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Yifan CHEN, Prof
Assistant Professor
,
Department of Computer Science
Assistant Professor (Affiliate)
,
Department of Mathematics
Email
yifanc
hkbu.edu
hk
2021
2025
Research activity per year
Overview
Fingerprint
Network
Projects / Grants
(1)
Research Output
(12)
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%
Approximated Matrix
66%
Transfer Learning
66%
Kernel Ridge Regression
66%
Graph Convolutional Network
66%
Natural Language Generation
55%
Natural-Language Understanding
55%
Space Complexity
50%
Neural Network
50%
Kernel Method
44%
Approximation (Algorithm)
44%
Theoretical Framework
33%
Kernel Estimator
33%
Gaussian Kernel
33%
Prompt Tuning
33%
Geometric Approach
33%
Time Complexity
33%
Computational Cost
33%
Kernel Machine
33%
Language Modeling
33%
Performance Gap
33%
Multiobjective
33%
Spectral Information
33%
Distance Graph
33%
Unified Framework
33%
Metric Space
33%
Sketching Method
33%
Machine Learning
33%
Natural Sciences Computing
33%
Learning System
33%
Attention (Machine Learning)
33%
Implementation Detail
33%
Classification Task
33%
Performance Gain
33%
Subgraphs
33%
Edge Information
33%
Graph Representation
33%
Coordinate System
16%
Quadratic Time Complexity
16%
Massive Amount
16%
Self-Attention Mechanism
16%
Estimation Variance
16%
approximation scheme
16%
Training Model
16%
Node Embedding
16%
Mathematical Convergence
16%
Analysis Framework
16%
Pareto Solution
16%
Real-World Problem
16%
Benchmark Problem
16%
Keyphrases
Self-attention
88%
Geometric Perspective
66%
Kernel Ridge Regression
66%
Pre-trained Language Model
55%
Kernel Methods
48%
Nystrm Method
44%
Matrix Approximation
44%
Sampling Methods
44%
Graph Convolutional Network
44%
Parameter Transfer Learning
37%
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%
Language Model
33%
Multilayer Perceptron
33%
Debiasing
33%
Sketching Techniques
33%
Pareto Set
33%
Unified Framework
33%
Adapter
33%
Hypervolume
33%
Causal Signals
33%
Quadratic Space
27%
Quadratic Time
27%
Kernel Learning
22%
Graph Spectrum
22%
Natural Language Understanding
20%
Natural Language Generation
20%
Empirical Kernel
17%
Kernel Matrix
17%
Spectral Norm
16%
Computational Cost
16%
Approximation Scheme
16%
Self-attention Mechanism
16%
Sampling without Replacement
16%
Layerwise Theory
16%
Training Model
16%
Mathematics
Approximated Matrix
66%
Modulo
40%
Matrix (Mathematics)
40%
Leverage Score
33%
Gaussian Distribution
33%
Polar Coordinate System
33%
Metric Space
33%
K-Means
33%
Adaptive Sampling
33%
Computational Cost
33%
Neural Network
33%
Ridge Regression
33%
Finite Number
33%
Convergence Analysis
33%
Probability Theory
33%
World Problem
33%
Dot Product
16%
Approximation Error
16%
Complexity Space
16%
Sampling Without Replacement
16%
Positive Semidefinite Matrix
16%
Variance Estimation
16%
Importance Sampling
16%
Subsample
6%
Analytic Formula
6%
Spectral Density
6%
Approximates
6%
Sampling Distribution
6%
Numerical Experiment
6%
Objective Function
6%
Linear Time
6%
