Symbolic Learning to Optimize: Towards Interpretability and Scalability

13 March 2022

Papers citing "Symbolic Learning to Optimize: Towards Interpretability and Scalability"

6 / 6 papers shown

Title
Symbolic Distillation for Learned TCP Congestion Control S. Sharan Wenqing Zheng Kuo-Feng Hsu Jiarong Xing Ang Chen Zhangyang Wang 10 5 0 24 Oct 2022
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases James Harrison Luke Metz Jascha Narain Sohl-Dickstein 39 22 0 22 Sep 2022
Practical tradeoffs between memory, compute, and performance in learned optimizers Luke Metz C. Freeman James Harrison Niru Maheswaranathan Jascha Narain Sohl-Dickstein 18 32 0 22 Mar 2022
Tutorial on amortized optimization Brandon Amos OffRL 67 42 0 01 Feb 2022
L $^2$ -GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks Yuning You Tianlong Chen Zhangyang Wang Yang Shen GNN 86 82 0 30 Mar 2020
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,658 0 28 Feb 2017