Kezhi Kong
9 papers · 2021–2025 · 6 venues · across top CS/AI venues
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Achievements
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🌍
Conference Polyglot · Silver
🗺️
Taxonomy Completionist
(26)
🐣
Hot Topic Early Bird
🏆
Grand Slam
🔥
Unstoppable · Bronze
Top co-authors
Research topics
Machine Learning
(7)
Deep Learning
(4)
Architectures
(4)
Artificial Intelligence
(3)
Application Areas
(3)
Learning Types · Machine Learning
(3)
Learning Types · Deep Learning
(3)
Core AI
(2)
Optimization & Theory
(2)
Natural Language Processing
(1)
Applications
(1)
Core Methods
(1)
Learning Paradigms · Artificial Intelligence
(1)
Learning Paradigms · Machine Learning
(1)
Models
(1)
Keywords
graph neural network
(3)
node classification
(3)
link prediction
(2)
data augmentation
(2)
semi-supervised learning
(1)
few-shot learning
(1)
stochastic gradient descent
(1)
sql generation
(1)
robust optimization
(1)
message passing
(1)
vector quantization
(1)
false positive rate
(1)
generative model
(1)
adam optimizer
(1)
graph transformer
(1)
variational autoencoder
(1)
adversarial perturbation
(1)
language model
(1)
synthetic data
(1)
graph classification
(1)
Papers
Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset
ACL 2025
On the Reliability of Watermarks for Large Language Models
ICLR 2024
OpenTab: Advancing Large Language Models as Open-domain Table Reasoners
ICLR 2024
GOAT: A Global Transformer on Large-scale Graphs
ICML 2023
Robust Optimization As Data Augmentation for Large-Scale Graphs
CVPR 2022
Data Augmentation for Meta-Learning
ICML 2021
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training
NeurIPS 2021
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations
AAAI 2021
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
NeurIPS 2021