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Found 22 papers, 4 papers with code
Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation
However, TRISO failure probabilities are small and the associated computational models are expensive.
Analysing Wideband Absorbance Immittance in Normal and Ears with Otitis Media with Effusion Using Machine Learning
Wideband Absorbance Immittance (WAI) has been available for more than a decade, however its clinical use still faces the challenges of limited understanding and poor interpretation of WAI results.
Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks
A large number of network embedding methods exist to learn vectorial node representations from general graphs with both homogeneous and heterogeneous node and edge types, including some that can specifically model the distinct properties of bipartite networks.
Pseudo-Loss Confidence Metric for Semi-Supervised Few-Shot Learning
Most semi-supervised few-shot learning methods select pseudo-labeled data of unlabeled set by task-specific confidence estimation.
Coherent Reconstruction of Multiple Humans from a Single Image
Our goal is to train a single network that learns to avoid these problems and generate a coherent 3D reconstruction of all the humans in the scene.
Ranked #1 on
3D Human Reconstruction
on AGORA
Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN
It has achieved great success on many tasks of network analysis such as link prediction and node classification.
Deep Snake for Real-Time Instance Segmentation
Based on deep snake, we develop a two-stage pipeline for instance segmentation: initial contour proposal and contour deformation, which can handle errors in object localization.
Ranked #1 on
Semantic Contour Prediction
on Sbd val
On the negation of a Dempster-Shafer belief structure based on maximum uncertainty allocation
Probability theory and Dempster-Shafer theory are two germane theories to represent and handle uncertain information.
Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views
This paper addresses the problem of 3D pose estimation for multiple people in a few calibrated camera views.
Ranked #5 on
3D Multi-Person Pose Estimation
on Shelf
A total uncertainty measure for D numbers based on belief intervals
As a generalization of Dempster-Shafer theory, the theory of D numbers is a new theoretical framework for uncertainty reasoning.
D numbers theory based game-theoretic framework in adversarial decision making under fuzzy environment
Within the proposed framework or model, fuzzy set theory is used to model the uncertain evaluations of decision makers to alternatives, the non-exclusiveness among fuzzy evaluations are taken into consideration by using DNT, and the conflict of interests among decision makers is considered in a two-person non-constant sum game theory perspective.
Uncertainty measurement with belief entropy on interference effect in Quantum-Like Bayesian Networks
Social dilemmas have been regarded as the essence of evolution game theory, in which the prisoner's dilemma game is the most famous metaphor for the problem of cooperation.
An evidential Markov decision making model
However, the state is allowed to be uncertain in the EM model before the final decision is made.
A New Medical Diagnosis Method Based on Z-Numbers
In this paper, a new decision making methodology based on Z-numbers is presented.
Quantum Mechanical Approach to Modelling Reliability of Sensor Reports
Dempster-Shafer evidence theory is wildly applied in multi-sensor data fusion.
Exploring the Combination Rules of D Numbers From a Perspective of Conflict Redistribution
Dempster-Shafer theory of evidence is widely applied to uncertainty modelling and knowledge reasoning because of its advantages in dealing with uncertain information.
Evidential supplier selection based on interval data fusion
However, the weights of criteria are kept as interval numbers to generate interval BPAs and do the fusion of interval BPAs.
A quantum dynamic belief model to explain the interference effects of categorization on decision making
However, the categorization process may interfere the decision making result and the law of total probability can be violated in some situations.
A new belief Markov chain model and its application in inventory prediction
The new belief Markov chain model overcomes the shortcomings of classical Markov chain and has an efficient ability in dealing with uncertain information.
A quantum dynamic belief decision making model
The uncertainty in actions is represented as an extra uncertainty state.
A modified Physarum-inspired model for the user equilibrium traffic assignment problem
The user equilibrium traffic assignment principle is very important in the traffic assignment problem.
A correlation coefficient of belief functions
The correlation coefficient can be used to measure the similarity of evidence in Dempster-Shafer evidence theory.
