Search Results for author:
Found 32 papers, 5 papers with code
Copula Mixed-Membership Stochastic Blockmodel for Intra-Subgroup Correlations
To this end, we introduce a \emph{Copula Mixed-Membership Stochastic Blockmodel (cMMSB)} where an individual Copula function is employed to jointly model the membership pairs of those nodes within the subgroup of interest.
Dynamic Infinite Mixed-Membership Stochastic Blockmodel
Directional and pairwise measurements are often used to model inter-relationships in a social network setting.
A non-parametric conditional factor regression model for high-dimensional input and response
In this paper, we propose a non-parametric conditional factor regression (NCFR)model for domains with high-dimensional input and response.
Learning Hidden Structures with Relational Models by Adequately Involving Rich Information in A Network
In this work, we propose an informative relational model (InfRM) framework to adequately involve rich information and its granularity in a network, including metadata information about each entity and various forms of link data.
An Adaptive Online HDP-HMM for Segmentation and Classification of Sequential Data
This infinite adaptive online approach is capable of segmenting and classifying the sequential data over unlimited number of classes, while meeting the memory and delay constraints of streaming contexts.
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.
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.
Bayesian nonparametric image segmentation using a generalized Swendsen-Wang algorithm
We introduce here a class of Bayesian nonparametric models to address this problem.
Smoothed Hierarchical Dirichlet Process: A Non-Parametric Approach to Constraint Measures
The key novelty of this model is that we place a temporal constraint amongst the nearby discrete measures $\{G_j\}$ in the form of symmetric Kullback-Leibler (KL) Divergence with a fixed bound $B$.
The Dependent Random Measures with Independent Increments in Mixture Models
One of the propositions of the dependent random measures is that the atoms of the posterior distribution are shared amongst groups, and hence groups can borrow information from each other.
Cooperative Hierarchical Dirichlet Processes: Superposition vs. Maximization
The cooperative hierarchical structure is a common and significant data structure observed in, or adopted by, many research areas, such as: text mining (author-paper-word) and multi-label classification (label-instance-feature).
Diverse Online Feature Selection
The framework aims to promote diversity based on the kernel produced on a feature level, through at most three stages: feature sampling, local criteria and global criteria for feature selection.
Magnitude Bounded Matrix Factorisation for Recommender Systems
Low rank matrix factorisation is often used in recommender systems as a way of extracting latent features.
GAN-based Gaussian Mixture Model Responsibility Learning
Instead of defining p(x|k, θ) explicitly, we devised a modified GAN to allow us to define the distribution using p(z|k, θ), where z is the corresponding latent representation of x, as well as p(k|x, θ) through an additional classification network which is trained with the GAN in an “end-to-end” fashion.
Gaussian Process Latent Variable Model Factorization for Context-aware Recommender Systems
Our work is primarily inspired by the Gaussian Process Latent Variable Model (GPLVM), which was a non-linear dimensionality reduction method.
Mean field theory for deep dropout networks: digging up gradient backpropagation deeply
In recent years, the mean field theory has been applied to the study of neural networks and has achieved a great deal of success.
On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization
The prevailing thinking is that orthogonal weights are crucial to enforcing dynamical isometry and speeding up training.
RGB-IR Cross-modality Person ReID based on Teacher-Student GAN Model
To solve this challenge, we proposed a Teacher-Student GAN model (TS-GAN) to adopt different domains and guide the ReID backbone to learn better ReID information.
Implicit bias of deep linear networks in the large learning rate phase
We claim that depending on the separation conditions of data, the gradient descent iterates will converge to a flatter minimum in the catapult phase.
Resolution-invariant Person ReID Based on Feature Transformation and Self-weighted Attention
In this paper, we propose a novel two-stream network with a lightweight resolution association ReID feature transformation (RAFT) module and a self-weighted attention (SWA) ReID module to evaluate features under different resolutions.
Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective
Inspired by our theoretical insights on trainability, we propose Critical DropEdge, a connectivity-aware and graph-adaptive sampling method, to alleviate the exponential decay problem more fundamentally.
Alleviating Mode Collapse in GAN via Diversity Penalty Module
We compare the proposed method with Unrolled GAN (Metz et al. 2016), BourGAN (Xiao, Zhong, and Zheng 2018), PacGAN (Lin et al. 2018), VEEGAN (Srivastava et al. 2017) and ALI (Dumoulin et al. 2016) on 2D synthetic dataset, and results show that the diversity penalty module can help GAN capture much more modes of the data distribution.
Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures
We propose using regularization for Multi-Agent Reinforcement Learning rather than learning explicit cooperative structures called {\em Multi-Agent Regularized Q-learning} (MARQ).
Dual Behavior Regularized Reinforcement Learning
We demonstrate the flexibility of this approach and how it can be adapted to online contexts where the environment is available to collect experiences and a variety of other contexts.
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning
This paper introduces Greedy UnMix (GUM) for cooperative multi-agent reinforcement learning (MARL).
Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning
In addition to being a pure generalization bound analysis tool, PAC-Bayesian bound can also be incorporated into an objective function to train a probabilistic neural network, making them a powerful and relevant framework that can numerically provide a tight generalization bound for supervised learning.
Domain Decorrelation with Potential Energy Ranking
PoER helps the neural networks to capture label-related features which contain the domain information first in shallow layers and then distills the label-discriminative representations out progressively, enforcing the neural networks to be aware of the characteristic of objects and background which is vital to the generation of domain-invariant features.
Free Lunch for Generating Effective Outlier Supervision
Generally, existing approaches in dealing with out-of-distribution (OOD) detection mainly focus on the statistical difference between the features of OOD and in-distribution (ID) data extracted by the classifiers.
A variational autoencoder-based nonnegative matrix factorisation model for deep dictionary learning
Construction of dictionaries using nonnegative matrix factorisation (NMF) has extensive applications in signal processing and machine learning.
KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation
Knowledge graph completion is a task that revolves around filling in missing triples based on the information available in a knowledge graph.
End-to-end Dynamic Matching Network for Multi-view Multi-person 3d Pose Estimation
To address this phenomenon, we propose a novel end-to-end training scheme that brings the three separate modules into a single model.
