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3,700 | DP-EM: Differentially Private Expectation Maximization | cs.LG | The iterative nature of the expectation maximization (EM) algorithm presents
a challenge for privacy-preserving estimation, as each iteration increases the
amount of noise needed. We propose a practical private EM algorithm that
overcomes this challenge using two innovations: (1) a novel moment perturbation
formulation... | computer science |
3,701 | Post-Inference Prior Swapping | stat.ML | While Bayesian methods are praised for their ability to incorporate useful
prior knowledge, in practice, convenient priors that allow for computationally
cheap or tractable inference are commonly used. In this paper, we investigate
the following question: for a given model, is it possible to compute an
inference result... | computer science |
3,702 | Active Ranking from Pairwise Comparisons and when Parametric Assumptions
Don't Help | cs.LG | We consider sequential or active ranking of a set of n items based on noisy
pairwise comparisons. Items are ranked according to the probability that a
given item beats a randomly chosen item, and ranking refers to partitioning the
items into sets of pre-specified sizes according to their scores. This notion
of ranking ... | computer science |
3,703 | Understanding Deep Neural Networks with Rectified Linear Units | cs.LG | In this paper we investigate the family of functions representable by deep
neural networks (DNN) with rectified linear units (ReLU). We give an algorithm
to train a ReLU DNN with one hidden layer to *global optimality* with runtime
polynomial in the data size albeit exponential in the input dimension. Further,
we impro... | computer science |
3,704 | Learning to Invert: Signal Recovery via Deep Convolutional Networks | stat.ML | The promise of compressive sensing (CS) has been offset by two significant
challenges. First, real-world data is not exactly sparse in a fixed basis.
Second, current high-performance recovery algorithms are slow to converge,
which limits CS to either non-real-time applications or scenarios where massive
back-end comput... | computer science |
3,705 | Fast k-Nearest Neighbour Search via Prioritized DCI | cs.LG | Most exact methods for k-nearest neighbour search suffer from the curse of
dimensionality; that is, their query times exhibit exponential dependence on
either the ambient or the intrinsic dimensionality. Dynamic Continuous Indexing
(DCI) offers a promising way of circumventing the curse and successfully
reduces the dep... | computer science |
3,706 | Resilience: A Criterion for Learning in the Presence of Arbitrary
Outliers | cs.LG | We introduce a criterion, resilience, which allows properties of a dataset
(such as its mean or best low rank approximation) to be robustly computed, even
in the presence of a large fraction of arbitrary additional data. Resilience is
a weaker condition than most other properties considered so far in the
literature, an... | computer science |
3,707 | Group invariance principles for causal generative models | stat.ML | The postulate of independence of cause and mechanism (ICM) has recently led
to several new causal discovery algorithms. The interpretation of independence
and the way it is utilized, however, varies across these methods. Our aim in
this paper is to propose a group theoretic framework for ICM to unify and
generalize the... | computer science |
3,708 | Finding Bottlenecks: Predicting Student Attrition with Unsupervised
Classifier | stat.ML | With pressure to increase graduation rates and reduce time to degree in
higher education, it is important to identify at-risk students early. Automated
early warning systems are therefore highly desirable. In this paper, we use
unsupervised clustering techniques to predict the graduation status of declared
majors in fi... | computer science |
3,709 | Emotion in Reinforcement Learning Agents and Robots: A Survey | cs.LG | This article provides the first survey of computational models of emotion in
reinforcement learning (RL) agents. The survey focuses on agent/robot emotions,
and mostly ignores human user emotions. Emotions are recognized as functional
in decision-making by influencing motivation and action selection. Therefore,
computa... | computer science |
3,710 | The Sample Complexity of Online One-Class Collaborative Filtering | cs.LG | We consider the online one-class collaborative filtering (CF) problem that
consists of recommending items to users over time in an online fashion based on
positive ratings only. This problem arises when users respond only occasionally
to a recommendation with a positive rating, and never with a negative one. We
study t... | computer science |
3,711 | Weakly Submodular Maximization Beyond Cardinality Constraints: Does
Randomization Help Greedy? | cs.DM | Submodular functions are a broad class of set functions, which naturally
arise in diverse areas. Many algorithms have been suggested for the
maximization of these functions. Unfortunately, once the function deviates from
submodularity, the known algorithms may perform arbitrarily poorly. Amending
this issue, by obtaini... | computer science |
3,712 | Worst-case vs Average-case Design for Estimation from Fixed Pairwise
Comparisons | cs.LG | Pairwise comparison data arises in many domains, including tournament
rankings, web search, and preference elicitation. Given noisy comparisons of a
fixed subset of pairs of items, we study the problem of estimating the
underlying comparison probabilities under the assumption of strong stochastic
transitivity (SST). We... | computer science |
3,713 | Discretization-free Knowledge Gradient Methods for Bayesian Optimization | stat.ML | This paper studies Bayesian ranking and selection (R&S) problems with
correlated prior beliefs and continuous domains, i.e. Bayesian optimization
(BO). Knowledge gradient methods [Frazier et al., 2008, 2009] have been widely
studied for discrete R&S problems, which sample the one-step Bayes-optimal
point. When used ove... | computer science |
3,714 | CausalGAN: Learning Causal Implicit Generative Models with Adversarial
Training | cs.LG | We propose an adversarial training procedure for learning a causal implicit
generative model for a given causal graph. We show that adversarial training
can be used to learn a generative model with true observational and
interventional distributions if the generator architecture is consistent with
the given causal grap... | computer science |
3,715 | Guided Deep Reinforcement Learning for Swarm Systems | cs.MA | In this paper, we investigate how to learn to control a group of cooperative
agents with limited sensing capabilities such as robot swarms. The agents have
only very basic sensor capabilities, yet in a group they can accomplish
sophisticated tasks, such as distributed assembly or search and rescue tasks.
Learning a pol... | computer science |
3,716 | Model-Powered Conditional Independence Test | stat.ML | We consider the problem of non-parametric Conditional Independence testing
(CI testing) for continuous random variables. Given i.i.d samples from the
joint distribution $f(x,y,z)$ of continuous random vectors $X,Y$ and $Z,$ we
determine whether $X \perp Y | Z$. We approach this by converting the
conditional independenc... | computer science |
3,717 | Enhanced Quantum Synchronization via Quantum Machine Learning | cs.AI | We study the quantum synchronization between a pair of two-level systems
inside two coupledcavities. Using a digital-analog decomposition of the master
equation that rules the system dynamics, we show that this approach leads to
quantum synchronization between both two-level systems. Moreover, we can
identify in this d... | computer science |
3,718 | Physics Informed Deep Learning (Part I): Data-driven Solutions of
Nonlinear Partial Differential Equations | cs.AI | We introduce physics informed neural networks -- neural networks that are
trained to solve supervised learning tasks while respecting any given law of
physics described by general nonlinear partial differential equations. In this
two part treatise, we present our developments in the context of solving two
main classes ... | computer science |
3,719 | Physics Informed Deep Learning (Part II): Data-driven Discovery of
Nonlinear Partial Differential Equations | cs.AI | We introduce physics informed neural networks -- neural networks that are
trained to solve supervised learning tasks while respecting any given law of
physics described by general nonlinear partial differential equations. In this
second part of our two-part treatise, we focus on the problem of data-driven
discovery of ... | computer science |
3,720 | The information bottleneck and geometric clustering | stat.ML | The information bottleneck (IB) approach to clustering takes a joint
distribution $P\!\left(X,Y\right)$ and maps the data $X$ to cluster labels $T$
which retain maximal information about $Y$ (Tishby et al., 1999). This
objective results in an algorithm that clusters data points based upon the
similarity of their condit... | computer science |
3,721 | Fairness in Supervised Learning: An Information Theoretic Approach | cs.LG | Automated decision making systems are increasingly being used in real-world
applications. In these systems for the most part, the decision rules are
derived by minimizing the training error on the available historical data.
Therefore, if there is a bias related to a sensitive attribute such as gender,
race, religion, e... | computer science |
3,722 | Expectation Learning for Adaptive Crossmodal Stimuli Association | cs.LG | The human brain is able to learn, generalize, and predict crossmodal stimuli.
Learning by expectation fine-tunes crossmodal processing at different levels,
thus enhancing our power of generalization and adaptation in highly dynamic
environments. In this paper, we propose a deep neural architecture trained by
using expe... | computer science |
3,723 | Counting and Uniform Sampling from Markov Equivalent DAGs | cs.DS | We propose an exact solution for the problem of finding the size of a Markov
equivalence class (MEC). For the bounded degree graphs, the proposed solution
is capable of computing the size of the MEC in polynomial time. Our proposed
approach is based on a recursive method for counting the number of the elements
of the M... | computer science |
3,724 | Identifiability of Nonparametric Mixture Models and Bayes Optimal
Clustering | math.ST | Motivated by problems in data clustering, we establish general conditions
under which families of nonparametric mixture models are identifiable by
introducing a novel framework for clustering overfitted \emph{parametric} (i.e.
misspecified) mixture models. These conditions generalize existing conditions
in the literatu... | computer science |
3,725 | Fully Decentralized Multi-Agent Reinforcement Learning with Networked
Agents | cs.LG | We consider the problem of \emph{fully decentralized} multi-agent
reinforcement learning (MARL), where the agents are located at the nodes of a
time-varying communication network. Specifically, we assume that the reward
functions of the agents might correspond to different tasks, and are only known
to the corresponding... | computer science |
3,726 | Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal
Modeling | cs.LG | Recent advances in the field of inverse reinforcement learning (IRL) have
yielded sophisticated frameworks which relax the original modeling assumption
that the behavior of an observed agent reflects only a single intention.
Instead, the demonstration data is typically divided into parts, to account for
the fact that d... | computer science |
3,727 | WNGrad: Learn the Learning Rate in Gradient Descent | stat.ML | Adjusting the learning rate schedule in stochastic gradient methods is an
important unresolved problem which requires tuning in practice. If certain
parameters of the loss function such as smoothness or strong convexity
constants are known, theoretical learning rate schedules can be applied.
However, in practice, such ... | computer science |
3,728 | Measurement-based adaptation protocol with quantum reinforcement
learning | cs.AI | Machine learning employs dynamical algorithms that mimic the human capacity
to learn, where the reinforcement learning ones are among the most similar to
humans in this respect. On the other hand, adaptability is an essential aspect
to perform any task efficiently in a changing environment, and it is
fundamental for ma... | computer science |
3,729 | Vulnerability of Deep Learning | stat.ML | The Renormalisation Group (RG) provides a framework in which it is possible
to assess whether a deep-learning network is sensitive to small changes in the
input data and hence prone to error, or susceptible to adversarial attack.
Distinct classification outputs are associated with different RG fixed points
and sensitiv... | computer science |
3,730 | Information Theoretic Interpretation of Deep learning | cs.LG | We interpret part of the experimental results of Shwartz-Ziv and Tishby
[2017]. Inspired by these results, we established a conjecture of the dynamics
of the machinary of deep neural network. This conjecture can be used to explain
the counterpart result by Saxe et al. [2018]. | computer science |
3,731 | Doubly Robust Policy Evaluation and Learning | cs.LG | We study decision making in environments where the reward is only partially
observed, but can be modeled as a function of an action and an observed
context. This setting, known as contextual bandits, encompasses a wide variety
of applications including health-care policy and Internet advertising. A
central task is eval... | computer science |
3,732 | Automorphism Groups of Graphical Models and Lifted Variational Inference | cs.AI | Using the theory of group action, we first introduce the concept of the
automorphism group of an exponential family or a graphical model, thus
formalizing the general notion of symmetry of a probabilistic model. This
automorphism group provides a precise mathematical framework for lifted
inference in the general expone... | computer science |
3,733 | An Analysis of Active Learning With Uniform Feature Noise | stat.ML | In active learning, the user sequentially chooses values for feature $X$ and
an oracle returns the corresponding label $Y$. In this paper, we consider the
effect of feature noise in active learning, which could arise either because
$X$ itself is being measured, or it is corrupted in transmission to the oracle,
or the o... | computer science |
3,734 | Variational Algorithms for Marginal MAP | cs.LG | Marginal MAP problems are notoriously difficult tasks for graphical models.
We derive a general variational framework for solving marginal MAP problems, in
which we apply analogues of the Bethe, tree-reweighted, and mean field
approximations. We then derive a "mixed" message passing algorithm and a
convergent alternati... | computer science |
3,735 | Statistical-mechanical analysis of pre-training and fine tuning in deep
learning | stat.ML | In this paper, we present a statistical-mechanical analysis of deep learning.
We elucidate some of the essential components of deep learning---pre-training
by unsupervised learning and fine tuning by supervised learning. We formulate
the extraction of features from the training data as a margin criterion in a
high-dime... | computer science |
3,736 | Bayesian Poisson Tensor Factorization for Inferring Multilateral
Relations from Sparse Dyadic Event Counts | stat.ML | We present a Bayesian tensor factorization model for inferring latent group
structures from dynamic pairwise interaction patterns. For decades, political
scientists have collected and analyzed records of the form "country $i$ took
action $a$ toward country $j$ at time $t$"---known as dyadic events---in order
to form an... | computer science |
3,737 | Simple, Robust and Optimal Ranking from Pairwise Comparisons | cs.LG | We consider data in the form of pairwise comparisons of n items, with the
goal of precisely identifying the top k items for some value of k < n, or
alternatively, recovering a ranking of all the items. We analyze the Copeland
counting algorithm that ranks the items in order of the number of pairwise
comparisons won, an... | computer science |
3,738 | Bayesian Poisson Tucker Decomposition for Learning the Structure of
International Relations | stat.ML | We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling
country--country interaction event data. These data consist of interaction
events of the form "country $i$ took action $a$ toward country $j$ at time
$t$." BPTD discovers overlapping country--community memberships, including the
number of latent com... | computer science |
3,739 | A Permutation-based Model for Crowd Labeling: Optimal Estimation and
Robustness | cs.LG | The aggregation and denoising of crowd labeled data is a task that has gained
increased significance with the advent of crowdsourcing platforms and massive
datasets. In this paper, we propose a permutation-based model for crowd labeled
data that is a significant generalization of the common Dawid-Skene model, and
intro... | computer science |
3,740 | Lazily Adapted Constant Kinky Inference for Nonparametric Regression and
Model-Reference Adaptive Control | math.OC | Techniques known as Nonlinear Set Membership prediction, Lipschitz
Interpolation or Kinky Inference are approaches to machine learning that
utilise presupposed Lipschitz properties to compute inferences over unobserved
function values. Provided a bound on the true best Lipschitz constant of the
target function is known... | computer science |
3,741 | Deep Probabilistic Programming | stat.ML | We propose Edward, a Turing-complete probabilistic programming language.
Edward defines two compositional representations---random variables and
inference. By treating inference as a first class citizen, on a par with
modeling, we show that probabilistic programming can be as flexible and
computationally efficient as t... | computer science |
3,742 | Stochastic Variance Reduction Methods for Policy Evaluation | cs.LG | Policy evaluation is a crucial step in many reinforcement-learning
procedures, which estimates a value function that predicts states' long-term
value under a given policy. In this paper, we focus on policy evaluation with
linear function approximation over a fixed dataset. We first transform the
empirical policy evalua... | computer science |
3,743 | Machine Teaching: A New Paradigm for Building Machine Learning Systems | cs.LG | The current processes for building machine learning systems require
practitioners with deep knowledge of machine learning. This significantly
limits the number of machine learning systems that can be created and has led
to a mismatch between the demand for machine learning systems and the ability
for organizations to b... | computer science |
3,744 | A Spectral Method for Activity Shaping in Continuous-Time Information
Cascades | stat.ML | Information Cascades Model captures dynamical properties of user activity in
a social network. In this work, we develop a novel framework for activity
shaping under the Continuous-Time Information Cascades Model which allows the
administrator for local control actions by allocating targeted resources that
can alter the... | computer science |
3,745 | Learning Complex Swarm Behaviors by Exploiting Local Communication
Protocols with Deep Reinforcement Learning | cs.MA | Swarm systems constitute a challenging problem for reinforcement learning
(RL) as the algorithm needs to learn decentralized control policies that can
cope with limited local sensing and communication abilities of the agents.
Although there have been recent advances of deep RL algorithms applied to
multi-agent systems,... | computer science |
3,746 | Fast Meta-Learning for Adaptive Hierarchical Classifier Design | cs.LG | We propose a new splitting criterion for a meta-learning approach to
multiclass classifier design that adaptively merges the classes into a
tree-structured hierarchy of increasingly difficult binary classification
problems. The classification tree is constructed from empirical estimates of
the Henze-Penrose bounds on t... | computer science |
3,747 | Approximate Ranking from Pairwise Comparisons | cs.LG | A common problem in machine learning is to rank a set of n items based on
pairwise comparisons. Here ranking refers to partitioning the items into sets
of pre-specified sizes according to their scores, which includes identification
of the top-k items as the most prominent special case. The score of a given
item is defi... | computer science |
3,748 | Deep Learning and Data Assimilation for Real-Time Production Prediction
in Natural Gas Wells | cs.LG | The prediction of the gas production from mature gas wells, due to their
complex end-of-life behavior, is challenging and crucial for operational
decision making. In this paper, we apply a modified deep LSTM model for
prediction of the gas flow rates in mature gas wells, including the
uncertainties in input parameters.... | computer science |
3,749 | Scaling-up Split-Merge MCMC with Locality Sensitive Sampling (LSS) | cs.LG | Split-Merge MCMC (Monte Carlo Markov Chain) is one of the essential and
popular variants of MCMC for problems when an MCMC state consists of an unknown
number of components. It is well known that state-of-the-art methods for
split-merge MCMC do not scale well. Strategies for rapid mixing requires smart
and informative ... | computer science |
3,750 | Contextual Bandits with Stochastic Experts | stat.ML | We consider the problem of contextual bandits with stochastic experts, which
is a variation of the traditional stochastic contextual bandit with experts
problem. In our problem setting, we assume access to a class of stochastic
experts, where each expert is a conditional distribution over the arms given a
context. We p... | computer science |
3,751 | A Discipline of Evolutionary Programming | cs.NE | Genetic fitness optimization using small populations or small population
updates across generations generally suffers from randomly diverging
evolutions. We propose a notion of highly probable fitness optimization through
feasible evolutionary computing runs on small size populations. Based on
rapidly mixing Markov cha... | computer science |
3,752 | Cross-Entropic Learning of a Machine for the Decision in a Partially
Observable Universe | math.OC | Revision of the paper previously entitled "Learning a Machine for the
Decision in a Partially Observable Markov Universe" In this paper, we are
interested in optimal decisions in a partially observable universe. Our
approach is to directly approximate an optimal strategic tree depending on the
observation. This approxi... | computer science |
3,753 | Efficient Methods for Unsupervised Learning of Probabilistic Models | cs.LG | In this thesis I develop a variety of techniques to train, evaluate, and
sample from intractable and high dimensional probabilistic models. Abstract
exceeds arXiv space limitations -- see PDF. | computer science |
3,754 | Estimating mutual information and multi--information in large networks | cs.IT | We address the practical problems of estimating the information relations
that characterize large networks. Building on methods developed for analysis of
the neural code, we show that reliable estimates of mutual information can be
obtained with manageable computational effort. The same methods allow
estimation of high... | computer science |
3,755 | Multi-Modal Human-Machine Communication for Instructing Robot Grasping
Tasks | cs.HC | A major challenge for the realization of intelligent robots is to supply them
with cognitive abilities in order to allow ordinary users to program them
easily and intuitively. One way of such programming is teaching work tasks by
interactive demonstration. To make this effective and convenient for the user,
the machine... | computer science |
3,756 | Evidence Feed Forward Hidden Markov Model: A New Type of Hidden Markov
Model | cs.AI | The ability to predict the intentions of people based solely on their visual
actions is a skill only performed by humans and animals. The intelligence of
current computer algorithms has not reached this level of complexity, but there
are several research efforts that are working towards it. With the number of
classific... | computer science |
3,757 | Comparative Study and Optimization of Feature-Extraction Techniques for
Content based Image Retrieval | cs.CV | The aim of a Content-Based Image Retrieval (CBIR) system, also known as Query
by Image Content (QBIC), is to help users to retrieve relevant images based on
their contents. CBIR technologies provide a method to find images in large
databases by using unique descriptors from a trained image. The image
descriptors includ... | computer science |
3,758 | Improving Semantic Embedding Consistency by Metric Learning for
Zero-Shot Classification | cs.CV | This paper addresses the task of zero-shot image classification. The key
contribution of the proposed approach is to control the semantic embedding of
images -- one of the main ingredients of zero-shot learning -- by formulating
it as a metric learning problem. The optimized empirical criterion associates
two types of ... | computer science |
3,759 | Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies | cs.RO | While deep learning has had significant successes in computer vision thanks
to the abundance of visual data, collecting sufficiently large real-world
datasets for robot learning can be costly. To increase the practicality of
these techniques on real robots, we propose a modular deep reinforcement
learning method capabl... | computer science |
3,760 | CBinfer: Change-Based Inference for Convolutional Neural Networks on
Video Data | cs.CV | Extracting per-frame features using convolutional neural networks for
real-time processing of video data is currently mainly performed on powerful
GPU-accelerated workstations and compute clusters. However, there are many
applications such as smart surveillance cameras that require or would benefit
from on-site process... | computer science |
3,761 | Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination | cs.RO | This paper introduces an end-to-end fine-tuning method to improve hand-eye
coordination in modular deep visuo-motor policies (modular networks) where each
module is trained independently. Benefiting from weighted losses, the
fine-tuning method significantly improves the performance of the policies for a
robotic planar ... | computer science |
3,762 | Acting Thoughts: Towards a Mobile Robotic Service Assistant for Users
with Limited Communication Skills | cs.AI | As autonomous service robots become more affordable and thus available also
for the general public, there is a growing need for user friendly interfaces to
control the robotic system. Currently available control modalities typically
expect users to be able to express their desire through either touch, speech or
gesture... | computer science |
3,763 | Sim-to-real Transfer of Visuo-motor Policies for Reaching in Clutter:
Domain Randomization and Adaptation with Modular Networks | cs.RO | A modular method is proposed to learn and transfer visuo-motor policies from
simulation to the real world in an efficient manner by combining domain
randomization and adaptation. The feasibility of the approach is demonstrated
in a table-top object reaching task where a 7 DoF arm is controlled in velocity
mode to reach... | computer science |
3,764 | MINOS: Multimodal Indoor Simulator for Navigation in Complex
Environments | cs.LG | We present MINOS, a simulator designed to support the development of
multisensory models for goal-directed navigation in complex indoor
environments. The simulator leverages large datasets of complex 3D environments
and supports flexible configuration of multimodal sensor suites. We use MINOS
to benchmark deep-learning... | computer science |
3,765 | On the Stability of Deep Networks | stat.ML | In this work we study the properties of deep neural networks (DNN) with
random weights. We formally prove that these networks perform a
distance-preserving embedding of the data. Based on this we then draw
conclusions on the size of the training data and the networks' structure. A
longer version of this paper with more... | computer science |
3,766 | Gradient descent with identity initialization efficiently learns
positive definite linear transformations by deep residual networks | cs.LG | We analyze algorithms for approximating a function $f(x) = \Phi x$ mapping
$\Re^d$ to $\Re^d$ using deep linear neural networks, i.e. that learn a
function $h$ parameterized by matrices $\Theta_1,...,\Theta_L$ and defined by
$h(x) = \Theta_L \Theta_{L-1} ... \Theta_1 x$. We focus on algorithms that
learn through gradie... | computer science |
3,767 | An Exponential Lower Bound on the Complexity of Regularization Paths | cs.LG | For a variety of regularized optimization problems in machine learning,
algorithms computing the entire solution path have been developed recently.
Most of these methods are quadratic programs that are parameterized by a single
parameter, as for example the Support Vector Machine (SVM). Solution path
algorithms do not ... | computer science |
3,768 | The Cyborg Astrobiologist: Testing a Novelty-Detection Algorithm on Two
Mobile Exploration Systems at Rivas Vaciamadrid in Spain and at the Mars
Desert Research Station in Utah | cs.CV | (ABRIDGED) In previous work, two platforms have been developed for testing
computer-vision algorithms for robotic planetary exploration (McGuire et al.
2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been
tested at geological and astrobiological field sites in Spain (Rivas
Vaciamadrid and Riba de S... | computer science |
3,769 | Tracking Tetrahymena Pyriformis Cells using Decision Trees | cs.CV | Matching cells over time has long been the most difficult step in cell
tracking. In this paper, we approach this problem by recasting it as a
classification problem. We construct a feature set for each cell, and compute a
feature difference vector between a cell in the current frame and a cell in a
previous frame. Then... | computer science |
3,770 | Self-Expressive Decompositions for Matrix Approximation and Clustering | cs.IT | Data-aware methods for dimensionality reduction and matrix decomposition aim
to find low-dimensional structure in a collection of data. Classical approaches
discover such structure by learning a basis that can efficiently express the
collection. Recently, "self expression", the idea of using a small subset of
data vect... | computer science |
3,771 | Sequential Dimensionality Reduction for Extracting Localized Features | cs.CV | Linear dimensionality reduction techniques are powerful tools for image
analysis as they allow the identification of important features in a data set.
In particular, nonnegative matrix factorization (NMF) has become very popular
as it is able to extract sparse, localized and easily interpretable features by
imposing an... | computer science |
3,772 | Nonparametric Basis Pursuit via Sparse Kernel-based Learning | cs.LG | Signal processing tasks as fundamental as sampling, reconstruction, minimum
mean-square error interpolation and prediction can be viewed under the prism of
reproducing kernel Hilbert spaces. Endowing this vantage point with
contemporary advances in sparsity-aware modeling and processing, promotes the
nonparametric basi... | computer science |
3,773 | Key point selection and clustering of swimmer coordination through
Sparse Fisher-EM | stat.ML | To answer the existence of optimal swimmer learning/teaching strategies, this
work introduces a two-level clustering in order to analyze temporal dynamics of
motor learning in breaststroke swimming. Each level have been performed through
Sparse Fisher-EM, a unsupervised framework which can be applied efficiently on
lar... | computer science |
3,774 | Voxelwise nonlinear regression toolbox for neuroimage analysis:
Application to aging and neurodegenerative disease modeling | stat.ML | This paper describes a new neuroimaging analysis toolbox that allows for the
modeling of nonlinear effects at the voxel level, overcoming limitations of
methods based on linear models like the GLM. We illustrate its features using a
relevant example in which distinct nonlinear trajectories of Alzheimer's
disease relate... | computer science |
3,775 | Forest Sparsity for Multi-channel Compressive Sensing | cs.LG | In this paper, we investigate a new compressive sensing model for
multi-channel sparse data where each channel can be represented as a
hierarchical tree and different channels are highly correlated. Therefore, the
full data could follow the forest structure and we call this property as
\emph{forest sparsity}. It exploi... | computer science |
3,776 | Feature Selection with Annealing for Computer Vision and Big Data
Learning | stat.ML | Many computer vision and medical imaging problems are faced with learning
from large-scale datasets, with millions of observations and features. In this
paper we propose a novel efficient learning scheme that tightens a sparsity
constraint by gradually removing variables based on a criterion and a schedule.
The attract... | computer science |
3,777 | Stochastic Gradient Descent, Weighted Sampling, and the Randomized
Kaczmarz algorithm | math.NA | We obtain an improved finite-sample guarantee on the linear convergence of
stochastic gradient descent for smooth and strongly convex objectives,
improving from a quadratic dependence on the conditioning $(L/\mu)^2$ (where
$L$ is a bound on the smoothness and $\mu$ on the strong convexity) to a linear
dependence on $L/... | computer science |
3,778 | Bayesian image segmentations by Potts prior and loopy belief propagation | cs.CV | This paper presents a Bayesian image segmentation model based on Potts prior
and loopy belief propagation. The proposed Bayesian model involves several
terms, including the pairwise interactions of Potts models, and the average
vectors and covariant matrices of Gauss distributions in color image modeling.
These terms a... | computer science |
3,779 | Analyzing sparse dictionaries for online learning with kernels | stat.ML | Many signal processing and machine learning methods share essentially the
same linear-in-the-parameter model, with as many parameters as available
samples as in kernel-based machines. Sparse approximation is essential in many
disciplines, with new challenges emerging in online learning with kernels. To
this end, severa... | computer science |
3,780 | Clustering multi-way data: a novel algebraic approach | cs.LG | In this paper, we develop a method for unsupervised clustering of two-way
(matrix) data by combining two recent innovations from different fields: the
Sparse Subspace Clustering (SSC) algorithm [10], which groups points coming
from a union of subspaces into their respective subspaces, and the t-product
[18], which was ... | computer science |
3,781 | Scalable Multi-Output Label Prediction: From Classifier Chains to
Classifier Trellises | stat.ML | Multi-output inference tasks, such as multi-label classification, have become
increasingly important in recent years. A popular method for multi-label
classification is classifier chains, in which the predictions of individual
classifiers are cascaded along a chain, thus taking into account inter-label
dependencies and... | computer science |
3,782 | Generalized Majorization-Minimization | cs.CV | Non-convex optimization is ubiquitous in machine learning. The
Majorization-Minimization (MM) procedure systematically optimizes non-convex
functions through an iterative construction and optimization of upper bounds on
the objective function. The bound at each iteration is required to \emph{touch}
the objective functi... | computer science |
3,783 | Online Learning to Sample | cs.LG | Stochastic Gradient Descent (SGD) is one of the most widely used techniques
for online optimization in machine learning. In this work, we accelerate SGD by
adaptively learning how to sample the most useful training examples at each
time step. First, we show that SGD can be used to learn the best possible
sampling distr... | computer science |
3,784 | Mining Brain Networks using Multiple Side Views for Neurological
Disorder Identification | cs.LG | Mining discriminative subgraph patterns from graph data has attracted great
interest in recent years. It has a wide variety of applications in disease
diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the
graph representation alone. However, in many real-world applications, the side
information ... | computer science |
3,785 | Gaussian Mixture Reduction Using Reverse Kullback-Leibler Divergence | stat.ML | We propose a greedy mixture reduction algorithm which is capable of pruning
mixture components as well as merging them based on the Kullback-Leibler
divergence (KLD). The algorithm is distinct from the well-known Runnalls' KLD
based method since it is not restricted to merging operations. The capability
of pruning (in ... | computer science |
3,786 | Coordinate Descent Methods for Symmetric Nonnegative Matrix
Factorization | cs.NA | Given a symmetric nonnegative matrix $A$, symmetric nonnegative matrix
factorization (symNMF) is the problem of finding a nonnegative matrix $H$,
usually with much fewer columns than $A$, such that $A \approx HH^T$. SymNMF
can be used for data analysis and in particular for various clustering tasks.
In this paper, we p... | computer science |
3,787 | Robust Classification by Pre-conditioned LASSO and Transductive
Diffusion Component Analysis | cs.LG | Modern machine learning-based recognition approaches require large-scale
datasets with large number of labelled training images. However, such datasets
are inherently difficult and costly to collect and annotate. Hence there is a
great and growing interest in automatic dataset collection methods that can
leverage the w... | computer science |
3,788 | A Short Survey on Data Clustering Algorithms | cs.DS | With rapidly increasing data, clustering algorithms are important tools for
data analytics in modern research. They have been successfully applied to a
wide range of domains; for instance, bioinformatics, speech recognition, and
financial analysis. Formally speaking, given a set of data instances, a
clustering algorith... | computer science |
3,789 | Clustering by Deep Nearest Neighbor Descent (D-NND): A Density-based
Parameter-Insensitive Clustering Method | stat.ML | Most density-based clustering methods largely rely on how well the underlying
density is estimated. However, density estimation itself is also a challenging
problem, especially the determination of the kernel bandwidth. A large
bandwidth could lead to the over-smoothed density estimation in which the
number of density ... | computer science |
3,790 | Multilinear Subspace Clustering | cs.IT | In this paper we present a new model and an algorithm for unsupervised
clustering of 2-D data such as images. We assume that the data comes from a
union of multilinear subspaces (UOMS) model, which is a specific structured
case of the much studied union of subspaces (UOS) model. For segmentation under
this model, we de... | computer science |
3,791 | How many faces can be recognized? Performance extrapolation for
multi-class classification | stat.ML | The difficulty of multi-class classification generally increases with the
number of classes. Using data from a subset of the classes, can we predict how
well a classifier will scale with an increased number of classes? Under the
assumption that the classes are sampled exchangeably, and under the assumption
that the cla... | computer science |
3,792 | Dense Associative Memory is Robust to Adversarial Inputs | cs.LG | Deep neural networks (DNN) trained in a supervised way suffer from two known
problems. First, the minima of the objective function used in learning
correspond to data points (also known as rubbish examples or fooling images)
that lack semantic similarity with the training data. Second, a clean input can
be changed by a... | computer science |
3,793 | Online Robust Principal Component Analysis with Change Point Detection | cs.LG | Robust PCA methods are typically batch algorithms which requires loading all
observations into memory before processing. This makes them inefficient to
process big data. In this paper, we develop an efficient online robust
principal component methods, namely online moving window robust principal
component analysis (OMW... | computer science |
3,794 | Introduction to Nonnegative Matrix Factorization | cs.NA | In this paper, we introduce and provide a short overview of nonnegative
matrix factorization (NMF). Several aspects of NMF are discussed, namely, the
application in hyperspectral imaging, geometry and uniqueness of NMF solutions,
complexity, algorithms, and its link with extended formulations of polyhedra.
In order to ... | computer science |
3,795 | Faster Coordinate Descent via Adaptive Importance Sampling | cs.LG | Coordinate descent methods employ random partial updates of decision
variables in order to solve huge-scale convex optimization problems. In this
work, we introduce new adaptive rules for the random selection of their
updates. By adaptive, we mean that our selection rules are based on the dual
residual or the primal-du... | computer science |
3,796 | Perspective: Energy Landscapes for Machine Learning | stat.ML | Machine learning techniques are being increasingly used as flexible
non-linear fitting and prediction tools in the physical sciences. Fitting
functions that exhibit multiple solutions as local minima can be analysed in
terms of the corresponding machine learning landscape. Methods to explore and
visualise molecular pot... | computer science |
3,797 | Subspace Clustering via Optimal Direction Search | cs.CV | This letter presents a new spectral-clustering-based approach to the subspace
clustering problem. Underpinning the proposed method is a convex program for
optimal direction search, which for each data point d finds an optimal
direction in the span of the data that has minimum projection on the other data
points and non... | computer science |
3,798 | Deep Convolutional Framelets: A General Deep Learning Framework for
Inverse Problems | stat.ML | Recently, deep learning approaches with various network architectures have
achieved significant performance improvement over existing iterative
reconstruction methods in various imaging problems. However, it is still
unclear why these deep learning architectures work for specific inverse
problems. To address these issu... | computer science |
3,799 | Exact Tensor Completion from Sparsely Corrupted Observations via Convex
Optimization | cs.LG | This paper conducts a rigorous analysis for provable estimation of
multidimensional arrays, in particular third-order tensors, from a random
subset of its corrupted entries. Our study rests heavily on a recently proposed
tensor algebraic framework in which we can obtain tensor singular value
decomposition (t-SVD) that ... | computer science |
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