Learning Rate Schedules
Exponential Decay
Exponential Decay is a learning rate schedule where we decay the learning rate with more iterations using an exponential function:
$$ \text{lr} = \text{lr}_{0}\exp\left(-kt\right) $$
Image Credit: Suki Lau
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Classification | 12 | 9.68% |
| Computational Efficiency | 5 | 4.03% |
| Reinforcement Learning | 5 | 4.03% |
| Classification | 5 | 4.03% |
| Time Series Analysis | 4 | 3.23% |
| Large Language Model | 3 | 2.42% |
| Quantum Machine Learning | 3 | 2.42% |
| Multi-agent Reinforcement Learning | 3 | 2.42% |
| Graph Neural Network | 3 | 2.42% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
