Effective Dataset Distillation for Spatio-Temporal Forecasting with Bi-dimensional Compression
Effective Dataset Distillation for Spatio-Temporal Forecasting with Bi-dimensional Compression explores STemDist is a novel dataset distillation method that enhances spatio-temporal forecasting by compressing both spatial and temporal dimensions.. Commercial viability score: 7/10 in Dataset Distillation.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisDataset Distillation Efficiently Encodes Low-Dimensional Representations from Gradient-Based Learning of Non-Linear TasksViability: 3.0
- Builds On ThisGrounding and Enhancing Informativeness and Utility in Dataset DistillationViability: 5.0
- Prior WorkBeyond Dataset Distillation: Lossless Dataset Concentration via Diffusion-Assisted Distribution AlignmentViability: 7.0
- Prior WorkAccelerating Large-Scale Dataset Distillation via Exploration-Exploitation OptimizationViability: 7.0
- Prior WorkMultimodal Dataset Distillation via Phased Teacher Models
Related Resources
- What are the limitations of existing dataset distillation methods focusing on sample reduction?(question)
- Here are 30-50 long-tail search questions for the topic of Dataset Distillation, based on the provided context:(question)
- How can dataset distillation improve deep learning model training efficiency?(question)