1.2k citations · 1.7k across the 15 of their papers we have counts for
5 papers · 1 filter
Program Synthesis with Large Language Models
Jacob Austin, Augustus Odena, Maxwell Nye +8
This paper explores the limits of the current generation of large language models for program synthesis in general purpose programming languages. We evaluate a collection of such m…
SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network
William Chan, Daniel Park, Chris Lee +3
We present SpeechStew, a speech recognition model that is trained on a combination of various publicly available speech recognition datasets: AMI, Broadcast News, Common Voice, Lib…
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…
EfficientNetV2: Smaller Models and Faster Training
Mingxing Tan, Quoc V. Le
This paper introduces EfficientNetV2, a new family of convolutional networks that have faster training speed and better parameter efficiency than previous models. To develop this f…
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…