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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Salawu, Emmanuel Oluwatobi Amazon Web Serv Machine Learning Solut Lab Metro Area Washington DC 98108 USA

ISBN: (纸本)9781728125473

The conformation spaces (CS) of macromolecules and their associated dynamics are of vital importance in the understanding of many biochemical functions as well as diseases and in the developments of drugs for curing or managing disease conditions. While the exploration of the CS is generally easier for molecules with fewer atoms (such as ligands and short peptides), achieving the same for larger molecules (such as nucleic acids and proteins) beyond a narrow local equilibrium is non-trivial and sometimes computationally prohibitive. In this work, we present Deep Enhanced Sampling of Nucleic Acids' Structures Using Deep-Learning-Derived Biasing Forces (DESNA, pronounced DES-na), that combines variational autoencoder, a special deep neural network (DNN), and molecular dynamics (MD) simulations to create a robust technique for enhanced sampling, in which DNN-learned latent space is used for inferring appropriate biasing potentials for guiding the MD simulations. The results obtained show that DESNA performs better than conventional MD simulations and efficiently samples wider CS than conventional MD simulations even when DESNA is allowed to run for as short as 10% of the length of conventional MD simulations. This suggests that DESNA is at least 10 times more efficient that conventional MD simulations in its sampling of CS of molecules.

关键词： Deep neural network variational autoencoder conformation space molecular dynamics nucleic acids DNA

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Neural networks based domain name generation

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2021年 61卷

作者： Wang, Zheng Guo, Yang NIST Gaithersburg MD 20899 USA

Domain generation algorithm (DGA) is used by botnets to build a stealthy command and control (C&C) communication channel between the C&C server and the bots. A DGA can periodically produce a large number of pseudo-random algorithmically generated domains (AGDs), a few of which direct the bots to the C&C server. AGD detection algorithms provide a lightweight, promising solution in response to the existing DGA techniques. In the constantly evolving attacker-defender game, attackers may seek more advanced DGA techniques to gain a better chance of evading detection by defenders. In this paper, we propose a new DGA, namely a neural networks-based domain name generation (NDG) architecture. NDG is based on a variational autoencoder (VAE), where the encoder and decoder networks use stacked gated convolutional neural networks (GCNNs) to learn the contextual structure hierarchically. NDG is experimentally validated using a set of state-of-the-art AGD detection algorithms. The existing DGAs of different classes following a DGA taxonomy are used to benchmark NDG. NDG shows the best overall anti-detection performance among all tested DGAs. We also demonstrate that NDG is effective in benchmarking AGD detection algorithms.

关键词： Domain generation algorithm Malicious domain name Classification variational autoencoder Deep learning

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Domain Adaptation for Synthesis of Hazy Images

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Journal of Computer and Communications 2021年第10期9卷 142-151页

作者： Haoying Sun Yutong Zheng Qing Lang School of Information Engineering Minzu University of China Beijing China

Most existing image dehazing methods based learning are less able to perform well to real hazy images. An important reason is that they are trained on synthetic hazy images whose distribution is different from real hazy images. To relieve this issue, this paper proposes a new hazy scene generation model based on domain adaptation, which uses a variational autoencoder to encode the synthetic hazy image pairs and the real hazy images into the latent space to adapt. The synthetic hazy image pairs guide the model to learn the mapping of clear images to hazy images, the real hazy images are used to adapt the synthetic hazy images’ latent space to real hazy images through generative adversarial loss, so as to make the generative hazy images’ distribution as close to the real hazy images’ distribution as possible. By comparing the results of the model with traditional physical scattering models and Adobe Lightroom CC software, the hazy images generated in this paper is more realistic. Our end-to-end domain adaptation model is also very convenient to synthesize hazy images without depth map. Using traditional method to dehaze the synthetic hazy images generated by this paper, both SSIM and PSNR have been improved, proved that the effectiveness of our method. The non-reference haze density evaluation algorithm and other quantitative evaluation also illustrate the advantages of our method in synthetic hazy images.

关键词： Haze Scene Domain Adaptation Generative Adversarial Net variational autoencoder Deep Learning

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variational Information Diffusion for Probabilistic Cascades Prediction 38

Variational Information Diffusion for Probabilistic Cascades...

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39th IEEE International Conference on Computer Communications (IEEE INFOCOM)

作者： Zhou, Fan Xu, Xovee Zhang, Kunpeng Trajcevski, Goce Zhong, Ting Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Peoples R China Univ Maryland Robert H Smith Sch Business College Pk MD 20742 USA Iowa State Univ Dept Elect & Comp Engn Ames IA USA

ISBN: (纸本)9781728164120

Understanding in-network information diffusion is a fundamental problem in many application domains and one of the primary challenges is to predict the size of the information cascade. Most of the existing models rely either on hypothesized point process (e.g., Poisson and Hawkes process), or simply predict the information propagation via deep neural networks. However, they fail to simultaneously capture the underlying structure of a cascade graph and the propagation of uncertainty in the diffusion, which may result in unsatisfactory prediction performance. To address these, in this work we propose a novel probabilistic cascade prediction framework: variational Cascade (VaCas) graph learning networks. VaCas allows a non-linear information diffusion inference and models the information diffusion process by learning the latent representation of both the structural and temporal information. It is a pattern-agnostic model leveraging variational inference to learn the node-level and cascade-level latent factors in an unsupervised manner. In addition, VaCas is capable of capturing both the cascade representation uncertainty and node infection uncertainty, while enabling hierarchical pattern learning of information diffusion. Extensive experiments conducted on real-world datasets demonstrate that VaCas significantly improves the prediction accuracy, compared to state-of-the-art approaches, while also enabling interpretability.

关键词： Information cascades variational autoencoder graph learning

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Diversity Regularized autoencoders for Text Generation 20

Diversity Regularized Autoencoders for Text Generation

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35th Annual ACM Symposium on Applied Computing (SAC)

作者： Ko, Hyeseon Lee, Junhyuk Kim, Jinhong Lee, Jongwuk Shim, Hyunjung Sungkyunkwan Univ Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9781450368667

In this paper, we propose a simple yet powerful text generation model, called diversity regularized autoencoders (DRAE). The key novelty of the proposed model lies in its ability to handle various sentence modifications such as insertions, deletions, substitutions, and maskings, and to take them as input. Because the noise injection strategy enables an encoder to make the latent distribution smooth and continuous, the proposed model can generate more diverse and coherent sentences. Also, we adopt the Wasserstein generative adversarial networks with a gradient penalty to achieve stable adversarial training of the prior distribution. We evaluate the proposed model using quantitative, qualitative, and human evaluations on two public datasets. Experimental results demonstrate that our model using a noise-injection strategy produces more natural and diverse sentences than several baseline models. Furthermore, it is found that our model shows the synergistic effect of grammar correction and paraphrase generation in an unsupervised way.

关键词： variational autoencoder Adversarial training Data augmentation

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Manifold-based Test Generation for Image Classifiers 2

Manifold-based Test Generation for Image Classifiers

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IEEE International Conference on Artificial Intelligence Testing (AITest)

作者： Byun, Taejoon Vijayakumar, Abhishek Rayadurgam, Sanjai Cofer, Darren Univ Minnesota Comp Sci & Eng Minneapolis MN 55455 USA Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA Collins Aerosp Adv Technol Ctr Bloomington MN USA

ISBN: (数字)9781728169842

ISBN: (纸本)9781728169842

Neural networks used for image classification tasks in critical applications must be tested with sufficient realistic data to assure their correctness. This raises two challenges: first, an adequate subset of the data points must be carefully chosen to inspire confidence, and second, the implicit requirements must be meaningfully extrapolated to data points beyond those in the explicit training set. This paper proposes a novel framework to address these challenges. Our approach is based on the premise that patterns in a large input data space can be effectively captured in a smaller manifold space, from which similar yet novel test cases-both the input and the label-can be sampled and generated. A variant of Conditional variational autoencoder (CVAE) is used for capturing this manifold with a generative function, and a search technique is applied on this manifold space to efficiently find fault-revealing inputs. Experiments show that this approach enables generation of thousands of realistic yet fault-revealing test cases efficiently even for well-trained models.

关键词： machine learning testing test generation neural networks variational autoencoder

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An Efficient Prediction Method for Coronary Heart Disease Risk Based on Two Deep Neural Networks Trained on Well-Ordered Training Datasets

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IEEE ACCESS 2021年 9卷 135210-135223页

作者： Amarbayasgalan, Tsatsral Pham, Van-Huy Theera-Umpon, Nipon Piao, Yongjun Ryu, Keun Ho Chungbuk Natl Univ Sch Elect & Comp Engn Database & Bioinformat Lab Cheongju 28644 South Korea Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh City 700000 Vietnam Chiang Mai Univ Biomed Engn Inst Chiang Mai 50200 Thailand Chiang Mai Univ Fac Engn Dept Elect Engn Chiang Mai 50200 Thailand Nankai Univ Sch Med Tianjin 300071 Peoples R China Tianjin Cent Hosp Gynecol Obstet Tianjin Key Lab Human Dev & Reprod Regulat Tianjin 300199 Peoples R China

This study proposes an efficient prediction method for coronary heart disease risk based on two deep neural networks trained on well-ordered training datasets. Most real datasets include an irregular subset with higher variance than most data, and predictive models do not learn well from these datasets. While most existing prediction models learned from the whole or randomly sampled training datasets, our suggested method draws up training datasets by separating regular and highly biased subsets to build accurate prediction models. We use a two-step approach to prepare the training dataset: (1) divide the initial training dataset into two groups, commonly distributed and highly biased using Principal Component Analysis, (2) enrich the highly biased group by variational autoencoders. Then, two deep neural network classifiers learn from the isolated training groups separately. The well-organized training groups enable a chance to build more accurate prediction models. When predicting the risk of coronary heart disease from the given input, only one appropriate model is selected based on the reconstruction error on the Principal Component Analysis model. Dataset used in this study was collected from the Korean National Health and Nutritional Examination Survey. We have conducted two types of experiments on the dataset. The first one proved how Principal Component Analysis and variational autoencoder models of the proposed method improves the performance of a single deep neural network. The second experiment compared the proposed method with existing machine learning algorithms, including Naive Bayes, Random Forest, K-Nearest Neighbor, Decision Tree, Support Vector Machine, and Adaptive Boosting. The experimental results show that the proposed method outperformed conventional machine learning algorithms by giving the accuracy of 0.892, specificity of 0.840, precision of 0.911, recall of 0.920, f-measure of 0.915, and AUC of 0.882.

关键词： Training Heart Principal component analysis Predictive models Diseases Deep learning Support vector machines Coronary heart disease deep neural network machine learning principal component analysis reconstruction error variational autoencoder

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Late Breaking Results: A Neural Network that Routes ICs 57

Late Breaking Results: A Neural Network that Routes ICs

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57th ACM/IEEE Design Automation Conference (DAC)

作者： Utyamishev, Dmitry Partin-Vaisband, Irina Univ Illinois Chicago IL 60607 USA

ISBN: (纸本)9781728110851

A global router is proposed that learns from routed circuits and autonomously routes unseen layouts. The uniqueness of this approach is in redefining the global routing as a classical image-to image processing problem. The imaging problem is efficiently solved with a deep learning system, comprising a variational autoencoder and custom loss function. This fundamentally new routing method provides a natural way for global routing parallelization. The deep router is designed, trained, and tested on an unseen 64x64 ISPD'98 benchmark circuit. The test results yield 3.2% decrease in routability and over 5X speedup in runtime as compared with the state-of-the-art FastRoute router.

关键词： Global routing deep learning variational autoencoder FastRoute

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Deep Learning Based Unsupervised and Semi-supervised Classification for Keratoconus

Deep Learning Based Unsupervised and Semi-supervised Classif...

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Hallett, Nicole Yi, Kai Dick, Josef Hodge, Christopher Sutton, Gerard Wang, Yu Guang You, Jingjing Univ Sydney Sydney Eye Hosp Sydney NSW Australia Univ New South Wales Sch Math & Stat Sydney NSW Australia

ISBN: (纸本)9781728169262

The transparent cornea is the window of the eye, facilitating the entry of light rays and controlling focusing the movement of the light within the eye. The cornea is critical, contributing to 75% of the refractive power of the eye. Keratoconus is a progressive and multifactorial corneal degenerative disease affecting 1 in 2000 individuals worldwide. Currently, there is no cure for keratoconus other than corneal transplantation for advanced stage keratoconus or corneal cross-linking, which can only halt KC progression. The ability to accurately identify subtle KC or KC progression is of vital clinical significance. To date, there has been little consensus on a useful model to classify KC patients, which therefore inhibits the ability to predict disease progression accurately. In this paper, we utilised machine learning to analyse data from 124 KC patients, including topographical and clinical variables. Both supervised multilayer perceptron and unsupervised variational autoencoder models were used to classify KC patients with reference to the existing Amsler-Krumeich (A-K) classification system. Both methods result in high accuracy, with the unsupervised method showing better performance. The result showed that the unsupervised method with a selection of 29 variables could be a powerful tool to provide an automatic classification tool for clinicians. These outcomes provide a platform for additional analysis for the progression and treatment of keratoconus.

关键词： variational autoencoder Multilayer Perceptron Cornea Keratoconus Bayesian Neural Networks Clustering Deep Learning Semi-supervised Learning Dimensionality Reduction Diagnosis Amsler-Krumeich Classification

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Text Generation in Discrete Space 29th

Text Generation in Discrete Space

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29th International Conference on Artificial Neural Networks (ICANN)

作者： Hu, Ting Meinel, Christoph Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9783030616168;9783030616151

variational autoencoders (VAEs) are applied to many generation tasks while suffering from posterior collapse issue. Vector Quantization (VQ) is recently employed in VAE model on image generation, which could get rid of the posterior collapse problem and show its potentiality for more generation tasks. In this paper, the VQ method is applied to VAE on text generation. We elaborately design the model architecture to mitigate the index collapse issue brought in by VQ process. Experiments show that our text generation model can achieve better reconstruction and generation performance than other VAE based approaches.

关键词： Text generation Vector quantization variational autoencoder

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