196 citations · 344 across the 6 of their papers we have counts for
8 papers · 1 filter
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
David Patterson, Joseph Gonzalez, Urs Hölzle +7
Machine Learning (ML) workloads have rapidly grown in importance, but raised concerns about their carbon footprint. Four best practices can reduce ML training energy by up to 100x…
Carbon Emissions and Large Neural Network Training
David Patterson, Joseph Gonzalez, Quoc Le +6
The computation demand for machine learning (ML) has grown rapidly recently, which comes with a number of costs. Estimating the energy cost helps measure its environmental impact a…
PyGlove: Symbolic Programming for Automated Machine Learning
Daiyi Peng, Xuanyi Dong, Esteban Real +7
Neural networks are sensitive to hyper-parameter and architecture choices. Automated Machine Learning (AutoML) is a promising paradigm for automating these choices. Current ML soft…
Compositional Generalization via Neural-Symbolic Stack Machines
Xinyun Chen, Chen Liang, Adams Wei Yu +2
Despite achieving tremendous success, existing deep learning models have exposed limitations in compositional generalization, the capability to learn compositional rules and apply…
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
Esteban Real, Chen Liang, David R. So +1
Machine learning research has advanced in multiple aspects, including model structures and learning methods. The effort to automate such research, known as AutoML, has also made si…
Learning to Generalize from Sparse and Underspecified Rewards
Rishabh Agarwal, Chen Liang, Dale Schuurmans +1
We consider the problem of learning from sparse and underspecified rewards, where an agent receives a complex input, such as a natural language instruction, and needs to generate a…