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12,101 | Deep Learning the Ising Model Near Criticality | cs.LG | It is well established that neural networks with deep architectures perform
better than shallow networks for many tasks in machine learning. In statistical
physics, while there has been recent interest in representing physical data
with generative modelling, the focus has been on shallow neural networks. A
natural ques... | computer science |
12,102 | Data-driven Advice for Applying Machine Learning to Bioinformatics
Problems | cs.LG | As the bioinformatics field grows, it must keep pace not only with new data
but with new algorithms. Here we contribute a thorough analysis of 13
state-of-the-art, commonly used machine learning algorithms on a set of 165
publicly available classification problems in order to provide data-driven
algorithm recommendatio... | computer science |
12,103 | Learning Universal Adversarial Perturbations with Generative Models | cs.CR | Neural networks are known to be vulnerable to adversarial examples, inputs
that have been intentionally perturbed to remain visually similar to the source
input, but cause a misclassification. It was recently shown that given a
dataset and classifier, there exists so called universal adversarial
perturbations, a single... | computer science |
12,104 | Efficient Use of Limited-Memory Accelerators for Linear Learning on
Heterogeneous Systems | cs.LG | We propose a generic algorithmic building block to accelerate training of
machine learning models on heterogeneous compute systems. Our scheme allows to
efficiently employ compute accelerators such as GPUs and FPGAs for the training
of large-scale machine learning models, when the training data exceeds their
memory cap... | computer science |
12,105 | Data-Driven Tree Transforms and Metrics | stat.ML | We consider the analysis of high dimensional data given in the form of a
matrix with columns consisting of observations and rows consisting of features.
Often the data is such that the observations do not reside on a regular grid,
and the given order of the features is arbitrary and does not convey a notion
of locality... | computer science |
12,106 | Stochastic Primal-Dual Proximal ExtraGradient Descent for Compositely
Regularized Optimization | cs.LG | We consider a wide range of regularized stochastic minimization problems with
two regularization terms, one of which is composed with a linear function. This
optimization model abstracts a number of important applications in artificial
intelligence and machine learning, such as fused Lasso, fused logistic
regression, a... | computer science |
12,107 | Nonparametric regression using deep neural networks with ReLU activation
function | math.ST | Consider the multivariate nonparametric regression model. It is shown that
estimators based on sparsely connected deep neural networks with ReLU
activation function and properly chosen network architecture achieve the
minimax rates of convergence (up to log n-factors) under a general composition
assumption on the regre... | computer science |
12,108 | Human experts vs. machines in taxa recognition | stat.ML | Biomonitoring of waterbodies is vital as the number of anthropogenic
stressors on aquatic ecosystems keeps growing. However, the continuous decrease
in funding makes it impossible to meet monitoring goals or sustain traditional
manual sample processing. In this paper, we review what kind of statistical
tools can be use... | computer science |
12,109 | Is Deep Learning Safe for Robot Vision? Adversarial Examples against the
iCub Humanoid | cs.LG | Deep neural networks have been widely adopted in recent years, exhibiting
impressive performances in several application domains. It has however been
shown that they can be fooled by adversarial examples, i.e., images altered by
a barely-perceivable adversarial noise, carefully crafted to mislead
classification. In thi... | computer science |
12,110 | Variational autoencoders for tissue heterogeneity exploration from
(almost) no preprocessed mass spectrometry imaging data | cs.LG | The paper presents the application of Variational Autoencoders (VAE) for data
dimensionality reduction and explorative analysis of mass spectrometry imaging
data (MSI). The results confirm that VAEs are capable of detecting the patterns
associated with the different tissue sub-types with performance than standard
appro... | computer science |
12,111 | Bayesian Learning of Clique Tree Structure | cs.LG | The problem of categorical data analysis in high dimensions is considered. A
discussion of the fundamental difficulties of probability modeling is provided,
and a solution to the derivation of high dimensional probability distributions
based on Bayesian learning of clique tree decomposition is presented. The main
contr... | computer science |
12,112 | Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian
Information | math.OC | We consider variants of trust-region and cubic regularization methods for
non-convex optimization, in which the Hessian matrix is approximated. Under
mild conditions on the inexact Hessian, and using approximate solution of the
corresponding sub-problems, we provide iteration complexity to achieve $
\epsilon $-approxim... | computer science |
12,113 | GALILEO: A Generalized Low-Entropy Mixture Model | stat.ML | We present a new method of generating mixture models for data with
categorical attributes. The keys to this approach are an entropy-based density
metric in categorical space and annealing of high-entropy/low-density
components from an initial state with many components. Pruning of low-density
components using the entro... | computer science |
12,114 | Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning
Workloads | cs.DB | We present ease.ml, a declarative machine learning service platform we built
to support more than ten research groups outside the computer science
departments at ETH Zurich for their machine learning needs. With ease.ml, a
user defines the high-level schema of a machine learning application and
submits the task via a W... | computer science |
12,115 | Bayesian Compressive Sensing Using Normal Product Priors | stat.ML | In this paper, we introduce a new sparsity-promoting prior, namely, the
"normal product" prior, and develop an efficient algorithm for sparse signal
recovery under the Bayesian framework. The normal product distribution is the
distribution of a product of two normally distributed variables with zero means
and possibly ... | computer science |
12,116 | Modular Learning Component Attacks: Today's Reality, Tomorrow's
Challenge | cs.CR | Many of today's machine learning (ML) systems are not built from scratch, but
are compositions of an array of {\em modular learning components} (MLCs). The
increasing use of MLCs significantly simplifies the ML system development
cycles. However, as most MLCs are contributed and maintained by third parties,
their lack ... | computer science |
12,117 | Second-Order Optimization for Non-Convex Machine Learning: An Empirical
Study | math.OC | While first-order optimization methods such as stochastic gradient descent
(SGD) are popular in machine learning (ML), they come with well-known
deficiencies, including relatively-slow convergence, sensitivity to the
settings of hyper-parameters such as learning rate, stagnation at high training
errors, and difficulty ... | computer science |
12,118 | Faster Clustering via Non-Backtracking Random Walks | stat.ML | This paper presents VEC-NBT, a variation on the unsupervised graph clustering
technique VEC, which improves upon the performance of the original algorithm
significantly for sparse graphs. VEC employs a novel application of the
state-of-the-art word2vec model to embed a graph in Euclidean space via random
walks on the n... | computer science |
12,119 | Plausible Deniability for Privacy-Preserving Data Synthesis | cs.CR | Releasing full data records is one of the most challenging problems in data
privacy. On the one hand, many of the popular techniques such as data
de-identification are problematic because of their dependence on the background
knowledge of adversaries. On the other hand, rigorous methods such as the
exponential mechanis... | computer science |
12,120 | On the Protection of Private Information in Machine Learning Systems:
Two Recent Approaches | stat.ML | The recent, remarkable growth of machine learning has led to intense interest
in the privacy of the data on which machine learning relies, and to new
techniques for preserving privacy. However, older ideas about privacy may well
remain valid and useful. This note reviews two recent works on privacy in the
light of the ... | computer science |
12,121 | ByRDiE: Byzantine-resilient distributed coordinate descent for
decentralized learning | cs.LG | Distributed machine learning algorithms enable processing of datasets that
are distributed over a network without gathering the data at a centralized
location. While efficient distributed algorithms have been developed under the
assumption of faultless networks, failures that can render these algorithms
nonfunctional i... | computer science |
12,122 | An inexact subsampled proximal Newton-type method for large-scale
machine learning | cs.LG | We propose a fast proximal Newton-type algorithm for minimizing regularized
finite sums that returns an $\epsilon$-suboptimal point in
$\tilde{\mathcal{O}}(d(n + \sqrt{\kappa d})\log(\frac{1}{\epsilon}))$ FLOPS,
where $n$ is number of samples, $d$ is feature dimension, and $\kappa$ is the
condition number. As long as $... | computer science |
12,123 | Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields | cs.LG | Generative adversarial networks (GANs) evolved into one of the most
successful unsupervised techniques for generating realistic images. Even though
it has recently been shown that GAN training converges, GAN models often end up
in local Nash equilibria that are associated with mode collapse or otherwise
fail to model t... | computer science |
12,124 | A Compressive Sensing Approach to Community Detection with Applications | cs.IT | The community detection problem for graphs asks one to partition the n
vertices V of a graph G into k communities, or clusters, such that there are
many intracluster edges and few intercluster edges. Of course this is
equivalent to finding a permutation matrix P such that, if A denotes the
adjacency matrix of G, then P... | computer science |
12,125 | Design and Analysis of the NIPS 2016 Review Process | cs.DL | Neural Information Processing Systems (NIPS) is a top-tier annual conference
in machine learning. The 2016 edition of the conference comprised more than
2,400 paper submissions, 3,000 reviewers, and 8,000 attendees, representing a
growth of nearly 40% in terms of submissions, 96% in terms of reviewers, and
over 100% in... | computer science |
12,126 | Low Permutation-rank Matrices: Structural Properties and Noisy
Completion | stat.ML | We consider the problem of noisy matrix completion, in which the goal is to
reconstruct a structured matrix whose entries are partially observed in noise.
Standard approaches to this underdetermined inverse problem are based on
assuming that the underlying matrix has low rank, or is well-approximated by a
low rank matr... | computer science |
12,127 | PassGAN: A Deep Learning Approach for Password Guessing | cs.CR | State-of-the-art password guessing tools, such as HashCat and John the
Ripper, enable users to check billions of passwords per second against password
hashes. In addition to performing straightforward dictionary attacks, these
tools can expand password dictionaries using password generation rules, such as
concatenation... | computer science |
12,128 | Communication-efficient Algorithm for Distributed Sparse Learning via
Two-way Truncation | stat.ML | We propose a communicationally and computationally efficient algorithm for
high-dimensional distributed sparse learning. At each iteration, local machines
compute the gradient on local data and the master machine solves one shifted
$l_1$ regularized minimization problem. The communication cost is reduced from
constant ... | computer science |
12,129 | Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario | cs.IR | Item cold-start is a classical issue in recommender systems that affects
anime and manga recommendations as well. This problem can be framed as follows:
how to predict whether a user will like a manga that received few ratings from
the community? Content-based techniques can alleviate this issue but require
extra infor... | computer science |
12,130 | Privacy Risk in Machine Learning: Analyzing the Connection to
Overfitting | cs.CR | Machine learning algorithms, when applied to sensitive data, pose a distinct
threat to privacy. A growing body of prior work demonstrates that models
produced by these algorithms may leak specific private information in the
training data to an attacker, either through the models' structure or their
observable behavior.... | computer science |
12,131 | Unsupervised Generative Modeling Using Matrix Product States | cs.LG | Generative modeling, which learns joint probability distribution from
training data and generates samples according to it, is an important task in
machine learning and artificial intelligence. Inspired by probabilistic
interpretation of quantum physics, we propose a generative model using matrix
product states, which i... | computer science |
12,132 | An Influence-Receptivity Model for Topic based Information Cascades | stat.ML | We consider the problem of estimating the latent structure of a social
network based on observational data on information diffusion processes, or {\it
cascades}. Here for a given cascade, we only observe the time a node/agent is
infected but not the source of infection. Existing literature has focused on
estimating net... | computer science |
12,133 | A Quasi-isometric Embedding Algorithm | stat.ML | The Whitney embedding theorem gives an upper bound on the smallest embedding
dimension of a manifold. If a data set lies on a manifold, a random projection
into this reduced dimension will retain the manifold structure. Here we present
an algorithm to find a projection that distorts the data as little as possible. | computer science |
12,134 | On Fairness and Calibration | cs.LG | The machine learning community has become increasingly concerned with the
potential for bias and discrimination in predictive models. This has motivated
a growing line of work on what it means for a classification procedure to be
"fair." In this paper, we investigate the tension between minimizing error
disparity acros... | computer science |
12,135 | A deep generative model for gene expression profiles from single-cell
RNA sequencing | cs.LG | We propose a probabilistic model for interpreting gene expression levels that
are observed through single-cell RNA sequencing. In the model, each cell has a
low-dimensional latent representation. Additional latent variables account for
technical effects that may erroneously set some observations of gene expression
leve... | computer science |
12,136 | Approximating meta-heuristics with homotopic recurrent neural networks | stat.ML | Much combinatorial optimisation problems constitute a non-polynomial (NP)
hard optimisation problem, i.e., they can not be solved in polynomial time. One
such problem is finding the shortest route between two nodes on a graph.
Meta-heuristic algorithms such as $A^{*}$ along with mixed-integer programming
(MIP) methods ... | computer science |
12,137 | Feature selection in high-dimensional dataset using MapReduce | cs.DC | This paper describes a distributed MapReduce implementation of the minimum
Redundancy Maximum Relevance algorithm, a popular feature selection method in
bioinformatics and network inference problems. The proposed approach handles
both tall/narrow and wide/short datasets. We further provide an open source
implementation... | computer science |
12,138 | Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift
Detection Methods for Evolving Data Streams | stat.ML | The last decade has seen a surge of interest in adaptive learning algorithms
for data stream classification, with applications ranging from predicting ozone
level peaks, learning stock market indicators, to detecting computer security
violations. In addition, a number of methods have been developed to detect
concept dr... | computer science |
12,139 | CuRTAIL: ChaRacterizing and Thwarting AdversarIal deep Learning | cs.CR | This paper proposes CuRTAIL, an end-to-end computing framework for
characterizing and thwarting adversarial space in the context of Deep Learning
(DL). The framework protects deep neural networks against adversarial samples,
which are perturbed inputs carefully crafted by malicious entities to mislead
the underlying DL... | computer science |
12,140 | Deep learning for undersampled MRI reconstruction | stat.ML | This paper presents a deep learning method for faster magnetic resonance
imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and
provides a rationale for why the proposed approach works well. Uniform
subsampling is used in the time-consuming phase-encoding direction to capture
high-resolution im... | computer science |
12,141 | A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism,
Composite Objectives, and Variational Bounds | cs.LG | Recently, much work has been done on extending the scope of online learning
and incremental stochastic optimization algorithms. In this paper we contribute
to this effort in two ways: First, based on a new regret decomposition and a
generalization of Bregman divergences, we provide a self-contained, modular
analysis of... | computer science |
12,142 | Towards Proving the Adversarial Robustness of Deep Neural Networks | cs.LG | Autonomous vehicles are highly complex systems, required to function reliably
in a wide variety of situations. Manually crafting software controllers for
these vehicles is difficult, but there has been some success in using deep
neural networks generated using machine-learning. However, deep neural networks
are opaque ... | computer science |
12,143 | A Brief Introduction to Machine Learning for Engineers | cs.LG | This monograph aims at providing an introduction to key concepts, algorithms,
and theoretical frameworks in machine learning, including supervised and
unsupervised learning, statistical learning theory, probabilistic graphical
models and approximate inference. The intended readership consists of
electrical engineers wi... | computer science |
12,144 | Convolutional Dictionary Learning | cs.LG | Convolutional sparse representations are a form of sparse representation with
a dictionary that has a structure that is equivalent to convolution with a set
of linear filters. While effective algorithms have recently been developed for
the convolutional sparse coding problem, the corresponding dictionary learning
probl... | computer science |
12,145 | A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary
Convex Regularizer | stat.ML | In this paper, we present a simple analysis of {\bf fast rates} with {\it
high probability} of {\bf empirical minimization} for {\it stochastic composite
optimization} over a finite-dimensional bounded convex set with exponential
concave loss functions and an arbitrary convex regularization. To the best of
our knowledg... | computer science |
12,146 | Bayesian bandits: balancing the exploration-exploitation tradeoff via
double sampling | stat.ML | Reinforcement learning studies how to balance exploration and exploitation in
real-world systems, optimizing interactions with the world while simultaneously
learning how the world works. One general class of algorithms for such learning
is the multi-armed bandit setting (in which sequential interactions are
independen... | computer science |
12,147 | Variational inference for the multi-armed contextual bandit | stat.ML | In many biomedical, science, and engineering problems, one must sequentially
decide which action to take next so as to maximize rewards. Reinforcement
learning is an area of machine learning that studies how this maximization
balances exploration and exploitation, optimizing interactions with the world
while simultaneo... | computer science |
12,148 | GIANT: Globally Improved Approximate Newton Method for Distributed
Optimization | cs.LG | For distributed computing environments, we consider the canonical machine
learning problem of empirical risk minimization (ERM) with quadratic
regularization, and we propose a distributed and communication-efficient
Newton-type optimization method. At every iteration, each worker locally finds
an Approximate NewTon (AN... | computer science |
12,149 | Identifying Genetic Risk Factors via Sparse Group Lasso with Group Graph
Structure | stat.ML | Genome-wide association studies (GWA studies or GWAS) investigate the
relationships between genetic variants such as single-nucleotide polymorphisms
(SNPs) and individual traits. Recently, incorporating biological priors
together with machine learning methods in GWA studies has attracted increasing
attention. However, ... | computer science |
12,150 | End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics
Optimization by Fully Convolutional Neural Networks | stat.ML | Speech enhancement model is used to map a noisy speech to a clean speech. In
the training stage, an objective function is often adopted to optimize the
model parameters. However, in most studies, there is an inconsistency between
the model optimization criterion and the evaluation criterion on the enhanced
speech. For ... | computer science |
12,151 | Community Recovery in Hypergraphs | cs.IT | Community recovery is a central problem that arises in a wide variety of
applications such as network clustering, motion segmentation, face clustering
and protein complex detection. The objective of the problem is to cluster data
points into distinct communities based on a set of measurements, each of which
is associat... | computer science |
12,152 | Support Spinor Machine | cs.LG | We generalize a support vector machine to a support spinor machine by using
the mathematical structure of wedge product over vector machine in order to
extend field from vector field to spinor field. The separated hyperplane is
extended to Kolmogorov space in time series data which allow us to extend a
structure of sup... | computer science |
12,153 | Linear Stochastic Approximation: Constant Step-Size and Iterate
Averaging | cs.LG | We consider $d$-dimensional linear stochastic approximation algorithms (LSAs)
with a constant step-size and the so called Polyak-Ruppert (PR) averaging of
iterates. LSAs are widely applied in machine learning and reinforcement
learning (RL), where the aim is to compute an appropriate $\theta_{*} \in
\mathbb{R}^d$ (that... | computer science |
12,154 | EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial
Examples | stat.ML | Recent studies have highlighted the vulnerability of deep neural networks
(DNNs) to adversarial examples - a visually indistinguishable adversarial image
can easily be crafted to cause a well-trained model to misclassify. Existing
methods for crafting adversarial examples are based on $L_2$ and $L_\infty$
distortion me... | computer science |
12,155 | Asymptotic Bayesian Generalization Error in a General Stochastic Matrix
Factorization for Markov Chain and Bayesian Network | math.ST | Stochastic matrix factorization (SMF) can be regarded as a restriction of
non-negative matrix factorization (NMF). SMF is useful for inference of topic
models, NMF for binary matrices data, Markov chains, and Bayesian networks.
However, SMF needs strong assumptions to reach a unique factorization and its
theoretical pr... | computer science |
12,156 | Generating Music Medleys via Playing Music Puzzle Games | stat.ML | Generating music medleys is about finding an optimal permutation of a given
set of music clips. Toward this goal, we propose a self-supervised learning
task, called the music puzzle game, to train neural network models to learn the
sequential patterns in music. In essence, such a game requires machines to
correctly sor... | computer science |
12,157 | Two-sample Statistics Based on Anisotropic Kernels | stat.ML | The paper introduces a new kernel-based Maximum Mean Discrepancy (MMD)
statistic for measuring the distance between two distributions given
finitely-many multivariate samples. When the distributions are locally
low-dimensional, the proposed test can be made more powerful to distinguish
certain alternatives by incorpora... | computer science |
12,158 | Optimal approximation of piecewise smooth functions using deep ReLU
neural networks | math.FA | We study the necessary and sufficient complexity of ReLU neural networks-in
terms of depth and number of weights-required for approximating classifier
functions in an $L^2$-sense.
As a model, we consider the set $\mathcal{E}^\beta (\mathbb{R}^d)$ of
possibly discontinuous piecewise $C^\beta$ functions $f : [-1/2, 1/2... | computer science |
12,159 | Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting
Neural Networks | cs.LG | We present a technique for efficiently synthesizing images of atmospheric
clouds using a combination of Monte Carlo integration and neural networks. The
intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming
aerosols make rendering of clouds---e.g. the characteristic silverlining and
the "whiteness" ... | computer science |
12,160 | Mitigating Evasion Attacks to Deep Neural Networks via Region-based
Classification | cs.CR | Deep neural networks (DNNs) have transformed several artificial intelligence
research areas including computer vision, speech recognition, and natural
language processing. However, recent studies demonstrated that DNNs are
vulnerable to adversarial manipulations at testing time. Specifically, suppose
we have a testing ... | computer science |
12,161 | Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep
Learning | cs.CR | In this paper, we focus on developing a novel mechanism to preserve
differential privacy in deep neural networks, such that: (1) The privacy budget
consumption is totally independent of the number of training steps; (2) It has
the ability to adaptively inject noise into features based on the contribution
of each to the... | computer science |
12,162 | Learning Depth-Three Neural Networks in Polynomial Time | cs.DS | We give a polynomial-time algorithm for learning neural networks with one
hidden layer of sigmoids feeding into any smooth, monotone activation function
(e.g., sigmoid or ReLU). We make no assumptions on the structure of the
network, and the algorithm succeeds with respect to {\em any} distribution on
the unit ball in ... | computer science |
12,163 | Accurate Genomic Prediction Of Human Height | cs.LG | We construct genomic predictors for heritable and extremely complex human
quantitative traits (height, heel bone density, and educational attainment)
using modern methods in high dimensional statistics (i.e., machine learning).
Replication tests show that these predictors capture, respectively, $\sim$40,
20, and 9 perc... | computer science |
12,164 | Distributed Training Large-Scale Deep Architectures | cs.DC | Scale of data and scale of computation infrastructures together enable the
current deep learning renaissance. However, training large-scale deep
architectures demands both algorithmic improvement and careful system
configuration. In this paper, we focus on employing the system approach to
speed up large-scale training.... | computer science |
12,165 | An Attention-based Collaboration Framework for Multi-View Network
Representation Learning | cs.SI | Learning distributed node representations in networks has been attracting
increasing attention recently due to its effectiveness in a variety of
applications. Existing approaches usually study networks with a single type of
proximity between nodes, which defines a single view of a network. However, in
reality there usu... | computer science |
12,166 | On the Design of LQR Kernels for Efficient Controller Learning | cs.SY | Finding optimal feedback controllers for nonlinear dynamic systems from data
is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful
framework for direct controller tuning from experimental trials. For selecting
the next query point and finding the global optimum, BO relies on a
probabilistic desc... | computer science |
12,167 | Near Optimal Sketching of Low-Rank Tensor Regression | cs.LG | We study the least squares regression problem \begin{align*} \min_{\Theta \in
\mathcal{S}_{\odot D,R}} \|A\Theta-b\|_2, \end{align*} where
$\mathcal{S}_{\odot D,R}$ is the set of $\Theta$ for which $\Theta =
\sum_{r=1}^{R} \theta_1^{(r)} \circ \cdots \circ \theta_D^{(r)}$ for vectors
$\theta_d^{(r)} \in \mathbb{R}^{p_d... | computer science |
12,168 | Deep Recurrent NMF for Speech Separation by Unfolding Iterative
Thresholding | cs.SD | In this paper, we propose a novel recurrent neural network architecture for
speech separation. This architecture is constructed by unfolding the iterations
of a sequential iterative soft-thresholding algorithm (ISTA) that solves the
optimization problem for sparse nonnegative matrix factorization (NMF) of
spectrograms.... | computer science |
12,169 | GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs | cs.RO | This work studies the problem of stochastic dynamic filtering and state
propagation with complex beliefs. The main contribution is GP-SUM, a filtering
algorithm tailored to dynamic systems and observation models expressed as
Gaussian processes (GP), that does not rely on linearizations or unimodal
Gaussian approximatio... | computer science |
12,170 | A Generative Model for Score Normalization in Speaker Recognition | stat.ML | We propose a theoretical framework for thinking about score normalization,
which confirms that normalization is not needed under (admittedly fragile)
ideal conditions. If, however, these conditions are not met, e.g. under
data-set shift between training and runtime, our theory reveals dependencies
between scores that c... | computer science |
12,171 | Sparse Hierarchical Regression with Polynomials | math.OC | We present a novel method for exact hierarchical sparse polynomial
regression. Our regressor is that degree $r$ polynomial which depends on at
most $k$ inputs, counting at most $\ell$ monomial terms, which minimizes the
sum of the squares of its prediction errors. The previous hierarchical sparse
specification aligns w... | computer science |
12,172 | Privacy Preserving Identification Using Sparse Approximation with
Ambiguization | cs.CR | In this paper, we consider a privacy preserving encoding framework for
identification applications covering biometrics, physical object security and
the Internet of Things (IoT). The proposed framework is based on a sparsifying
transform, which consists of a trained linear map, an element-wise
nonlinearity, and privacy... | computer science |
12,173 | Toward Scalable Machine Learning and Data Mining: the Bioinformatics
Case | cs.DC | In an effort to overcome the data deluge in computational biology and
bioinformatics and to facilitate bioinformatics research in the era of big
data, we identify some of the most influential algorithms that have been widely
used in the bioinformatics community. These top data mining and machine
learning algorithms cov... | computer science |
12,174 | A Versatile Approach to Evaluating and Testing Automated Vehicles based
on Kernel Methods | cs.LG | Evaluation and validation of complicated control systems are crucial to
guarantee usability and safety. Usually, failure happens in some very rarely
encountered situations, but once triggered, the consequence is disastrous.
Accelerated Evaluation is a methodology that efficiently tests those
rarely-occurring yet critic... | computer science |
12,175 | Weighted-SVD: Matrix Factorization with Weights on the Latent Factors | cs.IR | The Matrix Factorization models, sometimes called the latent factor models,
are a family of methods in the recommender system research area to (1) generate
the latent factors for the users and the items and (2) predict users' ratings
on items based on their latent factors. However, current Matrix Factorization
models p... | computer science |
12,176 | Remote Sensing Image Classification with Large Scale Gaussian Processes | cs.LG | Current remote sensing image classification problems have to deal with an
unprecedented amount of heterogeneous and complex data sources. Upcoming
missions will soon provide large data streams that will make land cover/use
classification difficult. Machine learning classifiers can help at this, and
many methods are cur... | computer science |
12,177 | Online and Distributed Robust Regressions under Adversarial Data
Corruption | cs.DS | In today's era of big data, robust least-squares regression becomes a more
challenging problem when considering the adversarial corruption along with
explosive growth of datasets. Traditional robust methods can handle the noise
but suffer from several challenges when applied in huge dataset including 1)
computational i... | computer science |
12,178 | Mean-field theory of input dimensionality reduction in unsupervised deep
neural networks | cs.LG | Deep neural networks as powerful tools are widely used in various domains.
However, the nature of computations at each layer of the deep networks is far
from being well understood. Increasing the interpretability of deep neural
networks is thus important. Here, we construct a mean-field framework to
understand how comp... | computer science |
12,179 | Constructing multi-modality and multi-classifier radiomics predictive
models through reliable classifier fusion | cs.LG | Radiomics aims to extract and analyze large numbers of quantitative features
from medical images and is highly promising in staging, diagnosing, and
predicting outcomes of cancer treatments. Nevertheless, several challenges need
to be addressed to construct an optimal radiomics predictive model. First, the
predictive p... | computer science |
12,180 | On the Sample Complexity of the Linear Quadratic Regulator | math.OC | This paper addresses the optimal control problem known as the Linear
Quadratic Regulator in the case when the dynamics are unknown. We propose a
multi-stage procedure, called Coarse-ID control, that estimates a model from a
few experimental trials, estimates the error in that model with respect to the
truth, and then d... | computer science |
12,181 | McDiarmid Drift Detection Methods for Evolving Data Streams | stat.ML | Increasingly, Internet of Things (IoT) domains, such as sensor networks,
smart cities, and social networks, generate vast amounts of data. Such data are
not only unbounded and rapidly evolving. Rather, the content thereof
dynamically evolves over time, often in unforeseen ways. These variations are
due to so-called con... | computer science |
12,182 | Reliable Learning of Bernoulli Mixture Models | cs.LG | In this paper, we have derived a set of sufficient conditions for reliable
clustering of data produced by Bernoulli Mixture Models (BMM), when the number
of clusters is unknown. A BMM refers to a random binary vector whose components
are independent Bernoulli trials with cluster-specific frequencies. The problem
of clu... | computer science |
12,183 | Accumulated Gradient Normalization | stat.ML | This work addresses the instability in asynchronous data parallel
optimization. It does so by introducing a novel distributed optimizer which is
able to efficiently optimize a centralized model under communication
constraints. The optimizer achieves this by pushing a normalized sequence of
first-order gradients to a pa... | computer science |
12,184 | Machine Learning for Drug Overdose Surveillance | cs.CY | We describe two recently proposed machine learning approaches for discovering
emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset
Scan enables early detection of emerging patterns in spatio-temporal data,
accounting for both the non-iid nature of the data and the fact that detecting
subtle p... | computer science |
12,185 | Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal
Distributions using Simulated Tempering Langevin Monte Carlo | cs.LG | A key task in Bayesian statistics is sampling from distributions that are
only specified up to a partition function (i.e., constant of proportionality).
However, without any assumptions, sampling (even approximately) can be #P-hard,
and few works have provided "beyond worst-case" guarantees for such settings.
For log... | computer science |
12,186 | Structural Feature Selection for Event Logs | cs.LG | We consider the problem of classifying business process instances based on
structural features derived from event logs. The main motivation is to provide
machine learning based techniques with quick response times for interactive
computer assisted root cause analysis. In particular, we create structural
features from p... | computer science |
12,187 | Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE,
and node2vec | cs.SI | Since the invention of word2vec, the skip-gram model has significantly
advanced the research of network embedding, such as the recent emergence of the
DeepWalk, LINE, PTE, and node2vec approaches. In this work, we show that all of
the aforementioned models with negative sampling can be unified into the matrix
factoriza... | computer science |
12,188 | CTD: Fast, Accurate, and Interpretable Method for Static and Dynamic
Tensor Decompositions | cs.NA | How can we find patterns and anomalies in a tensor, or multi-dimensional
array, in an efficient and directly interpretable way? How can we do this in an
online environment, where a new tensor arrives each time step? Finding patterns
and anomalies in a tensor is a crucial problem with many applications,
including buildi... | computer science |
12,189 | High-dimensional dynamics of generalization error in neural networks | stat.ML | We perform an average case analysis of the generalization dynamics of large
neural networks trained using gradient descent. We study the
practically-relevant "high-dimensional" regime where the number of free
parameters in the network is on the order of or even larger than the number of
examples in the dataset. Using r... | computer science |
12,190 | Adaptive multi-penalty regularization based on a generalized Lasso path | stat.ML | For many algorithms, parameter tuning remains a challenging and critical
task, which becomes tedious and infeasible in a multi-parameter setting.
Multi-penalty regularization, successfully used for solving undetermined sparse
regression of problems of unmixing type where signal and noise are additively
mixed, is one of... | computer science |
12,191 | Maximum Margin Interval Trees | stat.ML | Learning a regression function using censored or interval-valued output data
is an important problem in fields such as genomics and medicine. The goal is to
learn a real-valued prediction function, and the training output labels
indicate an interval of possible values. Whereas most existing algorithms for
this task are... | computer science |
12,192 | Local Convergence of Proximal Splitting Methods for Rank Constrained
Problems | math.OC | We analyze the local convergence of proximal splitting algorithms to solve
optimization problems that are convex besides a rank constraint. For this, we
show conditions under which the proximal operator of a function involving the
rank constraint is locally identical to the proximal operator of its convex
envelope, hen... | computer science |
12,193 | Improved Coresets for Kernel Density Estimates | cs.LG | We study the construction of coresets for kernel density estimates. That is
we show how to approximate the kernel density estimate described by a large
point set with another kernel density estimate with a much smaller point set.
For characteristic kernels (including Gaussian and Laplace kernels), our
approximation pre... | computer science |
12,194 | Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition | cs.LG | Spectral decomposition of the Koopman operator is attracting attention as a
tool for the analysis of nonlinear dynamical systems. Dynamic mode
decomposition is a popular numerical algorithm for Koopman spectral analysis;
however, we often need to prepare nonlinear observables manually according to
the underlying dynami... | computer science |
12,195 | An Improved Naive Bayes Classifier-based Noise Detection Technique for
Classifying User Phone Call Behavior | cs.LG | The presence of noisy instances in mobile phone data is a fundamental issue
for classifying user phone call behavior (i.e., accept, reject, missed and
outgoing), with many potential negative consequences. The classification
accuracy may decrease and the complexity of the classifiers may increase due to
the number of re... | computer science |
12,196 | Additivity of Information in Multilayer Networks via Additive Gaussian
Noise Transforms | cs.IT | Multilayer (or deep) networks are powerful probabilistic models based on
multiple stages of a linear transform followed by a non-linear (possibly
random) function. In general, the linear transforms are defined by matrices and
the non-linear functions are defined by information channels. These models have
gained great p... | computer science |
12,197 | On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for
Real-Time Streaming Data | stat.ML | Ever growing volume and velocity of data coupled with decreasing attention
span of end users underscore the critical need for real-time analytics. In this
regard, anomaly detection plays a key role as an application as well as a means
to verify data fidelity. Although the subject of anomaly detection has been
researche... | computer science |
12,198 | User Modelling for Avoiding Overfitting in Interactive Knowledge
Elicitation for Prediction | cs.HC | In human-in-the-loop machine learning, the user provides information beyond
that in the training data. Many algorithms and user interfaces have been
designed to optimize and facilitate this human--machine interaction; however,
fewer studies have addressed the potential defects the designs can cause.
Effective interacti... | computer science |
12,199 | Potential Conditional Mutual Information: Estimators, Properties and
Applications | cs.IT | The conditional mutual information I(X;Y|Z) measures the average information
that X and Y contain about each other given Z. This is an important primitive
in many learning problems including conditional independence testing, graphical
model inference, causal strength estimation and time-series problems. In
several appl... | computer science |
12,200 | Automated Scalable Bayesian Inference via Hilbert Coresets | stat.ML | The automation of posterior inference in Bayesian data analysis has enabled
experts and nonexperts alike to use more sophisticated models, engage in faster
exploratory modeling and analysis, and ensure experimental reproducibility.
However, standard automated posterior inference algorithms are not tractable at
the scal... | computer science |
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