This article proposes a novel causal discovery and inference method call...
Automatic few-shot font generation (AFFG), aiming at generating new font...
Reinforcement learning (RL) exhibits impressive performance when managin...
Existing normal estimation methods for point clouds are often less robus...
Sim-and-real training is a promising alternative to sim-to-real training...
This article introduces a causal discovery method to learn nonlinear
rel...
In explainable artificial intelligence, discriminative feature localizat...
Methods utilizing instrumental variables have been a fundamental statist...
Large-scale genome-wide association studies (GWAS) have offered an excit...
This paper investigates the mathematical properties of a stochastic vers...
Discovering governing equations from data is critical for diverse scient...
A critical step in virtual dental treatment planning is to accurately
de...
In this work we set the stage for a new probabilistic pathwise approach ...
As a popular representation of 3D data, point cloud may contain noise an...
Recently, barrier function-based safe reinforcement learning (RL) with t...
Inference of directed relations given some unspecified interventions, th...
A recommender system learns to predict the user-specific preference or
i...
This paper proposes a sparse Bayesian treatment of deep neural networks
...
Inertial measurement units are widely used in different fields to estima...
Deep reinforcement learning (DRL) has successfully solved various proble...
This paper presents a deep reinforcement learning (DRL) algorithm for
or...
An exciting recent development is the uptake of deep learning in many
sc...
Reinforcement learning (RL) is promising for complicated stochastic nonl...
In this paper, we consider the state estimation problem for nonlinear
st...
Prediction of human motions is key for safe navigation of autonomous rob...
Functional magnetic resonance imaging (fMRI) data have become increasing...
Decentralized multi-agent control has broad applications, ranging from
m...
Unobserved confounders are a long-standing issue in causal inference usi...
This paper presents a novel model-reference reinforcement learning algor...
Binary Convolutional Neural Networks (CNNs) can significantly reduce the...
Feature-preserving mesh denoising has received noticeable attention rece...
Deep Reinforcement Learning (DRL) has achieved impressive performance in...
This paper presents a novel model-reference reinforcement learning contr...
Minimizing Gaussian curvature of meshes is fundamentally important for
o...
Nonlinear system identification is important with a wide range of
applic...
Reinforcement learning is showing great potentials in robotics applicati...
Reinforcement learning is showing great potentials in robotics applicati...
This paper contains the latest installment of the authors' project on
de...
In this work, we apply a particle filter with three additional procedure...
One-Shot Neural Architecture Search (NAS) is a promising method to
signi...
This paper presents a simple yet effective method for feature-preserving...
Humans are capable of attributing latent mental contents such as beliefs...
Cyber-physical systems (CPSs) embed software into the physical world. Th...
Many high-dimensional hypothesis tests aim to globally examine marginal ...
We introduce a novel scheme to train binary convolutional neural network...
Even though many machine algorithms have been proposed for entity resolu...
This paper proposes a practical approach to addressing limitations posed...
Collective intelligence is believed to underly the remarkable success of...
Deep learning using multi-layer neural networks (NNs) architecture manif...
Clustering analysis is one of the most widely used statistical tools in ...