Topic Tag: Autoencoder

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 Learning compressed representations of blood samples time series with missing data

 

Clinical measurements collected over time are naturally represented as multivariate time series (MTS), which often contain missing data. An autoencoder can learn low dimensional vectorial representations of MTS that preserve important data characteristics, but cannot deal explicitly with missing da…


 Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis

  

An increasing number of sensors on mobile, Internet of things (IoT), and wearable devices generate time-series measurements of physical activities. Though access to the sensory data is critical to the success of many beneficial applications such as health monitoring or activity recognition, a wide …


 Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis

Anomaly detection in database management systems (DBMSs) is difficult because of increasing number of statistics (stat) and event metrics in big data system. In this paper, I propose an automatic DBMS diagnosis system that detects anomaly periods with abnormal DB stat metrics and finds causal event…


 Generating Nontrivial Melodies for Music as a Service

 

We present a hybrid neural network and rule-based system that generates pop music. Music produced by pure rule-based systems often sounds mechanical. Music produced by machine learning sounds better, but still lacks hierarchical temporal structure. We restore temporal hierarchy by augmenting machin…


 Learnable Explicit Density for Continuous Latent Space and Variational Inference

In this paper, we study two aspects of the variational autoencoder (VAE): the prior distribution over the latent variables and its corresponding posterior. First, we decompose the learning of VAEs into layerwise density estimation, and argue that having a flexible prior is beneficial to both sample…


 Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation

 

Domain mismatch between training and testing can lead to significant degradation in performance in many machine learning scenarios. Unfortunately, this is not a rare situation for automatic speech recognition deployments in real-world applications. Research on robust speech recognition can be regar…


 AutoCon: Regression Testing for Detecting Cache Contention Anomalies Using Autoencoder

Cache contention is an important type of performance anomaly in this multi-core and many-core era. It can cause a significant slowdown in parallel programs. However, it is hard to detect and often, not visible in the source code. As software changes over time, modifications in code can introduce ca…


 MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings

 

E-commerce websites such as Amazon, Alibaba, Flipkart, and Walmart sell billions of products. Machine learning (ML) algorithms involving products are often used to improve the customer experience and increase revenue, e.g., product similarity, recommendation, and price estimation. The products are …


 Quantum Autoencoders via Quantum Adders with Genetic Algorithms

 

The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connect…


 Variational Memory Addressing in Generative Models

Aiming to augment generative models with external memory, we interpret the output of a memory module with stochastic addressing as a conditional mixture distribution, where a read operation corresponds to sampling a discrete memory address and retrieving the corresponding content from memory. This …


 Embedding Deep Networks into Visual Explanations

   

In this paper, we propose a novel explanation module to explain the predictions made by deep learning. Explanation modules work by embedding a high-dimensional deep network layer nonlinearly into a low-dimensional explanation space, while retaining faithfulness in that the original deep learning pr…


 Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks

Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or forensic investigations. Nowadays, the majority of applied techniques refer to handcrafted rules derived from known fraud scenarios. While fairly successful, these rules e…


 Deep Asymmetric Multi-task Feature Learning

 

We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can learn deep representations shared across multiple tasks while effectively preventing negative transfer that may happen in the feature sharing process. Specifically, we introduce an asymmetric autoencoder term that allows r…


 Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets

  

The inverse mapping of GANs'(Generative Adversarial Nets) generator has a great potential value.Hence, some works have been developed to construct the inverse function of generator by directly learning or adversarial learning.While the results are encouraging, the problem is highly challenging and …


 Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning

   

Internet traffic classification has become more important with rapid growth of current Internet network and online applications. There have been numerous studies on this topic which have led to many different approaches. Most of these approaches exploit predefined features extracted by an expert in…


 Symmetric Variational Autoencoder and Connections to Adversarial Learning

A new form of the variational autoencoder (VAE) is proposed, based on the symmetric Kullback-Leibler divergence. It is demonstrated that learning of the resulting symmetric VAE (sVAE) has close connections to previously developed adversarial-learning methods. This relationship helps unify the previ…