# as a conference paper at ICLR 2018 O N THE CONVERGENCE OF A DAM AND B EYOND

@inproceedings{Uxiliary2018asAC, title={as a conference paper at ICLR 2018 O N THE CONVERGENCE OF A DAM AND B EYOND}, author={Uxiliary and Emma}, year={2018} }

Several recently proposed stochastic optimization methods that have been successfully used in training deep networks such as RMSPROP, ADAM, ADADELTA, NADAM are based on using gradient updates scaled by square roots of exponential moving averages of squared past gradients. In many applications, e.g. learning with large output spaces, it has been empirically observed that these algorithms fail to converge to an optimal solution (or a critical point in nonconvex settings). We show that one cause…

## 13 Citations

Dropout with Expectation-linear Regularization

- Computer ScienceICLR
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This work first formulate dropout as a tractable approximation of some latent variable model, leading to a clean view of parameter sharing and enabling further theoretical analysis, and introduces (approximate) expectation-linear dropout neural networks, whose inference gap the authors are able to formally characterize.

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Extensive experiments on the ImageNet classification task using almost all known deep CNN architectures including AlexNet, VGG-16, GoogleNet and ResNets well testify the efficacy of the proposed INQ, showing that at 5-bit quantization, models have improved accuracy than the 32-bit floating-point references.

Super-Resolution with Deep Convolutional Sufficient Statistics

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This paper proposes to use as conditional model a Gibbs distribution, where its sufficient statistics are given by deep convolutional neural networks, and the features computed by the network are stable to local deformation, and have reduced variance when the input is a stationary texture.

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We introduce the "Energy-based Generative Adversarial Network" model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and…

Deep Variational Information Bottleneck

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- 2017

It is shown that models trained with the VIB objective outperform those that are trained with other forms of regularization, in terms of generalization performance and robustness to adversarial attack.

Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer

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This work shows that, by properly defining attention for convolutional neural networks, this type of information can be used in order to significantly improve the performance of a student CNN network by forcing it to mimic the attention maps of a powerful teacher network.

Delving into Transferable Adversarial Examples and Black-box Attacks

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- 2017

This work is the first to conduct an extensive study of the transferability over large models and a large scale dataset, and it is also theFirst to study the transferabilities of targeted adversarial examples with their target labels.

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- Computer ScienceICLR
- 2016

This work develops a new convolutional network module that is specifically designed for dense prediction, and shows that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems.

Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks

- Computer ScienceICLR
- 2017

Sparsely-connected neural networks are proposed, by showing that the number of connections in fully-connected networks can be reduced by up to 90% while improving the accuracy performance on three popular datasets while proposing an efficient hardware architecture based on linear-feedback shift registers to reduce the memory requirements of the proposed sparsely- connected networks.

DeepCoder: Learning to Write Programs

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The approach is to train a neural network to predict properties of the program that generated the outputs from the inputs to augment search techniques from the programming languages community, including enumerative search and an SMT-based solver.

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