CS5720 - Week 7
Slide 126 of 140

Gated Recurrent Unit (GRU) - Simplified LSTM

Why GRU?

🎯 The Simplification Goal
LSTMs work great but are complex with 3 gates and separate cell state. Can we achieve similar performance with a simpler architecture?
GRU Key Innovations:

• Fewer parameters → Faster training
• Simpler architecture → Easier to understand
• Similar performance → LSTM-level results
• Two gates only → Reset & Update

GRU Features

  • ⚡ Fewer Parameters (25% less than LSTM)
  • 🚪 Only Two Gates (Reset & Update)
  • 🧠 No Separate Cell State
  • 🏃 Faster Training and Inference
  • 📊 Comparable Performance to LSTM
💡 Key Insight
GRU proves that you don't always need complexity to achieve great results. Sometimes, simpler is better!

GRU Architecture: Simplicity in Action

GRU Cell
r
Reset Gate
z
Update Gate
Hidden state: h_t
→
Simplified
from LSTM
LSTM Complexity
• 3 Gates (f, i, o)
• Separate cell state
• More parameters
• Complex interactions
GRU combines the forget and input gates into a single "update gate" and merges the cell state with the hidden state
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Prepared by Dr. Gorkem Kar