Search Results for author:
Found 6 papers, 2 papers with code
Why Random Pruning Is All We Need to Start Sparse
Random masks define surprisingly effective sparse neural network models, as has been shown empirically.
Dynamical Isometry for Residual Networks
We propose a random initialization scheme, RISOTTO, that achieves perfect dynamical isometry for residual networks with ReLU activation functions even for finite depth and width.
Lottery Tickets with Nonzero Biases
The strong lottery ticket hypothesis holds the promise that pruning randomly initialized deep neural networks could offer a computationally efficient alternative to deep learning with stochastic gradient descent.
Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation
We study the problem of estimating at a central server the mean of a set of vectors distributed across several nodes (one vector per node).
A Field Guide to Federated Optimization
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection.
Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning
Communication of model updates between client nodes and the central aggregating server is a major bottleneck in federated learning, especially in bandwidth-limited settings and high-dimensional models.
