Sparse Training
Definition
Training sparse models learns with a deliberately sparsely populated model structure.
Training sparse models maintains or generates a sparsely populated parameter structure during learning to reduce computational or memory requirements.
Simply explained
During learning, only selected connections are used or retained to save computation or memory.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A research team trains a network with fixed parameter density and measures real acceleration on target hardware.
Limits and pitfalls
- Unstructured sparsity often does not save time without suitable kernels; accuracy and reproducibility may suffer.
Origin & history
Dynamic sparsity methods such as Sparse Evolutionary Training and RigL have shown that connections can be adjusted during training.
Use in mid-sized companies
- Possible example: A research team trains a network with fixed parameter density and measures real acceleration on target hardware.
Frequently asked questions
What does training sparse models mean?
Training sparse models maintains or generates a sparsely populated parameter structure during learning to reduce computational or memory requirements. What should be considered? Unstructured sparsity often does not save time without suitable kernels; accuracy and reproducibility may suffer.
Sources
- Rigging the Lottery: Making All Tickets Winners · Evci et al., Rigging the Lottery: Making All Tickets Winners
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: