LLM4AAMAS: Add refs for quantization and distilation

README.md

@@ -44,6 +44,19 @@ to generative AAMAS. This list is a work in progress and will be regularly updat
    Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton (2012) Presented at *NeurIPS*
 
 
+- Quantization, and distillation are two popular techniques for deploying deep
+  learning models in resource-constrained environments by making these models
+  more lightweight without compromising too much on performance.
+
+   **[A survey of quantization methods for efficient neural 
+   network inference](https://www.crcpress.com/Low-Power-Computer-Vision/Gholami-Kim-Dong-Yao-Mahoney-Keutzer/p/book/9780367707095)**  
+   Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer (2022)
+   Published in *Low-Power Computer Vision*, Chapman and Hall/CRC, pp. 291–326.
+
+   **[Knowledge Distillation: A Survey](https://doi.org/10.1007/s11263-021-01453-z)**  
+   Jianping Gou, Baosheng Yu, Stephen J. Maybank, Dacheng Tao (2021) 
+   Published in *International Journal of Computer Vision*, Volume 129, pp. 1789–1819.  
+
 ## Large Language Models
 
 - The literature review of the recent advances in LLMs shown that scaling can