2 citations · 2 across the 2 of their papers we have counts for
4 papers
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems
Fei Mi, Wanhao Zhou, Fengyu Cai +3
As the labeling cost for different modules in task-oriented dialog (ToD) systems is expensive, a major challenge is to train different modules with the least amount of labeled data…
Consensus Control for Decentralized Deep Learning
Lingjing Kong, Tao Lin, Anastasia Koloskova +2
Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal…
Extrapolation for Large-batch Training in Deep Learning
Tao Lin, Lingjing Kong, Sebastian U. Stich +1
Deep learning networks are typically trained by Stochastic Gradient Descent (SGD) methods that iteratively improve the model parameters by estimating a gradient on a very small fra…
Ensemble Distillation for Robust Model Fusion in Federated Learning
Tao Lin, Lingjing Kong, Sebastian U. Stich +1
Federated Learning (FL) is a machine learning setting where many devices collaboratively train a machine learning model while keeping the training data decentralized. In most of th…