Today's state-of-the-art image classifiers fail to correctly classify carefully manipulated adversarial images. In this work, we develop a new, localized adversarial attack that generates adversarial examples by imperceptibly altering the backgrounds of normal images. We first use this attack to highlight the unnecessary sensitivity of neural networks to changes in the background of an image, then use it as part of a new training technique: localized adversarial training. By including locally adversarial images in the training set, we are able to create a classifier that suffers less loss than a non-adversarially trained counterpart model on both natural and adversarial inputs. The evaluation of our localized adversarial training algorithm on MNIST and CIFAR-10 datasets shows decreased accuracy loss on natural images, and increased robustness against adversarial inputs.

As one part of them, Reinforcement Learning has achieved incredible results in game playing. An intelligent agent will be created and trained with reinforcement learning algorithms to fulfill this tasks. In the Future of Go Summit 2017, Alpha Go which is an AI player trained with deep reinforcement learning algorithms won three games against the world best human player in Go. The success of reinforcement learning in this area shock the world and many researches are launched such as driverless cars. Deep learning methods such as convolutional neural network contributes a lot to this because these techniques solves the problem of dealing with high dimension input data and feature extraction. T-rex Runner is a dinosaur game from Google Chrome offline mode. The aim of the player is to escape all obstacles and get higher score until reaching the limitation which is 99999. The moving speed of the obstacles will increase as time goes by which make it difficult to get the highest score. The code of this project can be found in this link which is written in Python.

--Deep Neural Network has proved its potential in various perception tasks and hence become an appealing option for interpretation and data processing in security sensitive systems. However, security-sensitive systems demand not only high perception performance, but also design robustness under various circumstances. Unlike prior works that study network robustness from software level, we investigate from hardware perspective about the impact of Single Event Upset (SEU) induced parameter perturbation (SIPP) on neural networks. We systematically define the fault models of SEU and then provide the definition of sensitivity to SIPP as the robustness measure for the network. We are then able to analytically explore the weakness of a network and summarize the key findings for the impact of SIPP on different types of bits in a floating point parameter, layer-wise robustness within the same network and impact of network depth. Based on those findings, we propose two remedy solutions to protect DNNs from SIPPs, which can mitigate accuracy degradation from 28% to 0.27% for ResNet with merely 0.24-bit SRAM area overhead per parameter . Index T erms --component, formatting, style, styling, insert I. DNNs) have recently attracted enormous attention due to the success in various perception tasks [1], [2] and it is an appealing idea to adopt DNNs in security sensitive systems for in-depth inference and efficient data processing, such as autonomous automobile and medical monitoring. On the other hand, the robustness of DNN itself is of great concern for such security related applications and hence has been widely studied.

Among different types of networks, Residual Network (ResNet, He et al. (2016a)) is undoubted a milestone. ResNet is equipped with shortcut connections, which skip layers in the forward step of an input. Similar idea also appears in the Highway Networks (Srivastava et al., 2015), and further inspires densely connected convolutional networks (Huang et al., 2017). ResNet owes its great success to a surprisingly efficient training compared to the widely used feedforward Convolutional Neural Networks (CNN, Krizhevsky et al. (2012)). Feedforward CNNs are seldomly used with more than 30 layers in the existing literature. There are experimental results suggest that very deep feedforward CNNs are significantly slow to train, and yield worse performance than their shallow counterparts (He et al., 2016a). However, simple first order algorithms such as stochastic gradient descent and its variants are able to train ResNet with hundreds T. Liu, M. Chen, E. Zhou, and T. Zhao are affiliated with School of Industrial and Systems Engineering at Georgia Tech; M. Zhou is now affiliated with CS Department of Duke University; S. S. Du is now affiliated with Institute for Advanced Study; This work is done while M. Zhou is at Peking University and S. S. Du is a Ph.D. student at CMU. T. Liu and M. Chen contribute equally; T uo Zhao is the corresponding author; Email: tourzhao@gatech.edu. 1 arXiv:1909.04653v2

--Automated brain tumor segmentation plays an important role in the diagnosis and prognosis of the patient. The main focus of this paper is to segment tumor from BRA TS 2018 benchmark dataset and use age, shape and volumetric features to predict overall survival of patients. The random forest classifier achieves overall survival accuracy of 59% on the test dataset and 67% on the dataset with resection status as gross total resection. The proposed approach uses fewer features but achieves better accuracy than state-of- the-art methods. Medical fraternity considers brain tumor amongst the most fatal type of cancer [1]. Brain tumors are divided into two categories based on origin and malignancy. Former is further classified as primary and secondary.

Huawei Noah's Ark Lab Abstract Pruning of neural networks is one of the well-known and promising model simplification techniques. Most neural network models are large and require expensive computations to predict new instances. It is imperative to compress the network to deploy models on low resource devices. Most compression techniques, especially pruning have been focusing on computer vision and convolution neural networks. Existing techniques are complex and require multistage optimization and fine-tuning to recover the state-of-the-art accuracy. We introduce a Differentiable Mask Pruning (DMP), that simplifies the network while training, and can be used to induce sparsity on weight, filter, node or sub-network. Our method achieves competitive results on standard vision and NLP benchmarks, and is easy to integrate within the deep learning toolbox. Introduction Recent models on machine translation, self-driving cars, Alpha Go have shown game-changing breakthroughs.

We describe our methods to address both tasks of the ISIC 2019 challenge. The goal of this challenge is to provide the diagnostic for skin cancer using images and meta-data. There are nine classes in the dataset, nonetheless, one of them is an outlier and is not present on it. To tackle the challenge, we apply an ensemble of classifiers, which has 13 convolutional neural networks (CNN), we develop two approaches to handle the outlier class and we propose a straightforward method to use the meta-data along with the images. Throughout this report, we detail each methodology and parameters to make it easy to replicate our work. The results obtained are in accordance with the previous challenges and the approaches to detect the outlier class and to address the meta-data seem to be work properly.

Although deep neural networks have shown promising performances on various tasks, even achieving human-level performance on some, they are shown to be susceptible to incorrect predictions even with imperceptibly small perturbations to an input. There exists a large number of previous works which proposed to defend against such adversarial attacks either by robust inference or detection of adversarial inputs. Yet, most of them cannot effectively defend against whitebox attacks where an adversary has a knowledge of the model and defense. More importantly, they do not provide a convincing reason why the generated adversarial inputs successfully fool the target models. To address these shortcomings of the existing approaches, we hypothesize that the adversarial inputs are tied to latent features that are susceptible to adversarial perturbation, which we call vulnerable features. Then based on this intuition, we propose a minimax game formulation to disentangle the latent features of each instance into robust and vulnerable ones, using variational autoencoders with two latent spaces. We thoroughly validate our model for both blackbox and whitebox attacks on MNIST, Fashion MNIST5, and Cat & Dog datasets, whose results show that the adversarial inputs cannot bypass our detector without changing its semantics, in which case the attack has failed.

Generating forecasts for time series with multiple seasonal cycles is an important use-case for many industries nowadays. Accounting for the multi-seasonal patterns becomes necessary to generate more accurate and meaningful forecasts in these contexts. In this paper, we propose Long Short-Term Memory Multi-Seasonal Net (LSTM-MSNet), a decompositionbased, unified prediction framework to forecast time series with multiple seasonal patterns. The current state of the art in this space are typically univariate methods, in which the model parameters of each time series are estimated independently. Consequently, these models are unable to include key patterns and structures that may be shared by a collection of time series. In contrast, LSTM-MSNet is a globally trained Long Short-Term Memory network (LSTM), where a single prediction model is built across all the available time series to exploit the crossseries knowledge in a group of related time series. Furthermore, our methodology combines a series of state-of-the-art multiseasonal decomposition techniques to supplement the LSTM learning procedure. In our experiments, we are able to show that on datasets from disparate data sources, like e.g. the popular M4 forecasting competition, a decomposition step is beneficial, whereas in the common real-world situation of homogeneous series from a single application, exogenous seasonal variables or no seasonal preprocessing at all are better choices. All options are readily included in the framework and allow us to achieve competitive results for both cases, outperforming many state-ofthe-art multi-seasonal forecasting methods

We consider in this paper the optimal approximations of convex univariate functions with feed-forward Relu neural networks. We are interested in the following question: what is the minimal approximation error given the number of approximating linear pieces? We establish the necessary and sufficient conditions and uniqueness of optimal approximations, and give lower and upper bounds of the optimal approximation errors. Relu neural network architectures are then presented to generate these optimal approximations. Finally, we propose an algorithm to find the optimal approximations, as well as prove its convergence and validate it with experimental results.