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Stochastic gradient descent and its variants in machine learning
Stochastic Gradient Descent and Its Variants in Machine Learning | Journal of the Indian Institute
of Science Skip to main content Springer Nature Link Account Menu Find a journal Publish with …
of Science Skip to main content Springer Nature Link Account Menu Find a journal Publish with …
A survey of stochastic simulation and optimization methods in signal processing
Modern signal processing (SP) methods rely very heavily on probability and statistics to
solve challenging SP problems. SP methods are now expected to deal with ever more …
solve challenging SP problems. SP methods are now expected to deal with ever more …
Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation
In the past decade the mathematical theory of machine learning has lagged far behind the
triumphs of deep neural networks on practical challenges. However, the gap between theory …
triumphs of deep neural networks on practical challenges. However, the gap between theory …
A unified theory of decentralized SGD with changing topology and local updates
Decentralized stochastic optimization methods have gained a lot of attention recently, mainly
because of their cheap per iteration cost, data locality, and their communication-efficiency. In …
because of their cheap per iteration cost, data locality, and their communication-efficiency. In …
Randomized numerical linear algebra: Foundations and algorithms
This survey describes probabilistic algorithms for linear algebraic computations, such as
factorizing matrices and solving linear systems. It focuses on techniques that have a proven …
factorizing matrices and solving linear systems. It focuses on techniques that have a proven …
Sparsified SGD with memory
Huge scale machine learning problems are nowadays tackled by distributed optimization
algorithms, ie algorithms that leverage the compute power of many devices for training. The …
algorithms, ie algorithms that leverage the compute power of many devices for training. The …
SGD: General analysis and improved rates
We propose a general yet simple theorem describing the convergence of SGD under the
arbitrary sampling paradigm. Our theorem describes the convergence of an infinite array of …
arbitrary sampling paradigm. Our theorem describes the convergence of an infinite array of …
Federated optimization: Distributed machine learning for on-device intelligence
We introduce a new and increasingly relevant setting for distributed optimization in machine
learning, where the data defining the optimization are unevenly distributed over an …
learning, where the data defining the optimization are unevenly distributed over an …
Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent
Most distributed machine learning systems nowadays, including TensorFlow and CNTK, are
built in a centralized fashion. One bottleneck of centralized algorithms lies on high …
built in a centralized fashion. One bottleneck of centralized algorithms lies on high …
Qsparse-local-SGD: Distributed SGD with quantization, sparsification and local computations
Communication bottleneck has been identified as a significant issue in distributed
optimization of large-scale learning models. Recently, several approaches to mitigate this …
optimization of large-scale learning models. Recently, several approaches to mitigate this …