Zhili Li
7 papers · 2023–2026 · 4 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓
🌍
Conference Polyglot · Bronze
🌉
Interdisciplinary Bridge · Bronze
🗺️
Taxonomy Completionist
(26)
🐣
Hot Topic Early Bird
🗃️
Keyword Collector
(50)
Venues
Top co-authors
Research topics
Deep Learning
(6)
Machine Learning
(6)
Learning Types · Deep Learning
(4)
Computer Vision
(3)
Domain-Specific
(3)
Learning Types · Machine Learning
(3)
Application Areas
(2)
Core Methods
(2)
Optimization & Theory
(2)
Artificial Intelligence
(1)
Interdisciplinary
(1)
Natural Language Processing
(1)
Analysis
(1)
Core AI
(1)
Generation
(1)
Keywords
satellite imagery
(4)
remote sensing
(2)
transfer learning
(2)
attention mechanism
(1)
image regression
(1)
document understanding
(1)
uncertainty quantification
(1)
renewable energy
(1)
time series forecasting
(1)
machine learning
(1)
confidence estimation
(1)
confidence calibration
(1)
spatiotemporal dynamics
(1)
satellite data
(1)
domain adaptation
(1)
spatial regression
(1)
domain generalization
(1)
fairness-aware learning
(1)
spatial heterogeneity
(1)
unsupervised learning
(1)
Papers
EcoDiffusion: Uncertainty-Aware Emulation of Ecosystem Processes with Conditional Diffusion for Long Sequences with Single-Step Initialization
AAAI 2026
LLM×MapReduce: Simplified Long-Sequence Processing using Large Language Models
ACL 2025
SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models
AAAI 2024
SolarCube: An Integrative Benchmark Dataset Harnessing Satellite and In-situ Observations for Large-scale Solar Energy Forecasting
NeurIPS 2024
Auto-CM: Unsupervised Deep Learning for Satellite Imagery Composition and Cloud Masking Using Spatio-Temporal Dynamics
AAAI 2023
Confidence-based Self-Corrective Learning: An Application in Height Estimation Using Satellite LiDAR and Imagery
IJCAI 2023
Point-to-Region Co-learning for Poverty Mapping at High Resolution Using Satellite Imagery
AAAI 2023