Search Results for author:
Found 24 papers, 14 papers with code
Balancing property optimization and constraint satisfaction for constrained multi-property molecular optimization
Molecular optimization, which aims to discover improved molecules from a vast chemical search space, is a critical step in chemical development.
Diffusion-Inspired Cold Start with Sufficient Prior in Computerized Adaptive Testing
Redundant and extraneous cognitive states can lead to limited transfer and negative transfer effects.
DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis
To tackle the issues, this paper suggests a meta multigraph-assisted disentangled graph learning framework for CD (DisenGCD), which learns three types of representations on three disentangled graphs: student-exercise-concept interaction, exercise-concept relation, and concept dependency graphs, respectively.
DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation
To this end, in this paper, we propose DISCO, a hierarchical Disentanglement based Cognitive diagnosis framework, aimed at flexibly accommodating the underlying representation learning model for effective and interpretable job recommendations.
Reconstruction of Particle Flow Energy Distribution Using Deep Learning Algorithms
In high-energy particle physics, extracting information from complex detector signals is crucial for energy reconstruction.
Dual-stream Feature Augmentation for Domain Generalization
On the other hand, the causality in discriminative features is not involved in these methods, which harms the generalization ability of model due to the spurious correlations.
RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning Processes
To this end, in this paper, we propose RIGL, a unified Reciprocal model to trace knowledge states at both the individual and group levels, drawing from the Independent and Group Learning processes.
Towards Deeper Understanding of PPR-based Embedding Approaches: A Topological Perspective
In this work, we first show that state-of-the-art embedding approaches that factorize a PPR-related matrix can be unified into a closed-form framework.
Gradually Vanishing Gap in Prototypical Network for Unsupervised Domain Adaptation
Recent advancements in UDA models have demonstrated significant generalization capabilities on the target domain.
SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection
We introduce Individual Smoothing Patterns (ISP) and Neighborhood Smoothing Patterns (NSP), which indicate that the representations of anomalous nodes are harder to smooth than those of normal ones.
Dual-State Personalized Knowledge Tracing with Emotional Incorporation
To alleviate this limitation, we explicitly models the personalized learning process by incorporating the emotions, a representative personalized behavior in the learning process, into KT framework.
KPG: Key Propagation Graph Generator for Rumor Detection based on Reinforcement Learning
In this paper, we propose Key Propagation Graph Generator (KPG), a novel reinforcement learning-based rumor detection framework that generates contextually coherent and informative propagation patterns for events with insufficient topology information, while also identifies indicative substructures for events with redundant and noisy propagation structures.
Personalized Forgetting Mechanism with Concept-Driven Knowledge Tracing
To address these problems, we propose a Concept-driven Personalized Forgetting knowledge tracing model (CPF) which integrates hierarchical relationships between knowledge concepts and incorporates students' personalized cognitive abilities.
ReliCD: A Reliable Cognitive Diagnosis Framework with Confidence Awareness
To address this, in this paper, we propose a novel Reliable Cognitive Diagnosis(ReliCD) framework, which can quantify the confidence of the diagnosis feedback and is flexible for different cognitive diagnostic functions.
Enhancing Cognitive Diagnosis using Un-interacted Exercises: A Collaboration-aware Mixed Sampling Approach
Finally, we demonstrate the effectiveness and interpretability of our framework through comprehensive experiments on real-world datasets.
Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection
Moreover, we prove that the accumulated spectral energy of the graph signal can be represented by its Rayleigh Quotient, indicating that the Rayleigh Quotient is a driving factor behind the anomalous properties of graphs.
Evolutionary Neural Architecture Search for Transformer in Knowledge Tracing
Knowledge tracing (KT) aims to trace students' knowledge states by predicting whether students answer correctly on exercises.
EQ-Net: Elastic Quantization Neural Networks
In this paper, we explore a one-shot network quantization regime, named Elastic Quantization Neural Networks (EQ-Net), which aims to train a robust weight-sharing quantization supernet.
Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search
Then, we propose multi-objective genetic programming (MOGP) to explore the NAS task's search space by maximizing model performance and interpretability.
Self-supervised Graph Learning for Long-tailed Cognitive Diagnosis
Cognitive diagnosis is a fundamental yet critical research task in the field of intelligent education, which aims to discover the proficiency level of different students on specific knowledge concepts.
Accelerating Evolutionary Neural Architecture Search via Multi-Fidelity Evaluation
Evolutionary neural architecture search (ENAS) has recently received increasing attention by effectively finding high-quality neural architectures, which however consumes high computational cost by training the architecture encoded by each individual for complete epochs in individual evaluation.
Learning Based Proximity Matrix Factorization for Node Embedding
Recent progress on node embedding shows that proximity matrix factorization methods gain superb performance and scale to large graphs with millions of nodes.
Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics
In the past three decades, a large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems.
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization
To address these issues, we have developed a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes more than 50 multi-objective evolutionary algorithms and more than 100 multi-objective test problems, along with several widely used performance indicators.
