5 papers
Gradient Descent in RKHS with Importance Labeling
Tomoya Murata, Taiji Suzuki
Labeling cost is often expensive and is a fundamental limitation of supervised learning. In this paper, we study importance labeling problem, in which we are given many unlabeled d…
Accelerated Sparsified SGD with Error Feedback
Tomoya Murata, Taiji Suzuki
A stochastic gradient method for synchronous distributed optimization is studied. For reducing communication cost, we particularly focus on utilization of compression of communicat…
Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation
Tomoya Murata, Taiji Suzuki
We develop new stochastic gradient methods for efficiently solving sparse linear regression in a partial attribute observation setting, where learners are only allowed to observe a…
Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error
Taiji Suzuki, Hiroshi Abe, Tomoya Murata +6
Compression techniques for deep neural network models are becoming very important for the efficient execution of high-performance deep learning systems on edge-computing devices. T…
Stochastic dual averaging methods using variance reduction techniques for regularized empirical risk minimization problems
Tomoya Murata, Taiji Suzuki
We consider a composite convex minimization problem associated with regularized empirical risk minimization, which often arises in machine learning. We propose two new stochastic g…