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Yifan CHEN, Prof
Assistant Professor
,
Department of Computer Science
Assistant Professor (Affiliate)
,
Department of Mathematics
https://orcid.org/0000-0001-9462-9122
Email
yifanc
hkbu.edu
hk
2021
2025
Research activity per year
Overview
Fingerprint
Network
Projects / Grants
(3)
Research Output
(16)
Similar Scholars
(3)
Fingerprint
Dive into the research topics where Yifan CHEN is active. Topic labels come from the works of this scholar. Together they form a unique fingerprint.
Sort by:
Weight
Alphabetically
Computer Science
Pre-Trained Language Models
100%
Multiobjective
100%
Self-Attention
100%
Approximation (Algorithm)
77%
Transfer Learning
66%
Kernel Ridge Regression
66%
Graph Convolutional Network
66%
Language Modeling
66%
Natural Language Generation
55%
Neural Network
50%
Kernel Method
44%
Approximated Matrix
41%
Machine Learning
40%
Learning System
40%
Natural Language Understanding
38%
Kernel Estimator
33%
Gaussian Kernel
33%
Prompt Tuning
33%
Geometric Approach
33%
Computational Cost
33%
Kernel Machine
33%
Performance Gap
33%
Spectral Information
33%
Distance Graph
33%
Unified Framework
33%
Metric Space
33%
Sketching Method
33%
Natural Sciences Computing
33%
Attention (Machine Learning)
33%
Implementation Detail
33%
Classification Task
33%
Performance Gain
33%
Subgraphs
33%
Edge Information
33%
Graph Representation
33%
Optimal Threshold
33%
Common Practice
33%
Training Process
33%
Effective Approach
33%
Predicted Probability
33%
Wasserstein Barycenter
33%
Mixture-of-Experts LLM
33%
Sparsity
33%
Large Language Model
33%
Contrastive Learning
33%
Stochastic Policy
33%
Markov Decision Process
33%
Testing Process
33%
Process Framework
33%
Likelihood Ratio
33%
Keyphrases
Self-attention
88%
Geometric Perspective
66%
Kernel Ridge Regression
66%
Pre-trained Language Model
55%
Multi-objective Optimization
50%
Kernel Methods
48%
Nystrm Method
44%
Matrix Approximation
44%
Sampling Methods
44%
Graph Convolutional Network
44%
Pareto Set
44%
Graph Classification
44%
Language Model
41%
Transformer
41%
Parameter Transfer Learning
37%
Pareto Optimal Solution
34%
Statistical Leverage
33%
Set Learning
33%
Random Sketching
33%
Gaussian Kernel
33%
Adapter Tuning
33%
Prefix Tuning
33%
Neural Tangent Kernel
33%
Kernel Machines
33%
Model Fine-tuning
33%
Gromov-Wasserstein
33%
Graph Coarsening
33%
Long Sequence
33%
Sub-Gaussian
33%
Kernel Structure
33%
Inducer
33%
Sampling Probability
33%
Multilayer Perceptron
33%
Debiasing
33%
Sketching Techniques
33%
Unified Framework
33%
Adapter
33%
Hypervolume
33%
Causal Signals
33%
Gradient-based
33%
Optimization Library
33%
Multi-objective Optimization Problem
33%
PyTorch
33%
Gradient Clipping
33%
Noisy Label Learning
33%
Efficient Compression
33%
Label Imbalance
33%
Domain Generalization
33%
Pareto Front
33%
Mixture-of-experts
33%
