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12,401 | Brain EEG Time Series Selection: A Novel Graph-Based Approach for
Classification | cs.LG | Brain Electroencephalography (EEG) classification is widely applied to
analyze cerebral diseases in recent years. Unfortunately, invalid/noisy EEGs
degrade the diagnosis performance and most previously developed methods ignore
the necessity of EEG selection for classification. To this end, this paper
proposes a novel m... | computer science |
12,402 | Towards Imperceptible and Robust Adversarial Example Attacks against
Neural Networks | cs.LG | Machine learning systems based on deep neural networks, being able to produce
state-of-the-art results on various perception tasks, have gained mainstream
adoption in many applications. However, they are shown to be vulnerable to
adversarial example attack, which generates malicious output by adding slight
perturbation... | computer science |
12,403 | Sparsity-based Defense against Adversarial Attacks on Linear Classifiers | stat.ML | Deep neural networks represent the state of the art in machine learning in a
growing number of fields, including vision, speech and natural language
processing. However, recent work raises important questions about the
robustness of such architectures, by showing that it is possible to induce
classification errors thro... | computer science |
12,404 | Improving Orbit Prediction Accuracy through Supervised Machine Learning | cs.CE | Due to the lack of information such as the space environment condition and
resident space objects' (RSOs') body characteristics, current orbit predictions
that are solely grounded on physics-based models may fail to achieve required
accuracy for collision avoidance and have led to satellite collisions already.
This pap... | computer science |
12,405 | On the Complexity of the Weighted Fused Lasso | cs.LG | The solution path of the 1D fused lasso for an $n$-dimensional input is
piecewise linear with $\mathcal{O}(n)$ segments (Hoefling et al. 2010 and
Tibshirani et al 2011). However, existing proofs of this bound do not hold for
the weighted fused lasso. At the same time, results for the generalized lasso,
of which the wei... | computer science |
12,406 | Multi-Label Learning from Medical Plain Text with Convolutional Residual
Models | stat.ML | Predicting diagnoses from Electronic Health Records (EHRs) is an important
medical application of multi-label learning. We propose a convolutional
residual model for multi-label classification from doctor notes in EHR data. A
given patient may have multiple diagnoses, and therefore multi-label learning
is required. We ... | computer science |
12,407 | MORF: A Framework for MOOC Predictive Modeling and Replication At Scale | cs.SE | The MOOC Replication Framework (MORF) is a novel software system for feature
extraction, model training/testing, and evaluation of predictive dropout models
in Massive Open Online Courses (MOOCs). MORF makes large-scale replication of
complex machine-learned models tractable and accessible for researchers, and
enables ... | computer science |
12,408 | On the Direction of Discrimination: An Information-Theoretic Analysis of
Disparate Impact in Machine Learning | cs.IT | In the context of machine learning, disparate impact refers to a form of
systematic discrimination whereby the output distribution of a model depends on
the value of a sensitive attribute (e.g., race or gender). In this paper, we
present an information-theoretic framework to analyze the disparate impact of a
binary cla... | computer science |
12,409 | Combinatorial Preconditioners for Proximal Algorithms on Graphs | math.OC | We present a novel preconditioning technique for proximal optimization
methods that relies on graph algorithms to construct effective preconditioners.
Such combinatorial preconditioners arise from partitioning the graph into
forests. We prove that certain decompositions lead to a theoretically optimal
condition number.... | computer science |
12,410 | Automatic Classification of Music Genre using Masked Conditional Neural
Networks | cs.SD | Neural network based architectures used for sound recognition are usually
adapted from other application domains such as image recognition, which may not
harness the time-frequency representation of a signal. The ConditionaL Neural
Networks (CLNN) and its extension the Masked ConditionaL Neural Networks
(MCLNN) are des... | computer science |
12,411 | Network Representation Learning: A Survey | cs.SI | With the widespread use of information technologies, information networks
have increasingly become popular to capture complex relationships across
various disciplines, such as social networks, citation networks,
telecommunication networks, and biological networks. Analyzing these networks
sheds light on different aspec... | computer science |
12,412 | Active Community Detection: A Maximum Likelihood Approach | cs.SI | We propose novel semi-supervised and active learning algorithms for the
problem of community detection on networks. The algorithms are based on
optimizing the likelihood function of the community assignments given a graph
and an estimate of the statistical model that generated it. The optimization
framework is inspired... | computer science |
12,413 | Deep Learning: An Introduction for Applied Mathematicians | math.HO | Multilayered artificial neural networks are becoming a pervasive tool in a
host of application fields. At the heart of this deep learning revolution are
familiar concepts from applied and computational mathematics; notably, in
calculus, approximation theory, optimization and linear algebra. This article
provides a very... | computer science |
12,414 | When Does Stochastic Gradient Algorithm Work Well? | stat.ML | In this paper, we consider a general stochastic optimization problem which is
often at the core of supervised learning, such as deep learning and linear
classification. We consider a standard stochastic gradient descent (SGD) method
with a fixed, large step size and propose a novel assumption on the objective
function,... | computer science |
12,415 | Introducing ReQuEST: an Open Platform for Reproducible and
Quality-Efficient Systems-ML Tournaments | stat.ML | Co-designing efficient machine learning based systems across the whole
hardware/software stack to trade off speed, accuracy, energy and costs is
becoming extremely complex and time consuming. Researchers often struggle to
evaluate and compare different published works across rapidly evolving software
frameworks, hetero... | computer science |
12,416 | Deep Hidden Physics Models: Deep Learning of Nonlinear Partial
Differential Equations | stat.ML | A long-standing problem at the interface of artificial intelligence and
applied mathematics is to devise an algorithm capable of achieving human level
or even superhuman proficiency in transforming observed data into predictive
mathematical models of the physical world. In the current era of abundance of
data and advan... | computer science |
12,417 | Optimal Rates for Spectral-regularized Algorithms with Least-Squares
Regression over Hilbert Spaces | stat.ML | In this paper, we study regression problems over a separable Hilbert space
with the square loss, covering non-parametric regression over a reproducing
kernel Hilbert space. We investigate a class of spectral-regularized
algorithms, including ridge regression, principal component analysis, and
gradient methods. We prove... | computer science |
12,418 | Time series kernel similarities for predicting Paroxysmal Atrial
Fibrillation from ECGs | cs.LG | We tackle the problem of classifying Electrocardiography (ECG) signals with
the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF). Atrial
fibrillation is the most common type of arrhythmia, but in many cases PAF
episodes are asymptomatic. Therefore, in order to help diagnosing PAF, it is
important to ... | computer science |
12,419 | Scale-invariant Feature Extraction of Neural Network and Renormalization
Group Flow | cs.LG | Theoretical understanding of how deep neural network (DNN) extracts features
from input images is still unclear, but it is widely believed that the
extraction is performed hierarchically through a process of coarse-graining. It
reminds us of the basic concept of renormalization group (RG) in statistical
physics. In ord... | computer science |
12,420 | Algorithmic Bio-surveillance For Precise Spatio-temporal Prediction of
Zoonotic Emergence | cs.LG | Viral zoonoses have emerged as the key drivers of recent pandemics. Human
infection by zoonotic viruses are either spillover events -- isolated
infections that fail to cause a widespread contagion -- or species jumps, where
successful adaptation to the new host leads to a pandemic. Despite expensive
bio-surveillance ef... | computer science |
12,421 | Incremental Eigenpair Computation for Graph Laplacian Matrices: Theory
and Applications | cs.LG | The smallest eigenvalues and the associated eigenvectors (i.e., eigenpairs)
of a graph Laplacian matrix have been widely used in spectral clustering and
community detection. However, in real-life applications the number of clusters
or communities (say, $K$) is generally unknown a-priori. Consequently, the
majority of t... | computer science |
12,422 | Data-Driven Impulse Response Regularization via Deep Learning | cs.SY | We consider the problem of impulse response estimation for stable linear
single-input single-output systems. It is a well-studied problem where flexible
non-parametric models recently offered a leap in performance compared to the
classical finite-dimensional model structures. Inspired by this development and
the succes... | computer science |
12,423 | Deep Learning in Pharmacogenomics: From Gene Regulation to Patient
Stratification | cs.LG | This Perspective provides examples of current and future applications of deep
learning in pharmacogenomics, including: (1) identification of novel regulatory
variants located in noncoding domains and their function as applied to
pharmacoepigenomics; (2) patient stratification from medical records; and (3)
prediction of... | computer science |
12,424 | Algorithmic Linearly Constrained Gaussian Processes | stat.ML | We algorithmically construct multi-output Gaussian process priors which
satisfy linear differential equations. Our approach attempts to parametrize all
solutions of the equations using Gr\"obner bases. If successful, a push forward
Gaussian process along the paramerization is the desired prior. We consider
several exam... | computer science |
12,425 | Less is more: sampling chemical space with active learning | cs.LG | The development of accurate and transferable machine learning (ML) potentials
for predicting molecular energetics is a challenging task. The process of data
generation to train such ML potentials is a task neither well understood nor
researched in detail. In this work, we present a fully automated approach for
the gene... | computer science |
12,426 | Cardiac Arrhythmia Detection from ECG Combining Convolutional and Long
Short-Term Memory Networks | eess.SP | Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder
associated with deadly and debilitating consequences including heart failure,
stroke, poor mental health, reduced quality of life and death. Having an
automatic system that diagnoses various types of cardiac arrhythmias would
assist cardiologists to... | computer science |
12,427 | Evaluating the Robustness of Neural Networks: An Extreme Value Theory
Approach | stat.ML | The robustness of neural networks to adversarial examples has received great
attention due to security implications. Despite various attack approaches to
crafting visually imperceptible adversarial examples, little has been developed
towards a comprehensive measure of robustness. In this paper, we provide a
theoretical... | computer science |
12,428 | Matrix completion with deterministic pattern - a geometric perspective | cs.LG | We consider the matrix completion problem with a deterministic pattern of
observed entries and aim to find conditions such that there will be (at least
locally) unique solution to the non-convex Minimum Rank Matrix Completion
(MRMC) formulation. We answer the question from a somewhat different point of
view and to give... | computer science |
12,429 | Distributed Newton Methods for Deep Neural Networks | stat.ML | Deep learning involves a difficult non-convex optimization problem with a
large number of weights between any two adjacent layers of a deep structure. To
handle large data sets or complicated networks, distributed training is needed,
but the calculation of function, gradient, and Hessian is expensive. In
particular, th... | computer science |
12,430 | A Nonparametric Delayed Feedback Model for Conversion Rate Prediction | cs.LG | Predicting conversion rates (CVRs) in display advertising (e.g., predicting
the proportion of users who purchase an item (i.e., a conversion) after its
corresponding ad is clicked) is important when measuring the effects of ads
shown to users and to understanding the interests of the users. There is
generally a time de... | computer science |
12,431 | Sensitivity Sampling Over Dynamic Geometric Data Streams with
Applications to $k$-Clustering | cs.DS | Sensitivity based sampling is crucial for constructing nearly-optimal coreset
for $k$-means / median clustering. In this paper, we provide a novel data
structure that enables sensitivity sampling over a dynamic data stream, where
points from a high dimensional discrete Euclidean space can be either inserted
or deleted.... | computer science |
12,432 | Modeling polypharmacy side effects with graph convolutional networks | cs.LG | The use of multiple drugs, termed polypharmacy, is common to treat patients
with complex diseases or co-existing medical conditions. However, a major
consequence of polypharmacy is a much higher risk of side effects for the
patient. Polypharmacy side effects emerge because of drug interactions, in
which activity of one... | computer science |
12,433 | A Generative Model for Natural Sounds Based on Latent Force Modelling | cs.LG | Recent advances in analysis of subband amplitude envelopes of natural sounds
have resulted in convincing synthesis, showing subband amplitudes to be a
crucial component of perception. Probabilistic latent variable analysis is
particularly revealing, but existing approaches don't incorporate prior
knowledge about the ph... | computer science |
12,434 | VIBNN: Hardware Acceleration of Bayesian Neural Networks | cs.LG | Bayesian Neural Networks (BNNs) have been proposed to address the problem of
model uncertainty in training and inference. By introducing weights associated
with conditioned probability distributions, BNNs are capable of resolving the
overfitting issue commonly seen in conventional neural networks and allow for
small-da... | computer science |
12,435 | Deep UQ: Learning deep neural network surrogate models for high
dimensional uncertainty quantification | cs.LG | State-of-the-art computer codes for simulating real physical systems are
often characterized by a vast number of input parameters. Performing
uncertainty quantification (UQ) tasks with Monte Carlo (MC) methods is almost
always infeasible because of the need to perform hundreds of thousands or even
millions of forward m... | computer science |
12,436 | Bayesian Renewables Scenario Generation via Deep Generative Networks | math.OC | We present a method to generate renewable scenarios using Bayesian
probabilities by implementing the Bayesian generative adversarial
network~(Bayesian GAN), which is a variant of generative adversarial networks
based on two interconnected deep neural networks. By using a Bayesian
formulation, generators can be construc... | computer science |
12,437 | Non-Gaussian information from weak lensing data via deep learning | cs.LG | Weak lensing maps contain information beyond two-point statistics on small
scales. Much recent work has tried to extract this information through a range
of different observables or via nonlinear transformations of the lensing field.
Here we train and apply a 2D convolutional neural network to simulated
noiseless lensi... | computer science |
12,438 | Hardening Deep Neural Networks via Adversarial Model Cascades | cs.LG | Deep neural networks (DNNs) have been shown to be vulnerable to adversarial
examples - malicious inputs which are crafted by the adversary to induce the
trained model to produce erroneous outputs. This vulnerability has inspired a
lot of research on how to secure neural networks against these kinds of
attacks. Although... | computer science |
12,439 | Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave
Saddle Point Problems without Strong Convexity | math.OC | We consider the convex-concave saddle point problem $\min_{x}\max_{y}
f(x)+y^\top A x-g(y)$ where $f$ is smooth and convex and $g$ is smooth and
strongly convex. We prove that if the coupling matrix $A$ has full column rank,
the vanilla primal-dual gradient method can achieve linear convergence even if
$f$ is not stron... | computer science |
12,440 | Weakly-supervised Dictionary Learning | eess.SP | We present a probabilistic modeling and inference framework for
discriminative analysis dictionary learning under a weak supervision setting.
Dictionary learning approaches have been widely used for tasks such as
low-level signal denoising and restoration as well as high-level classification
tasks, which can be applied... | computer science |
12,441 | Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent | stat.ML | Coherent uncertainty quantification is a key strength of Bayesian methods.
But modern algorithms for approximate Bayesian posterior inference often
sacrifice accurate posterior uncertainty estimation in the pursuit of
scalability. This work shows that previous Bayesian coreset construction
algorithms---which build a sm... | computer science |
12,442 | Near-Optimal Coresets of Kernel Density Estimates | cs.LG | We construct near-optimal coresets for kernel density estimate for points in
$\mathbb{R^d}$ when the kernel is positive definite. Specifically we show a
polynomial time construction for a coreset of size $O(\sqrt{d\log
(1/\epsilon)}/\epsilon)$, and we show a near-matching lower bound of size
$\Omega(\sqrt{d}/\epsilon)$... | computer science |
12,443 | Learning One Convolutional Layer with Overlapping Patches | cs.LG | We give the first provably efficient algorithm for learning a one hidden
layer convolutional network with respect to a general class of (potentially
overlapping) patches. Additionally, our algorithm requires only mild conditions
on the underlying distribution. We prove that our framework captures commonly
used schemes ... | computer science |
12,444 | Predicting Hurricane Trajectories using a Recurrent Neural Network | cs.LG | Hurricanes are cyclones circulating about a defined center whose closed wind
speeds exceed 75 mph originating over tropical and subtropical waters. At
landfall, hurricanes can result in severe disasters. The accuracy of predicting
their trajectory paths is critical to reduce economic loss and save human
lives. Given th... | computer science |
12,445 | Recognition of Acoustic Events Using Masked Conditional Neural Networks | cs.LG | Automatic feature extraction using neural networks has accomplished
remarkable success for images, but for sound recognition, these models are
usually modified to fit the nature of the multi-dimensional temporal
representation of the audio signal in spectrograms. This may not efficiently
harness the time-frequency repr... | computer science |
12,446 | Gradient conjugate priors and deep neural networks | math.ST | The paper deals with learning the probability distribution of the observed
data by artificial neural networks. We suggest a so-called gradient conjugate
prior (GCP) update appropriate for neural networks, which is a modification of
the classical Bayesian update for conjugate priors. We establish a connection
between th... | computer science |
12,447 | Geometry Score: A Method For Comparing Generative Adversarial Networks | cs.LG | One of the biggest challenges in the research of generative adversarial
networks (GANs) is assessing the quality of generated samples and detecting
various levels of mode collapse. In this work, we construct a novel measure of
performance of a GAN by comparing geometrical properties of the underlying data
manifold and ... | computer science |
12,448 | Neural Network Renormalization Group | cs.LG | We present a variational renormalization group approach using deep generative
model composed of bijectors. The model can learn hierarchical transformations
between physical variables and renormalized collective variables. It can
directly generate statistically independent physical configurations by
iterative refinement... | computer science |
12,449 | State Compression of Markov Processes via Empirical Low-Rank Estimation | stat.ML | Model reduction is a central problem in analyzing complex systems and
high-dimensional data. We study the state compression of finite-state Markov
process from its empirical trajectories. We adopt a low-rank model which is
motivated by the state aggregation of controlled systems. A spectral method is
proposed for estim... | computer science |
12,450 | Detection of Adversarial Training Examples in Poisoning Attacks through
Anomaly Detection | stat.ML | Machine learning has become an important component for many systems and
applications including computer vision, spam filtering, malware and network
intrusion detection, among others. Despite the capabilities of machine learning
algorithms to extract valuable information from data and produce accurate
predictions, it ha... | computer science |
12,451 | Mini-Batch Stochastic ADMMs for Nonconvex Nonsmooth Optimization | math.OC | In the paper, we study the mini-batch stochastic ADMMs (alternating direction
method of multipliers) for the nonconvex nonsmooth optimization. We prove that,
given an appropriate mini-batch size, the mini-batch stochastic ADMM without
variance reduction (VR) technique is convergent and reaches the convergence
rate of $... | computer science |
12,452 | Large Scale Constrained Linear Regression Revisited: Faster Algorithms
via Preconditioning | cs.LG | In this paper, we revisit the large-scale constrained linear regression
problem and propose faster methods based on some recent developments in
sketching and optimization. Our algorithms combine (accelerated) mini-batch SGD
with a new method called two-step preconditioning to achieve an approximate
solution with a time... | computer science |
12,453 | Deep Learning for Malicious Flow Detection | cs.LG | Cyber security has grown up to be a hot issue in recent years. How to
identify potential malware becomes a challenging task. To tackle this
challenge, we adopt deep learning approaches and perform flow detection on real
data. However, real data often encounters an issue of imbalanced data
distribution which will lead t... | computer science |
12,454 | UMAP: Uniform Manifold Approximation and Projection for Dimension
Reduction | stat.ML | UMAP (Uniform Manifold Approximation and Projection) is a novel manifold
learning technique for dimension reduction. UMAP is constructed from a
theoretical framework based in Riemannian geometry and algebraic topology. The
result is a practical scalable algorithm that applies to real world data. The
UMAP algorithm is c... | computer science |
12,455 | A Critical View of Global Optimality in Deep Learning | cs.LG | We investigate the loss surface of deep linear and nonlinear neural networks.
We show that for deep linear networks with differentiable losses, critical
points after the multilinear parameterization inherit the structure of critical
points of the underlying loss with linear parameterization. As corollaries we
obtain "l... | computer science |
12,456 | Riemannian Manifold Kernel for Persistence Diagrams | stat.ML | Algebraic topology methods have recently played an important role for
statistical analysis with complicated geometric structured data. Among them,
persistent homology is a well-known tool to extract robust topological
features, and outputs as persistence diagrams. Unfortunately, persistence
diagrams are point multi-set... | computer science |
12,457 | Feature-Distributed SVRG for High-Dimensional Linear Classification | cs.LG | Linear classification has been widely used in many high-dimensional
applications like text classification. To perform linear classification for
large-scale tasks, we often need to design distributed learning methods on a
cluster of multiple machines. In this paper, we propose a new distributed
learning method, called f... | computer science |
12,458 | Understanding Convolutional Networks with APPLE : Automatic Patch
Pattern Labeling for Explanation | cs.LG | With the success of deep learning, recent efforts have been focused on
analyzing how learned networks make their classifications. We are interested in
analyzing the network output based on the network structure and information
flow through the network layers. We contribute an algorithm for 1) analyzing a
deep network t... | computer science |
12,459 | Evolving Latent Space Model for Dynamic Networks | cs.SI | Networks observed in the real world like social networks, collaboration
networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or
disappear over time. In this paper, we propose a generative, latent space
based, statistical model for such networks (called dynamic networks). We
consider the case where ... | computer science |
12,460 | Convex Formulations for Fair Principal Component Analysis | cs.LG | Though there is a growing body of literature on fairness for supervised
learning, the problem of incorporating fairness into unsupervised learning has
been less well-studied. This paper studies fairness in the context of principal
component analysis (PCA). We first present a definition of fairness for
dimensionality re... | computer science |
12,461 | Drug response prediction by ensemble learning and drug-induced gene
expression signatures | cs.LG | Chemotherapeutic response of cancer cells to a given compound is one of the
most fundamental information one requires to design anti-cancer drugs. Recent
advances in producing large drug screens against cancer cell lines provided an
opportunity to apply machine learning methods for this purpose. In addition to
cytotoxi... | computer science |
12,462 | SGD and Hogwild! Convergence Without the Bounded Gradients Assumption | math.OC | Stochastic gradient descent (SGD) is the optimization algorithm of choice in
many machine learning applications such as regularized empirical risk
minimization and training deep neural networks. The classical analysis of
convergence of SGD is carried out under the assumption that the norm of the
stochastic gradient is ... | computer science |
12,463 | Uncharted Forest a Technique for Exploratory Data Analysis of Provenance
Studies | stat.ML | Exploratory data analysis is a crucial task for developing effective
classification models from high dimensional datasets. We explore the utility of
a new unsupervised tree ensemble which we call, uncharted forest, for purposes
of elucidating class associations, sample-sample associations, class
heterogeneity, and unin... | computer science |
12,464 | Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex
Optimization | cs.LG | The problem of minimizing sum-of-nonconvex functions (i.e., convex functions
that are average of non-convex ones) is becoming increasingly important in
machine learning, and is the core machinery for PCA, SVD, regularized Newton's
method, accelerated non-convex optimization, and more.
We show how to provably obtain a... | computer science |
12,465 | Q-learning with Nearest Neighbors | cs.LG | We consider the problem of model-free reinforcement learning for
infinite-horizon discounted Markov Decision Processes (MDPs) with a continuous
state space and unknown transition kernels, when only a single sample path of
the system is available. We focus on the classical approach of Q-learning where
the goal is to lea... | computer science |
12,466 | Spectral Filtering for General Linear Dynamical Systems | cs.LG | We give a polynomial-time algorithm for learning latent-state linear
dynamical systems without system identification, and without assumptions on the
spectral radius of the system's transition matrix. The algorithm extends the
recently introduced technique of spectral filtering, previously applied only to
systems with a... | computer science |
12,467 | Fair and Diverse DPP-based Data Summarization | cs.LG | Sampling methods that choose a subset of the data proportional to its
diversity in the feature space are popular for data summarization. However,
recent studies have noted the occurrence of bias (under- or over-representation
of a certain gender or race) in such data summarization methods. In this paper
we initiate a s... | computer science |
12,468 | Efficient Empirical Risk Minimization with Smooth Loss Functions in
Non-interactive Local Differential Privacy | cs.LG | In this paper, we study the Empirical Risk Minimization problem in the
non-interactive local model of differential privacy. We first show that if the
ERM loss function is $(\infty, T)$-smooth, then we can avoid a dependence of
the sample complexity, to achieve error $\alpha$, on the exponential of the
dimensionality $p... | computer science |
12,469 | Dimension Reduction Using Active Manifolds | cs.LG | Scientists and engineers rely on accurate mathematical models to quantify the
objects of their studies, which are often high-dimensional. Unfortunately,
high-dimensional models are inherently difficult, i.e. when observations are
sparse or expensive to determine. One way to address this problem is to
approximate the or... | computer science |
12,470 | Learning a Neural-network-based Representation for Open Set Recognition | cs.LG | Open set recognition problems exist in many domains. For example in security,
new malware classes emerge regularly; therefore malware classification systems
need to identify instances from unknown classes in addition to discriminating
between known classes. In this paper we present a neural network based
representation... | computer science |
12,471 | Tempered Adversarial Networks | stat.ML | Generative adversarial networks (GANs) have been shown to produce realistic
samples from high-dimensional distributions, but training them is considered
hard. A possible explanation for training instabilities is the inherent
imbalance between the networks: While the discriminator is trained directly on
both real and fa... | computer science |
12,472 | A comparative study of fairness-enhancing interventions in machine
learning | stat.ML | Computers are increasingly used to make decisions that have significant
impact in people's lives. Often, these predictions can affect different
population subgroups disproportionately. As a result, the issue of fairness has
received much recent interest, and a number of fairness-enhanced classifiers
and predictors have... | computer science |
12,473 | A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex
Optimization | math.OC | We analyze stochastic gradient algorithms for optimizing nonconvex, nonsmooth
finite-sum problems. In particular, the objective function is given by the
summation of a differentiable (possibly nonconvex) component, together with a
possibly non-differentiable but convex component. We propose a proximal
stochastic gradie... | computer science |
12,474 | Analysis of Minimax Error Rate for Crowdsourcing and Its Application to
Worker Clustering Model | stat.ML | While crowdsourcing has become an important means to label data, crowdworkers
are not always experts---sometimes they can even be adversarial. Therefore,
there is great interest in estimating the ground truth from unreliable labels
produced by crowdworkers. The Dawid and Skene (DS) model is one of the most
well-known m... | computer science |
12,475 | Recovering Loss to Followup Information Using Denoising Autoencoders | cs.LG | Loss to followup is a significant issue in healthcare and has serious
consequences for a study's validity and cost. Methods available at present for
recovering loss to followup information are restricted by their expressive
capabilities and struggle to model highly non-linear relations and complex
interactions. In this... | computer science |
12,476 | Stochastic Variance-Reduced Hamilton Monte Carlo Methods | stat.ML | We propose a fast stochastic Hamilton Monte Carlo (HMC) method, for sampling
from a smooth and strongly log-concave distribution. At the core of our
proposed method is a variance reduction technique inspired by the recent
advance in stochastic optimization. We show that, to achieve $\epsilon$
accuracy in 2-Wasserstein ... | computer science |
12,477 | Leveraging the Exact Likelihood of Deep Latent Variable Models | stat.ML | Deep latent variable models combine the approximation abilities of deep
neural networks and the statistical foundations of generative models. The
induced data distribution is an infinite mixture model whose density is
extremely delicate to compute. Variational methods are consequently used for
inference, following the ... | computer science |
12,478 | Persistence Codebooks for Topological Data Analysis | stat.ML | Topological data analysis, such as persistent homology has shown beneficial
properties for machine learning in many tasks. Topological representations,
such as the persistence diagram (PD), however, have a complex structure
(multiset of intervals) which makes it difficult to combine with typical
machine learning workfl... | computer science |
12,479 | Understanding Membership Inferences on Well-Generalized Learning Models | cs.CR | Membership Inference Attack (MIA) determines the presence of a record in a
machine learning model's training data by querying the model. Prior work has
shown that the attack is feasible when the model is overfitted to its training
data or when the adversary controls the training algorithm. However, when the
model is no... | computer science |
12,480 | Linear-Time Algorithm for Learning Large-Scale Sparse Graphical Models | stat.ML | The sparse inverse covariance estimation problem is commonly solved using an
$\ell_{1}$-regularized Gaussian maximum likelihood estimator known as
"graphical lasso", but its computational cost becomes prohibitive for large
data sets. A recent line of results showed--under mild assumptions--that the
graphical lasso esti... | computer science |
12,481 | Benchmarking Framework for Performance-Evaluation of Causal Inference
Analysis | stat.ME | Causal inference analysis is the estimation of the effects of actions on
outcomes. In the context of healthcare data this means estimating the outcome
of counter-factual treatments (i.e. including treatments that were not
observed) on a patient's outcome. Compared to classic machine learning methods,
evaluation and val... | computer science |
12,482 | Toward Deeper Understanding of Nonconvex Stochastic Optimization with
Momentum using Diffusion Approximations | cs.LG | Momentum Stochastic Gradient Descent (MSGD) algorithm has been widely applied
to many nonconvex optimization problems in machine learning. Popular examples
include training deep neural networks, dimensionality reduction, and etc. Due
to the lack of convexity and the extra momentum term, the optimization theory
of MSGD ... | computer science |
12,483 | Security Analysis and Enhancement of Model Compressed Deep Learning
Systems under Adversarial Attacks | cs.LG | DNN is presenting human-level performance for many complex intelligent tasks
in real-world applications. However, it also introduces ever-increasing
security concerns. For example, the emerging adversarial attacks indicate that
even very small and often imperceptible adversarial input perturbations can
easily mislead t... | computer science |
12,484 | Generative Models for Spear Phishing Posts on Social Media | cs.CR | Historically, machine learning in computer security has prioritized defense:
think intrusion detection systems, malware classification, and botnet traffic
identification. Offense can benefit from data just as well. Social networks,
with their access to extensive personal data, bot-friendly APIs, colloquial
syntax, and ... | computer science |
12,485 | Distributionally Robust Submodular Maximization | cs.LG | Submodular functions have applications throughout machine learning, but in
many settings, we do not have direct access to the underlying function $f$. We
focus on stochastic functions that are given as an expectation of functions
over a distribution $P$. In practice, we often have only a limited set of
samples $f_i$ fr... | computer science |
12,486 | Differentially Private Empirical Risk Minimization Revisited: Faster and
More General | cs.LG | In this paper we study the differentially private Empirical Risk Minimization
(ERM) problem in different settings. For smooth (strongly) convex loss function
with or without (non)-smooth regularization, we give algorithms that achieve
either optimal or near optimal utility bounds with less gradient complexity
compared ... | computer science |
12,487 | Designing Random Graph Models Using Variational Autoencoders With
Applications to Chemical Design | cs.LG | Deep generative models have been praised for their ability to learn smooth
latent representation of images, text, and audio, which can then be used to
generate new, plausible data. However, current generative models are unable to
work with graphs due to their unique characteristics--their underlying
structure is not Eu... | computer science |
12,488 | 500+ Times Faster Than Deep Learning (A Case Study Exploring Faster
Methods for Text Mining StackOverflow) | cs.SE | Deep learning methods are useful for high-dimensional data and are becoming
widely used in many areas of software engineering. Deep learners utilizes
extensive computational power and can take a long time to train-- making it
difficult to widely validate and repeat and improve their results. Further,
they are not the b... | computer science |
12,489 | Stealing Hyperparameters in Machine Learning | cs.CR | Hyperparameters are critical in machine learning, as different
hyperparameters often result in models with significantly different
performance. Hyperparameters may be deemed confidential because of their
commercial value and the confidentiality of the proprietary algorithms that the
learner uses to learn them. In this ... | computer science |
12,490 | A Progressive Batching L-BFGS Method for Machine Learning | math.OC | The standard L-BFGS method relies on gradient approximations that are not
dominated by noise, so that search directions are descent directions, the line
search is reliable, and quasi-Newton updating yields useful quadratic models of
the objective function. All of this appears to call for a full batch approach,
but sinc... | computer science |
12,491 | "Dependency Bottleneck" in Auto-encoding Architectures: an Empirical
Study | cs.IT | Recent works investigated the generalization properties in deep neural
networks (DNNs) by studying the Information Bottleneck in DNNs. However, the
mea- surement of the mutual information (MI) is often inaccurate due to the
density estimation. To address this issue, we propose to measure the dependency
instead of MI be... | computer science |
12,492 | Convolutional Analysis Operator Learning: Acceleration, Convergence,
Application, and Neural Networks | stat.ML | Convolutional operator learning is increasingly gaining attention in many
signal processing and computer vision applications. Learning kernels has mostly
relied on so-called local approaches that extract and store many overlapping
patches across training signals. Due to memory demands, local approaches have
limitations... | computer science |
12,493 | Adversarial Risk and the Dangers of Evaluating Against Weak Attacks | cs.LG | This paper investigates recently proposed approaches for defending against
adversarial examples and evaluating adversarial robustness. The existence of
adversarial examples in trained neural networks reflects the fact that expected
risk alone does not capture the model's performance against worst-case inputs.
We motiva... | computer science |
12,494 | Simulation assisted machine learning | stat.ML | Predicting how a proposed cancer treatment will affect a given tumor can be
cast as a machine learning problem, but the complexity of biological systems,
the number of potentially relevant genomic and clinical features, and the lack
of very large scale patient data repositories make this a unique challenge.
"Pure data"... | computer science |
12,495 | Inferring relevant features: from QFT to PCA | cs.LG | In many-body physics, renormalization techniques are used to extract aspects
of a statistical or quantum state that are relevant at large scale, or for low
energy experiments. Recent works have proposed that these features can be
formally identified as those perturbations of the states whose
distinguishability most res... | computer science |
12,496 | Stochastic Wasserstein Barycenters | cs.LG | We present a stochastic algorithm to compute the barycenter of a set of
probability distributions under the Wasserstein metric from optimal transport.
Unlike previous approaches, our method extends to continuous input
distributions and allows the support of the barycenter to be adjusted in each
iteration. We tackle the... | computer science |
12,497 | Quantum Variational Autoencoder | cs.LG | Variational autoencoders (VAEs) are powerful generative models with the
salient ability to perform inference. Here, we introduce a \emph{quantum
variational autoencoder} (QVAE): a VAE whose latent generative process is
implemented as a quantum Boltzmann machine (QBM). We show that our model can be
trained end-to-end by... | computer science |
12,498 | Masked Conditional Neural Networks for Automatic Sound Events
Recognition | cs.LG | Deep neural network architectures designed for application domains other than
sound, especially image recognition, may not optimally harness the
time-frequency representation when adapted to the sound recognition problem. In
this work, we explore the ConditionaL Neural Network (CLNN) and the Masked
ConditionaL Neural N... | computer science |
12,499 | Variational Autoencoders for Collaborative Filtering | stat.ML | We extend variational autoencoders (VAEs) to collaborative filtering for
implicit feedback. This non-linear probabilistic model enables us to go beyond
the limited modeling capacity of linear factor models which still largely
dominate collaborative filtering research.We introduce a generative model with
multinomial lik... | computer science |
12,500 | Online Machine Learning in Big Data Streams | cs.DC | The area of online machine learning in big data streams covers algorithms
that are (1) distributed and (2) work from data streams with only a limited
possibility to store past data. The first requirement mostly concerns software
architectures and efficient algorithms. The second one also imposes nontrivial
theoretical ... | computer science |
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