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arXiv: Learning
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#1Prasad Cheema (USYD: University of Sydney)H-Index: 3
#2Mehrisadat Makki Alamdari (UNSW: University of New South Wales)H-Index: 9
Last.Gareth A. Vio (USYD: University of Sydney)H-Index: 10
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In many contexts the modal properties of a structure change, either due to the impact of a changing environment, fatigue, or due to the presence of structural damage. For example during flight, an aircraft’s modal properties are known to change with both altitude and velocity. It is thus important to quantify these changes given only a truncated set of modal data, which is usually the case experimentally. This procedure is formally known as the generalised inverse eigenvalue problem. In this pap...
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#1Ahmad Alzahrani (University of Regina)H-Index: 1
#2Samira Sadaoui (University of Regina)H-Index: 6
Although shill bidding is a common fraud in online auctions, it is however very tough to detect because there is no obvious evidence of it happening. There are limited studies on SB classification because training data are difficult to produce. In this study, we build a high-quality labeled shill bidding dataset based on recently scraped auctions from eBay. Labeling shill biding instances with multidimensional features is a tedious task but critical for developing efficient classification models...
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#1Rahul Vashisht (IIST: Indian Institute of Space Science and Technology)
#2H. Viji (VSSC: Vikram Sarabhai Space Centre)
Last.S. Sumitra (IIST: Indian Institute of Space Science and Technology)H-Index: 1
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The advancement of machine learning algorithms has opened a wide scope for vibration-based Structural Health Monitoring (SHM). Vibration-based SHM is based on the fact that damage will alter the dynamic properties, viz., structural response, frequencies, mode shapes, etc. of the structure. The responses measured using sensors, which are high dimensional in nature, can be intelligently analysed using machine learning techniques for damage assessment. Neural networks employing multilayer architect...
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#1Marco Schreyer (HSG: University of St. Gallen)
#2Timur SattarovH-Index: 1
Last.Damian Borth (HSG: University of St. Gallen)H-Index: 15
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Nowadays, organizations collect vast quantities of accounting relevant transactions, referred to as 'journal entries', in 'Enterprise Resource Planning' (ERP) systems. The aggregation of those entries ultimately defines an organization's financial statement. To detect potential misstatements and fraud, international audit standards demand auditors to directly assess journal entries using 'Computer Assisted AuditTechniques' (CAATs). At the same time, discoveries in deep learning research revealed...
We study the variance of the REINFORCE policy gradient estimator in environments with continuous state and action spaces, linear dynamics, quadratic cost, and Gaussian noise. These simple environments allow us to derive bounds on the estimator variance in terms of the environment and noise parameters. We compare the predictions of our bounds to the empirical variance in simulation experiments.
#1Dieterich LawsonH-Index: 6
#2George TuckerH-Index: 16
Last.Rajesh RanganathH-Index: 23
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Energy-based models (EBMs) are powerful probabilistic models, but suffer from intractable sampling and density evaluation due to the partition function. As a result, inference in EBMs relies on approximate sampling algorithms, leading to a mismatch between the model and inference. Motivated by this, we consider the sampler-induced distribution as the model of interest and maximize the likelihood of this model. This yields a class of energy-inspired models (EIMs) that incorporate learned energy f...
Deep learning is increasingly used for state estimation problems such as tracking, navigation, and pose estimation. The uncertainties associated with these measurements are typically assumed to be a fixed covariance matrix. For many scenarios this assumption is inaccurate, leading to worse subsequent filtered state estimates. We show how to model multivariate uncertainty for regression problems with neural networks, incorporating both aleatoric and epistemic sources of heteroscedastic uncertaint...
#1Dushyant RaoH-Index: 9
#2Francesco VisinH-Index: 9
Last.Raia HadsellH-Index: 26
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Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially. Prior art in the field has largely considered supervised or reinforcement learning tasks, and often assumes full knowledge of task labels and boundaries. In this work, we propose an approach (CURL) to tackle a more general problem that we will refer to as unsupervised continual learning. The focus is on learning repr...
#1Hui GuanH-Index: 2
#2Lin NingH-Index: 2
Last.Seung-Hwan LimH-Index: 8
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Convolutional Neural Networks (CNN) are being actively explored for safety-critical applications such as autonomous vehicles and aerospace, where it is essential to ensure the reliability of inference results in the presence of possible memory faults. Traditional methods such as error correction codes (ECC) and Triple Modular Redundancy (TMR) are CNN-oblivious and incur substantial memory overhead and energy cost. This paper introduces in-place zero-space ECC assisted with a new training scheme ...
Auto Composing is an active and appealing research area in the past few years, and lots of efforts have been put into inventing more robust models to solve this problem. With the fast evolution of deep learning techniques, some deep neural network-based language models are becoming dominant. Notably, the transformer structure has been proven to be very efficient and promising in modeling texts. However, the transformer-based language models usually contain huge number of parameters and the size ...
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Machine learning
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