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7,000 | Deep Convolutional Neural Networks for Raman Spectrum Recognition: A
Unified Solution | cs.LG | Machine learning methods have found many applications in Raman spectroscopy,
especially for the identification of chemical species. However, almost all of
these methods require non-trivial preprocessing such as baseline correction
and/or PCA as an essential step. Here we describe our unified solution for the
identifica... | computer science |
7,001 | THAP: A Matlab Toolkit for Learning with Hawkes Processes | stat.ML | As a powerful tool of asynchronous event sequence analysis, point processes
have been studied for a long time and achieved numerous successes in different
fields. Among various point process models, Hawkes process and its variants
attract many researchers in statistics and computer science these years because
they capt... | computer science |
7,002 | Efficient Convolutional Network Learning using Parametric Log based
Dual-Tree Wavelet ScatterNet | cs.LG | We propose a DTCWT ScatterNet Convolutional Neural Network (DTSCNN) formed by
replacing the first few layers of a CNN network with a parametric log based
DTCWT ScatterNet. The ScatterNet extracts edge based invariant representations
that are used by the later layers of the CNN to learn high-level features. This
improve... | computer science |
7,003 | Efficient tracking of a growing number of experts | stat.ML | We consider a variation on the problem of prediction with expert advice,
where new forecasters that were unknown until then may appear at each round. As
often in prediction with expert advice, designing an algorithm that achieves
near-optimal regret guarantees is straightforward, using aggregation of
experts. However, ... | computer science |
7,004 | A State-Space Approach to Dynamic Nonnegative Matrix Factorization | cs.LG | Nonnegative matrix factorization (NMF) has been actively investigated and
used in a wide range of problems in the past decade. A significant amount of
attention has been given to develop NMF algorithms that are suitable to model
time series with strong temporal dependencies. In this paper, we propose a
novel state-spac... | computer science |
7,005 | Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel | stat.ML | Support vector data description (SVDD) is a machine learning technique that
is used for single-class classification and outlier detection. The idea of SVDD
is to find a set of support vectors that defines a boundary around data. When
dealing with online or large data, existing batch SVDD methods have to be rerun
in eac... | computer science |
7,006 | Two-Step Disentanglement for Financial Data | cs.LG | In this work, we address the problem of disentanglement of factors that
generate a given data into those that are correlated with the labeling and
those that are not. Our solution is simpler than previous solutions and employs
adversarial training in a straightforward manner. We demonstrate the new method
on visual dat... | computer science |
7,007 | On Identifiability of Nonnegative Matrix Factorization | cs.LG | In this letter, we propose a new identification criterion that guarantees the
recovery of the low-rank latent factors in the nonnegative matrix factorization
(NMF) model, under mild conditions. Specifically, using the proposed criterion,
it suffices to identify the latent factors if the rows of one factor are
\emph{suf... | computer science |
7,008 | SamBaTen: Sampling-based Batch Incremental Tensor Decomposition | stat.ML | Tensor decompositions are invaluable tools in analyzing multimodal datasets.
In many real-world scenarios, such datasets are far from being static, to the
contrary they tend to grow over time. For instance, in an online social network
setting, as we observe new interactions over time, our dataset gets updated in
its "t... | computer science |
7,009 | Learning Implicit Generative Models Using Differentiable Graph Tests | stat.ML | Recently, there has been a growing interest in the problem of learning rich
implicit models - those from which we can sample, but can not evaluate their
density. These models apply some parametric function, such as a deep network,
to a base measure, and are learned end-to-end using stochastic optimization.
One strategy... | computer science |
7,010 | Balancing Interpretability and Predictive Accuracy for Unsupervised
Tensor Mining | stat.ML | The PARAFAC tensor decomposition has enjoyed an increasing success in
exploratory multi-aspect data mining scenarios. A major challenge remains the
estimation of the number of latent factors (i.e., the rank) of the
decomposition, which yields high-quality, interpretable results. Previously, we
have proposed an automate... | computer science |
7,011 | Random Subspace with Trees for Feature Selection Under Memory
Constraints | stat.ML | Dealing with datasets of very high dimension is a major challenge in machine
learning. In this paper, we consider the problem of feature selection in
applications where the memory is not large enough to contain all features. In
this setting, we propose a novel tree-based feature selection approach that
builds a sequenc... | computer science |
7,012 | Discriminative Similarity for Clustering and Semi-Supervised Learning | stat.ML | Similarity-based clustering and semi-supervised learning methods separate the
data into clusters or classes according to the pairwise similarity between the
data, and the pairwise similarity is crucial for their performance. In this
paper, we propose a novel discriminative similarity learning framework which
learns dis... | computer science |
7,013 | Inhomogeneous Hypergraph Clustering with Applications | cs.LG | Hypergraph partitioning is an important problem in machine learning, computer
vision and network analytics. A widely used method for hypergraph partitioning
relies on minimizing a normalized sum of the costs of partitioning hyperedges
across clusters. Algorithmic solutions based on this approach assume that
different p... | computer science |
7,014 | Spectral Mixture Kernels for Multi-Output Gaussian Processes | stat.ML | Early approaches to multiple-output Gaussian processes (MOGPs) relied on
linear combinations of independent, latent, single-output Gaussian processes
(GPs). This resulted in cross-covariance functions with limited parametric
interpretation, thus conflicting with the ability of single-output GPs to
understand lengthscal... | computer science |
7,015 | Recovery Conditions and Sampling Strategies for Network Lasso | stat.ML | The network Lasso is a recently proposed convex optimization method for
machine learning from massive network structured datasets, i.e., big data over
networks. It is a variant of the well-known least absolute shrinkage and
selection operator (Lasso), which is underlying many methods in learning and
signal processing i... | computer science |
7,016 | Deep learning: Technical introduction | stat.ML | This note presents in a technical though hopefully pedagogical way the three
most common forms of neural network architectures: Feedforward, Convolutional
and Recurrent. For each network, their fundamental building blocks are
detailed. The forward pass and the update rules for the backpropagation
algorithm are then der... | computer science |
7,017 | A Statistical Approach to Increase Classification Accuracy in Supervised
Learning Algorithms | cs.LG | Probabilistic mixture models have been widely used for different machine
learning and pattern recognition tasks such as clustering, dimensionality
reduction, and classification. In this paper, we focus on trying to solve the
most common challenges related to supervised learning algorithms by using
mixture probability d... | computer science |
7,018 | Learning the PE Header, Malware Detection with Minimal Domain Knowledge | stat.ML | Many efforts have been made to use various forms of domain knowledge in
malware detection. Currently there exist two common approaches to malware
detection without domain knowledge, namely byte n-grams and strings. In this
work we explore the feasibility of applying neural networks to malware
detection and feature lear... | computer science |
7,019 | Boosting Deep Learning Risk Prediction with Generative Adversarial
Networks for Electronic Health Records | cs.LG | The rapid growth of Electronic Health Records (EHRs), as well as the
accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting
widespread interests and attentions. Recent progress in the design and
applications of deep learning methods has shown promising results and is
forcing massive changes in h... | computer science |
7,020 | Probabilistic Rule Realization and Selection | cs.LG | Abstraction and realization are bilateral processes that are key in deriving
intelligence and creativity. In many domains, the two processes are approached
through rules: high-level principles that reveal invariances within similar yet
diverse examples. Under a probabilistic setting for discrete input spaces, we
focus ... | computer science |
7,021 | Optimal Sub-sampling with Influence Functions | stat.ML | Sub-sampling is a common and often effective method to deal with the
computational challenges of large datasets. However, for most statistical
models, there is no well-motivated approach for drawing a non-uniform
subsample. We show that the concept of an asymptotically linear estimator and
the associated influence func... | computer science |
7,022 | Symmetric Variational Autoencoder and Connections to Adversarial
Learning | stat.ML | 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 previously
distinct techni... | computer science |
7,023 | The low-rank hurdle model | stat.ML | A composite loss framework is proposed for low-rank modeling of data
consisting of interesting and common values, such as excess zeros or missing
values. The methodology is motivated by the generalized low-rank framework and
the hurdle method which is commonly used to analyze zero-inflated counts. The
model is demonstr... | computer science |
7,024 | Neural Networks Regularization Through Class-wise Invariant
Representation Learning | cs.LG | Training deep neural networks is known to require a large number of training
samples. However, in many applications only few training samples are available.
In this work, we tackle the issue of training neural networks for
classification task when few training samples are available. We attempt to
solve this issue by pr... | computer science |
7,025 | Convolutional Gaussian Processes | stat.ML | We present a practical way of introducing convolutional structure into
Gaussian processes, making them more suited to high-dimensional inputs like
images. The main contribution of our work is the construction of an
inter-domain inducing point approximation that is well-tailored to the
convolutional kernel. This allows ... | computer science |
7,026 | Less Is More: A Comprehensive Framework for the Number of Components of
Ensemble Classifiers | cs.LG | The number of component classifiers chosen for an ensemble has a great impact
on its prediction ability. In this paper, we use a geometric framework for a
priori determining the ensemble size, applicable to most of the existing batch
and online ensemble classifiers. There are only a limited number of studies on
the ens... | computer science |
7,027 | Deep Residual Networks and Weight Initialization | cs.LG | Residual Network (ResNet) is the state-of-the-art architecture that realizes
successful training of really deep neural network. It is also known that good
weight initialization of neural network avoids problem of vanishing/exploding
gradients. In this paper, simplified models of ResNets are analyzed. We argue
that good... | computer science |
7,028 | Classifying Unordered Feature Sets with Convolutional Deep Averaging
Networks | cs.LG | Unordered feature sets are a nonstandard data structure that traditional
neural networks are incapable of addressing in a principled manner. Providing a
concatenation of features in an arbitrary order may lead to the learning of
spurious patterns or biases that do not actually exist. Another complication is
introduced ... | computer science |
7,029 | R2N2: Residual Recurrent Neural Networks for Multivariate Time Series
Forecasting | cs.LG | Multivariate time-series modeling and forecasting is an important problem
with numerous applications. Traditional approaches such as VAR (vector
auto-regressive) models and more recent approaches such as RNNs (recurrent
neural networks) are indispensable tools in modeling time-series data. In many
multivariate time ser... | computer science |
7,030 | Semi-Supervised Active Clustering with Weak Oracles | stat.ML | Semi-supervised active clustering (SSAC) utilizes the knowledge of a domain
expert to cluster data points by interactively making pairwise "same-cluster"
queries. However, it is impractical to ask human oracles to answer every
pairwise query. In this paper, we study the influence of allowing "not-sure"
answers from a w... | computer science |
7,031 | Ensemble Methods as a Defense to Adversarial Perturbations Against Deep
Neural Networks | stat.ML | Deep learning has become the state of the art approach in many machine
learning problems such as classification. It has recently been shown that deep
learning is highly vulnerable to adversarial perturbations. Taking the camera
systems of self-driving cars as an example, small adversarial perturbations can
cause the sy... | computer science |
7,032 | Learning Graph-Level Representation for Drug Discovery | cs.LG | Predicating macroscopic influences of drugs on human body, like efficacy and
toxicity, is a central problem of small-molecule based drug discovery.
Molecules can be represented as an undirected graph, and we can utilize graph
convolution networks to predication molecular properties. However, graph
convolutional network... | computer science |
7,033 | Learning with Bounded Instance- and Label-dependent Label Noise | stat.ML | Instance- and label-dependent label noise (ILN) is widely existed in
real-world datasets but has been rarely studied. In this paper, we focus on a
particular case of ILN where the label noise rates, representing the
probabilities that the true labels of examples flip into the corrupted labels,
have upper bounds. We pro... | computer science |
7,034 | Dual Discriminator Generative Adversarial Nets | cs.LG | We propose in this paper a novel approach to tackle the problem of mode
collapse encountered in generative adversarial network (GAN). Our idea is
intuitive but proven to be very effective, especially in addressing some key
limitations of GAN. In essence, it combines the Kullback-Leibler (KL) and
reverse KL divergences ... | computer science |
7,035 | High-Dimensional Dependency Structure Learning for Physical Processes | cs.LG | In this paper, we consider the use of structure learning methods for
probabilistic graphical models to identify statistical dependencies in
high-dimensional physical processes. Such processes are often synthetically
characterized using PDEs (partial differential equations) and are observed in a
variety of natural pheno... | computer science |
7,036 | Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits | cs.LG | In this paper, we propose and study opportunistic bandits - a new variant of
bandits where the regret of pulling a suboptimal arm varies under different
environmental conditions, such as network load or produce price. When the
load/price is low, so is the cost/regret of pulling a suboptimal arm (e.g.,
trying a suboptim... | computer science |
7,037 | Tight Semi-Nonnegative Matrix Factorization | stat.ML | The nonnegative matrix factorization is a widely used, flexible matrix
decomposition, finding applications in biology, image and signal processing and
information retrieval, among other areas. Here we present a related matrix
factorization. A multi-objective optimization problem finds conical
combinations of templates ... | computer science |
7,038 | Normalized Direction-preserving Adam | cs.LG | Optimization algorithms for training deep models not only affects the
convergence rate and stability of the training process, but are also highly
related to the generalization performance of the models. While adaptive
algorithms, such as Adam and RMSprop, have shown better optimization
performance than stochastic gradi... | computer science |
7,039 | Interpretable Graph-Based Semi-Supervised Learning via Flows | stat.ML | In this paper, we consider the interpretability of the foundational
Laplacian-based semi-supervised learning approaches on graphs. We introduce a
novel flow-based learning framework that subsumes the foundational approaches
and additionally provides a detailed, transparent, and easily understood
expression of the learn... | computer science |
7,040 | Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework
for Traffic Forecasting | cs.LG | Timely accurate traffic forecast is crucial for urban traffic control and
guidance. Due to the high nonlinearity and complexity of traffic flow,
traditional methods cannot satisfy the requirements of mid-and-long term
prediction tasks and often neglect spatial and temporal dependencies. In this
paper, we propose a nove... | computer science |
7,041 | LSTM Fully Convolutional Networks for Time Series Classification | cs.LG | Fully convolutional neural networks (FCN) have been shown to achieve
state-of-the-art performance on the task of classifying time series sequences.
We propose the augmentation of fully convolutional networks with long short
term memory recurrent neural network (LSTM RNN) sub-modules for time series
classification. Our ... | computer science |
7,042 | Road Friction Estimation for Connected Vehicles using Supervised Machine
Learning | cs.LG | In this paper, the problem of road friction prediction from a fleet of
connected vehicles is investigated. A framework is proposed to predict the road
friction level using both historical friction data from the connected cars and
data from weather stations, and comparative results from different methods are
presented. ... | computer science |
7,043 | Subset Labeled LDA for Large-Scale Multi-Label Classification | stat.ML | Labeled Latent Dirichlet Allocation (LLDA) is an extension of the standard
unsupervised Latent Dirichlet Allocation (LDA) algorithm, to address
multi-label learning tasks. Previous work has shown it to perform in par with
other state-of-the-art multi-label methods. Nonetheless, with increasing label
sets sizes LLDA enc... | computer science |
7,044 | Relevant Ensemble of Trees | stat.ML | Tree ensembles are flexible predictive models that can capture relevant
variables and to some extent their interactions in a compact and interpretable
manner. Most algorithms for obtaining tree ensembles are based on versions of
boosting or Random Forest. Previous work showed that boosting algorithms
exhibit a cyclic b... | computer science |
7,045 | Deep Automated Multi-task Learning | cs.LG | Multi-task learning (MTL) has recently contributed to learning better
representations in service of various NLP tasks. MTL aims at improving the
performance of a primary task, by jointly training on a secondary task. This
paper introduces automated tasks, which exploit the sequential nature of the
input data, as second... | computer science |
7,046 | Learning Mixtures of Multi-Output Regression Models by Correlation
Clustering for Multi-View Data | stat.ML | In many datasets, different parts of the data may have their own patterns of
correlation, a structure that can be modeled as a mixture of local linear
correlation models. The task of finding these mixtures is known as correlation
clustering. In this work, we propose a linear correlation clustering method for
datasets w... | computer science |
7,047 | Multi-Entity Dependence Learning with Rich Context via Conditional
Variational Auto-encoder | cs.LG | Multi-Entity Dependence Learning (MEDL) explores conditional correlations
among multiple entities. The availability of rich contextual information
requires a nimble learning scheme that tightly integrates with deep neural
networks and has the ability to capture correlation structures among
exponentially many outcomes. ... | computer science |
7,048 | Neonatal Seizure Detection using Convolutional Neural Networks | stat.ML | This study presents a novel end-to-end architecture that learns hierarchical
representations from raw EEG data using fully convolutional deep neural
networks for the task of neonatal seizure detection. The deep neural network
acts as both feature extractor and classifier, allowing for end-to-end
optimization of the sei... | computer science |
7,049 | Why Pay More When You Can Pay Less: A Joint Learning Framework for
Active Feature Acquisition and Classification | cs.LG | We consider the problem of active feature acquisition, where we sequentially
select the subset of features in order to achieve the maximum prediction
performance in the most cost-effective way. In this work, we formulate this
active feature acquisition problem as a reinforcement learning problem, and
provide a novel fr... | computer science |
7,050 | N2N Learning: Network to Network Compression via Policy Gradient
Reinforcement Learning | cs.LG | While bigger and deeper neural network architectures continue to advance the
state-of-the-art for many computer vision tasks, real-world adoption of these
networks is impeded by hardware and speed constraints. Conventional model
compression methods attempt to address this problem by modifying the
architecture manually ... | computer science |
7,051 | A Note on Tight Lower Bound for MNL-Bandit Assortment Selection Models | stat.ML | In this note we prove a tight lower bound for the MNL-bandit assortment
selection model that matches the upper bound given in (Agrawal et al., 2016a,b)
for all parameters, up to logarithmic factors. | computer science |
7,052 | A Probabilistic Framework for Nonlinearities in Stochastic Neural
Networks | stat.ML | We present a probabilistic framework for nonlinearities, based on doubly
truncated Gaussian distributions. By setting the truncation points
appropriately, we are able to generate various types of nonlinearities within a
unified framework, including sigmoid, tanh and ReLU, the most commonly used
nonlinearities in neural... | computer science |
7,053 | Scalable Estimation of Dirichlet Process Mixture Models on Distributed
Data | stat.ML | We consider the estimation of Dirichlet Process Mixture Models (DPMMs) in
distributed environments, where data are distributed across multiple computing
nodes. A key advantage of Bayesian nonparametric models such as DPMMs is that
they allow new components to be introduced on the fly as needed. This, however,
posts an ... | computer science |
7,054 | Analogical-based Bayesian Optimization | cs.LG | Some real-world problems revolve to solve the optimization problem
\max_{x\in\mathcal{X}}f\left(x\right) where f\left(.\right) is a black-box
function and X might be the set of non-vectorial objects (e.g., distributions)
where we can only define a symmetric and non-negative similarity score on it.
This setting requires... | computer science |
7,055 | Triangle Generative Adversarial Networks | cs.LG | A Triangle Generative Adversarial Network ($\Delta$-GAN) is developed for
semi-supervised cross-domain joint distribution matching, where the training
data consists of samples from each domain, and supervision of domain
correspondence is provided by only a few paired samples. $\Delta$-GAN consists
of four neural networ... | computer science |
7,056 | Deep Reinforcement Learning that Matters | cs.LG | In recent years, significant progress has been made in solving challenging
problems across various domains using deep reinforcement learning (RL).
Reproducing existing work and accurately judging the improvements offered by
novel methods is vital to sustaining this progress. Unfortunately, reproducing
results for state... | computer science |
7,057 | A textual transform of multivariate time-series for prognostics | stat.ML | Prognostics or early detection of incipient faults is an important industrial
challenge for condition-based and preventive maintenance. Physics-based
approaches to modeling fault progression are infeasible due to multiple
interacting components, uncontrolled environmental factors and observability
constraints. Moreover... | computer science |
7,058 | Deep Lattice Networks and Partial Monotonic Functions | stat.ML | We propose learning deep models that are monotonic with respect to a
user-specified set of inputs by alternating layers of linear embeddings,
ensembles of lattices, and calibrators (piecewise linear functions), with
appropriate constraints for monotonicity, and jointly training the resulting
network. We implement the l... | computer science |
7,059 | Contrastive Principal Component Analysis | stat.ML | We present a new technique called contrastive principal component analysis
(cPCA) that is designed to discover low-dimensional structure that is unique to
a dataset, or enriched in one dataset relative to other data. The technique is
a generalization of standard PCA, for the setting where multiple datasets are
availabl... | computer science |
7,060 | Bandits with Delayed, Aggregated Anonymous Feedback | stat.ML | We study a variant of the stochastic $K$-armed bandit problem, which we call
"bandits with delayed, aggregated anonymous feedback". In this problem, when
the player pulls an arm, a reward is generated, however it is not immediately
observed. Instead, at the end of each round the player observes only the sum of
a number... | computer science |
7,061 | Structured Probabilistic Pruning for Convolutional Neural Network
Acceleration | cs.LG | Although deep Convolutional Neural Network (CNN) has shown better performance
in various computer vision tasks, its application is restricted by a
significant increase in storage and computation. Among CNN simplification
techniques, parameter pruning is a promising approach which aims at reducing
the number of weights ... | computer science |
7,062 | Learning RBM with a DC programming Approach | cs.LG | By exploiting the property that the RBM log-likelihood function is the
difference of convex functions, we formulate a stochastic variant of the
difference of convex functions (DC) programming to minimize the negative
log-likelihood. Interestingly, the traditional contrastive divergence algorithm
is a special case of th... | computer science |
7,063 | SpectralLeader: Online Spectral Learning for Single Topic Models | cs.LG | We study the problem of learning a latent variable model from a stream of
data. Latent variable models are popular in practice because they can explain
observed data in terms of unobserved concepts. These models have been
traditionally studied in the offline setting. The online EM is arguably the
most popular algorithm... | computer science |
7,064 | Perturbative Black Box Variational Inference | stat.ML | Black box variational inference (BBVI) with reparameterization gradients
triggered the exploration of divergence measures other than the
Kullback-Leibler (KL) divergence, such as alpha divergences. In this paper, we
view BBVI with generalized divergences as a form of estimating the marginal
likelihood via biased import... | computer science |
7,065 | Total stability of kernel methods | stat.ML | Regularized empirical risk minimization using kernels and their corresponding
reproducing kernel Hilbert spaces (RKHSs) plays an important role in machine
learning. However, the actually used kernel often depends on one or on a few
hyperparameters or the kernel is even data dependent in a much more complicated
manner. ... | computer science |
7,066 | Approximate Bayesian Inference in Linear State Space Models for
Intermittent Demand Forecasting at Scale | stat.ML | We present a scalable and robust Bayesian inference method for linear state
space models. The method is applied to demand forecasting in the context of a
large e-commerce platform, paying special attention to intermittent and bursty
target statistics. Inference is approximated by the Newton-Raphson algorithm,
reduced t... | computer science |
7,067 | Ensemble Multi-task Gaussian Process Regression with Multiple Latent
Processes | stat.ML | Multi-task/Multi-output learning seeks to exploit correlation among tasks to
enhance performance over learning or solving each task independently. In this
paper, we investigate this problem in the context of Gaussian Processes (GPs)
and propose a new model which learns a mixture of latent processes by
decomposing the c... | computer science |
7,068 | House Price Prediction Using LSTM | cs.LG | In this paper, we use the house price data ranging from January 2004 to
October 2016 to predict the average house price of November and December in
2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply
Autoregressive Integrated Moving Average model to generate the baseline while
LSTM networks to... | computer science |
7,069 | Predictive-State Decoders: Encoding the Future into Recurrent Networks | stat.ML | Recurrent neural networks (RNNs) are a vital modeling technique that rely on
internal states learned indirectly by optimization of a supervised,
unsupervised, or reinforcement training loss. RNNs are used to model dynamic
processes that are characterized by underlying latent states whose form is
often unknown, precludi... | computer science |
7,070 | Understanding a Version of Multivariate Symmetric Uncertainty to assist
in Feature Selection | cs.LG | In this paper, we analyze the behavior of the multivariate symmetric
uncertainty (MSU) measure through the use of statistical simulation techniques
under various mixes of informative and non-informative randomly generated
features. Experiments show how the number of attributes, their cardinalities,
and the sample size ... | computer science |
7,071 | On the regularization of Wasserstein GANs | stat.ML | Since their invention, generative adversarial networks (GANs) have become a
popular approach for learning to model a distribution of real (unlabeled) data.
Convergence problems during training are overcome by Wasserstein GANs which
minimize the distance between the model and the empirical distribution in terms
of a dif... | computer science |
7,072 | AutoEncoder by Forest | cs.LG | Auto-encoding is an important task which is typically realized by deep neural
networks (DNNs) such as convolutional neural networks (CNN). In this paper, we
propose EncoderForest (abbrv. eForest), the first tree ensemble based
auto-encoder. We present a procedure for enabling forests to do backward
reconstruction by ut... | computer science |
7,073 | Output Range Analysis for Deep Neural Networks | cs.LG | Deep neural networks (NN) are extensively used for machine learning tasks
such as image classification, perception and control of autonomous systems.
Increasingly, these deep NNs are also been deployed in high-assurance
applications. Thus, there is a pressing need for developing techniques to
verify neural networks to ... | computer science |
7,074 | SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine | cs.LG | Traditional medicine typically applies one-size-fits-all treatment for the
entire patient population whereas precision medicine develops tailored
treatment schemes for different patient subgroups. The fact that some factors
may be more significant for a specific patient subgroup motivates clinicians
and medical researc... | computer science |
7,075 | Introducing DeepBalance: Random Deep Belief Network Ensembles to Address
Class Imbalance | stat.ML | Class imbalance problems manifest in domains such as financial fraud
detection or network intrusion analysis, where the prevalence of one class is
much higher than another. Typically, practitioners are more interested in
predicting the minority class than the majority class as the minority class may
carry a higher misc... | computer science |
7,076 | L1-norm Kernel PCA | stat.ML | We present the first model and algorithm for L1-norm kernel PCA. While
L2-norm kernel PCA has been widely studied, there has been no work on L1-norm
kernel PCA. For this non-convex and non-smooth problem, we offer geometric
understandings through reformulations and present an efficient algorithm where
the kernel trick ... | computer science |
7,077 | Comparison of PCA with ICA from data distribution perspective | stat.ML | We performed an empirical comparison of ICA and PCA algorithms by applying
them on two simulated noisy time series with varying distribution parameters
and level of noise. In general, ICA shows better results than PCA because it
takes into account higher moments of data distribution. On the other hand, PCA
remains quit... | computer science |
7,078 | A Nonlinear Orthogonal Non-Negative Matrix Factorization Approach to
Subspace Clustering | stat.ML | A recent theoretical analysis shows the equivalence between non-negative
matrix factorization (NMF) and spectral clustering based approach to subspace
clustering. As NMF and many of its variants are essentially linear, we
introduce a nonlinear NMF with explicit orthogonality and derive general
kernel-based orthogonal m... | computer science |
7,079 | Convergence Analysis of Distributed Stochastic Gradient Descent with
Shuffling | stat.ML | When using stochastic gradient descent to solve large-scale machine learning
problems, a common practice of data processing is to shuffle the training data,
partition the data across multiple machines if needed, and then perform several
epochs of training on the re-shuffled (either locally or globally) data. The
above ... | computer science |
7,080 | Language-depedent I-Vectors for LRE15 | stat.ML | A standard recipe for spoken language recognition is to apply a Gaussian
back-end to i-vectors. This ignores the uncertainty in the i-vector extraction,
which could be important especially for short utterances. A recent paper by
Cumani, Plchot and Fer proposes a solution to propagate that uncertainty into
the backend. ... | computer science |
7,081 | The Deep Ritz method: A deep learning-based numerical algorithm for
solving variational problems | cs.LG | We propose a deep learning based method, the Deep Ritz Method, for
numerically solving variational problems, particularly the ones that arise from
partial differential equations. The Deep Ritz method is naturally nonlinear,
naturally adaptive and has the potential to work in rather high dimensions. The
framework is qui... | computer science |
7,082 | To prune, or not to prune: exploring the efficacy of pruning for model
compression | stat.ML | Model pruning seeks to induce sparsity in a deep neural network's various
connection matrices, thereby reducing the number of nonzero-valued parameters
in the model. Recent reports (Han et al., 2015; Narang et al., 2017) prune deep
networks at the cost of only a marginal loss in accuracy and achieve a sizable
reduction... | computer science |
7,083 | Porcupine Neural Networks: (Almost) All Local Optima are Global | stat.ML | Neural networks have been used prominently in several machine learning and
statistics applications. In general, the underlying optimization of neural
networks is non-convex which makes their performance analysis challenging. In
this paper, we take a novel approach to this problem by asking whether one can
constrain neu... | computer science |
7,084 | Linear-Time Sequence Classification using Restricted Boltzmann Machines | cs.LG | Classification of sequence data is the topic of interest for dynamic Bayesian
models and Recurrent Neural Networks (RNNs). While the former can explicitly
model the temporal dependencies between class variables, the latter have a
capability of learning representations. Several attempts have been made to
improve perform... | computer science |
7,085 | Discovering Playing Patterns: Time Series Clustering of Free-To-Play
Game Data | stat.ML | The classification of time series data is a challenge common to all
data-driven fields. However, there is no agreement about which are the most
efficient techniques to group unlabeled time-ordered data. This is because a
successful classification of time series patterns depends on the goal and the
domain of interest, i... | computer science |
7,086 | Ranking and Selection as Stochastic Control | cs.LG | Under a Bayesian framework, we formulate the fully sequential sampling and
selection decision in statistical ranking and selection as a stochastic control
problem, and derive the associated Bellman equation. Using value function
approximation, we derive an approximately optimal allocation policy. We show
that this poli... | computer science |
7,087 | Bayesian Alignments of Warped Multi-Output Gaussian Processes | stat.ML | We present a Bayesian extension to convolution processes which defines a
representation between multiple functions by an embedding in a shared latent
space. The proposed model allows for both arbitrary alignments of the inputs
and and also non-parametric output warpings to transform the observations. This
gives rise to... | computer science |
7,088 | Enhancing Transparency of Black-box Soft-margin SVM by Integrating
Data-based Prior Information | stat.ML | The lack of transparency often makes the black-box models difficult to be
applied to many practical domains. For this reason, the current work, from the
black-box model input port, proposes to incorporate data-based prior
information into the black-box soft-margin SVM model to enhance its
transparency. The concept and ... | computer science |
7,089 | Unifying Local and Global Change Detection in Dynamic Networks | cs.LG | Many real-world networks are complex dynamical systems, where both local
(e.g., changing node attributes) and global (e.g., changing network topology)
processes unfold over time. Local dynamics may provoke global changes in the
network, and the ability to detect such effects could have profound
implications for a numbe... | computer science |
7,090 | Sum-Product Networks for Hybrid Domains | cs.LG | While all kinds of mixed data -from personal data, over panel and scientific
data, to public and commercial data- are collected and stored, building
probabilistic graphical models for these hybrid domains becomes more difficult.
Users spend significant amounts of time in identifying the parametric form of
the random va... | computer science |
7,091 | Safe Semi-Supervised Learning of Sum-Product Networks | stat.ML | In several domains obtaining class annotations is expensive while at the same
time unlabelled data are abundant. While most semi-supervised approaches
enforce restrictive assumptions on the data distribution, recent work has
managed to learn semi-supervised models in a non-restrictive regime. However,
so far such appro... | computer science |
7,092 | An Analysis of Dropout for Matrix Factorization | cs.LG | Dropout is a simple yet effective algorithm for regularizing neural networks
by randomly dropping out units through Bernoulli multiplicative noise, and for
some restricted problem classes, such as linear or logistic regression, several
theoretical studies have demonstrated the equivalence between dropout and a
fully de... | computer science |
7,093 | Fast and Strong Convergence of Online Learning Algorithms | cs.LG | In this paper, we study the online learning algorithm without explicit
regularization terms. This algorithm is essentially a stochastic gradient
descent scheme in a reproducing kernel Hilbert space (RKHS). The polynomially
decaying step size in each iteration can play a role of regularization to
ensure the generalizati... | computer science |
7,094 | LinXGBoost: Extension of XGBoost to Generalized Local Linear Models | cs.LG | XGBoost is often presented as the algorithm that wins every ML competition.
Surprisingly, this is true even though predictions are piecewise constant. This
might be justified in high dimensional input spaces, but when the number of
features is low, a piecewise linear model is likely to perform better. XGBoost
was exten... | computer science |
7,095 | Using Task Descriptions in Lifelong Machine Learning for Improved
Performance and Zero-Shot Transfer | cs.LG | Knowledge transfer between tasks can improve the performance of learned
models, but requires an accurate estimate of the inter-task relationships to
identify the relevant knowledge to transfer. These inter-task relationships are
typically estimated based on training data for each task, which is inefficient
in lifelong ... | computer science |
7,096 | Quantized Minimum Error Entropy Criterion | stat.ML | Comparing with traditional learning criteria, such as mean square error
(MSE), the minimum error entropy (MEE) criterion is superior in nonlinear and
non-Gaussian signal processing and machine learning. The argument of the
logarithm in Renyis entropy estimator, called information potential (IP), is a
popular MEE cost i... | computer science |
7,097 | Efficient Data-Driven Geologic Feature Detection from Pre-stack Seismic
Measurements using Randomized Machine-Learning Algorithm | cs.LG | Conventional seismic techniques for detecting the subsurface geologic
features are challenged by limited data coverage, computational inefficiency,
and subjective human factors. We developed a novel data-driven geological
feature detection approach based on pre-stack seismic measurements. Our
detection method employs a... | computer science |
7,098 | A Unified Neural Network Approach for Estimating Travel Time and
Distance for a Taxi Trip | stat.ML | In building intelligent transportation systems such as taxi or rideshare
services, accurate prediction of travel time and distance is crucial for
customer experience and resource management. Using the NYC taxi dataset, which
contains taxi trips data collected from GPS-enabled taxis [23], this paper
investigates the use... | computer science |
7,099 | Deep Learning in Multiple Multistep Time Series Prediction | stat.ML | The project aims to research on combining deep learning specifically
Long-Short Memory (LSTM) and basic statistics in multiple multistep time series
prediction. LSTM can dive into all the pages and learn the general trends of
variation in a large scope, while the well selected medians for each page can
keep the special... | computer science |
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