Search Results for author:
Found 26 papers, 5 papers with code
3D Implicit Transporter for Temporally Consistent Keypoint Discovery
To address this issue, the Transporter method was introduced for 2D data, which reconstructs the target frame from the source frame to incorporate both spatial and temporal information.
Learning to Branch in Combinatorial Optimization with Graph Pointer Networks
The proposed model, which combines the graph neural network and the pointer mechanism, can effectively map from the solver state to the branching variable decisions.
Enhancing the Robustness of QMIX against State-adversarial Attacks
To increase the robustness of multi-agent reinforcement learning (MARL) algorithms, we train models using a variety of attacks in this research.
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks
To account for the uncertainty caused by the limited training tasks, we propose a variational MetaModulation where the modulation parameters are treated as latent variables.
STEPS: Joint Self-supervised Nighttime Image Enhancement and Depth Estimation
By fitting a bridge-shaped curve to the illumination map distribution, both regions are suppressed and two tasks are bridged naturally.
Coevolutionary Framework for Generalized Multimodal Multi-objective Optimization
To address the above two issues, in this study, a novel coevolutionary framework termed CoMMEA for multimodal multi-objective optimization is proposed to better obtain both global and local PSs, and simultaneously, to improve the convergence performance in dealing with high-dimension MMOPs.
VDDB: a comprehensive resource and machine learning platform for antiviral drug discovery
Virus infection is one of the major diseases that seriously threaten human health.
HiGNN: Hierarchical Informative Graph Neural Networks for Molecular Property Prediction Equipped with Feature-Wise Attention
Elucidating and accurately predicting the druggability and bioactivities of molecules plays a pivotal role in drug design and discovery and remains an open challenge.
Large-scale matrix optimization based multi microgrid topology design with a constrained differential evolution algorithm
Binary matrix optimization commonly arise in the real world, e. g., multi-microgrid network structure design problem (MGNSDP), which is to minimize the total length of the power supply line under certain constraints.
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction
In addition, we analyzed the influence of different molecular fingerprints, and the effects of molecular graphs and molecular fingerprints on the performance of the FP-GNN model.
Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer
The model is trained using a deep reinforcement learning method offline, and is used to optimize the UAV routing problem online.
Temporal Convolution Domain Adaptation Learning for Crops Growth Prediction
We are the first to use the temporal convolution filters as the backbone to construct a domain adaptation network architecture which is suitable for deep learning regression models with very limited training data of the target domain.
Pyramid Architecture for Multi-Scale Processing in Point Cloud Segmentation
Semantic segmentation of point cloud data is a critical task for autonomous driving and other applications.
Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?
Using XAI, we quantified the contribution that specific drugs had on these ACS predictions, thus creating an XAI-based technique for pharmacovigilance monitoring, using ACS as an example of the adverse outcome to detect.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Time-consistent mean-variance reinsurance-investment problem with long-range dependent mortality rate
We also use a numerical study to reveal the influence of LRD on equilibrium strategies.
Optimal Expansion of Business Opportunity
The length of the waiting period is related to the opportunity cost, return, and risk of the expanded business.
Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling
In a set of experiments on three real-world financial markets: stocks, cryptocurrencies, and ETFs, LARA significantly outperforms several machine learning based methods on the Qlib quantitative investment platform.
LAI Estimation of Cucumber Crop Based on Improved Fully Convolutional Network
How to measure LAI accurately and efficiently is the key to the crop yield estimation problem.
A Deep Learning-Based Approach to Extracting Periosteal and Endosteal Contours of Proximal Femur in Quantitative CT Images
In this study, we proposed an approach based on deep learning for the automatic extraction of the periosteal and endosteal contours of proximal femur in order to differentiate cortical and trabecular bone compartments.
Progressive Defense Against Adversarial Attacks for Deep Learning as a Service in Internet of Things
To reduce the computational cost without loss of generality, we present a defense strategy called a progressive defense against adversarial attacks (PDAAA) for efficiently and effectively filtering out the adversarial pixel mutations, which could mislead the neural network towards erroneous outputs, without a-priori knowledge about the attack type.
Volterra mortality model: Actuarial valuation and risk management with long-range dependence
While abundant empirical studies support the long-range dependence (LRD) of mortality rates, the corresponding impact on mortality securities are largely unknown due to the lack of appropriate tractable models for valuation and risk management purposes.
Balancing Common Treatment and Epidemic Control in Medical Procurement during COVID-19: Transform-and-Divide Evolutionary Optimization
In this paper, we present an approach that first transforms the original high-dimensional, constrained multiobjective optimization problem to a low-dimensional, unconstrained multiobjective optimization problem, and then evaluates each solution to the transformed problem by solving a set of simple single-objective optimization subproblems, such that the problem can be efficiently solved by existing evolutionary multiobjective algorithms.
Frontoparietal Connectivity Neurofeedback Training for Promotion of Working Memory: An fNIRS Study in Healthy Male Participants
Neurofeedback cognitive training is a promising tool used to promote cognitive functions effectively and efficiently.
Model Embedded DRL for Intelligent Greenhouse Control
Greenhouse environment is the key to influence crops production.
Retrosynthesis with Attention-Based NMT Model and Chemical Analysis of the "Wrong" Predictions
We cast retrosynthesis as a machine translation problem by introducing a special Tensor2Tensor, an entire attention-based and fully data-driven model.
Sample-Efficient Policy Learning based on Completely Behavior Cloning
Direct policy search is one of the most important algorithm of reinforcement learning.
