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Training a Two-Layer ReLU Network Analytically
Sensors, 2023 Neural networks are usually trained with different variants of gradient descent-based optimization algorithms such as the stochastic gradient descent or the Adam optimizer.
Adrian Barbu
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Stochastic gradient descent on GPUs [PDF]
Proceedings of the 8th Workshop on General Purpose Processing using GPUs, 2015 Irregular algorithms such as Stochastic Gradient Descent (SGD) can benefit from the massive parallelism available on GPUs. However, unlike in data-parallel algorithms, synchronization patterns in SGD are quite complex. Furthermore, scheduling for scale-free graphs is challenging.
Rashid Kaleem +2 more
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Adaptive Stochastic Gradient Descent Method for Convex and Non-Convex Optimization
Fractal and Fractional, 2022 Stochastic gradient descent is the method of choice for solving large-scale optimization problems in machine learning. However, the question of how to effectively select the step-sizes in stochastic gradient descent methods is challenging, and can ...
Ruijuan Chen, Xiaoquan Tang, Xiuting Li
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Stochastic gradient descent for hybrid quantum-classical optimization [PDF]
Quantum, 2020 Within the context of hybrid quantum-classical optimization, gradient descent based optimizers typically require the evaluation of expectation values with respect to the outcome of parameterized quantum circuits. In this work, we explore the consequences
Ryan Sweke +6 more
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The effective noise of stochastic gradient descent
Journal of Statistical Mechanics: Theory and Experiment, 2022 Abstract Stochastic gradient descent (SGD) is the workhorse algorithm of deep learning technology. At each step of the training phase, a mini batch of samples is drawn from the training dataset and the weights of the neural network are adjusted according to the performance on this specific subset of examples.
Mignacco, Francesca +1 more
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Pipelined Stochastic Gradient Descent with Taylor Expansion
Applied Sciences, 2023 Stochastic gradient descent (SGD) is an optimization method typically used in deep learning to train deep neural network (DNN) models. In recent studies for DNN training, pipeline parallelism, a type of model parallelism, is proposed to accelerate SGD ...
Bongwon Jang, Inchul Yoo, Dongsuk Yook
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Scaling transition from momentum stochastic gradient descent to plain stochastic gradient descent
CoRR, 2021 The plain stochastic gradient descent and momentum stochastic gradient descent have extremely wide applications in deep learning due to their simple settings and low computational complexity. The momentum stochastic gradient descent uses the accumulated gradient as the updated direction of the current parameters, which has a faster training speed ...
Kun Zeng +3 more
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Natural Evolutionary Gradient Descent Strategy for Variational Quantum Algorithms
Intelligent Computing, 2023 Recent research has demonstrated that parametric quantum circuits (PQCs) are affected by gradients that progressively vanish to zero as a function of the number of qubits.
Jianshe Xie +4 more
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Stochastic gradient-free descents
CoRR, 2019 In this paper we propose stochastic gradient-free methods and accelerated methods with momentum for solving stochastic optimization problems. All these methods rely on stochastic directions rather than stochastic gradients. We analyze the convergence behavior of these methods under the mean-variance framework, and also provide a theoretical analysis ...
Xiaopeng Luo, Xin Xu 0006
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On the discrepancy principle for stochastic gradient descent
Inverse Problems, 2020 Abstract Stochastic gradient descent (SGD) is a promising numerical method for solving large-scale inverse problems. However, its theoretical properties remain largely underexplored in the lens of classical regularization theory. In this note, we study the classical discrepancy principle, one of the most popular a posteriori choice rules,
Tim Jahn, Bangti Jin
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