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We study regularized stochastic convex optimization subject to linear equality constraints. This class of problems was recently also studied by Ouyang et al. (2013) and Suzuki (2013); both introduced similar stochastic alternating direction method of multipliers (SADMM) algorithms. However, the analysis of both papers led to suboptimal convergence rates.… (More)

Precisely-labeled data sets with sufficient amount of samples are notably important for training deep convolutional neural networks (CNNs). However, many of the available real-world data sets contain erroneously labeled samples and the error in labels of training sample makes it a daunting task to learn a well-performing deep CNN model. In this work, we… (More)

Investigations of biological ultrastructure, such as comprehensive mapping of connections within a nervous system, increasingly rely on large, high-resolution electron microscopy (EM) image volumes. However, discontinuities between the registered section images from which these volumes are assembled, due to variations in imaging conditions and section… (More)

- Samaneh Azadi, Suvrit Sra
- 2014

1. The strongly convex case 1.1. Proof of Lemma 1 Lemma 1. Let f be µ-strongly convex, and let x k+1 , y k+1 and λ k+1 be computed as per Alg. 2. For all x ∈ X and y ∈ Y, and w ∈ Ω, it holds for k ≥ 0 that f (x k) − f (x) + h(y k+1) − h(y) + ⟨w k+1 − w, F (w k+1)⟩ ≤ η k 2 ∥g k ∥ 2 2 − µ 2 ∆ k + 1 2η k [∆ k − ∆ k+1 ] + β 2 [A k − A k+1 ] + 1 2β [L k − L k+1… (More)

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