Search Results for author:
Found 10 papers, 5 papers with code
FormalGeo: An Extensible Formalized Framework for Olympiad Geometric Problem Solving
In this paper, we have constructed a consistent formal plane geometry system.
Back to Prior Knowledge: Joint Event Causality Extraction via Convolutional Semantic Infusion
Joint event and causality extraction is a challenging yet essential task in information retrieval and data mining.
A Targeted Universal Attack on Graph Convolutional Network
The experiments on three popular datasets show that the average attack success rate of the proposed attack on any victim node in the graph reaches 83% when using only 3 attack nodes and 6 fake nodes.
Emotion Correlation Mining Through Deep Learning Models on Natural Language Text
That means, journalists may try to attract attention using fear and joy words but arouse the emotion love instead; After news release, netizens generate emotional comments to express their intense emotions, i. e., anger, sadness, and love.
Enhancing Relation Extraction Using Syntactic Indicators and Sentential Contexts
To leverage both syntactic indicators and sentential contexts, we propose an indicator-aware approach for relation extraction.
Ranked #7 on
Relation Extraction
on SemEval-2010 Task-8
TeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain
Neuron morphology is recognized as a key determinant of cell type, yet the quantitative profiling of a mammalian neuron’s complete three-dimensional (3-D) morphology remains arduous when the neuron has complex arborization and long projection.
Proximal Policy Optimization with Mixed Distributed Training
Instability and slowness are two main problems in deep reinforcement learning.
Dependent Indian Buffet Process-based Sparse Nonparametric Nonnegative Matrix Factorization
Under this same framework, two classes of correlation function are proposed (1) using Bivariate beta distribution and (2) using Copula function.
Nonparametric Relational Topic Models through Dependent Gamma Processes
Traditional Relational Topic Models provide a way to discover the hidden topics from a document network.
Infinite Author Topic Model based on Mixed Gamma-Negative Binomial Process
One branch of these works is the so-called Author Topic Model (ATM), which incorporates the authors's interests as side information into the classical topic model.
