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CS480/680 Lecture 6: Model compression for NLP (Ashutosh Adhikari)
Quantization vs Pruning vs Distillation: Optimizing NNs for Inference
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
[ITSC 2024 oral] Comb, Prune, Distill: Towards Unified Pruning for Vision Model Compression
Pruning Makes Faster and Smaller Neural Networks | Two Minute Papers #229
Model Selection (2) - Thresholding & Pruning
Simple Post-Training Robustness Using Test Time Augmentations and Random Forest
CAP6412 21Spring-Robust Pre-Training by Adversarial Contrastive Learning
How Model Compression Works with Real Examples in 10 minutes
Adversarial Training and Robustness for Multiple Perturbations
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Last Updated: August 22, 2026
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![[ITSC 2024 oral] Comb, Prune, Distill: Towards Unified Pruning for Vision Model Compression](https://i.ytimg.com/vi/JubQUGzYvy8/mqdefault.jpg)
