12 citations · 53 across the 37 of their papers we have counts for
4 papers · 1 filter
On the Convergence of Shallow Neural Network Training with Randomly Masked Neurons
Fangshuo Liao, Anastasios Kyrillidis
With the motive of training all the parameters of a neural network, we study why and when one can achieve this by iteratively creating, training, and combining randomly selected su…
Convergence and Stability of the Stochastic Proximal Point Algorithm with Momentum
Junhyung Lyle Kim, Panos Toulis, Anastasios Kyrillidis
Stochastic gradient descent with momentum (SGDM) is the dominant algorithm in many optimization scenarios, including convex optimization instances and non-convex neural network tra…
Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks
Zhenwei Dai, Chen Dun, Yuxin Tang +2
Federated learning enables many local devices to train a deep learning model jointly without sharing the local data. Currently, most of federated training schemes learns a global m…
How much pre-training is enough to discover a good subnetwork?
Cameron R. Wolfe, Fangshuo Liao, Qihan Wang +2
Neural network pruning is useful for discovering efficient, high-performing subnetworks within pre-trained, dense network architectures. More often than not, it involves a three-st…