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6,600 | On Bayesian Exponentially Embedded Family for Model Order Selection | stat.ML | In this paper, we derive a Bayesian model order selection rule by using the
exponentially embedded family method, termed Bayesian EEF. Unlike many other
Bayesian model selection methods, the Bayesian EEF can use vague proper priors
and improper noninformative priors to be objective in the elicitation of
parameter prior... | computer science |
6,601 | Diving into the shallows: a computational perspective on large-scale
shallow learning | stat.ML | In this paper we first identify a basic limitation in gradient descent-based
optimization methods when used in conjunctions with smooth kernels. An analysis
based on the spectral properties of the kernel demonstrates that only a
vanishingly small portion of the function space is reachable after a polynomial
number of g... | computer science |
6,602 | BEGAN: Boundary Equilibrium Generative Adversarial Networks | cs.LG | We propose a new equilibrium enforcing method paired with a loss derived from
the Wasserstein distance for training auto-encoder based Generative Adversarial
Networks. This method balances the generator and discriminator during training.
Additionally, it provides a new approximate convergence measure, fast and
stable t... | computer science |
6,603 | Spectral Methods for Nonparametric Models | cs.LG | Nonparametric models are versatile, albeit computationally expensive, tool
for modeling mixture models. In this paper, we introduce spectral methods for
the two most popular nonparametric models: the Indian Buffet Process (IBP) and
the Hierarchical Dirichlet Process (HDP). We show that using spectral methods
for the in... | computer science |
6,604 | Improved Training of Wasserstein GANs | cs.LG | Generative Adversarial Networks (GANs) are powerful generative models, but
suffer from training instability. The recently proposed Wasserstein GAN (WGAN)
makes progress toward stable training of GANs, but sometimes can still generate
only low-quality samples or fail to converge. We find that these problems are
often du... | computer science |
6,605 | A New Measure of Conditional Dependence | stat.ML | Measuring conditional dependencies among the variables of a network is of
great interest to many disciplines. This paper studies some shortcomings of the
existing dependency measures in detecting direct causal influences or their
lack of ability for group selection to capture strong dependencies and
accordingly introdu... | computer science |
6,606 | Geometric Insights into Support Vector Machine Behavior using the KKT
Conditions | stat.ML | The Support Vector Machine (SVM) is a powerful and widely used classification
algorithm. Its performance is well known to be impacted by a tuning parameter
which is frequently selected by cross-validation. This paper uses the
Karush-Kuhn-Tucker conditions to provide rigorous mathematical proof for new
insights into the... | computer science |
6,607 | A comparative study of counterfactual estimators | stat.ML | We provide a comparative study of several widely used off-policy estimators
(Empirical Average, Basic Importance Sampling and Normalized Importance
Sampling), detailing the different regimes where they are individually
suboptimal. We then exhibit properties optimal estimators should possess. In
the case where examples ... | computer science |
6,608 | Time Series Cluster Kernel for Learning Similarities between
Multivariate Time Series with Missing Data | stat.ML | Similarity-based approaches represent a promising direction for time series
analysis. However, many such methods rely on parameter tuning, and some have
shortcomings if the time series are multivariate (MTS), due to dependencies
between attributes, or the time series contain missing data. In this paper, we
address thes... | computer science |
6,609 | Linear Additive Markov Processes | cs.LG | We introduce LAMP: the Linear Additive Markov Process. Transitions in LAMP
may be influenced by states visited in the distant history of the process, but
unlike higher-order Markov processes, LAMP retains an efficient
parametrization. LAMP also allows the specific dependence on history to be
learned efficiently from da... | computer science |
6,610 | AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning | cs.LG | The AMIDST Toolbox is a software for scalable probabilistic machine learning
with a spe- cial focus on (massive) streaming data. The toolbox supports a
flexible modeling language based on probabilistic graphical models with latent
variables and temporal dependencies. The specified models can be learnt from
large data s... | computer science |
6,611 | Bag-of-Words Method Applied to Accelerometer Measurements for the
Purpose of Classification and Energy Estimation | cs.LG | Accelerometer measurements are the prime type of sensor information most
think of when seeking to measure physical activity. On the market, there are
many fitness measuring devices which aim to track calories burned and steps
counted through the use of accelerometers. These measurements, though good
enough for the aver... | computer science |
6,612 | Learning Combinatorial Optimization Algorithms over Graphs | cs.LG | The design of good heuristics or approximation algorithms for NP-hard
combinatorial optimization problems often requires significant specialized
knowledge and trial-and-error. Can we automate this challenging, tedious
process, and learn the algorithms instead? In many real-world applications, it
is typically the case t... | computer science |
6,613 | Learning Certifiably Optimal Rule Lists for Categorical Data | stat.ML | We present the design and implementation of a custom discrete optimization
technique for building rule lists over a categorical feature space. Our
algorithm produces rule lists with optimal training performance, according to
the regularized empirical risk, with a certificate of optimality. By leveraging
algorithmic bou... | computer science |
6,614 | An Online Hierarchical Algorithm for Extreme Clustering | cs.LG | Many modern clustering methods scale well to a large number of data items, N,
but not to a large number of clusters, K. This paper introduces PERCH, a new
non-greedy algorithm for online hierarchical clustering that scales to both
massive N and K--a problem setting we term extreme clustering. Our algorithm
efficiently ... | computer science |
6,615 | On the Statistical Efficiency of Compositional Nonparametric Prediction | stat.ML | In this paper, we propose a compositional nonparametric method in which a
model is expressed as a labeled binary tree of $2k+1$ nodes, where each node is
either a summation, a multiplication, or the application of one of the $q$
basis functions to one of the $p$ covariates. We show that in order to recover
a labeled bi... | computer science |
6,616 | Training Triplet Networks with GAN | cs.LG | Triplet networks are widely used models that are characterized by good
performance in classification and retrieval tasks. In this work we propose to
train a triplet network by putting it as the discriminator in Generative
Adversarial Nets (GANs). We make use of the good capability of representation
learning of the disc... | computer science |
6,617 | Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive
Models | cs.LG | The Gibbs sampler is a particularly popular Markov chain used for learning
and inference problems in Graphical Models (GMs). These tasks are
computationally intractable in general, and the Gibbs sampler often suffers
from slow mixing. In this paper, we study the Swendsen-Wang dynamics which is a
more sophisticated Mark... | computer science |
6,618 | Fast Spectral Clustering Using Autoencoders and Landmarks | cs.LG | In this paper, we introduce an algorithm for performing spectral clustering
efficiently. Spectral clustering is a powerful clustering algorithm that
suffers from high computational complexity, due to eigen decomposition. In this
work, we first build the adjacency matrix of the corresponding graph of the
dataset. To bui... | computer science |
6,619 | Joint Probabilistic Linear Discriminant Analysis | cs.LG | Standard probabilistic linear discriminant analysis (PLDA) for speaker
recognition assumes that the sample's features (usually, i-vectors) are given
by a sum of three terms: a term that depends on the speaker identity, a term
that models the within-speaker variability and is assumed independent across
samples, and a fi... | computer science |
6,620 | Bayesian Recurrent Neural Networks | cs.LG | In this work we explore a straightforward variational Bayes scheme for
Recurrent Neural Networks. Firstly, we show that a simple adaptation of
truncated backpropagation through time can yield good quality uncertainty
estimates and superior regularisation at only a small extra computational cost
during training, also re... | computer science |
6,621 | A Dual-Stage Attention-Based Recurrent Neural Network for Time Series
Prediction | cs.LG | The Nonlinear autoregressive exogenous (NARX) model, which predicts the
current value of a time series based upon its previous values as well as the
current and past values of multiple driving (exogenous) series, has been
studied for decades. Despite the fact that various NARX models have been
developed, few of them ca... | computer science |
6,622 | Federated Tensor Factorization for Computational Phenotyping | cs.LG | Tensor factorization models offer an effective approach to convert massive
electronic health records into meaningful clinical concepts (phenotypes) for
data analysis. These models need a large amount of diverse samples to avoid
population bias. An open challenge is how to derive phenotypes jointly across
multiple hospi... | computer science |
6,623 | Parametric Gaussian Process Regression for Big Data | stat.ML | This work introduces the concept of parametric Gaussian processes (PGPs),
which is built upon the seemingly self-contradictory idea of making Gaussian
processes parametric. Parametric Gaussian processes, by construction, are
designed to operate in "big data" regimes where one is interested in
quantifying the uncertaint... | computer science |
6,624 | MAGAN: Margin Adaptation for Generative Adversarial Networks | cs.LG | We propose the Margin Adaptation for Generative Adversarial Networks (MAGANs)
algorithm, a novel training procedure for GANs to improve stability and
performance by using an adaptive hinge loss function. We estimate the
appropriate hinge loss margin with the expected energy of the target
distribution, and derive princi... | computer science |
6,625 | Determining Song Similarity via Machine Learning Techniques and Tagging
Information | cs.LG | The task of determining item similarity is a crucial one in a recommender
system. This constitutes the base upon which the recommender system will work
to determine which items are more likely to be enjoyed by a user, resulting in
more user engagement. In this paper we tackle the problem of determining song
similarity ... | computer science |
6,626 | Virtual Adversarial Training: a Regularization Method for Supervised and
Semi-supervised Learning | stat.ML | We propose a new regularization method based on virtual adversarial loss: a
new measure of local smoothness of the output distribution. Virtual adversarial
loss is defined as the robustness of the model's posterior distribution against
local perturbation around each input data point. Our method is similar to
adversaria... | computer science |
6,627 | Stochastic Gradient Descent as Approximate Bayesian Inference | stat.ML | Stochastic Gradient Descent with a constant learning rate (constant SGD)
simulates a Markov chain with a stationary distribution. With this perspective,
we derive several new results. (1) We show that constant SGD can be used as an
approximate Bayesian posterior inference algorithm. Specifically, we show how
to adjust ... | computer science |
6,628 | Asynchronous Parallel Empirical Variance Guided Algorithms for the
Thresholding Bandit Problem | stat.ML | This paper considers the multi-armed thresholding bandit problem --
identifying all arms whose expected rewards are above a predefined threshold
via as few pulls (or rounds) as possible -- proposed by Locatelli et al. [2016]
recently. Although the proposed algorithm in Locatelli et al. [2016] achieves
the optimal round... | computer science |
6,629 | Machine Learning and the Future of Realism | stat.ML | The preceding three decades have seen the emergence, rise, and proliferation
of machine learning (ML). From half-recognised beginnings in perceptrons,
neural nets, and decision trees, algorithms that extract correlations (that is,
patterns) from a set of data points have broken free from their origin in
computational c... | computer science |
6,630 | Random Walk Sampling for Big Data over Networks | stat.ML | It has been shown recently that graph signals with small total variation can
be accurately recovered from only few samples if the sampling set satisfies a
certain condition, referred to as the network nullspace property. Based on this
recovery condition, we propose a sampling strategy for smooth graph signals
based on ... | computer science |
6,631 | Bayesian Hybrid Matrix Factorisation for Data Integration | stat.ML | We introduce a novel Bayesian hybrid matrix factorisation model (HMF) for
data integration, based on combining multiple matrix factorisation methods,
that can be used for in- and out-of-matrix prediction of missing values. The
model is very general and can be used to integrate many datasets across
different entity type... | computer science |
6,632 | Multimodal Prediction and Personalization of Photo Edits with Deep
Generative Models | stat.ML | Professional-grade software applications are powerful but
complicated$-$expert users can achieve impressive results, but novices often
struggle to complete even basic tasks. Photo editing is a prime example: after
loading a photo, the user is confronted with an array of cryptic sliders like
"clarity", "temp", and "high... | computer science |
6,633 | Fast multi-output relevance vector regression | cs.LG | This paper aims to decrease the time complexity of multi-output relevance
vector regression from O(VM^3) to O(V^3+M^3), where V is the number of output
dimensions, M is the number of basis functions, and V<M. The experimental
results demonstrate that the proposed method is more competitive than the
existing method, wit... | computer science |
6,634 | O$^2$TD: (Near)-Optimal Off-Policy TD Learning | cs.LG | Temporal difference learning and Residual Gradient methods are the most
widely used temporal difference based learning algorithms; however, it has been
shown that none of their objective functions is optimal w.r.t approximating the
true value function $V$. Two novel algorithms are proposed to approximate the
true value... | computer science |
6,635 | Learning Piece-wise Linear Models from Large Scale Data for Ad Click
Prediction | stat.ML | CTR prediction in real-world business is a difficult machine learning problem
with large scale nonlinear sparse data. In this paper, we introduce an
industrial strength solution with model named Large Scale Piece-wise Linear
Model (LS-PLM). We formulate the learning problem with $L_1$ and $L_{2,1}$
regularizers, leadin... | computer science |
6,636 | Large-Scale Online Semantic Indexing of Biomedical Articles via an
Ensemble of Multi-Label Classification Models | stat.ML | Background: In this paper we present the approaches and methods employed in
order to deal with a large scale multi-label semantic indexing task of
biomedical papers. This work was mainly implemented within the context of the
BioASQ challenge of 2014. Methods: The main contribution of this work is a
multi-label ensemble... | computer science |
6,637 | SAFS: A Deep Feature Selection Approach for Precision Medicine | cs.LG | In this paper, we propose a new deep feature selection method based on deep
architecture. Our method uses stacked auto-encoders for feature representation
in higher-level abstraction. We developed and applied a novel feature learning
approach to a specific precision medicine problem, which focuses on assessing
and prio... | computer science |
6,638 | Every Untrue Label is Untrue in its Own Way: Controlling Error Type with
the Log Bilinear Loss | cs.LG | Deep learning has become the method of choice in many application domains of
machine learning in recent years, especially for multi-class classification
tasks. The most common loss function used in this context is the cross-entropy
loss, which reduces to the log loss in the typical case when there is a single
correct r... | computer science |
6,639 | Dynamic Graph Convolutional Networks | cs.LG | Many different classification tasks need to manage structured data, which are
usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that
the vertices/edges of each graph may change during time. Our goal is to jointly
exploit structured data and temporal information through the use of a neural
networ... | computer science |
6,640 | Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption | stat.ML | We consider the robust phase retrieval problem of recovering the unknown
signal from the magnitude-only measurements, where the measurements can be
contaminated by both sparse arbitrary corruption and bounded random noise. We
propose a new nonconvex algorithm for robust phase retrieval, namely Robust
Wirtinger Flow to ... | computer science |
6,641 | Learned D-AMP: Principled Neural Network based Compressive Image
Recovery | stat.ML | Compressive image recovery is a challenging problem that requires fast and
accurate algorithms. Recently, neural networks have been applied to this
problem with promising results. By exploiting massively parallel GPU processing
architectures and oodles of training data, they can run orders of magnitude
faster than exis... | computer science |
6,642 | Feature selection algorithm based on Catastrophe model to improve the
performance of regression analysis | cs.LG | In this paper we introduce a new feature selection algorithm to remove the
irrelevant or redundant features in the data sets. In this algorithm the
importance of a feature is based on its fitting to the Catastrophe model.
Akaike information crite- rion value is used for ranking the features in the
data set. The propose... | computer science |
6,643 | A Neural Network model with Bidirectional Whitening | stat.ML | We present here a new model and algorithm which performs an efficient Natural
gradient descent for Multilayer Perceptrons. Natural gradient descent was
originally proposed from a point of view of information geometry, and it
performs the steepest descent updates on manifolds in a Riemannian space. In
particular, we ext... | computer science |
6,644 | Learning from Comparisons and Choices | stat.ML | When tracking user-specific online activities, each user's preference is
revealed in the form of choices and comparisons. For example, a user's purchase
history tracks her choices, i.e. which item was chosen among a subset of
offerings. A user's comparisons are observed either explicitly as in movie
ratings or implicit... | computer science |
6,645 | A Riemannian approach for structured low-rank matrix learning | stat.ML | We consider the problem of learning a low-rank matrix, constrained to lie in
a linear subspace, and introduce a novel factorization for modeling such
matrices. A salient feature of the proposed factorization scheme is it
decouples the low-rank and the structural constraints onto separate factors. We
formulate the optim... | computer science |
6,646 | Active Bias: Training More Accurate Neural Networks by Emphasizing High
Variance Samples | stat.ML | Self-paced learning and hard example mining re-weight training instances to
improve learning accuracy. This paper presents two improved alternatives based
on lightweight estimates of sample uncertainty in stochastic gradient descent
(SGD): the variance in predicted probability of the correct class across
iterations of ... | computer science |
6,647 | Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum
Disorder Classification | stat.ML | Using predictive models to identify patterns that can act as biomarkers for
different neuropathoglogical conditions is becoming highly prevalent. In this
paper, we consider the problem of Autism Spectrum Disorder (ASD)
classification. While non-invasive imaging measurements, such as the rest state
fMRI, are typically u... | computer science |
6,648 | Dynamic Model Selection for Prediction Under a Budget | stat.ML | We present a dynamic model selection approach for resource-constrained
prediction. Given an input instance at test-time, a gating function identifies
a prediction model for the input among a collection of models. Our objective is
to minimize overall average cost without sacrificing accuracy. We learn gating
and predict... | computer science |
6,649 | Deep Over-sampling Framework for Classifying Imbalanced Data | cs.LG | Class imbalance is a challenging issue in practical classification problems
for deep learning models as well as traditional models. Traditionally
successful countermeasures such as synthetic over-sampling have had limited
success with complex, structured data handled by deep learning models. In this
paper, we propose D... | computer science |
6,650 | Stochastic Optimization from Distributed, Streaming Data in Rate-limited
Networks | stat.ML | Motivated by machine learning applications in networks of sensors,
internet-of-things (IoT) devices, and autonomous agents, we propose techniques
for distributed stochastic convex learning from high-rate data streams. The
setup involves a network of nodes---each one of which has a stream of data
arriving at a constant ... | computer science |
6,651 | Reward Maximization Under Uncertainty: Leveraging Side-Observations on
Networks | cs.LG | We study the stochastic multi-armed bandit (MAB) problem in the presence of
side-observations across actions that occur as a result of an underlying
network structure. In our model, a bipartite graph captures the relationship
between actions and a common set of unknowns such that choosing an action
reveals observations... | computer science |
6,652 | Training L1-Regularized Models with Orthant-Wise Passive Descent
Algorithms | cs.LG | The $L_1$-regularized models are widely used for sparse regression or
classification tasks. In this paper, we propose the orthant-wise passive
descent algorithm (OPDA) for optimizing $L_1$-regularized models, as an
improved substitute of proximal algorithms, which are the standard tools for
optimizing the models nowada... | computer science |
6,653 | Exploiting random projections and sparsity with random forests and
gradient boosting methods -- Application to multi-label and multi-output
learning, random forest model compression and leveraging input sparsity | stat.ML | Within machine learning, the supervised learning field aims at modeling the
input-output relationship of a system, from past observations of its behavior.
Decision trees characterize the input-output relationship through a series of
nested $if-then-else$ questions, the testing nodes, leading to a set of
predictions, th... | computer science |
6,654 | Pruning variable selection ensembles | stat.ML | In the context of variable selection, ensemble learning has gained increasing
interest due to its great potential to improve selection accuracy and to reduce
false discovery rate. A novel ordering-based selective ensemble learning
strategy is designed in this paper to obtain smaller but more accurate
ensembles. In part... | computer science |
6,655 | Limits of End-to-End Learning | cs.LG | End-to-end learning refers to training a possibly complex learning system by
applying gradient-based learning to the system as a whole. End-to-end learning
system is specifically designed so that all modules are differentiable. In
effect, not only a central learning machine, but also all "peripheral" modules
like repre... | computer science |
6,656 | Identifying Similarities in Epileptic Patients for Drug Resistance
Prediction | cs.LG | Currently, approximately 30% of epileptic patients treated with antiepileptic
drugs (AEDs) remain resistant to treatment (known as refractory patients). This
project seeks to understand the underlying similarities in refractory patients
vs. other epileptic patients, identify features contributing to drug resistance
acr... | computer science |
6,657 | Large-scale Feature Selection of Risk Genetic Factors for Alzheimer's
Disease via Distributed Group Lasso Regression | cs.LG | Genome-wide association studies (GWAS) have achieved great success in the
genetic study of Alzheimer's disease (AD). Collaborative imaging genetics
studies across different research institutions show the effectiveness of
detecting genetic risk factors. However, the high dimensionality of GWAS data
poses significant cha... | computer science |
6,658 | A Siamese Deep Forest | stat.ML | A Siamese Deep Forest (SDF) is proposed in the paper. It is based on the Deep
Forest or gcForest proposed by Zhou and Feng and can be viewed as a gcForest
modification. It can be also regarded as an alternative to the well-known
Siamese neural networks. The SDF uses a modified training set consisting of
concatenated pa... | computer science |
6,659 | Learning Quadratic Variance Function (QVF) DAG models via OverDispersion
Scoring (ODS) | stat.ML | Learning DAG or Bayesian network models is an important problem in
multi-variate causal inference. However, a number of challenges arises in
learning large-scale DAG models including model identifiability and
computational complexity since the space of directed graphs is huge. In this
paper, we address these issues in ... | computer science |
6,660 | DeepArchitect: Automatically Designing and Training Deep Architectures | stat.ML | In deep learning, performance is strongly affected by the choice of
architecture and hyperparameters. While there has been extensive work on
automatic hyperparameter optimization for simple spaces, complex spaces such as
the space of deep architectures remain largely unexplored. As a result, the
choice of architecture ... | computer science |
6,661 | Adaptation and learning over networks for nonlinear system modeling | stat.ML | In this chapter, we analyze nonlinear filtering problems in distributed
environments, e.g., sensor networks or peer-to-peer protocols. In these
scenarios, the agents in the environment receive measurements in a streaming
fashion, and they are required to estimate a common (nonlinear) model by
alternating local computat... | computer science |
6,662 | Mostly Exploration-Free Algorithms for Contextual Bandits | stat.ML | The contextual bandit literature has traditionally focused on algorithms that
address the exploration-exploitation tradeoff. In particular, greedy algorithms
that exploit current estimates without any exploration may be sub-optimal in
general. However, exploration-free greedy algorithms are desirable in practical
setti... | computer science |
6,663 | Scaling Active Search using Linear Similarity Functions | stat.ML | Active Search has become an increasingly useful tool in information retrieval
problems where the goal is to discover as many target elements as possible
using only limited label queries. With the advent of big data, there is a
growing emphasis on the scalability of such techniques to handle very large and
very complex ... | computer science |
6,664 | Targeted matrix completion | cs.LG | Matrix completion is a problem that arises in many data-analysis settings
where the input consists of a partially-observed matrix (e.g., recommender
systems, traffic matrix analysis etc.). Classical approaches to matrix
completion assume that the input partially-observed matrix is low rank. The
success of these methods... | computer science |
6,665 | Learning Multimodal Transition Dynamics for Model-Based Reinforcement
Learning | stat.ML | In this paper we study how to learn stochastic, multimodal transition
dynamics in reinforcement learning (RL) tasks. We focus on evaluating
transition function estimation, while we defer planning over this model to
future work. Stochasticity is a fundamental property of many task environments.
However, discriminative f... | computer science |
6,666 | Determinantal Point Processes for Mini-Batch Diversification | cs.LG | We study a mini-batch diversification scheme for stochastic gradient descent
(SGD). While classical SGD relies on uniformly sampling data points to form a
mini-batch, we propose a non-uniform sampling scheme based on the Determinantal
Point Process (DPP). The DPP relies on a similarity measure between data points
and g... | computer science |
6,667 | Convex-constrained Sparse Additive Modeling and Its Extensions | cs.LG | Sparse additive modeling is a class of effective methods for performing
high-dimensional nonparametric regression. In this work we show how shape
constraints such as convexity/concavity and their extensions, can be integrated
into additive models. The proposed sparse difference of convex additive models
(SDCAM) can est... | computer science |
6,668 | Regularizing Model Complexity and Label Structure for Multi-Label Text
Classification | stat.ML | Multi-label text classification is a popular machine learning task where each
document is assigned with multiple relevant labels. This task is challenging
due to high dimensional features and correlated labels. Multi-label text
classifiers need to be carefully regularized to prevent the severe over-fitting
in the high ... | computer science |
6,669 | One-Class Semi-Supervised Learning: Detecting Linearly Separable Class
by its Mean | stat.ML | In this paper, we presented a novel semi-supervised one-class classification
algorithm which assumes that class is linearly separable from other elements.
We proved theoretically that class is linearly separable if and only if it is
maximal by probability within the sets with the same mean. Furthermore, we
presented an... | computer science |
6,670 | Deep Learning for Tumor Classification in Imaging Mass Spectrometry | stat.ML | Motivation: Tumor classification using Imaging Mass Spectrometry (IMS) data
has a high potential for future applications in pathology. Due to the
complexity and size of the data, automated feature extraction and
classification steps are required to fully process the data. Deep learning
offers an approach to learn featu... | computer science |
6,671 | Summarized Network Behavior Prediction | cs.LG | This work studies the entity-wise topical behavior from massive network logs.
Both the temporal and the spatial relationships of the behavior are explored
with the learning architectures combing the recurrent neural network (RNN) and
the convolutional neural network (CNN). To make the behavioral data appropriate
for th... | computer science |
6,672 | Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering | cs.LG | In this work we study the non-parametric reconstruction of spatio-temporal
dynamical Gaussian processes (GPs) via GP regression from sparse and noisy
data. GPs have been mainly applied to spatial regression where they represent
one of the most powerful estimation approaches also thanks to their universal
representing p... | computer science |
6,673 | Semi-supervised cross-entropy clustering with information bottleneck
constraint | cs.LG | In this paper, we propose a semi-supervised clustering method, CEC-IB, that
models data with a set of Gaussian distributions and that retrieves clusters
based on a partial labeling provided by the user (partition-level side
information). By combining the ideas from cross-entropy clustering (CEC) with
those from the inf... | computer science |
6,674 | Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning | stat.ML | Maximizing the area under the receiver operating characteristic curve (AUC)
is a standard approach to imbalanced classification. So far, various supervised
AUC optimization methods have been developed and they are also extended to
semi-supervised scenarios to cope with small sample problems. However, existing
semi-supe... | computer science |
6,675 | Semi-supervised model-based clustering with controlled clusters leakage | cs.LG | In this paper, we focus on finding clusters in partially categorized data
sets. We propose a semi-supervised version of Gaussian mixture model, called
C3L, which retrieves natural subgroups of given categories. In contrast to
other semi-supervised models, C3L is parametrized by user-defined leakage
level, which control... | computer science |
6,676 | Learning with Confident Examples: Rank Pruning for Robust Classification
with Noisy Labels | stat.ML | Noisy PN learning is the problem of binary classification when training
examples may be mislabeled (flipped) uniformly with noise rate rho1 for
positive examples and rho0 for negative examples. We propose Rank Pruning (RP)
to solve noisy PN learning and the open problem of estimating the noise rates,
i.e. the fraction ... | computer science |
6,677 | KATE: K-Competitive Autoencoder for Text | stat.ML | Autoencoders have been successful in learning meaningful representations from
image datasets. However, their performance on text datasets has not been widely
studied. Traditional autoencoders tend to learn possibly trivial
representations of text documents due to their confounding properties such as
high-dimensionality... | computer science |
6,678 | Matrix Completion via Factorizing Polynomials | stat.ML | Predicting unobserved entries of a partially observed matrix has found wide
applicability in several areas, such as recommender systems, computational
biology, and computer vision. Many scalable methods with rigorous theoretical
guarantees have been developed for algorithms where the matrix is factored into
low-rank co... | computer science |
6,679 | A comprehensive study of batch construction strategies for recurrent
neural networks in MXNet | cs.LG | In this work we compare different batch construction methods for mini-batch
training of recurrent neural networks. While popular implementations like
TensorFlow and MXNet suggest a bucketing approach to improve the
parallelization capabilities of the recurrent training process, we propose a
simple ordering strategy tha... | computer science |
6,680 | Face Super-Resolution Through Wasserstein GANs | cs.LG | Generative adversarial networks (GANs) have received a tremendous amount of
attention in the past few years, and have inspired applications addressing a
wide range of problems. Despite its great potential, GANs are difficult to
train. Recently, a series of papers (Arjovsky & Bottou, 2017a; Arjovsky et al.
2017b; and Gu... | computer science |
6,681 | MIDA: Multiple Imputation using Denoising Autoencoders | cs.LG | Missing data is a significant problem impacting all domains. State-of-the-art
framework for minimizing missing data bias is multiple imputation, for which
the choice of an imputation model remains nontrivial. We propose a multiple
imputation model based on overcomplete deep denoising autoencoders. Our
proposed model is... | computer science |
6,682 | Non-negative Matrix Factorization via Archetypal Analysis | stat.ML | Given a collection of data points, non-negative matrix factorization (NMF)
suggests to express them as convex combinations of a small set of `archetypes'
with non-negative entries. This decomposition is unique only if the true
archetypes are non-negative and sufficiently sparse (or the weights are
sufficiently sparse),... | computer science |
6,683 | Generative Adversarial Trainer: Defense to Adversarial Perturbations
with GAN | cs.LG | We propose a novel technique to make neural network robust to adversarial
examples using a generative adversarial network. We alternately train both
classifier and generator networks. The generator network generates an
adversarial perturbation that can easily fool the classifier network by using a
gradient of each imag... | computer science |
6,684 | Comments on the proof of adaptive submodular function minimization | stat.ML | We point out an issue with Theorem 5 appearing in "Group-based active query
selection for rapid diagnosis in time-critical situations". Theorem 5 bounds
the expected number of queries for a greedy algorithm to identify the class of
an item within a constant factor of optimal. The Theorem is based on
correctness of a re... | computer science |
6,685 | Fast Stochastic Variance Reduced ADMM for Stochastic Composition
Optimization | cs.LG | We consider the stochastic composition optimization problem proposed in
\cite{wang2017stochastic}, which has applications ranging from estimation to
statistical and machine learning. We propose the first ADMM-based algorithm
named com-SVR-ADMM, and show that com-SVR-ADMM converges linearly for strongly
convex and Lipsc... | computer science |
6,686 | Bayesian Approaches to Distribution Regression | stat.ML | Distribution regression has recently attracted much interest as a generic
solution to the problem of supervised learning where labels are available at
the group level, rather than at the individual level. Current approaches,
however, do not propagate the uncertainty in observations due to sampling
variability in the gr... | computer science |
6,687 | The Network Nullspace Property for Compressed Sensing of Big Data over
Networks | stat.ML | We present a novel condition, which we term the net- work nullspace property,
which ensures accurate recovery of graph signals representing massive
network-structured datasets from few signal values. The network nullspace
property couples the cluster structure of the underlying network-structure with
the geometry of th... | computer science |
6,688 | Detecting Statistical Interactions from Neural Network Weights | stat.ML | Interpreting neural networks is a crucial and challenging task in machine
learning. In this paper, we develop a novel framework for detecting statistical
interactions captured by a feedforward multilayer neural network by directly
interpreting its learned weights. Depending on the desired interactions, our
method can a... | computer science |
6,689 | Active Learning for Graph Embedding | cs.LG | Graph embedding provides an efficient solution for graph analysis by
converting the graph into a low-dimensional space which preserves the structure
information. In contrast to the graph structure data, the i.i.d. node embedding
can be processed efficiently in terms of both time and space. Current
semi-supervised graph... | computer science |
6,690 | Bandit Regret Scaling with the Effective Loss Range | cs.LG | We study how the regret guarantees of nonstochastic multi-armed bandits can
be improved, if the effective range of the losses in each round is small (e.g.
the maximal difference between two losses in a given round). Despite a recent
impossibility result, we show how this can be made possible under certain mild
addition... | computer science |
6,691 | Learning Convex Regularizers for Optimal Bayesian Denoising | cs.LG | We propose a data-driven algorithm for the maximum a posteriori (MAP)
estimation of stochastic processes from noisy observations. The primary
statistical properties of the sought signal is specified by the penalty
function (i.e., negative logarithm of the prior probability density function).
Our alternating direction m... | computer science |
6,692 | Learning how to explain neural networks: PatternNet and
PatternAttribution | stat.ML | DeConvNet, Guided BackProp, LRP, were invented to better understand deep
neural networks. We show that these methods do not produce the theoretically
correct explanation for a linear model. Yet they are used on multi-layer
networks with millions of parameters. This is a cause for concern since linear
models are simple ... | computer science |
6,693 | To tune or not to tune the number of trees in random forest? | stat.ML | The number of trees T in the random forest (RF) algorithm for supervised
learning has to be set by the user. It is controversial whether T should simply
be set to the largest computationally manageable value or whether a smaller T
may in some cases be better. While the principle underlying bagging is that
"more trees a... | computer science |
6,694 | Hierarchical Temporal Representation in Linear Reservoir Computing | cs.LG | Recently, studies on deep Reservoir Computing (RC) highlighted the role of
layering in deep recurrent neural networks (RNNs). In this paper, the use of
linear recurrent units allows us to bring more evidence on the intrinsic
hierarchical temporal representation in deep RNNs through frequency analysis
applied to the sta... | computer science |
6,695 | Maximum Margin Principal Components | stat.ML | Principal Component Analysis (PCA) is a very successful dimensionality
reduction technique, widely used in predictive modeling. A key factor in its
widespread use in this domain is the fact that the projection of a dataset onto
its first $K$ principal components minimizes the sum of squared errors between
the original ... | computer science |
6,696 | Sample-Efficient Algorithms for Recovering Structured Signals from
Magnitude-Only Measurements | stat.ML | We consider the problem of recovering a signal $\mathbf{x}^* \in
\mathbf{R}^n$, from magnitude-only measurements $y_i =
|\left\langle\mathbf{a}_i,\mathbf{x}^*\right\rangle|$ for $i=[m]$. Also called
the phase retrieval, this is a fundamental challenge in bio-,astronomical
imaging and speech processing. The problem abov... | computer science |
6,697 | Delving into adversarial attacks on deep policies | stat.ML | Adversarial examples have been shown to exist for a variety of deep learning
architectures. Deep reinforcement learning has shown promising results on
training agent policies directly on raw inputs such as image pixels. In this
paper we present a novel study into adversarial attacks on deep reinforcement
learning polic... | computer science |
6,698 | Discovering the Graph Structure in the Clustering Results | stat.ML | In a standard cluster analysis, such as k-means, in addition to clusters
locations and distances between them, it's important to know if they are
connected or well separated from each other. The main focus of this paper is
discovering the relations between the resulting clusters. We propose a new
method which is based ... | computer science |
6,699 | Learning Effective Representations from Clinical Notes | stat.ML | Clinical notes are a rich source of information about patient state. However,
using them effectively presents many challenges. In this work we present two
methods for summarizing clinical notes into patient-level representations. The
resulting representations are evaluated on a range of prediction tasks and
cohort size... | computer science |
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