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7,400 | Ballpark Crowdsourcing: The Wisdom of Rough Group Comparisons | stat.ML | Crowdsourcing has become a popular method for collecting labeled training
data. However, in many practical scenarios traditional labeling can be
difficult for crowdworkers (for example, if the data is high-dimensional or
unintuitive, or the labels are continuous).
In this work, we develop a novel model for crowdsourc... | computer science |
7,401 | FFT-Based Deep Learning Deployment in Embedded Systems | cs.LG | Deep learning has delivered its powerfulness in many application domains,
especially in image and speech recognition. As the backbone of deep learning,
deep neural networks (DNNs) consist of multiple layers of various types with
hundreds to thousands of neurons. Embedded platforms are now becoming essential
for deep le... | computer science |
7,402 | Predicting Station-level Hourly Demands in a Large-scale Bike-sharing
Network: A Graph Convolutional Neural Network Approach | stat.ML | Bike sharing is a vital piece in a modern multi-modal transportation system.
However, it suffers from the bike unbalancing problem due to fluctuating
spatial and temporal demands. Accurate bike sharing demand predictions can help
operators to make optimal routes and schedules for bike redistributions, and
therefore enh... | computer science |
7,403 | Deep Prior | stat.ML | The recent literature on deep learning offers new tools to learn a rich
probability distribution over high dimensional data such as images or sounds.
In this work we investigate the possibility of learning the prior distribution
over neural network parameters using such tools. Our resulting variational
Bayes algorithm ... | computer science |
7,404 | Learning Compact Recurrent Neural Networks with Block-Term Tensor
Decomposition | cs.LG | Recurrent Neural Networks (RNNs) are powerful sequence modeling tools.
However, when dealing with high dimensional inputs, the training of RNNs
becomes computational expensive due to the large number of model parameters.
This hinders RNNs from solving many important computer vision tasks, such as
Action Recognition in ... | computer science |
7,405 | Graph-Sparse Logistic Regression | cs.LG | We introduce Graph-Sparse Logistic Regression, a new algorithm for
classification for the case in which the support should be sparse but connected
on a graph. We val- idate this algorithm against synthetic data and benchmark
it against L1-regularized Logistic Regression. We then explore our technique in
the bioinformat... | computer science |
7,406 | Safe Policy Search with Gaussian Process Models | stat.ML | We propose a method to optimise the parameters of a policy which will be used
to safely perform a given task in a data-efficient manner. We train a Gaussian
process model to capture the system dynamics, based on the PILCO framework. Our
model has useful analytic properties, which allow closed form computation of
error ... | computer science |
7,407 | BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition | stat.ML | Recently, deep neural networks (DNNs) have been regarded as the
state-of-the-art classification methods in a wide range of applications,
especially in image classification. Despite the success, the huge number of
parameters blocks its deployment to situations with light computing resources.
Researchers resort to the re... | computer science |
7,408 | Quantization and Training of Neural Networks for Efficient
Integer-Arithmetic-Only Inference | cs.LG | The rising popularity of intelligent mobile devices and the daunting
computational cost of deep learning-based models call for efficient and
accurate on-device inference schemes. We propose a quantization scheme that
allows inference to be carried out using integer-only arithmetic, which can be
implemented more efficie... | computer science |
7,409 | On reproduction of On the regularization of Wasserstein GANs | cs.LG | This report has several purposes. First, our report is written to investigate
the reproducibility of the submitted paper On the regularization of Wasserstein
GANs (2018). Second, among the experiments performed in the submitted paper,
five aspects were emphasized and reproduced: learning speed, stability,
robustness ag... | computer science |
7,410 | Structured Optimal Transport | stat.ML | Optimal Transport has recently gained interest in machine learning for
applications ranging from domain adaptation, sentence similarities to deep
learning. Yet, its ability to capture frequently occurring structure beyond the
"ground metric" is limited. In this work, we develop a nonlinear generalization
of (discrete) ... | computer science |
7,411 | Predicting Individual Physiologically Acceptable States for Discharge
from a Pediatric Intensive Care Unit | stat.ML | Objective: Predict patient-specific vitals deemed medically acceptable for
discharge from a pediatric intensive care unit (ICU). Design: The means of each
patient's hr, sbp and dbp measurements between their medical and physical
discharge from the ICU were computed as a proxy for their physiologically
acceptable state ... | computer science |
7,412 | A Survey on Multi-View Clustering | cs.LG | With the fast development of information technology, especially the
popularization of internet, multi-view learning becomes more and more popular
in machine learning and data mining fields. As we all know that, multi-view
semi-supervised learning, such as co-training, co-regularization has gained
considerable attention... | computer science |
7,413 | Deep Neural Generative Model of Functional MRI Images for Psychiatric
Disorder Diagnosis | stat.ML | Accurate diagnosis of psychiatric disorders plays a critical role in
improving quality of life for patients and potentially supports the development
of new treatments. Many studies have been conducted on machine learning
techniques that seek brain imaging data for specific biomarkers of disorders.
These studies have en... | computer science |
7,414 | A Bridge Between Hyperparameter Optimization and Larning-to-learn | stat.ML | We consider a class of a nested optimization problems involving inner and
outer objectives. We observe that by taking into explicit account the
optimization dynamics for the inner objective it is possible to derive a
general framework that unifies gradient-based hyperparameter optimization and
meta-learning (or learnin... | computer science |
7,415 | The Power of Interpolation: Understanding the Effectiveness of SGD in
Modern Over-parametrized Learning | cs.LG | Stochastic Gradient Descent (SGD) with small mini-batch is a key component in
modern large-scale learning. However, its efficiency has not been easy to
analyze as most theoretical results require adaptive rates and show convergence
rates far slower than that for gradient descent, making computational
comparisons diffic... | computer science |
7,416 | MEBoost: Mixing Estimators with Boosting for Imbalanced Data
Classification | cs.LG | Class imbalance problem has been a challenging research problem in the fields
of machine learning and data mining as most real life datasets are imbalanced.
Several existing machine learning algorithms try to maximize the accuracy
classification by correctly identifying majority class samples while ignoring
the minorit... | computer science |
7,417 | Accurate Inference for Adaptive Linear Models | stat.ML | Estimators computed from adaptively collected data do not behave like their
non-adaptive brethren. Rather, the sequential dependence of the collection
policy can lead to severe distributional biases that persist even in the
infinite data limit. We develop a general method decorrelation procedure --
W-decorrelation -- f... | computer science |
7,418 | On Data-Dependent Random Features for Improved Generalization in
Supervised Learning | stat.ML | The randomized-feature approach has been successfully employed in large-scale
kernel approximation and supervised learning. The distribution from which the
random features are drawn impacts the number of features required to
efficiently perform a learning task. Recently, it has been shown that employing
data-dependent ... | computer science |
7,419 | Exploring High-Dimensional Structure via Axis-Aligned Decomposition of
Linear Projections | stat.ML | Two-dimensional embeddings remain the dominant approach to visualize high
dimensional data. The choice of embeddings ranges from highly non-linear ones,
which can capture complex relationships but are difficult to interpret
quantitatively, to axis-aligned projections, which are easy to interpret but
are limited to biva... | computer science |
7,420 | Approximate Profile Maximum Likelihood | cs.LG | We propose an efficient algorithm for approximate computation of the profile
maximum likelihood (PML), a variant of maximum likelihood maximizing the
probability of observing a sufficient statistic rather than the empirical
sample. The PML has appealing theoretical properties, but is difficult to
compute exactly. Inspi... | computer science |
7,421 | Discovery of Shifting Patterns in Sequence Classification | cs.LG | In this paper, we investigate the multi-variate sequence classification
problem from a multi-instance learning perspective. Real-world sequential data
commonly show discriminative patterns only at specific time periods. For
instance, we can identify a cropland during its growing season, but it looks
similar to a barren... | computer science |
7,422 | Adversarial Structured Prediction for Multivariate Measures | stat.ML | Many predicted structured objects (e.g., sequences, matchings, trees) are
evaluated using the F-score, alignment error rate (AER), or other multivariate
performance measures. Since inductively optimizing these measures using
training data is typically computationally difficult, empirical risk
minimization of surrogate ... | computer science |
7,423 | ADINE: An Adaptive Momentum Method for Stochastic Gradient Descent | stat.ML | Two major momentum-based techniques that have achieved tremendous success in
optimization are Polyak's heavy ball method and Nesterov's accelerated
gradient. A crucial step in all momentum-based methods is the choice of the
momentum parameter $m$ which is always suggested to be set to less than $1$.
Although the choice... | computer science |
7,424 | Fast kNN mode seeking clustering applied to active learning | stat.ML | A significantly faster algorithm is presented for the original kNN mode
seeking procedure. It has the advantages over the well-known mean shift
algorithm that it is feasible in high-dimensional vector spaces and results in
uniquely, well defined modes. Moreover, without any additional computational
effort it may yield ... | computer science |
7,425 | Deep Unsupervised Clustering Using Mixture of Autoencoders | cs.LG | Unsupervised clustering is one of the most fundamental challenges in machine
learning. A popular hypothesis is that data are generated from a union of
low-dimensional nonlinear manifolds; thus an approach to clustering is
identifying and separating these manifolds. In this paper, we present a novel
approach to solve th... | computer science |
7,426 | Combining Static and Dynamic Features for Multivariate Sequence
Classification | cs.LG | Model precision in a classification task is highly dependent on the feature
space that is used to train the model. Moreover, whether the features are
sequential or static will dictate which classification method can be applied as
most of the machine learning algorithms are designed to deal with either one or
another ty... | computer science |
7,427 | Fair Forests: Regularized Tree Induction to Minimize Model Bias | stat.ML | The potential lack of fairness in the outputs of machine learning algorithms
has recently gained attention both within the research community as well as in
society more broadly. Surprisingly, there is no prior work developing
tree-induction algorithms for building fair decision trees or fair random
forests. These metho... | computer science |
7,428 | Linear centralization classifier | cs.LG | A classification algorithm, called the Linear Centralization Classifier
(LCC), is introduced. The algorithm seeks to find a transformation that best
maps instances from the feature space to a space where they concentrate towards
the center of their own classes, while maximimizing the distance between class
centers. We ... | computer science |
7,429 | Learning and Transferring IDs Representation in E-commerce | cs.LG | Many machine intelligence techniques are developed in E-commerce and one of
the most essential components is the representation of IDs, including user ID,
item ID, product ID, store ID, brand ID, category ID etc. The classical
encoding based methods (like one-hot encoding) are inefficient in that it
suffers sparsity pr... | computer science |
7,430 | Diversifying Support Vector Machines for Boosting using Kernel
Perturbation: Applications to Class Imbalance and Small Disjuncts | cs.LG | The diversification (generating slightly varying separating discriminators)
of Support Vector Machines (SVMs) for boosting has proven to be a challenge due
to the strong learning nature of SVMs. Based on the insight that perturbing the
SVM kernel may help in diversifying SVMs, we propose two kernel perturbation
based b... | computer science |
7,431 | Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields | stat.ML | This work investigates training Conditional Random Fields (CRF) by Stochastic
Dual Coordinate Ascent (SDCA). SDCA enjoys a linear convergence rate and a
strong empirical performance for independent classification problems. However,
it has never been used to train CRF. Yet it benefits from an exact line search
with a si... | computer science |
7,432 | Least-Squares Temporal Difference Learning for the Linear Quadratic
Regulator | cs.LG | Reinforcement learning (RL) has been successfully used to solve many
continuous control tasks. Despite its impressive results however, fundamental
questions regarding the sample complexity of RL on continuous problems remain
open. We study the performance of RL in this setting by considering the
behavior of the Least-S... | computer science |
7,433 | Dropout Feature Ranking for Deep Learning Models | cs.LG | Deep neural networks (DNNs) achieve state-of-the-art results in a variety of
domains. Unfortunately, DNNs are notorious for their non-interpretability, and
thus limit their applicability in hypothesis-driven domains such as biology and
healthcare. Moreover, in the resource-constraint setting, it is critical to
design t... | computer science |
7,434 | An Approximate Bayesian Long Short-Term Memory Algorithm for Outlier
Detection | cs.LG | Long Short-Term Memory networks trained with gradient descent and
back-propagation have received great success in various applications. However,
point estimation of the weights of the networks is prone to over-fitting
problems and lacks important uncertainty information associated with the
estimation. However, exact Ba... | computer science |
7,435 | Weighted Data Normalization Based on Eigenvalues for Artificial Neural
Network Classification | cs.LG | Artificial neural network (ANN) is a very useful tool in solving learning
problems. Boosting the performances of ANN can be mainly concluded from two
aspects: optimizing the architecture of ANN and normalizing the raw data for
ANN. In this paper, a novel method which improves the effects of ANN by
preprocessing the raw... | computer science |
7,436 | Spurious Local Minima are Common in Two-Layer ReLU Neural Networks | cs.LG | We consider the optimization problem associated with training simple ReLU
neural networks of the form $\mathbf{x}\mapsto
\sum_{i=1}^{k}\max\{0,\mathbf{w}_i^\top \mathbf{x}\}$ with respect to the
squared loss. We provide a computer-assisted proof that even if the input
distribution is standard Gaussian, even if the dime... | computer science |
7,437 | Kernel Regression with Sparse Metric Learning | cs.LG | Kernel regression is a popular non-parametric fitting technique. It aims at
learning a function which estimates the targets for test inputs as precise as
possible. Generally, the function value for a test input is estimated by a
weighted average of the surrounding training examples. The weights are
typically computed b... | computer science |
7,438 | Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding | cs.LG | t-distributed Stochastic Neighborhood Embedding (t-SNE) is a method for
dimensionality reduction and visualization that has become widely popular in
recent years. Efficient implementations of t-SNE are available, but they scale
poorly to datasets with hundreds of thousands to millions of high dimensional
data-points. W... | computer science |
7,439 | On Connecting Stochastic Gradient MCMC and Differential Privacy | stat.ML | Significant success has been realized recently on applying machine learning
to real-world applications. There have also been corresponding concerns on the
privacy of training data, which relates to data security and confidentiality
issues. Differential privacy provides a principled and rigorous privacy
guarantee on mac... | computer science |
7,440 | Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization
properties of Entropy-SGD and data-dependent priors | stat.ML | We show that Entropy-SGD (Chaudhari et al., 2017), when viewed as a learning
algorithm, optimizes a PAC-Bayes bound on the risk of a Gibbs (posterior)
classifier, i.e., a randomized classifier obtained by a risk-sensitive
perturbation of the weights of a learned classifier. Entropy-SGD works by
optimizing the bound's p... | computer science |
7,441 | Robust Loss Functions under Label Noise for Deep Neural Networks | stat.ML | In many applications of classifier learning, training data suffers from label
noise. Deep networks are learned using huge training data where the problem of
noisy labels is particularly relevant. The current techniques proposed for
learning deep networks under label noise focus on modifying the network
architecture and... | computer science |
7,442 | Tensor Regression Networks with various Low-Rank Tensor Approximations | cs.LG | Tensor regression networks achieve high rate of compression of model
parameters in multilayer perceptrons (MLP) while having slight impact on
performances. Tensor regression layer imposes low-rank constraints on the
tensor regression layer which replaces the flattening operation of traditional
MLP. We investigate tenso... | computer science |
7,443 | PixelSNAIL: An Improved Autoregressive Generative Model | cs.LG | Autoregressive generative models consistently achieve the best results in
density estimation tasks involving high dimensional data, such as images or
audio. They pose density estimation as a sequence modeling task, where a
recurrent neural network (RNN) models the conditional distribution over the
next element conditio... | computer science |
7,444 | Random Feature-based Online Multi-kernel Learning in Environments with
Unknown Dynamics | stat.ML | Kernel-based methods exhibit well-documented performance in various nonlinear
learning tasks. Most of them rely on a preselected kernel, whose prudent choice
presumes task-specific prior information. Especially when the latter is not
available, multi-kernel learning has gained popularity thanks to its
flexibility in ch... | computer science |
7,445 | Robust Covariate Shift Prediction with General Losses and Feature Views | cs.LG | Covariate shift relaxes the widely-employed independent and identically
distributed (IID) assumption by allowing different training and testing input
distributions. Unfortunately, common methods for addressing covariate shift by
trying to remove the bias between training and testing distributions using
importance weigh... | computer science |
7,446 | Application of Convolutional Neural Network to Predict Airfoil Lift
Coefficient | stat.ML | The adaptability of the convolutional neural network (CNN) technique for
aerodynamic meta-modeling tasks is probed in this work. The primary objective
is to develop suitable CNN architecture for variable flow conditions and object
geometry, in addition to identifying a sufficient data preparation process.
Multiple CNN ... | computer science |
7,447 | Parameter-free online learning via model selection | cs.LG | We introduce an efficient algorithmic framework for model selection in online
learning, also known as parameter-free online learning. Departing from previous
work, which has focused on highly structured function classes such as nested
balls in Hilbert space, we propose a generic meta-algorithm framework that
achieves o... | computer science |
7,448 | PAC-Bayesian Margin Bounds for Convolutional Neural Networks - Technical
Report | cs.LG | Recently the generalisation error of deep neural networks has been analysed
through the PAC-Bayesian framework, for the case of fully connected layers. We
adapt this approach to the convolutional setting. | computer science |
7,449 | Deep Reinforcement Learning for List-wise Recommendations | cs.LG | Recommender systems play a crucial role in mitigating the problem of
information overload by suggesting users' personalized items or services. The
vast majority of traditional recommender systems consider the recommendation
procedure as a static process and make recommendations following a fixed
strategy. In this paper... | computer science |
7,450 | Using Deep Neural Network Approximate Bayesian Network | cs.LG | We present a new method to approximate posterior probabilities of Bayesian
Network using Deep Neural Network. Experiment results on several public
Bayesian Network datasets shows that Deep Neural Network is capable of learning
joint probability distri- bution of Bayesian Network by learning from a few
observation and p... | computer science |
7,451 | Restricted Boltzmann Machines for Robust and Fast Latent Truth Discovery | cs.LG | We address the problem of latent truth discovery, LTD for short, where the
goal is to discover the underlying true values of entity attributes in the
presence of noisy, conflicting or incomplete information. Despite a multitude
of algorithms to address the LTD problem that can be found in literature, only
little is kno... | computer science |
7,452 | ZOOpt: Toolbox for Derivative-Free Optimization | cs.LG | Recent advances of derivative-free optimization allow efficient approximating
the global optimal solutions of sophisticated functions, such as functions with
many local optima, non-differentiable and non-continuous functions. This
article describes the ZOOpt (https://github.com/eyounx/ZOOpt) toolbox that
provides effic... | computer science |
7,453 | Theoretical Analysis of Sparse Subspace Clustering with Missing Entries | cs.LG | Sparse Subspace Clustering (SSC) is a popular unsupervised machine learning
method for clustering data lying close to an unknown union of low-dimensional
linear subspaces; a problem with numerous applications in pattern recognition
and computer vision. Even though the behavior of SSC for complete data is by
now well-un... | computer science |
7,454 | Towards Practical Conditional Risk Minimization | stat.ML | We study conditional risk minimization (CRM), i.e. the problem of learning a
hypothesis of minimal risk for prediction at the next step of a sequentially
arriving dependent data. Despite it being a fundamental problem, successful
learning in the CRM sense has so far only been demonstrated using theoretical
algorithms t... | computer science |
7,455 | Network-Scale Traffic Modeling and Forecasting with Graphical Lasso and
Neural Networks | cs.LG | Traffic flow forecasting, especially the short-term case, is an important
topic in intelligent transportation systems (ITS). This paper does a lot of
research on network-scale modeling and forecasting of short-term traffic flows.
Firstly, we propose the concepts of single-link and multi-link models of
traffic flow fore... | computer science |
7,456 | Intrinsic Gaussian processes on complex constrained domains | stat.ML | We propose a class of intrinsic Gaussian processes (in-GPs) for
interpolation, regression and classification on manifolds with a primary focus
on complex constrained domains or irregular shaped spaces arising as subsets or
submanifolds of R, R2, R3 and beyond. For example, in-GPs can accommodate
spatial domains arising... | computer science |
7,457 | Demystifying MMD GANs | stat.ML | We investigate the training and performance of generative adversarial
networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs.
As our main theoretical contribution, we clarify the situation with bias in GAN
loss functions raised by recent work: we show that gradient estimators used in
the optimizat... | computer science |
7,458 | Clustering of Data with Missing Entries | cs.LG | The analysis of large datasets is often complicated by the presence of
missing entries, mainly because most of the current machine learning algorithms
are designed to work with full data. The main focus of this work is to
introduce a clustering algorithm, that will provide good clustering even in the
presence of missin... | computer science |
7,459 | SpectralNet: Spectral Clustering using Deep Neural Networks | stat.ML | Spectral clustering is a leading and popular technique in unsupervised data
analysis. Two of its major limitations are scalability and generalization of
the spectral embedding (i.e., out-of-sample-extension). In this paper we
introduce a deep learning approach to spectral clustering that overcomes the
above shortcoming... | computer science |
7,460 | Nonparametric Stochastic Contextual Bandits | cs.LG | We analyze the $K$-armed bandit problem where the reward for each arm is a
noisy realization based on an observed context under mild nonparametric
assumptions. We attain tight results for top-arm identification and a sublinear
regret of $\widetilde{O}\Big(T^{\frac{1+D}{2+D}}\Big)$, where $D$ is the
context dimension, f... | computer science |
7,461 | Closed-form marginal likelihood in Gamma-Poisson factorization | stat.ML | We present novel understandings of the Gamma-Poisson (GaP) model, a
probabilistic matrix factorization model for count data. We show that GaP can
be rewritten free of the score/activation matrix. This gives us new insights
about the estimation of the topic/dictionary matrix by maximum marginal
likelihood estimation. In... | computer science |
7,462 | Clustering with Outlier Removal | cs.LG | Cluster analysis and outlier detection are strongly coupled tasks in data
mining area. Cluster structure can be easily destroyed by few outliers; on the
contrary, the outliers are defined by the concept of cluster, which are
recognized as the points belonging to none of the clusters. However, most
existing studies hand... | computer science |
7,463 | Generating Neural Networks with Neural Networks | stat.ML | Hypernetworks are neural networks that transform a random input vector into
weights for a specified target neural network. We formulate the hypernetwork
training objective as a compromise between accuracy and diversity, where the
diversity takes into account trivial symmetry transformations of the target
network. We sh... | computer science |
7,464 | Adversarial Perturbation Intensity Achieving Chosen Intra-Technique
Transferability Level for Logistic Regression | stat.ML | Machine Learning models have been shown to be vulnerable to adversarial
examples, ie. the manipulation of data by a attacker to defeat a defender's
classifier at test time. We present a novel probabilistic definition of
adversarial examples in perfect or limited knowledge setting using prior
probability distributions o... | computer science |
7,465 | A Note on the Inception Score | stat.ML | Deep generative models are powerful tools that have produced impressive
results in recent years. These advances have been for the most part empirically
driven, making it essential that we use high quality evaluation metrics. In
this paper, we provide new insights into the Inception Score, a recently
proposed and widely... | computer science |
7,466 | Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal
Demonstrations | stat.ML | This paper considers the problem of inverse reinforcement learning in
zero-sum stochastic games when expert demonstrations are known to be not
optimal. Compared to previous works that decouple agents in the game by
assuming optimality in expert strategies, we introduce a new objective function
that directly pits expert... | computer science |
7,467 | Threshold Auto-Tuning Metric Learning | cs.LG | It has been reported repeatedly that discriminative learning of distance
metric boosts the pattern recognition performance. A weak point of ITML-based
methods is that the distance threshold for similarity/dissimilarity constraints
must be determined manually and it is sensitive to generalization performance,
although t... | computer science |
7,468 | Applying an Ensemble Learning Method for Improving Multi-label
Classification Performance | cs.LG | In recent years, multi-label classification problem has become a
controversial issue. In this kind of classification, each sample is associated
with a set of class labels. Ensemble approaches are supervised learning
algorithms in which an operator takes a number of learning algorithms, namely
base-level algorithms and ... | computer science |
7,469 | Gradient Layer: Enhancing the Convergence of Adversarial Training for
Generative Models | stat.ML | We propose a new technique that boosts the convergence of training generative
adversarial networks. Generally, the rate of training deep models reduces
severely after multiple iterations. A key reason for this phenomenon is that a
deep network is expressed using a highly non-convex finite-dimensional model,
and thus th... | computer science |
7,470 | Denoising Dictionary Learning Against Adversarial Perturbations | stat.ML | We propose denoising dictionary learning (DDL), a simple yet effective
technique as a protection measure against adversarial perturbations. We
examined denoising dictionary learning on MNIST and CIFAR10 perturbed under two
different perturbation techniques, fast gradient sign (FGSM) and jacobian
saliency maps (JSMA). W... | computer science |
7,471 | Deep Nearest Class Mean Model for Incremental Odor Classification | cs.LG | In recent years, more and more machine learning algorithms have been applied
to odor recognition. These odor recognition algorithms usually assume that the
training dataset is static. However, for some odor recognition tasks, the odor
dataset is dynamically growing where not only the training samples but also the
numbe... | computer science |
7,472 | Convexification of Neural Graph | cs.LG | Traditionally, most complex intelligence architectures are extremely
non-convex, which could not be well performed by convex optimization. However,
this paper decomposes complex structures into three types of nodes: operators,
algorithms and functions. Iteratively, propagating from node to node along
edge, we prove tha... | computer science |
7,473 | Online Cluster Validity Indices for Streaming Data | stat.ML | Cluster analysis is used to explore structure in unlabeled data sets in a
wide range of applications. An important part of cluster analysis is validating
the quality of computationally obtained clusters. A large number of different
internal indices have been developed for validation in the offline setting.
However, thi... | computer science |
7,474 | An efficient K -means clustering algorithm for massive data | stat.ML | The analysis of continously larger datasets is a task of major importance in
a wide variety of scientific fields. In this sense, cluster analysis algorithms
are a key element of exploratory data analysis, due to their easiness in the
implementation and relatively low computational cost. Among these algorithms,
the K -m... | computer science |
7,475 | A Predictive Approach Using Deep Feature Learning for Electronic Medical
Records: A Comparative Study | cs.LG | Massive amount of electronic medical records accumulating from patients and
populations motivates clinicians and data scientists to collaborate for the
advanced analytics to extract knowledge that is essential to address the
extensive personalized insights needed for patients, clinicians, providers,
scientists, and hea... | computer science |
7,476 | Adaptive Graph Convolutional Neural Networks | cs.LG | Graph Convolutional Neural Networks (Graph CNNs) are generalizations of
classical CNNs to handle graph data such as molecular data, point could and
social networks. Current filters in graph CNNs are built for fixed and shared
graph structure. However, for most real data, the graph structures varies in
both size and con... | computer science |
7,477 | More Adaptive Algorithms for Adversarial Bandits | cs.LG | We develop a novel and generic algorithm for the adversarial multi-armed
bandit problem (or more generally the combinatorial semi-bandit problem). When
instantiated differently, our algorithm achieves various new data-dependent
regret bounds improving previous work. Examples include: 1) a regret bound
depending on the ... | computer science |
7,478 | Weakly Supervised One-Shot Detection with Attention Siamese Networks | stat.ML | We consider the task of weakly supervised one-shot detection. In this task,
we attempt to perform a detection task over a set of unseen classes, when
training only using weak binary labels that indicate the existence of a class
instance in a given example. The model is conditioned on a single exemplar of
an unseen clas... | computer science |
7,479 | Approximation beats concentration? An approximation view on inference
with smooth radial kernels | cs.LG | Positive definite kernels and their associated Reproducing Kernel Hilbert
Spaces provide a mathematically compelling and practically competitive
framework for learning from data.
In this paper we take the approximation theory point of view to explore
various aspects of smooth kernels related to their inferential prop... | computer science |
7,480 | Inference Suboptimality in Variational Autoencoders | cs.LG | Amortized inference allows latent-variable models trained via variational
learning to scale to large datasets. The quality of approximate inference is
determined by two factors: a) the capacity of the variational distribution to
match the true posterior and b) the ability of the recognition network to
produce good vari... | computer science |
7,481 | Autoencoders and Probabilistic Inference with Missing Data: An Exact
Solution for The Factor Analysis Case | cs.LG | Latent variable models can be used to probabilistically "fill-in" missing
data entries. The variational autoencoder architecture (Kingma and Welling,
2014; Rezende et al., 2014) includes a "recognition" or "encoder" network that
infers the latent variables given the data variables. However, it is not clear
how to handl... | computer science |
7,482 | Noisy Expectation-Maximization: Applications and Generalizations | stat.ML | We present a noise-injected version of the Expectation-Maximization (EM)
algorithm: the Noisy Expectation Maximization (NEM) algorithm. The NEM
algorithm uses noise to speed up the convergence of the EM algorithm. The NEM
theorem shows that injected noise speeds up the average convergence of the EM
algorithm to a local... | computer science |
7,483 | A3T: Adversarially Augmented Adversarial Training | cs.LG | Recent research showed that deep neural networks are highly sensitive to
so-called adversarial perturbations, which are tiny perturbations of the input
data purposely designed to fool a machine learning classifier. Most
classification models, including deep learning models, are highly vulnerable to
adversarial attacks.... | computer science |
7,484 | MINE: Mutual Information Neural Estimation | cs.LG | We argue that the estimation of mutual information between high dimensional
continuous random variables can be achieved by gradient descent over neural
networks. We present a Mutual Information Neural Estimator (MINE) that is
linearly scalable in dimensionality as well as in sample size, trainable
through back-prop, an... | computer science |
7,485 | Deep Learning for Sampling from Arbitrary Probability Distributions | cs.LG | This paper proposes a fully connected neural network model to map samples
from a uniform distribution to samples of any explicitly known probability
density function. During the training, the Jensen-Shannon divergence between
the distribution of the model's output and the target distribution is
minimized. We experiment... | computer science |
7,486 | Multivariate LSTM-FCNs for Time Series Classification | cs.LG | Over the past decade, multivariate time series classification has been
receiving a lot of attention. We propose augmenting the existing univariate
time series classification models, LSTM-FCN and ALSTM-FCN with a squeeze and
excitation block to further improve performance. Our proposed models outperform
most of the stat... | computer science |
7,487 | Global Convergence of Policy Gradient Methods for Linearized Control
Problems | cs.LG | Direct policy gradient methods for reinforcement learning and continuous
control problems are a popular approach for a variety of reasons: 1) they are
easy to implement without explicit knowledge of the underlying model 2) they
are an "end-to-end" approach, directly optimizing the performance metric of
interest 3) they... | computer science |
7,488 | Understanding the Disharmony between Dropout and Batch Normalization by
Variance Shift | cs.LG | This paper first answers the question "why do the two most powerful
techniques Dropout and Batch Normalization (BN) often lead to a worse
performance when they are combined together?" in both theoretical and
statistical aspects. Theoretically, we find that Dropout would shift the
variance of a specific neural unit when... | computer science |
7,489 | Deep Canonically Correlated LSTMs | stat.ML | We examine Deep Canonically Correlated LSTMs as a way to learn nonlinear
transformations of variable length sequences and embed them into a correlated,
fixed dimensional space. We use LSTMs to transform multi-view time-series data
non-linearly while learning temporal relationships within the data. We then
perform corre... | computer science |
7,490 | Deep Neural Networks for Survival Analysis Based on a Multi-Task
Framework | stat.ML | Survival analysis/time-to-event models are extremely useful as they can help
companies predict when a customer will buy a product, churn or default on a
loan, and therefore help them improve their ROI. In this paper, we introduce a
new method to calculate survival functions using the Multi-Task Logistic
Regression (MTL... | computer science |
7,491 | Composite Functional Gradient Learning of Generative Adversarial Models | stat.ML | Generative adversarial networks (GAN) have become popular for generating data
that mimic observations by learning a suitable variable transformation from a
random variable. However, empirically, GAN is known to suffer from instability.
Also, the theory provided based on the minimax optimization formulation of GAN
canno... | computer science |
7,492 | Global overview of Imitation Learning | stat.ML | Imitation Learning is a sequential task where the learner tries to mimic an
expert's action in order to achieve the best performance. Several algorithms
have been proposed recently for this task. In this project, we aim at proposing
a wide review of these algorithms, presenting their main features and comparing
them on... | computer science |
7,493 | On the Iteration Complexity Analysis of Stochastic Primal-Dual Hybrid
Gradient Approach with High Probability | cs.LG | In this paper, we propose a stochastic Primal-Dual Hybrid Gradient (PDHG)
approach for solving a wide spectrum of regularized stochastic minimization
problems, where the regularization term is composite with a linear function. It
has been recognized that solving this kind of problem is challenging since the
closed-form... | computer science |
7,494 | Offline A/B testing for Recommender Systems | stat.ML | Before A/B testing online a new version of a recommender system, it is usual
to perform some offline evaluations on historical data. We focus on evaluation
methods that compute an estimator of the potential uplift in revenue that could
generate this new technology. It helps to iterate faster and to avoid losing
money b... | computer science |
7,495 | Optimizing Prediction Intervals by Tuning Random Forest via
Meta-Validation | cs.LG | Recent studies have shown that tuning prediction models increases prediction
accuracy and that Random Forest can be used to construct prediction intervals.
However, to our best knowledge, no study has investigated the need to, and the
manner in which one can, tune Random Forest for optimizing prediction intervals
{ thi... | computer science |
7,496 | Rover Descent: Learning to optimize by learning to navigate on
prototypical loss surfaces | cs.LG | Learning to optimize - the idea that we can learn from data algorithms that
optimize a numerical criterion - has recently been at the heart of a growing
number of research efforts. One of the most challenging issues within this
approach is to learn a policy that is able to optimize over classes of
functions that are fa... | computer science |
7,497 | Convergence of Value Aggregation for Imitation Learning | cs.LG | Value aggregation is a general framework for solving imitation learning
problems. Based on the idea of data aggregation, it generates a policy sequence
by iteratively interleaving policy optimization and evaluation in an online
learning setting. While the existence of a good policy in the policy sequence
can be guarant... | computer science |
7,498 | The Hybrid Bootstrap: A Drop-in Replacement for Dropout | stat.ML | Regularization is an important component of predictive model building. The
hybrid bootstrap is a regularization technique that functions similarly to
dropout except that features are resampled from other training points rather
than replaced with zeros. We show that the hybrid bootstrap offers superior
performance to dr... | computer science |
7,499 | Generalized two-dimensional linear discriminant analysis with
regularization | cs.LG | Recent advances show that two-dimensional linear discriminant analysis
(2DLDA) is a successful matrix based dimensionality reduction method. However,
2DLDA may encounter the singularity issue theoretically and the sensitivity to
outliers. In this paper, a generalized Lp-norm 2DLDA framework with
regularization for an a... | computer science |
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