Five ML Concepts - #27 | Software Wrighter Lab Blog

Five ML concepts in under 30 seconds each: Elastic Weight Consolidation (protecting important parameters during new task learning), Replay Buffers (mixing past examples to prevent forgetting), Parameter Routing (activating task-specific parameter subsets), Memory-Augmented Networks (external memory modules for neural networks), Model Editing (targeted weight updates without full retraining).

5 machine learning concepts. Under 30 seconds each.

Resource	Link
Papers	Links in References section
Video	Five ML Concepts #27

References

Concept	Reference
Elastic Weight Consolidation	Overcoming catastrophic forgetting (Kirkpatrick et al. 2017)
Replay Buffers	Experience Replay for Continual Learning (Rolnick et al. 2019)
Parameter Routing	Sparsely-Gated Mixture-of-Experts (Shazeer et al. 2017)
Memory-Augmented Networks	Neural Turing Machines (Graves et al. 2014)
Model Editing	Editing Large Language Models (Yao et al. 2023)

Today’s Five

1. Elastic Weight Consolidation

Adding a penalty that discourages changing parameters important to previous tasks. Importance is estimated using Fisher information from prior training.

This helps models learn new tasks without catastrophic forgetting.

Like protecting well-worn neural pathways while building new ones.

2. Replay Buffers

Storing examples from earlier tasks and mixing them into new training. Past data is replayed alongside current examples during optimization.

This reinforces previous knowledge while learning new data.

Like reviewing old flashcards while studying new material.

3. Parameter Routing

Activating different subsets of model parameters depending on the task or input. Mixture-of-experts and conditional computation route inputs to specialized weights.

Enables specialization without fully separate models.

Like having different experts handle different questions.

4. Memory-Augmented Networks

Adding external memory modules that neural networks can read from and write to. The model learns to store and retrieve information during inference.

Extends beyond purely weight-based memory to explicit storage.

Like giving a calculator access to a notepad.

5. Model Editing

Targeted weight updates to modify specific behaviors without full retraining. Locate and adjust the parameters responsible for particular facts or behaviors.

Allows fast corrections and knowledge updates post-training.

Like editing a specific entry in an encyclopedia instead of rewriting the whole book.

Quick Reference

Concept	One-liner
Elastic Weight Consolidation	Protecting important parameters during new learning
Replay Buffers	Mixing past examples to prevent forgetting
Parameter Routing	Activating task-specific parameter subsets
Memory-Augmented Networks	External memory modules for neural networks
Model Editing	Targeted weight updates without full retraining

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