Congratulations to the #ICML2022 outstanding paper award winners

The International Conference on Machine Learning (ICML) Outstanding Paper awards are given to papers from the current conference that are "strong representatives of solid theoretical and empirical work in the field". This year, there were 15 awards. Monarch: Expressive structured matrices for efficient and accurate training Tri Dao, Beidi Chen, Nimit Sohoni, Arjun Desai, Michael Poli, Jessica Grogan, Alexander Liu, Aniruddh Rao, Atri Rudra, Christopher Re Abstract: Large neural networks excel in many domains, but they are expensive to train and fine-tune. A popular approach to reduce their compute or memory requirements is to replace dense weight matrices with structured ones (e.g., sparse, low-rank, Fourier transform). These methods have not seen widespread adoption (1) in end-to-end training due to unfavorable efficiency–quality tradeoffs, and (2) in dense-to-sparse fine-tuning due to lack of tractable algorithms to approximate a given dense weight matrix.