Training Machine Learning Models More Efficiently with Dataset Distillation
Interpretable Deep Learning for Time Series Forecasting
A Fast WordPiece Tokenization System
More Efficient In-Context Learning with GLaM
General and Scalable Parallelization for Neural Networks
Improving Vision Transformer Efficiency and Accuracy by Learning to Tokenize
Google at NeurIPS 2021
Evaluating Syntactic Abilities of Language Models
RLDS: An Ecosystem to Generate, Share, and Use Datasets in Reinforcement Learning
MURAL: Multimodal, Multi-task Retrieval Across Languages
Predicting Text Selections with Federated Learning
