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- Samaneh Azadi, Suvrit Sra
- ICML
- 2014

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)

- Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, Trevor Darrell
- ArXiv
- 2015

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)

- Samaneh Azadi, Jeremy Maitin-Shepard, Pieter Abbeel
- ECCV
- 2014

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)

- Mohsen Fallahi, Samaneh Azadi
- 2009 International Conference on Education…
- 2009

In this paper, sliding mode control method is studied for controlling DC motor because of its robustness against model uncertainties and external disturbances, and also its ability in controlling nonlinear and MIMO systems. In this method, using high control gain to overcome uncertainties lead to occur chattering phenomena in control law which can excite… (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)

- Samaneh Azadi, Jiashi Feng, Trevor Darrell
- ArXiv
- 2017

To predict a set of diverse and informative proposals with enriched representations, this paper introduces a differentiable Determinantal Point Process (DPP) layer that is able to augment the object detection architectures. Most modern object detection architectures, such as Faster R-CNN, learn to localize objects by minimizing deviations from the… (More)

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