13 Result(s)
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Article
Learning structured communication for multi-agent reinforcement learning
This work explores the large-scale multi-agent communication mechanism for multi-agent reinforcement learning (MARL). We summarize the general topology categories for communication structures, which are often ...
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Chapter and Conference Paper
A Particle-Evolving Method for Approximating the Optimal Transport Plan
We propose an innovative algorithm that iteratively evolves a particle system to approximate the sample-wised Optimal Transport plan for given continuous probability densities. Our algorithm is proposed via th...
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Chapter and Conference Paper
Parametric Fokker-Planck Equation
We derive the Fokker-Planck equation on the parametric space. It is...
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Chapter and Conference Paper
Personalized Prescription for Comorbidity
Personalized medicine (PM) aiming at tailoring medical treatment to individual patient is critical in guiding precision prescription. An important challenge for PM is comorbidity due to the complex interrelati...
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Chapter and Conference Paper
Parallel Randomized Block Coordinate Descent for Neural Probabilistic Language Model with High-Dimensional Output Targets
Training a large probabilistic neural network language model, with typical high-dimensional output is excessively time-consuming, which is one of the main reasons that more simplified models such as n-gram is oft...
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Chapter and Conference Paper
Graduated Consistency-Regularized Optimization for Multi-graph Matching
Graph matching has a wide spectrum of computer vision applications such as finding feature point correspondences across images. The problem of graph matching is generally NP-hard, so most existing work pursues...
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Chapter and Conference Paper
Learning the Hotness of Information Diffusions with Multi-dimensional Hawkes Processes
Modeling the information cascading process over networks has attracted a lot of research attention due to its wide applications in viral marketing, epidemiology and recommendation systems. In particular, infor...
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Chapter and Conference Paper
Variational Graph Embedding for Globally and Locally Consistent Feature Extraction
Existing feature extraction methods explore either global statistical or local geometric information underlying the data. In this paper, we propose a general framework to learn features that account for both t...
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Chapter and Conference Paper
IKNN: Informative K-Nearest Neighbor Pattern Classification
The K-nearest neighbor (KNN) decision rule has been a ubiquitous classification tool with good scalability. Past experience has shown that the optimal choice of K depends upon the data, making it laborious to tun...
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Chapter and Conference Paper
Spectral Clustering for Robust Motion Segmentation
In this paper, we propose a robust motion segmentation method using the techniques of matrix factorization and subspace separation. We first show that the shape interaction matrix can be derived using QR decompos...
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Chapter and Conference Paper
Extracting Shared Topics of Multiple Documents
In this paper, we present a weighted graph based method to simultaneously compare the textual content of two or more documents and extract the shared (sub)topics of them, if available. A set of documents are m...
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Chapter and Conference Paper
Nonlinear Dimension Reduction via Local Tangent Space Alignment
In this paper we present a new algorithm for manifold learning and nonlinear dimension reduction. Based on a set of unorganized data points sampled with noise from the manifold, we represent the local geometry...
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Chapter and Conference Paper
Unsupervised Learning: Self-aggregation in Scaled Principal Component Space*
We demonstrate that data clustering amounts to a dynamic process of self-aggregation in which data objects move towards each other to form clusters, revealing the inherent pattern of similarity. Selfaggregation i...
