180 citations · 234 across the 7 of their papers we have counts for
4 papers · 1 filter
Reverse engineering learned optimizers reveals known and novel mechanisms
Niru Maheswaranathan, David Sussillo, Luke Metz +2
Learned optimizers are algorithms that can themselves be trained to solve optimization problems. In contrast to baseline optimizers (such as momentum or Adam) that use simple updat…
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
Li Li, Stephan Hoyer, Ryan Pederson +4
Including prior knowledge is important for effective machine learning models in physics, and is usually achieved by explicitly adding loss terms or constraints on model architectur…
Energy-based View of Retrosynthesis
Ruoxi Sun, Hanjun Dai, Li Li +2
Retrosynthesis -- the process of identifying a set of reactants to synthesize a target molecule -- is of vital importance to material design and drug discovery. Existing machine le…
Using a thousand optimization tasks to learn hyperparameter search strategies
Luke Metz, Niru Maheswaranathan, Ruoxi Sun +3
We present TaskSet, a dataset of tasks for use in training and evaluating optimizers. TaskSet is unique in its size and diversity, containing over a thousand tasks ranging from ima…