189 research outputs found
Isometric Gaussian Process Latent Variable Model for Dissimilarity Data
We present a probabilistic model where the latent variable respects both the
distances and the topology of the modeled data. The model leverages the
Riemannian geometry of the generated manifold to endow the latent space with a
well-defined stochastic distance measure, which is modeled locally as Nakagami
distributions. These stochastic distances are sought to be as similar as
possible to observed distances along a neighborhood graph through a censoring
process. The model is inferred by variational inference based on observations
of pairwise distances. We demonstrate how the new model can encode invariances
in the learned manifolds.Comment: ICML 202
Distribution-Dissimilarities in Machine Learning
Any binary classifier (or score-function) can be used to define a dissimilarity
between two distributions. Many well-known distribution-dissimilarities are
actually classifier-based: total variation, KL- or JS-divergence, Hellinger
distance, etc. And many recent popular generative modeling algorithms compute
or approximate these distribution-dissimilarities by explicitly training a
classifier: e.g. generative adversarial networks (GAN) and their variants.
This thesis introduces and studies such classifier-based
distribution-dissimilarities. After a general introduction, the first part
analyzes the influence of the classifiers' capacity on the dissimilarity's
strength for the special case of maximum mean discrepancies (MMD) and provides
applications. The second part studies applications of classifier-based
distribution-dissimilarities in the context of generative modeling and presents
two new algorithms: Wasserstein Auto-Encoders (WAE) and AdaGAN. The third and
final part focuses on adversarial examples, i.e. targeted but imperceptible
input-perturbations that lead to drastically different predictions of an
artificial classifier. It shows that adversarial vulnerability of neural network
based classifiers typically increases with the input-dimension, independently
of the network topology
Quantitative Analysis of Evaluation Criteria for Generative Models
Machine Learning (ML) is rapidly becoming integrated in critical aspects of cybersecurity today, particularly in the area of network intrusion/anomaly detection. However, ML techniques require large volumes of data to be effective. The available data is a critical aspect of the ML process for training, classification, and testing purposes. One solution to the problem is to generate synthetic data that is realistic. With the application of ML to this area, one promising application is the use of ML to perform the data generation. With the ability to generate synthetic data comes the need to evaluate the “realness” of the generated data. This research focuses specifically on the problem of evaluating the evaluation criteria. Quantitative analysis of evaluation criteria is important so that future research can have quantitative evidence for the evaluation criteria they utilize. The goal of this research is to provide a framework that can be used to inform and improve the process of generating synthetic semi-structured sequential data. A series of experiments evaluating a chosen set of metrics on discriminative ability and efficiency is performed. This research shows that the choice of feature space in which distances are calculated in is critical. The ability to discriminate between real and generated data hinges on the space that the distances are calculated in. Additionally, the choice of metric significantly affects the sample distance distributions in a suitable feature space. There are three main contributions from this work. First, this work provides the first known framework for evaluating metrics for semi-structured sequential synthetic data generation. Second, this work provides a “black box” evaluation framework which is generator agnostic. Third, this research provides the first known evaluation of metrics for semi-structured sequential data
Penalty Gradient Normalization for Generative Adversarial Networks
In this paper, we propose a novel normalization method called penalty
gradient normalization (PGN) to tackle the training instability of Generative
Adversarial Networks (GANs) caused by the sharp gradient space. Unlike existing
work such as gradient penalty and spectral normalization, the proposed PGN only
imposes a penalty gradient norm constraint on the discriminator function, which
increases the capacity of the discriminator. Moreover, the proposed penalty
gradient normalization can be applied to different GAN architectures with
little modification. Extensive experiments on three datasets show that GANs
trained with penalty gradient normalization outperform existing methods in
terms of both Frechet Inception and Distance and Inception Score.Comment: Under Revie
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