Re-weighted gradient descent via distributionally robust optimization
Neural network pruning with combinatorial optimization
Pre-trained Gaussian processes for Bayesian optimization
Enhancing Backpropagation via Local Loss Optimization
MLGO: A Machine Learning Framework for Compiler Optimization
Offline Optimization for Architecting Hardware Accelerators
Robust Routing Using Electrical Flows
Improving Sparse Training with RigL
Speeding Up Neural Network Training with Data Echoing
Bi-Tempered Logistic Loss for Training Neural Nets with Noisy Data
Measuring the Limits of Data Parallel Training for Neural Networks
