknown, but lies within a Wasserstein ball centered around empirical data distribution.
RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment
Entity alignment (EA) is the task to discover entities referring to the same real-world object from different knowledge graphs (KGs), which is the most crucial step in integrating multi-source KGs.
A Bidirectional Tree Tagging Scheme for Joint Medical Relation Extraction
In this paper, inspired by the tree-like relation structures in the medical text, we propose a novel scheme called Bidirectional Tree Tagging (BiTT) to form the medical relation triples into two two binary trees and convert the trees into a word-level tags sequence.
Ranked #1 on
Relation Extraction
on DuIE
Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data
This robust learning task entails an infinite-dimensional optimization problem, which is challenging.
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks
When the scaling factor is set to zero, it is equivalent to removing the corresponding filter.
On the Convergence of SARAH and Beyond
For convex problems, when adopting an $n$-dependent step size, the complexity of L2S is ${\cal O}(n+ \sqrt{n}/\epsilon)$; while for more frequently adopted $n$-independent step size, the complexity is ${\cal O}(n+ n/\epsilon)$.
Kernel-based Inference of Functions over Graphs
The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced.
Kernel-based Reconstruction of Graph Signals
A number of SPoG notions such as bandlimitedness, graph filters, and the graph Fourier transform are naturally accommodated in the kernel framework.
