On the Dynamics of a Recurrent Hopfield Network

In this research paper novel real/complex valued recurrent Hopfield Neural Network (RHNN) is proposed. The method of synthesizing the energy landscape of such a network and the experimental investigation of dynamics of Recurrent Hopfield Network is discussed. Parallel modes of operation (other than fully parallel mode) in layered RHNN is proposed. Also, certain potential applications are proposed.Comment: 6 pages, 6 figures, 1 table, submitted to IJCNN-201

Unsupervised Anomaly Detection in Unstructured Log-Data for Root-Cause-Analysis

Anomaly detection has attracted the attention of researchers from a variety of backgrounds as it finds numerous applications in the industry. As a subfield, fault detection plays a crucial role in growing telecommunications networks since failures lead to dissatisfaction and hence financial drawbacks. It aims at identifying unusual events in the system log files. System logs are messages from the elements of the network to highlight their status. The main challenge is to cope with the rate the data volume grows. Traditional methods such as expert systems are no longer practical making machine learning approaches more valuable. In this thesis work, unsupervised anomaly (fault) detection in unstructured system logs is investigated. The effect of various feature extraction methods are investigated in terms of the gain they provide. Also, the baseline dimensionality reduction method Principal Component Analysis (PCA) and its effects are given. Additionally, autoencoders are studied as an alternative dimensionality reduction technique. Four different methods based on statistics and clustering as well as a framework to clean datasets from anomalies are discussed. A high detection (classification) rate with 99:69% precision and 0:07% false alarm rate are achieved in one of the datasets while similar results have been achieved with variations in the recall in the other dataset. The studies show that the dimensionality reduction can greatly improve the performance of the classifiers used and reduce the computational complexity in anomaly detection

Estimating Small Differences in Car-Pose from Orbits

Distinction among nearby poses and among symmetries of an object is challenging. In this paper, we propose a unified, group-theoretic approach to tackle both. Different from existing works which directly predict absolute pose, our method measures the pose of an object relative to another pose, i.e., the pose difference. The proposed method generates the complete orbit of an object from a single view of the object with respect to the subgroup of SO(3) of rotations around the z-axis, and compares the orbit of the object with another orbit using a novel orbit metric to estimate the pose difference. The generated orbit in the latent space records all the differences in pose in the original observational space, and as a result, the method is capable of finding subtle differences in pose. We demonstrate the effectiveness of the proposed method on cars, where identifying the subtle pose differences is vital.Comment: to appear in BMVC201

Gauge Equivariant Convolutional Networks and the Icosahedral CNN

The principle of equivariance to symmetry transformations enables a theoretically grounded approach to neural network architecture design. Equivariant networks have shown excellent performance and data efficiency on vision and medical imaging problems that exhibit symmetries. Here we show how this principle can be extended beyond global symmetries to local gauge transformations. This enables the development of a very general class of convolutional neural networks on manifolds that depend only on the intrinsic geometry, and which includes many popular methods from equivariant and geometric deep learning. We implement gauge equivariant CNNs for signals defined on the surface of the icosahedron, which provides a reasonable approximation of the sphere. By choosing to work with this very regular manifold, we are able to implement the gauge equivariant convolution using a single conv2d call, making it a highly scalable and practical alternative to Spherical CNNs. Using this method, we demonstrate substantial improvements over previous methods on the task of segmenting omnidirectional images and global climate patterns.Comment: Proceedings of the International Conference on Machine Learning (ICML), 201

A Layer-Based Sequential Framework for Scene Generation with GANs

The visual world we sense, interpret and interact everyday is a complex composition of interleaved physical entities. Therefore, it is a very challenging task to generate vivid scenes of similar complexity using computers. In this work, we present a scene generation framework based on Generative Adversarial Networks (GANs) to sequentially compose a scene, breaking down the underlying problem into smaller ones. Different than the existing approaches, our framework offers an explicit control over the elements of a scene through separate background and foreground generators. Starting with an initially generated background, foreground objects then populate the scene one-by-one in a sequential manner. Via quantitative and qualitative experiments on a subset of the MS-COCO dataset, we show that our proposed framework produces not only more diverse images but also copes better with affine transformations and occlusion artifacts of foreground objects than its counterparts.Comment: This paper was accepted at AAAI 201

Unsupervised Anomaly Detection in Unstructured Log-Data for Root-Cause-Analysis

Anomaly detection has attracted the attention of researchers from a variety of backgrounds as it finds numerous applications in the industry. As a subfield, fault detection plays a crucial role in growing telecommunications networks since failures lead to dissatisfaction and hence financial drawbacks. It aims at identifying unusual events in the system log files. System logs are messages from the elements of the network to highlight their status. The main challenge is to cope with the rate the data volume grows. Traditional methods such as expert systems are no longer practical making machine learning approaches more valuable. In this thesis work, unsupervised anomaly (fault) detection in unstructured system logs is investigated. The effect of various feature extraction methods are investigated in terms of the gain they provide. Also, the baseline dimensionality reduction method Principal Component Analysis (PCA) and its effects are given. Additionally, autoencoders are studied as an alternative dimensionality reduction technique. Four different methods based on statistics and clustering as well as a framework to clean datasets from anomalies are discussed. A high detection (classification) rate with 99:69% precision and 0:07% false alarm rate are achieved in one of the datasets while similar results have been achieved with variations in the recall in the other dataset. The studies show that the dimensionality reduction can greatly improve the performance of the classifiers used and reduce the computational complexity in anomaly detection

On the Dynamics of a Recurrent Hopfield Network On the Dynamics of a Recurrent Hopfield Network

Abstract-In this research paper novel real/complex valued recurrent Hopfield Neural Network (RHNN) is proposed. The method of synthesizing the energy landscape of such a network and the experimental investigation of dynamics of Recurrent Hopfield Network is discussed. Parallel modes of operation (other than fully parallel mode) in layered RHNN is proposed. Also, certain potential applications are proposed

A Layer-Based Sequential Framework for Scene Generation with GANs

The visual world we sense, interpret and interact everyday is a complex composition of interleaved physical entities. Therefore, it is a very challenging task to generate vivid scenes of similar complexity using computers. In this work, we present a scene generation framework based on Generative Adversarial Networks (GANs) to sequentially compose a scene, breaking down the underlying problem into smaller ones. Different than the existing approaches, our framework offers an explicit control over the elements of a scene through separate background and foreground generators. Starting with an initially generated background, foreground objects then populate the scene one-by-one in a sequential manner. Via quantitative and qualitative experiments on a subset of the MS-COCO dataset, we show that our proposed framework produces not only more diverse images but also copes better with affine transformations and occlusion artifacts of foreground objects than its counterparts