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Found 37 papers, 7 papers with code
Causal Structure Learning Supervised by Large Language Model
Causal discovery from observational data is pivotal for deciphering complex relationships.
Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems
The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem with broad real-world applications.
From Query Tools to Causal Architects: Harnessing Large Language Models for Advanced Causal Discovery from Data
In this paper, we advance the current research of LLM-driven causal discovery by proposing a novel framework that combines knowledge-based LLM causal analysis with data-driven causal structure learning.
Mitigating Prior Errors in Causal Structure Learning: Towards LLM driven Prior Knowledge
LLM presents strong capability in discovering causal relationships between variables with the "text" inputs defining the investigated variables, leading to a potential new hierarchy and new ladder of causality.
Underground Diagnosis Based on GPR and Learning in the Model Space
In this paper, a GPR B-scan image diagnosis method based on learning in the model space is proposed.
Improving the Anomaly Detection in GPR Images by Fine-Tuning CNNs with Synthetic Data
In this paper, a novel method is proposed to improve the subsurface anomaly detection from GPR B-scan images.
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering
It is therefore currently impossible to ensure Byzantine robustness and confidentiality of updates without assuming a semi-honest majority.
Estimating the Direction and Radius of Pipe from GPR Image by Ellipse Inversion Model
By minimizing the sum of the algebraic distances from the extracted point set to the inverted ellipse, the most likely pipeline's direction and radius are determined.
Mapping the Buried Cable by Ground Penetrating Radar and Gaussian-Process Regression
On the basis of the established coordinate system and the derived points on the cable, the clustering method and cable fitting algorithm based on Gaussian-process regression are proposed to find the most likely locations of the underground cables.
UniDS: A Unified Dialogue System for Chit-Chat and Task-oriented Dialogues
With the advances in deep learning, tremendous progress has been made with chit-chat dialogue systems and task-oriented dialogue systems.
A Survey on Reinforcement Learning for Recommender Systems
To understand the challenges and relevant solutions, there should be a reference for researchers and practitioners working on RL-based recommender systems.
Semi-supervised Learning with Missing Values Imputation
SSCFlow explicitly utilizes the label information to facilitate the imputation and classification simultaneously by estimating the conditional distribution of incomplete instances with a novel semi-supervised normalizing flow.
Do Models Learn the Directionality of Relations? A New Evaluation: Relation Direction Recognition
Deep neural networks such as BERT have made great progress in relation classification.
Relation Classification with Entity Type Restriction
Specially, the mutual restrictions of relations and entity types are formalized and introduced into relation classification.
Ranked #5 on
Relation Extraction
on TACRED
When Non-Elitism Meets Time-Linkage Problems
However, only two elitist algorithms (1+1)EA and ($\mu$+1)EA are analyzed, and it is unknown whether the non-elitism mechanism could help to escape the local optima existed in OneMax$_{(0, 1^n)}$.
Brain-inspired Search Engine Assistant based on Knowledge Graph
However, when a query is complex, developers need to repeatedly refine the search keywords and open a large number of web pages to find and summarize answers.
A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes
To handle different types of Many-Objective Optimization Problems (MaOPs), Many-Objective Evolutionary Algorithms (MaOEAs) need to simultaneously maintain convergence and population diversity in the high-dimensional objective space.
Trustworthy Preference Completion in Social Choice
As from time to time it is impractical to ask agents to provide linear orders over all alternatives, for these partial rankings it is necessary to conduct preference completion.
Multi-label Causal Variable Discovery: Learning Common Causal Variables and Label-specific Causal Variables
Analyzing these mechanisms, we provide the theoretical property of common causal variables, based on which the discovery and distinguishing algorithm is designed to identify these two types of variables.
A Weighted Heterogeneous Graph Based Dialogue System
Most research on disease diagnosis dialogue systems highly rely on data-driven methods and statistical features, lacking profound comprehension of symptom-disease relations and symptom-symptom relations.
Unsupervised Change Detection in Satellite Images with Generative Adversarial Network
Detecting changed regions in paired satellite images plays a key role in many remote sensing applications.
A Systematic Survey of Regularization and Normalization in GANs
Although a handful number of regularization and normalization methods have been proposed for GANs, to the best of our knowledge, there exists no comprehensive survey that primarily focuses on objectives and development of these methods, apart from some in-comprehensive and limited scope studies.
Correction of Faulty Background Knowledge based on Condition Aware and Revise Transformer for Question Answering
CAR-Transformer (1) revises each condition value based on the whole conversation and original conditions values, and (2) it encodes the revised conditions and utilizes the conditions embedding to select an answer.
Probabilistic Classification Vector Machine for Multi-Class Classification
The probabilistic classification vector machine (PCVM) synthesizes the advantages of both the support vector machine and the relevant vector machine, delivering a sparse Bayesian solution to classification problems.
Analysis of Evolutionary Algorithms on Fitness Function with Time-linkage Property
In real-world applications, many optimization problems have the time-linkage property, that is, the objective function value relies on the current solution as well as the historical solutions.
Use Short Isometric Shapelets to Accelerate Binary Time Series Classification
In the research area of time series classification, the ensemble shapelet transform algorithm is one of state-of-the-art algorithms for classification.
When does Diversity Help Generalization in Classification Ensembles?
To reveal the effect of diversity on the generalization of classification ensembles, we investigate three issues on diversity, i. e., the measurement of diversity, the relationship between the proposed diversity and the generalization error, and the utilization of this relationship for ensemble pruning.
Sub-Architecture Ensemble Pruning in Neural Architecture Search
Neural architecture search (NAS) is gaining more and more attention in recent years due to its flexibility and remarkable capability to reduce the burden of neural network design.
Converse Attention Knowledge Transfer for Low-Resource Named Entity Recognition
In the process of translation, CAN obtain the attention matrices that align the two languages.
Low Resource Named Entity Recognition
named-entity-recognition
+4
Skeleton-based Activity Recognition with Local Order Preserving Match of Linear Patches
In this paper, we first design an efficient division method to decompose a manifold into ordered continuous maximal linear patches (CMLPs) that denote meaningful action snippets of the action sequence.
Multiple Kernel $k$-Means Clustering by Selecting Representative Kernels
Despite the success of multiple kernel $k$-means clustering in various scenarios, few of the existing work update the combination coefficients based on the diversity of kernels, which leads to the result that the selected kernels contain high redundancy and would degrade the clustering performance and efficiency.
Ensemble Pruning based on Objection Maximization with a General Distributed Framework
Ensemble pruning, selecting a subset of individual learners from an original ensemble, alleviates the deficiencies of ensemble learning on the cost of time and space.
Disturbance Grassmann Kernels for Subspace-Based Learning
In this paper, we focus on subspace-based learning problems, where data elements are linear subspaces instead of vectors.
Dynamic State Warping
Thus DSW is able to yield alignment that is semantically more interpretable than that of DTW.
Probabilistic Feature Selection and Classification Vector Machine
The proposed method, called probabilistic feature selection and classification vector machine (PFCVMLP ), is able to simultaneously select relevant features and samples for classification tasks.
