With the advent of foundation models and the emergence of capabilities at scale, algorithms designed to improve the training and inference efficiency of machine learning systems have become critical research topics. Despite their importance, expertise in training the most efficient systems is often in short supply, limiting the ability of teams to scale experiments, work at maximum productivity, develop foundation models for underexplored domains, and make the best use of our computing resources.
This seminar series seeks to educate, provide a platform for, and facilitate collaborative research opportunities for all algorithmic and technological questions arising in the quest for more efficient machine learning at scale. The desired outcomes of the seminar series are:
- A proliferation of knowledge on state-of-the-art approaches directly employable by diverse research teams hoping to scale training or make use of large-scale models.
- The provision of a research discussion forum on emerging topics to help catalyze further research and form connections among researchers invested in researching these questions.