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Survey of resampling techniques for improving classification performance
in unbalanced datasets | stat.AP cs.LG stat.ML | A number of classification problems need to deal with data imbalance between
classes. Often it is desired to have a high recall on the minority class while
maintaining a high precision on the majority class. In this paper, we review a
number of resampling techniques proposed in literature to handle unbalanced
datasets and study their effect on classification performance.
| Ajinkya More | null | 1608.06048 | null | null |
Local Binary Convolutional Neural Networks | cs.LG cs.CV | We propose local binary convolution (LBC), an efficient alternative to
convolutional layers in standard convolutional neural networks (CNN). The
design principles of LBC are motivated by local binary patterns (LBP). The LBC
layer comprises of a set of fixed sparse pre-defined binary convolutional
filters that are not updated during the training process, a non-linear
activation function and a set of learnable linear weights. The linear weights
combine the activated filter responses to approximate the corresponding
activated filter responses of a standard convolutional layer. The LBC layer
affords significant parameter savings, 9x to 169x in the number of learnable
parameters compared to a standard convolutional layer. Furthermore, the sparse
and binary nature of the weights also results in up to 9x to 169x savings in
model size compared to a standard convolutional layer. We demonstrate both
theoretically and experimentally that our local binary convolution layer is a
good approximation of a standard convolutional layer. Empirically, CNNs with
LBC layers, called local binary convolutional neural networks (LBCNN), achieves
performance parity with regular CNNs on a range of visual datasets (MNIST,
SVHN, CIFAR-10, and ImageNet) while enjoying significant computational savings.
| Felix Juefei-Xu, Vishnu Naresh Boddeti, Marios Savvides | null | 1608.06049 | null | null |
Uniform Generalization, Concentration, and Adaptive Learning | cs.LG cs.IT math.IT stat.ML | One fundamental goal in any learning algorithm is to mitigate its risk for
overfitting. Mathematically, this requires that the learning algorithm enjoys a
small generalization risk, which is defined either in expectation or in
probability. Both types of generalization are commonly used in the literature.
For instance, generalization in expectation has been used to analyze
algorithms, such as ridge regression and SGD, whereas generalization in
probability is used in the VC theory, among others. Recently, a third notion of
generalization has been studied, called uniform generalization, which requires
that the generalization risk vanishes uniformly in expectation across all
bounded parametric losses. It has been shown that uniform generalization is, in
fact, equivalent to an information-theoretic stability constraint, and that it
recovers classical results in learning theory. It is achievable under various
settings, such as sample compression schemes, finite hypothesis spaces, finite
domains, and differential privacy. However, the relationship between uniform
generalization and concentration remained unknown. In this paper, we answer
this question by proving that, while a generalization in expectation does not
imply a generalization in probability, a uniform generalization in expectation
does imply concentration. We establish a chain rule for the uniform
generalization risk of the composition of hypotheses and use it to derive a
large deviation bound. Finally, we prove that the bound is tight.
| Ibrahim Alabdulmohsin | null | 1608.06072 | null | null |
Multi-Sensor Prognostics using an Unsupervised Health Index based on
LSTM Encoder-Decoder | cs.LG cs.AI | Many approaches for estimation of Remaining Useful Life (RUL) of a machine,
using its operational sensor data, make assumptions about how a system degrades
or a fault evolves, e.g., exponential degradation. However, in many domains
degradation may not follow a pattern. We propose a Long Short Term Memory based
Encoder-Decoder (LSTM-ED) scheme to obtain an unsupervised health index (HI)
for a system using multi-sensor time-series data. LSTM-ED is trained to
reconstruct the time-series corresponding to healthy state of a system. The
reconstruction error is used to compute HI which is then used for RUL
estimation. We evaluate our approach on publicly available Turbofan Engine and
Milling Machine datasets. We also present results on a real-world industry
dataset from a pulverizer mill where we find significant correlation between
LSTM-ED based HI and maintenance costs.
| Pankaj Malhotra, Vishnu TV, Anusha Ramakrishnan, Gaurangi Anand,
Lovekesh Vig, Puneet Agarwal, Gautam Shroff | null | 1608.06154 | null | null |
Computational and Statistical Tradeoffs in Learning to Rank | cs.LG cs.IT math.IT stat.ML | For massive and heterogeneous modern datasets, it is of fundamental interest
to provide guarantees on the accuracy of estimation when computational
resources are limited. In the application of learning to rank, we provide a
hierarchy of rank-breaking mechanisms ordered by the complexity in thus
generated sketch of the data. This allows the number of data points collected
to be gracefully traded off against computational resources available, while
guaranteeing the desired level of accuracy. Theoretical guarantees on the
proposed generalized rank-breaking implicitly provide such trade-offs, which
can be explicitly characterized under certain canonical scenarios on the
structure of the data.
| Ashish Khetan, Sewoong Oh | null | 1608.06203 | null | null |
Adaptive Probabilistic Trajectory Optimization via Efficient Approximate
Inference | cs.RO cs.LG | Robotic systems must be able to quickly and robustly make decisions when
operating in uncertain and dynamic environments. While Reinforcement Learning
(RL) can be used to compute optimal policies with little prior knowledge about
the environment, it suffers from slow convergence. An alternative approach is
Model Predictive Control (MPC), which optimizes policies quickly, but also
requires accurate models of the system dynamics and environment. In this paper
we propose a new approach, adaptive probabilistic trajectory optimization, that
combines the benefits of RL and MPC. Our method uses scalable approximate
inference to learn and updates probabilistic models in an online incremental
fashion while also computing optimal control policies via successive local
approximations. We present two variations of our algorithm based on the Sparse
Spectrum Gaussian Process (SSGP) model, and we test our algorithm on three
learning tasks, demonstrating the effectiveness and efficiency of our approach.
| Yunpeng Pan, Xinyan Yan, Evangelos Theodorou and Byron Boots | null | 1608.06235 | null | null |
Multi-Dueling Bandits and Their Application to Online Ranker Evaluation | cs.IR cs.LG stat.ML | New ranking algorithms are continually being developed and refined,
necessitating the development of efficient methods for evaluating these
rankers. Online ranker evaluation focuses on the challenge of efficiently
determining, from implicit user feedback, which ranker out of a finite set of
rankers is the best. Online ranker evaluation can be modeled by dueling ban-
dits, a mathematical model for online learning under limited feedback from
pairwise comparisons. Comparisons of pairs of rankers is performed by
interleaving their result sets and examining which documents users click on.
The dueling bandits model addresses the key issue of which pair of rankers to
compare at each iteration, thereby providing a solution to the
exploration-exploitation trade-off. Recently, methods for simultaneously
comparing more than two rankers have been developed. However, the question of
which rankers to compare at each iteration was left open. We address this
question by proposing a generalization of the dueling bandits model that uses
simultaneous comparisons of an unrestricted number of rankers. We evaluate our
algorithm on synthetic data and several standard large-scale online ranker
evaluation datasets. Our experimental results show that the algorithm yields
orders of magnitude improvement in performance compared to stateof- the-art
dueling bandit algorithms.
| Brian Brost and Yevgeny Seldin and Ingemar J. Cox and Christina Lioma | null | 1608.06253 | null | null |
LFADS - Latent Factor Analysis via Dynamical Systems | cs.LG q-bio.NC stat.ML | Neuroscience is experiencing a data revolution in which many hundreds or
thousands of neurons are recorded simultaneously. Currently, there is little
consensus on how such data should be analyzed. Here we introduce LFADS (Latent
Factor Analysis via Dynamical Systems), a method to infer latent dynamics from
simultaneously recorded, single-trial, high-dimensional neural spiking data.
LFADS is a sequential model based on a variational auto-encoder. By making a
dynamical systems hypothesis regarding the generation of the observed data,
LFADS reduces observed spiking to a set of low-dimensional temporal factors,
per-trial initial conditions, and inferred inputs. We compare LFADS to existing
methods on synthetic data and show that it significantly out-performs them in
inferring neural firing rates and latent dynamics.
| David Sussillo, Rafal Jozefowicz, L. F. Abbott, Chethan Pandarinath | null | 1608.06315 | null | null |
Deep Double Sparsity Encoder: Learning to Sparsify Not Only Features But
Also Parameters | cs.LG cs.CV | This paper emphasizes the significance to jointly exploit the problem
structure and the parameter structure, in the context of deep modeling. As a
specific and interesting example, we describe the deep double sparsity encoder
(DDSE), which is inspired by the double sparsity model for dictionary learning.
DDSE simultaneously sparsities the output features and the learned model
parameters, under one unified framework. In addition to its intuitive model
interpretation, DDSE also possesses compact model size and low complexity.
Extensive simulations compare DDSE with several carefully-designed baselines,
and verify the consistently superior performance of DDSE. We further apply DDSE
to the novel application domain of brain encoding, with promising preliminary
results achieved.
| Zhangyang Wang, Thomas S. Huang | null | 1608.06374 | null | null |
Online Learning to Rank with Top-k Feedback | cs.LG | We consider two settings of online learning to rank where feedback is
restricted to top ranked items. The problem is cast as an online game between a
learner and sequence of users, over $T$ rounds. In both settings, the learners
objective is to present ranked list of items to the users. The learner's
performance is judged on the entire ranked list and true relevances of the
items. However, the learner receives highly restricted feedback at end of each
round, in form of relevances of only the top $k$ ranked items, where $k \ll m$.
The first setting is \emph{non-contextual}, where the list of items to be
ranked is fixed. The second setting is \emph{contextual}, where lists of items
vary, in form of traditional query-document lists. No stochastic assumption is
made on the generation process of relevances of items and contexts. We provide
efficient ranking strategies for both the settings. The strategies achieve
$O(T^{2/3})$ regret, where regret is based on popular ranking measures in first
setting and ranking surrogates in second setting. We also provide impossibility
results for certain ranking measures and a certain class of surrogates, when
feedback is restricted to the top ranked item, i.e. $k=1$. We empirically
demonstrate the performance of our algorithms on simulated and real world
datasets.
| Sougata Chaudhuri and Ambuj Tewari | null | 1608.06408 | null | null |
Learning to Communicate: Channel Auto-encoders, Domain Specific
Regularizers, and Attention | cs.LG cs.IT cs.NI math.IT | We address the problem of learning efficient and adaptive ways to communicate
binary information over an impaired channel. We treat the problem as
reconstruction optimization through impairment layers in a channel autoencoder
and introduce several new domain-specific regularizing layers to emulate common
channel impairments. We also apply a radio transformer network based attention
model on the input of the decoder to help recover canonical signal
representations. We demonstrate some promising initial capacity results from
this architecture and address several remaining challenges before such a system
could become practical.
| Timothy J O'Shea, Kiran Karra, T. Charles Clancy | null | 1608.06409 | null | null |
Fathom: Reference Workloads for Modern Deep Learning Methods | cs.LG | Deep learning has been popularized by its recent successes on challenging
artificial intelligence problems. One of the reasons for its dominance is also
an ongoing challenge: the need for immense amounts of computational power.
Hardware architects have responded by proposing a wide array of promising
ideas, but to date, the majority of the work has focused on specific algorithms
in somewhat narrow application domains. While their specificity does not
diminish these approaches, there is a clear need for more flexible solutions.
We believe the first step is to examine the characteristics of cutting edge
models from across the deep learning community.
Consequently, we have assembled Fathom: a collection of eight archetypal deep
learning workloads for study. Each of these models comes from a seminal work in
the deep learning community, ranging from the familiar deep convolutional
neural network of Krizhevsky et al., to the more exotic memory networks from
Facebook's AI research group. Fathom has been released online, and this paper
focuses on understanding the fundamental performance characteristics of each
model. We use a set of application-level modeling tools built around the
TensorFlow deep learning framework in order to analyze the behavior of the
Fathom workloads. We present a breakdown of where time is spent, the
similarities between the performance profiles of our models, an analysis of
behavior in inference and training, and the effects of parallelism on scaling.
| Robert Adolf, Saketh Rama, Brandon Reagen, Gu-Yeon Wei, David Brooks | 10.1109/IISWC.2016.7581275 | 1608.06581 | null | null |
Self-Averaging Expectation Propagation | cs.IT cs.LG math.IT | We investigate the problem of approximate Bayesian inference for a general
class of observation models by means of the expectation propagation (EP)
framework for large systems under some statistical assumptions. Our approach
tries to overcome the numerical bottleneck of EP caused by the inversion of
large matrices. Assuming that the measurement matrices are realizations of
specific types of ensembles we use the concept of freeness from random matrix
theory to show that the EP cavity variances exhibit an asymptotic
self-averaging property. They can be pre-computed using specific generating
functions, i.e. the R- and/or S-transforms in free probability, which do not
require matrix inversions. Our approach extends the framework of (generalized)
approximate message passing -- assumes zero-mean iid entries of the measurement
matrix -- to a general class of random matrix ensembles. The generalization is
via a simple formulation of the R- and/or S-transforms of the limiting
eigenvalue distribution of the Gramian of the measurement matrix. We
demonstrate the performance of our approach on a signal recovery problem of
nonlinear compressed sensing and compare it with that of EP.
| Burak \c{C}akmak, Manfred Opper, Bernard H. Fleury and Ole Winther | null | 1608.06602 | null | null |
Infinite-Label Learning with Semantic Output Codes | cs.LG | We develop a new statistical machine learning paradigm, named infinite-label
learning, to annotate a data point with more than one relevant labels from a
candidate set, which pools both the finite labels observed at training and a
potentially infinite number of previously unseen labels. The infinite-label
learning fundamentally expands the scope of conventional multi-label learning,
and better models the practical requirements in various real-world
applications, such as image tagging, ads-query association, and article
categorization. However, how can we learn a labeling function that is capable
of assigning to a data point the labels omitted from the training set? To
answer the question, we seek some clues from the recent work on zero-shot
learning, where the key is to represent a class/label by a vector of semantic
codes, as opposed to treating them as atomic labels. We validate the
infinite-label learning by a PAC bound in theory and some empirical studies on
both synthetic and real data.
| Yang Zhang, Rupam Acharyya, Ji Liu, Boqing Gong | null | 1608.06608 | null | null |
Unsupervised, Efficient and Semantic Expertise Retrieval | cs.IR cs.AI cs.CL cs.LG | We introduce an unsupervised discriminative model for the task of retrieving
experts in online document collections. We exclusively employ textual evidence
and avoid explicit feature engineering by learning distributed word
representations in an unsupervised way. We compare our model to
state-of-the-art unsupervised statistical vector space and probabilistic
generative approaches. Our proposed log-linear model achieves the retrieval
performance levels of state-of-the-art document-centric methods with the low
inference cost of so-called profile-centric approaches. It yields a
statistically significant improved ranking over vector space and generative
models in most cases, matching the performance of supervised methods on various
benchmarks. That is, by using solely text we can do as well as methods that
work with external evidence and/or relevance feedback. A contrastive analysis
of rankings produced by discriminative and generative approaches shows that
they have complementary strengths due to the ability of the unsupervised
discriminative model to perform semantic matching.
| Christophe Van Gysel, Maarten de Rijke, Marcel Worring | 10.1145/2872427.2882974 | 1608.06651 | null | null |
Topic Grids for Homogeneous Data Visualization | cs.LG cs.IR | We propose the topic grids to detect anomaly and analyze the behavior based
on the access log content. Content-based behavioral risk is quantified in the
high dimensional space where the topics are generated from the log. The topics
are being projected homogeneously into a space that is perception- and
interaction-friendly to the human experts.
| Shih-Chieh Su, Joseph Vaughn and Jean-Laurent Huynh | null | 1608.06664 | null | null |
Deep learning is competing random forest in computational docking | q-bio.BM cs.LG | Computational docking is the core process of computer-aided drug design; it
aims at predicting the best orientation and conformation of a small drug
molecule when bound to a target large protein receptor. The docking quality is
typically measured by a scoring function: a mathematical predictive model that
produces a score representing the binding free energy and hence the stability
of the resulting complex molecule. We analyze the performance of both learning
techniques on the scoring power, the ranking power, docking power, and
screening power using the PDBbind 2013 database. For the scoring and ranking
powers, the proposed learning scoring functions depend on a wide range of
features (energy terms, pharmacophore, intermolecular) that entirely
characterize the protein-ligand complexes. For the docking and screening
powers, the proposed learning scoring functions depend on the intermolecular
features of the RF-Score to utilize a larger number of training complexes. For
the scoring power, the DL\_RF scoring function achieves Pearson's correlation
coefficient between the predicted and experimentally measured binding
affinities of 0.799 versus 0.758 of the RF scoring function. For the ranking
power, the DL scoring function ranks the ligands bound to fixed target protein
with accuracy 54% for the high-level ranking and with accuracy 78% for the
low-level ranking while the RF scoring function achieves (46% and 62%)
respectively. For the docking power, the DL\_RF scoring function has a success
rate when the three best-scored ligand binding poses are considered within 2
\AA\ root-mean-square-deviation from the native pose of 36.0% versus 30.2% of
the RF scoring function. For the screening power, the DL scoring function has
an average enrichment factor and success rate at the top 1% level of (2.69 and
6.45%) respectively versus (1.61 and 4.84%) respectively of the RF scoring
function.
| Mohamed Khamis, Walid Gomaa, Basem Galal | null | 1608.06665 | null | null |
Efficient Training for Positive Unlabeled Learning | cs.LG | Positive unlabeled (PU) learning is useful in various practical situations,
where there is a need to learn a classifier for a class of interest from an
unlabeled data set, which may contain anomalies as well as samples from unknown
classes. The learning task can be formulated as an optimization problem under
the framework of statistical learning theory. Recent studies have theoretically
analyzed its properties and generalization performance, nevertheless, little
effort has been made to consider the problem of scalability, especially when
large sets of unlabeled data are available. In this work we propose a novel
scalable PU learning algorithm that is theoretically proven to provide the
optimal solution, while showing superior computational and memory performance.
Experimental evaluation confirms the theoretical evidence and shows that the
proposed method can be successfully applied to a large variety of real-world
problems involving PU learning.
| Emanuele Sansone, Francesco G.B. De Natale, Zhi-Hua Zhou | 10.1109/TPAMI.2018.2860995 | 1608.06807 | null | null |
Kullback-Leibler Penalized Sparse Discriminant Analysis for
Event-Related Potential Classification | cs.CV cs.LG stat.ML | A brain computer interface (BCI) is a system which provides direct
communication between the mind of a person and the outside world by using only
brain activity (EEG). The event-related potential (ERP)-based BCI problem
consists of a binary pattern recognition. Linear discriminant analysis (LDA) is
widely used to solve this type of classification problems, but it fails when
the number of features is large relative to the number of observations. In this
work we propose a penalized version of the sparse discriminant analysis (SDA),
called Kullback-Leibler penalized sparse discriminant analysis (KLSDA). This
method inherits both the discriminative feature selection and classification
properties of SDA and it also improves SDA performance through the addition of
Kullback-Leibler class discrepancy information. The KLSDA method is design to
automatically select the optimal regularization parameters. Numerical
experiments with two real ERP-EEG datasets show that this new method
outperforms standard SDA.
| Victoria Peterson, Hugo Leonardo Rufiner, Ruben Daniel Spies | null | 1608.06863 | null | null |
AIDE: Fast and Communication Efficient Distributed Optimization | math.OC cs.LG stat.ML | In this paper, we present two new communication-efficient methods for
distributed minimization of an average of functions. The first algorithm is an
inexact variant of the DANE algorithm that allows any local algorithm to return
an approximate solution to a local subproblem. We show that such a strategy
does not affect the theoretical guarantees of DANE significantly. In fact, our
approach can be viewed as a robustification strategy since the method is
substantially better behaved than DANE on data partition arising in practice.
It is well known that DANE algorithm does not match the communication
complexity lower bounds. To bridge this gap, we propose an accelerated variant
of the first method, called AIDE, that not only matches the communication lower
bounds but can also be implemented using a purely first-order oracle. Our
empirical results show that AIDE is superior to other communication efficient
algorithms in settings that naturally arise in machine learning applications.
| Sashank J. Reddi, Jakub Kone\v{c}n\'y, Peter Richt\'arik, Barnab\'as
P\'ocz\'os, Alex Smola | null | 1608.06879 | null | null |
Towards Bayesian Deep Learning: A Framework and Some Existing Methods | stat.ML cs.CV cs.LG cs.NE | While perception tasks such as visual object recognition and text
understanding play an important role in human intelligence, the subsequent
tasks that involve inference, reasoning and planning require an even higher
level of intelligence. The past few years have seen major advances in many
perception tasks using deep learning models. For higher-level inference,
however, probabilistic graphical models with their Bayesian nature are still
more powerful and flexible. To achieve integrated intelligence that involves
both perception and inference, it is naturally desirable to tightly integrate
deep learning and Bayesian models within a principled probabilistic framework,
which we call Bayesian deep learning. In this unified framework, the perception
of text or images using deep learning can boost the performance of higher-level
inference and in return, the feedback from the inference process is able to
enhance the perception of text or images. This paper proposes a general
framework for Bayesian deep learning and reviews its recent applications on
recommender systems, topic models, and control. In this paper, we also discuss
the relationship and differences between Bayesian deep learning and other
related topics like Bayesian treatment of neural networks.
| Hao Wang and Dit-Yan Yeung | null | 1608.06884 | null | null |
Learning an Optimization Algorithm through Human Design Iterations | cs.LG | Solving optimal design problems through crowdsourcing faces a dilemma: On one
hand, human beings have been shown to be more effective than algorithms at
searching for good solutions of certain real-world problems with
high-dimensional or discrete solution spaces; on the other hand, the cost of
setting up crowdsourcing environments, the uncertainty in the crowd's
domain-specific competence, and the lack of commitment of the crowd, all
contribute to the lack of real-world application of design crowdsourcing. We
are thus motivated to investigate a solution-searching mechanism where an
optimization algorithm is tuned based on human demonstrations on solution
searching, so that the search can be continued after human participants abandon
the problem. To do so, we model the iterative search process as a Bayesian
Optimization (BO) algorithm, and propose an inverse BO (IBO) algorithm to find
the maximum likelihood estimators of the BO parameters based on human
solutions. We show through a vehicle design and control problem that the search
performance of BO can be improved by recovering its parameters based on an
effective human search. Thus, IBO has the potential to improve the success rate
of design crowdsourcing activities, by requiring only good search strategies
instead of good solutions from the crowd.
| Thurston Sexton and Max Yi Ren | null | 1608.06984 | null | null |
Densely Connected Convolutional Networks | cs.CV cs.LG | Recent work has shown that convolutional networks can be substantially
deeper, more accurate, and efficient to train if they contain shorter
connections between layers close to the input and those close to the output. In
this paper, we embrace this observation and introduce the Dense Convolutional
Network (DenseNet), which connects each layer to every other layer in a
feed-forward fashion. Whereas traditional convolutional networks with L layers
have L connections - one between each layer and its subsequent layer - our
network has L(L+1)/2 direct connections. For each layer, the feature-maps of
all preceding layers are used as inputs, and its own feature-maps are used as
inputs into all subsequent layers. DenseNets have several compelling
advantages: they alleviate the vanishing-gradient problem, strengthen feature
propagation, encourage feature reuse, and substantially reduce the number of
parameters. We evaluate our proposed architecture on four highly competitive
object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet).
DenseNets obtain significant improvements over the state-of-the-art on most of
them, whilst requiring less computation to achieve high performance. Code and
pre-trained models are available at https://github.com/liuzhuang13/DenseNet .
| Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger | null | 1608.06993 | null | null |
Incremental Minimax Optimization based Fuzzy Clustering for Large
Multi-view Data | cs.AI cs.LG stat.ML | Incremental clustering approaches have been proposed for handling large data
when given data set is too large to be stored. The key idea of these approaches
is to find representatives to represent each cluster in each data chunk and
final data analysis is carried out based on those identified representatives
from all the chunks. However, most of the incremental approaches are used for
single view data. As large multi-view data generated from multiple sources
becomes prevalent nowadays, there is a need for incremental clustering
approaches to handle both large and multi-view data. In this paper we propose a
new incremental clustering approach called incremental minimax optimization
based fuzzy clustering (IminimaxFCM) to handle large multi-view data. In
IminimaxFCM, representatives with multiple views are identified to represent
each cluster by integrating multiple complementary views using minimax
optimization. The detailed problem formulation, updating rules derivation, and
the in-depth analysis of the proposed IminimaxFCM are provided. Experimental
studies on several real world multi-view data sets have been conducted. We
observed that IminimaxFCM outperforms related incremental fuzzy clustering in
terms of clustering accuracy, demonstrating the great potential of IminimaxFCM
for large multi-view data analysis.
| Yangtao Wang, Lihui Chen, Xiaoli Li | null | 1608.07001 | null | null |
Multi-View Fuzzy Clustering with Minimax Optimization for Effective
Clustering of Data from Multiple Sources | cs.AI cs.LG stat.ML | Multi-view data clustering refers to categorizing a data set by making good
use of related information from multiple representations of the data. It
becomes important nowadays because more and more data can be collected in a
variety of ways, in different settings and from different sources, so each data
set can be represented by different sets of features to form different views of
it. Many approaches have been proposed to improve clustering performance by
exploring and integrating heterogeneous information underlying different views.
In this paper, we propose a new multi-view fuzzy clustering approach called
MinimaxFCM by using minimax optimization based on well-known Fuzzy c means. In
MinimaxFCM the consensus clustering results are generated based on minimax
optimization in which the maximum disagreements of different weighted views are
minimized. Moreover, the weight of each view can be learned automatically in
the clustering process. In addition, there is only one parameter to be set
besides the fuzzifier. The detailed problem formulation, updating rules
derivation, and the in-depth analysis of the proposed MinimaxFCM are provided
here. Experimental studies on nine multi-view data sets including real world
image and document data sets have been conducted. We observed that MinimaxFCM
outperforms related multi-view clustering approaches in terms of clustering
accuracy, demonstrating the great potential of MinimaxFCM for multi-view data
analysis.
| Yangtao Wang, Lihui Chen | null | 1608.07005 | null | null |
Comparison among dimensionality reduction techniques based on Random
Projection for cancer classification | cs.LG stat.ML | Random Projection (RP) technique has been widely applied in many scenarios
because it can reduce high-dimensional features into low-dimensional space
within short time and meet the need of real-time analysis of massive data.
There is an urgent need of dimensionality reduction with fast increase of big
genomics data. However, the performance of RP is usually lower. We attempt to
improve classification accuracy of RP through combining other reduction
dimension methods such as Principle Component Analysis (PCA), Linear
Discriminant Analysis (LDA), and Feature Selection (FS). We compared
classification accuracy and running time of different combination methods on
three microarray datasets and a simulation dataset. Experimental results show a
remarkable improvement of 14.77% in classification accuracy of FS followed by
RP compared to RP on BC-TCGA dataset. LDA followed by RP also helps RP to yield
a more discriminative subspace with an increase of 13.65% on classification
accuracy on the same dataset. FS followed by RP outperforms other combination
methods in classification accuracy on most of the datasets.
| Haozhe Xie, Jie Li, Qiaosheng Zhang and Yadong Wang | 10.1016/j.compbiolchem.2016.09.010 | 1608.07019 | null | null |
Learning Points and Routes to Recommend Trajectories | cs.LG cs.IR | The problem of recommending tours to travellers is an important and broadly
studied area. Suggested solutions include various approaches of
points-of-interest (POI) recommendation and route planning. We consider the
task of recommending a sequence of POIs, that simultaneously uses information
about POIs and routes. Our approach unifies the treatment of various sources of
information by representing them as features in machine learning algorithms,
enabling us to learn from past behaviour. Information about POIs are used to
learn a POI ranking model that accounts for the start and end points of tours.
Data about previous trajectories are used for learning transition patterns
between POIs that enable us to recommend probable routes. In addition, a
probabilistic model is proposed to combine the results of POI ranking and the
POI to POI transitions. We propose a new F$_1$ score on pairs of POIs that
capture the order of visits. Empirical results show that our approach improves
on recent methods, and demonstrate that combining points and routes enables
better trajectory recommendations.
| Dawei Chen, Cheng Soon Ong, Lexing Xie | 10.1145/2983323.2983672 | 1608.07051 | null | null |
Active Robust Learning | cs.LG math.OC | In many practical applications of learning algorithms, unlabeled data is
cheap and abundant whereas labeled data is expensive. Active learning
algorithms developed to achieve better performance with lower cost. Usually
Representativeness and Informativeness are used in active learning algoirthms.
Advanced recent active learning methods consider both of these criteria.
Despite its vast literature, very few active learning methods consider noisy
instances, i.e. label noisy and outlier instances. Also, these methods didn't
consider accuracy in computing representativeness and informativeness. Based on
the idea that inaccuracy in these measures and not taking noisy instances into
consideration are two sides of a coin and are inherently related, a new loss
function is proposed. This new loss function helps to decrease the effect of
noisy instances while at the same time, reduces bias. We defined "instance
complexity" as a new notion of complexity for instances of a learning problem.
It is proved that noisy instances in the data if any, are the ones with maximum
instance complexity. Based on this loss function which has two functions for
classifying ordinary and noisy instances, a new classifier, named
"Simple-Complex Classifier" is proposed. In this classifier there are a simple
and a complex function, with the complex function responsible for selecting
noisy instances. The resulting optimization problem for both learning and
active learning is highly non-convex and very challenging. In order to solve
it, a convex relaxation is proposed.
| Hossein Ghafarian and Hadi Sadoghi Yazdi | null | 1608.07159 | null | null |
Minimizing Quadratic Functions in Constant Time | cs.LG cs.DS stat.ML | A sampling-based optimization method for quadratic functions is proposed. Our
method approximately solves the following $n$-dimensional quadratic
minimization problem in constant time, which is independent of $n$:
$z^*=\min_{\mathbf{v} \in \mathbb{R}^n}\langle\mathbf{v}, A \mathbf{v}\rangle +
n\langle\mathbf{v}, \mathrm{diag}(\mathbf{d})\mathbf{v}\rangle +
n\langle\mathbf{b}, \mathbf{v}\rangle$, where $A \in \mathbb{R}^{n \times n}$
is a matrix and $\mathbf{d},\mathbf{b} \in \mathbb{R}^n$ are vectors. Our
theoretical analysis specifies the number of samples $k(\delta, \epsilon)$ such
that the approximated solution $z$ satisfies $|z - z^*| = O(\epsilon n^2)$ with
probability $1-\delta$. The empirical performance (accuracy and runtime) is
positively confirmed by numerical experiments.
| Kohei Hayashi, Yuichi Yoshida | null | 1608.07179 | null | null |
Semantics derived automatically from language corpora contain human-like
biases | cs.AI cs.CL cs.CY cs.LG | Artificial intelligence and machine learning are in a period of astounding
growth. However, there are concerns that these technologies may be used, either
with or without intention, to perpetuate the prejudice and unfairness that
unfortunately characterizes many human institutions. Here we show for the first
time that human-like semantic biases result from the application of standard
machine learning to ordinary language---the same sort of language humans are
exposed to every day. We replicate a spectrum of standard human biases as
exposed by the Implicit Association Test and other well-known psychological
studies. We replicate these using a widely used, purely statistical
machine-learning model---namely, the GloVe word embedding---trained on a corpus
of text from the Web. Our results indicate that language itself contains
recoverable and accurate imprints of our historic biases, whether these are
morally neutral as towards insects or flowers, problematic as towards race or
gender, or even simply veridical, reflecting the {\em status quo} for the
distribution of gender with respect to careers or first names. These
regularities are captured by machine learning along with the rest of semantics.
In addition to our empirical findings concerning language, we also contribute
new methods for evaluating bias in text, the Word Embedding Association Test
(WEAT) and the Word Embedding Factual Association Test (WEFAT). Our results
have implications not only for AI and machine learning, but also for the fields
of psychology, sociology, and human ethics, since they raise the possibility
that mere exposure to everyday language can account for the biases we replicate
here.
| Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan | 10.1126/science.aal4230 | 1608.07187 | null | null |
Benchmarking State-of-the-Art Deep Learning Software Tools | cs.DC cs.LG | Deep learning has been shown as a successful machine learning method for a
variety of tasks, and its popularity results in numerous open-source deep
learning software tools. Training a deep network is usually a very
time-consuming process. To address the computational challenge in deep
learning, many tools exploit hardware features such as multi-core CPUs and
many-core GPUs to shorten the training time. However, different tools exhibit
different features and running performance when training different types of
deep networks on different hardware platforms, which makes it difficult for end
users to select an appropriate pair of software and hardware. In this paper, we
aim to make a comparative study of the state-of-the-art GPU-accelerated deep
learning software tools, including Caffe, CNTK, MXNet, TensorFlow, and Torch.
We first benchmark the running performance of these tools with three popular
types of neural networks on two CPU platforms and three GPU platforms. We then
benchmark some distributed versions on multiple GPUs. Our contribution is
two-fold. First, for end users of deep learning tools, our benchmarking results
can serve as a guide to selecting appropriate hardware platforms and software
tools. Second, for software developers of deep learning tools, our in-depth
analysis points out possible future directions to further optimize the running
performance.
| Shaohuai Shi, Qiang Wang, Pengfei Xu, Xiaowen Chu | null | 1608.07249 | null | null |
Large-scale Collaborative Imaging Genetics Studies of Risk Genetic
Factors for Alzheimer's Disease Across Multiple Institutions | cs.LG stat.ML | Genome-wide association studies (GWAS) offer new opportunities to identify
genetic risk factors for Alzheimer's disease (AD). Recently, collaborative
efforts across different institutions emerged that enhance the power of many
existing techniques on individual institution data. However, a major barrier to
collaborative studies of GWAS is that many institutions need to preserve
individual data privacy. To address this challenge, we propose a novel
distributed framework, termed Local Query Model (LQM) to detect risk SNPs for
AD across multiple research institutions. To accelerate the learning process,
we propose a Distributed Enhanced Dual Polytope Projection (D-EDPP) screening
rule to identify irrelevant features and remove them from the optimization. To
the best of our knowledge, this is the first successful run of the
computationally intensive model selection procedure to learn a consistent model
across different institutions without compromising their privacy while ranking
the SNPs that may collectively affect AD. Empirical studies are conducted on
809 subjects with 5.9 million SNP features which are distributed across three
individual institutions. D-EDPP achieved a 66-fold speed-up by effectively
identifying irrelevant features.
| Qingyang Li, Tao Yang, Liang Zhan, Derrek Paul Hibar, Neda Jahanshad,
Yalin Wang, Jieping Ye, Paul M. Thompson, Jie Wang | null | 1608.07251 | null | null |
Learning in games with continuous action sets and unknown payoff
functions | math.OC cs.GT cs.LG | This paper examines the convergence of no-regret learning in games with
continuous action sets. For concreteness, we focus on learning via "dual
averaging", a widely used class of no-regret learning schemes where players
take small steps along their individual payoff gradients and then "mirror" the
output back to their action sets. In terms of feedback, we assume that players
can only estimate their payoff gradients up to a zero-mean error with bounded
variance. To study the convergence of the induced sequence of play, we
introduce the notion of variational stability, and we show that stable
equilibria are locally attracting with high probability whereas globally stable
equilibria are globally attracting with probability 1. We also discuss some
applications to mixed-strategy learning in finite games, and we provide
explicit estimates of the method's convergence speed.
| Panayotis Mertikopoulos and Zhengyuan Zhou | 10.1007/s10107-017-1228-2 | 1608.0731 | null | null |
Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing | cs.LG cs.IT math.IT | Digital crowdsourcing (CS) is a modern approach to perform certain large
projects using small contributions of a large crowd. In CS, a taskmaster
typically breaks down the project into small batches of tasks and assigns them
to so-called workers with imperfect skill levels. The crowdsourcer then
collects and analyzes the results for inference and serving the purpose of the
project. In this work, the CS problem, as a human-in-the-loop computation
problem, is modeled and analyzed in an information theoretic rate-distortion
framework. The purpose is to identify the ultimate fidelity that one can
achieve by any form of query from the crowd and any decoding (inference)
algorithm with a given budget. The results are established by a joint source
channel (de)coding scheme, which represent the query scheme and inference, over
parallel noisy channels, which model workers with imperfect skill levels. We
also present and analyze a query scheme dubbed $k$-ary incidence coding and
study optimized query pricing in this setting.
| Farshad Lahouti, Babak Hassibi | null | 1608.07328 | null | null |
Collaborative Filtering with Recurrent Neural Networks | cs.IR cs.LG | We show that collaborative filtering can be viewed as a sequence prediction
problem, and that given this interpretation, recurrent neural networks offer
very competitive approach. In particular we study how the long short-term
memory (LSTM) can be applied to collaborative filtering, and how it compares to
standard nearest neighbors and matrix factorization methods on movie
recommendation. We show that the LSTM is competitive in all aspects, and
largely outperforms other methods in terms of item coverage and short term
predictions.
| Robin Devooght and Hugues Bersini | null | 1608.074 | null | null |
Hard Negative Mining for Metric Learning Based Zero-Shot Classification | cs.LG cs.AI cs.CV stat.ML | Zero-Shot learning has been shown to be an efficient strategy for domain
adaptation. In this context, this paper builds on the recent work of Bucher et
al. [1], which proposed an approach to solve Zero-Shot classification problems
(ZSC) by introducing a novel metric learning based objective function. This
objective function allows to learn an optimal embedding of the attributes
jointly with a measure of similarity between images and attributes. This paper
extends their approach by proposing several schemes to control the generation
of the negative pairs, resulting in a significant improvement of the
performance and giving above state-of-the-art results on three challenging ZSC
datasets.
| Maxime Bucher (Palaiseau), St\'ephane Herbin (Palaiseau), Fr\'ed\'eric
Jurie | null | 1608.07441 | null | null |
Entity Embedding-based Anomaly Detection for Heterogeneous Categorical
Events | cs.LG cs.CR stat.ML | Anomaly detection plays an important role in modern data-driven security
applications, such as detecting suspicious access to a socket from a process.
In many cases, such events can be described as a collection of categorical
values that are considered as entities of different types, which we call
heterogeneous categorical events. Due to the lack of intrinsic distance
measures among entities, and the exponentially large event space, most existing
work relies heavily on heuristics to calculate abnormal scores for events.
Different from previous work, we propose a principled and unified probabilistic
model APE (Anomaly detection via Probabilistic pairwise interaction and Entity
embedding) that directly models the likelihood of events. In this model, we
embed entities into a common latent space using their observed co-occurrence in
different events. More specifically, we first model the compatibility of each
pair of entities according to their embeddings. Then we utilize the weighted
pairwise interactions of different entity types to define the event
probability. Using Noise-Contrastive Estimation with "context-dependent" noise
distribution, our model can be learned efficiently regardless of the large
event space. Experimental results on real enterprise surveillance data show
that our methods can accurately detect abnormal events compared to other
state-of-the-art abnormal detection techniques.
| Ting Chen, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, Kai Zhang | null | 1608.07502 | null | null |
Leveraging over intact priors for boosting control and dexterity of
prosthetic hands by amputees | cs.LG stat.ML | Non-invasive myoelectric prostheses require a long training time to obtain
satisfactory control dexterity. These training times could possibly be reduced
by leveraging over training efforts by previous subjects. So-called domain
adaptation algorithms formalize this strategy and have indeed been shown to
significantly reduce the amount of required training data for intact subjects
for myoelectric movements classification. It is not clear, however, whether
these results extend also to amputees and, if so, whether prior information
from amputees and intact subjects is equally useful. To overcome this problem,
we evaluated several domain adaptation algorithms on data coming from both
amputees and intact subjects. Our findings indicate that: (1) the use of
previous experience from other subjects allows us to reduce the training time
by about an order of magnitude; (2) this improvement holds regardless of
whether an amputee exploits previous information from other amputees or from
intact subjects.
| Valentina Gregori and Barbara Caputo | null | 1608.07536 | null | null |
Clustering and Community Detection with Imbalanced Clusters | stat.ML cs.LG cs.SI | Spectral clustering methods which are frequently used in clustering and
community detection applications are sensitive to the specific graph
constructions particularly when imbalanced clusters are present. We show that
ratio cut (RCut) or normalized cut (NCut) objectives are not tailored to
imbalanced cluster sizes since they tend to emphasize cut sizes over cut
values. We propose a graph partitioning problem that seeks minimum cut
partitions under minimum size constraints on partitions to deal with imbalanced
cluster sizes. Our approach parameterizes a family of graphs by adaptively
modulating node degrees on a fixed node set, yielding a set of parameter
dependent cuts reflecting varying levels of imbalance. The solution to our
problem is then obtained by optimizing over these parameters. We present
rigorous limit cut analysis results to justify our approach and demonstrate the
superiority of our method through experiments on synthetic and real datasets
for data clustering, semi-supervised learning and community detection.
| Cem Aksoylar, Jing Qian, Venkatesh Saligrama | 10.1109/TSIPN.2016.2601022 | 1608.07605 | null | null |
Interacting with Massive Behavioral Data | cs.LG | In this short paper, we propose the split-diffuse (SD) algorithm that takes
the output of an existing word embedding algorithm, and distributes the data
points uniformly across the visualization space. The result improves the
perceivability and the interactability by the human.
We apply the SD algorithm to analyze the user behavior through access logs
within the cyber security domain. The result, named the topic grids, is a set
of grids on various topics generated from the logs. On the same set of grids,
different behavioral metrics can be shown on different targets over different
periods of time, to provide visualization and interaction to the human experts.
Analysis, investigation, and other types of interaction can be performed on
the topic grids more efficiently than on the output of existing dimension
reduction methods. In addition to the cyber security domain, the topic grids
can be further applied to other domains like e-commerce, credit card
transaction, customer service to analyze the behavior in a large scale.
| Shih-Chieh Su | null | 1608.07619 | null | null |
Large Scale Behavioral Analytics via Topical Interaction | cs.LG | We propose the split-diffuse (SD) algorithm that takes the output of an
existing dimension reduction algorithm, and distributes the data points
uniformly across the visualization space. The result, called the topic grids,
is a set of grids on various topics which are generated from the free-form text
content of any domain of interest. The topic grids efficiently utilizes the
visualization space to provide visual summaries for massive data. Topical
analysis, comparison and interaction can be performed on the topic grids in a
more perceivable way.
| Shih-Chieh Su | null | 1608.07625 | null | null |
Global analysis of Expectation Maximization for mixtures of two
Gaussians | math.ST cs.LG stat.CO stat.ML stat.TH | Expectation Maximization (EM) is among the most popular algorithms for
estimating parameters of statistical models. However, EM, which is an iterative
algorithm based on the maximum likelihood principle, is generally only
guaranteed to find stationary points of the likelihood objective, and these
points may be far from any maximizer. This article addresses this disconnect
between the statistical principles behind EM and its algorithmic properties.
Specifically, it provides a global analysis of EM for specific models in which
the observations comprise an i.i.d. sample from a mixture of two Gaussians.
This is achieved by (i) studying the sequence of parameters from idealized
execution of EM in the infinite sample limit, and fully characterizing the
limit points of the sequence in terms of the initial parameters; and then (ii)
based on this convergence analysis, establishing statistical consistency (or
lack thereof) for the actual sequence of parameters produced by EM.
| Ji Xu, Daniel Hsu, Arian Maleki | null | 1608.0763 | null | null |
Learning Temporal Dependence from Time-Series Data with Latent Variables | cs.LG stat.ML | We consider the setting where a collection of time series, modeled as random
processes, evolve in a causal manner, and one is interested in learning the
graph governing the relationships of these processes. A special case of wide
interest and applicability is the setting where the noise is Gaussian and
relationships are Markov and linear. We study this setting with two additional
features: firstly, each random process has a hidden (latent) state, which we
use to model the internal memory possessed by the variables (similar to hidden
Markov models). Secondly, each variable can depend on its latent memory state
through a random lag (rather than a fixed lag), thus modeling memory recall
with differing lags at distinct times. Under this setting, we develop an
estimator and prove that under a genericity assumption, the parameters of the
model can be learned consistently. We also propose a practical adaption of this
estimator, which demonstrates significant performance gains in both synthetic
and real-world datasets.
| Hossein Hosseini, Sreeram Kannan, Baosen Zhang and Radha Poovendran | null | 1608.07636 | null | null |
Learning to generalize to new compositions in image understanding | cs.CV cs.AI cs.CL cs.LG | Recurrent neural networks have recently been used for learning to describe
images using natural language. However, it has been observed that these models
generalize poorly to scenes that were not observed during training, possibly
depending too strongly on the statistics of the text in the training data. Here
we propose to describe images using short structured representations, aiming to
capture the crux of a description. These structured representations allow us to
tease-out and evaluate separately two types of generalization: standard
generalization to new images with similar scenes, and generalization to new
combinations of known entities. We compare two learning approaches on the
MS-COCO dataset: a state-of-the-art recurrent network based on an LSTM (Show,
Attend and Tell), and a simple structured prediction model on top of a deep
network. We find that the structured model generalizes to new compositions
substantially better than the LSTM, ~7 times the accuracy of predicting
structured representations. By providing a concrete method to quantify
generalization for unseen combinations, we argue that structured
representations and compositional splits are a useful benchmark for image
captioning, and advocate compositional models that capture linguistic and
visual structure.
| Yuval Atzmon, Jonathan Berant, Vahid Kezami, Amir Globerson and Gal
Chechik | null | 1608.07639 | null | null |
KSR: A Semantic Representation of Knowledge Graph within a Novel
Unsupervised Paradigm | cs.LG cs.AI | Knowledge representation is a long-history topic in AI, which is very
important. A variety of models have been proposed for knowledge graph
embedding, which projects symbolic entities and relations into continuous
vector space. However, most related methods merely focus on the data-fitting of
knowledge graph, and ignore the interpretable semantic expression. Thus,
traditional embedding methods are not friendly for applications that require
semantic analysis, such as question answering and entity retrieval. To this
end, this paper proposes a semantic representation method for knowledge graph
\textbf{(KSR)}, which imposes a two-level hierarchical generative process that
globally extracts many aspects and then locally assigns a specific category in
each aspect for every triple. Since both aspects and categories are
semantics-relevant, the collection of categories in each aspect is treated as
the semantic representation of this triple. Extensive experiments show that our
model outperforms other state-of-the-art baselines substantially.
| Han Xiao, Minlie Huang, Xiaoyan Zhu | null | 1608.07685 | null | null |
A Boundary Tilting Persepective on the Phenomenon of Adversarial
Examples | cs.LG stat.ML | Deep neural networks have been shown to suffer from a surprising weakness:
their classification outputs can be changed by small, non-random perturbations
of their inputs. This adversarial example phenomenon has been explained as
originating from deep networks being "too linear" (Goodfellow et al., 2014). We
show here that the linear explanation of adversarial examples presents a number
of limitations: the formal argument is not convincing, linear classifiers do
not always suffer from the phenomenon, and when they do their adversarial
examples are different from the ones affecting deep networks.
We propose a new perspective on the phenomenon. We argue that adversarial
examples exist when the classification boundary lies close to the submanifold
of sampled data, and present a mathematical analysis of this new perspective in
the linear case. We define the notion of adversarial strength and show that it
can be reduced to the deviation angle between the classifier considered and the
nearest centroid classifier. Then, we show that the adversarial strength can be
made arbitrarily high independently of the classification performance due to a
mechanism that we call boundary tilting. This result leads us to defining a new
taxonomy of adversarial examples. Finally, we show that the adversarial
strength observed in practice is directly dependent on the level of
regularisation used and the strongest adversarial examples, symptomatic of
overfitting, can be avoided by using a proper level of regularisation.
| Thomas Tanay and Lewis Griffin | null | 1608.0769 | null | null |
Random Forest for Label Ranking | cs.LG stat.ML | Label ranking aims to learn a mapping from instances to rankings over a
finite number of predefined labels. Random forest is a powerful and one of the
most successful general-purpose machine learning algorithms of modern times. In
this paper, we present a powerful random forest label ranking method which uses
random decision trees to retrieve nearest neighbors. We have developed a novel
two-step rank aggregation strategy to effectively aggregate neighboring
rankings discovered by the random forest into a final predicted ranking.
Compared with existing methods, the new random forest method has many
advantages including its intrinsically scalable tree data structure, highly
parallel-able computational architecture and much superior performance. We
present extensive experimental results to demonstrate that our new method
achieves the highly competitive performance compared with state-of-the-art
methods for datasets with complete ranking and datasets with only partial
ranking information.
| Yangming Zhou and Guoping Qiu | null | 1608.0771 | null | null |
Temperature-Based Deep Boltzmann Machines | cs.LG | Deep learning techniques have been paramount in the last years, mainly due to
their outstanding results in a number of applications, that range from speech
recognition to face-based user identification. Despite other techniques
employed for such purposes, Deep Boltzmann Machines are among the most used
ones, which are composed of layers of Restricted Boltzmann Machines (RBMs)
stacked on top of each other. In this work, we evaluate the concept of
temperature in DBMs, which play a key role in Boltzmann-related distributions,
but it has never been considered in this context up to date. Therefore, the
main contribution of this paper is to take into account this information and to
evaluate its influence in DBMs considering the task of binary image
reconstruction. We expect this work can foster future research considering the
usage of different temperatures during learning in DBMs.
| Leandro Aparecido Passos Junior and Joao Paulo Papa | null | 1608.07719 | null | null |
Bayesian selection for the l2-Potts model regularization parameter: 1D
piecewise constant signal denoising | cs.LG stat.ML | Piecewise constant denoising can be solved either by deterministic
optimization approaches, based on the Potts model, or by stochastic Bayesian
procedures. The former lead to low computational time but require the selection
of a regularization parameter, whose value significantly impacts the achieved
solution, and whose automated selection remains an involved and challenging
problem. Conversely, fully Bayesian formalisms encapsulate the regularization
parameter selection into hierarchical models, at the price of high
computational costs. This contribution proposes an operational strategy that
combines hierarchical Bayesian and Potts model formulations, with the double
aim of automatically tuning the regularization parameter and of maintaining
computational effciency. The proposed procedure relies on formally connecting a
Bayesian framework to a l2-Potts functional. Behaviors and performance for the
proposed piecewise constant denoising and regularization parameter tuning
techniques are studied qualitatively and assessed quantitatively, and shown to
compare favorably against those of a fully Bayesian hierarchical procedure,
both in accuracy and in computational load.
| Jordan Frecon, Nelly Pustelnik, Nicolas Dobigeon, Herwig Wendt and
Patrice Abry | 10.1109/TSP.2017.2715000 | 1608.07739 | null | null |
Online Monotone Optimization | cs.LG math.OC | This paper presents a new framework for analyzing and designing no-regret
algorithms for dynamic (possibly adversarial) systems. The proposed framework
generalizes the popular online convex optimization framework and extends it to
its natural limit allowing it to capture a notion of regret that is intuitive
for more general problems such as those encountered in game theory and
variational inequalities. The framework hinges on a special choice of a
system-wide loss function we have developed. Using this framework, we prove
that a simple update scheme provides a no-regret algorithm for monotone
systems. While previous results in game theory prove individual agents can
enjoy unilateral no-regret guarantees, our result proves monotonicity
sufficient for guaranteeing no-regret when considering the adjustments of
multiple agent strategies in parallel. Furthermore, to our knowledge, this is
the first framework to provide a suitable notion of regret for variational
inequalities. Most importantly, our proposed framework ensures monotonicity a
sufficient condition for employing multiple online learners safely in parallel.
| Ian Gemp and Sridhar Mahadevan | null | 1608.07888 | null | null |
Optimizing Recurrent Neural Networks Architectures under Time
Constraints | stat.ML cs.LG | Recurrent neural network (RNN)'s architecture is a key factor influencing its
performance. We propose algorithms to optimize hidden sizes under running time
constraint. We convert the discrete optimization into a subset selection
problem. By novel transformations, the objective function becomes submodular
and constraint becomes supermodular. A greedy algorithm with bounds is
suggested to solve the transformed problem. And we show how transformations
influence the bounds. To speed up optimization, surrogate functions are
proposed which balance exploration and exploitation. Experiments show that our
algorithms can find more accurate models or faster models than manually tuned
state-of-the-art and random search. We also compare popular RNN architectures
using our algorithms.
| Junqi Jin, Ziang Yan, Kun Fu, Nan Jiang, Changshui Zhang | null | 1608.07892 | null | null |
Human-Algorithm Interaction Biases in the Big Data Cycle: A Markov Chain
Iterated Learning Framework | cs.LG cs.HC | Early supervised machine learning algorithms have relied on reliable expert
labels to build predictive models. However, the gates of data generation have
recently been opened to a wider base of users who started participating
increasingly with casual labeling, rating, annotating, etc. The increased
online presence and participation of humans has led not only to a
democratization of unchecked inputs to algorithms, but also to a wide
democratization of the "consumption" of machine learning algorithms' outputs by
general users. Hence, these algorithms, many of which are becoming essential
building blocks of recommender systems and other information filters, started
interacting with users at unprecedented rates. The result is machine learning
algorithms that consume more and more data that is unchecked, or at the very
least, not fitting conventional assumptions made by various machine learning
algorithms. These include biased samples, biased labels, diverging training and
testing sets, and cyclical interaction between algorithms, humans, information
consumed by humans, and data consumed by algorithms. Yet, the continuous
interaction between humans and algorithms is rarely taken into account in
machine learning algorithm design and analysis. In this paper, we present a
preliminary theoretical model and analysis of the mutual interaction between
humans and algorithms, based on an iterated learning framework that is inspired
from the study of human language evolution. We also define the concepts of
human and algorithm blind spots and outline machine learning approaches to mend
iterated bias through two novel notions: antidotes and reactive learning.
| Olfa Nasraoui and Patrick Shafto | null | 1608.07895 | null | null |
Relevant based structure learning for feature selection | cs.LG stat.ML | Feature selection is an important task in many problems occurring in pattern
recognition, bioinformatics, machine learning and data mining applications. The
feature selection approach enables us to reduce the computation burden and the
falling accuracy effect of dealing with huge number of features in typical
learning problems. There is a variety of techniques for feature selection in
supervised learning problems based on different selection metrics. In this
paper, we propose a novel unified framework for feature selection built on the
graphical models and information theoretic tools. The proposed approach
exploits the structure learning among features to select more relevant and less
redundant features to the predictive modeling problem according to a primary
novel likelihood based criterion. In line with the selection of the optimal
subset of features through the proposed method, it provides us the Bayesian
network classifier without the additional cost of model training on the
selected subset of features. The optimal properties of our method are
established through empirical studies and computational complexity analysis.
Furthermore the proposed approach is evaluated on a bunch of benchmark datasets
based on the well-known classification algorithms. Extensive experiments
confirm the significant improvement of the proposed approach compared to the
earlier works.
| Hadi Zare and Mojtaba Niazi | 10.1016/j.engappai.2016.06.001 | 1608.07934 | null | null |
Learning-Based Resource Allocation Scheme for TDD-Based CRAN System | cs.NI cs.IT cs.LG math.IT | Explosive growth in the use of smart wireless devices has necessitated the
provision of higher data rates and always-on connectivity, which are the main
motivators for designing the fifth generation (5G) systems. To achieve higher
system efficiency, massive antenna deployment with tight coordination is one
potential strategy for designing 5G systems, but has two types of associated
system overhead. First is the synchronization overhead, which can be reduced by
implementing a cloud radio access network (CRAN)-based architecture design,
that separates the baseband processing and radio access functionality to
achieve better system synchronization. Second is the overhead for acquiring
channel state information (CSI) of the users present in the system, which,
however, increases tremendously when instantaneous CSI is used to serve
high-mobility users. To serve a large number of users, a CRAN system with a
dense deployment of remote radio heads (RRHs) is considered, such that each
user has a line-of-sight (LOS) link with the corresponding RRH. Since, the
trajectory of movement for high-mobility users is predictable; therefore,
fairly accurate position estimates for those users can be obtained, and can be
used for resource allocation to serve the considered users. The resource
allocation is dependent upon various correlated system parameters, and these
correlations can be learned using well-known \emph{machine learning}
algorithms. This paper proposes a novel \emph{learning-based resource
allocation scheme} for time division duplex (TDD) based 5G CRAN systems with
dense RRH deployment, by using only the users' position estimates for resource
allocation, thus avoiding the need for CSI acquisition. This reduces the
overall system overhead significantly, while still achieving near-optimal
system performance; thus, better (effective) system efficiency is achieved.
(See the paper for full abstract)
| Sahar Imtiaz, Hadi Ghauch, M. Mahboob Ur Rahman, George Koudouridis,
and James Gross | null | 1608.07949 | null | null |
Robust Discriminative Clustering with Sparse Regularizers | stat.ML cs.LG | Clustering high-dimensional data often requires some form of dimensionality
reduction, where clustered variables are separated from "noise-looking"
variables. We cast this problem as finding a low-dimensional projection of the
data which is well-clustered. This yields a one-dimensional projection in the
simplest situation with two clusters, and extends naturally to a multi-label
scenario for more than two clusters. In this paper, (a) we first show that this
joint clustering and dimension reduction formulation is equivalent to
previously proposed discriminative clustering frameworks, thus leading to
convex relaxations of the problem, (b) we propose a novel sparse extension,
which is still cast as a convex relaxation and allows estimation in higher
dimensions, (c) we propose a natural extension for the multi-label scenario,
(d) we provide a new theoretical analysis of the performance of these
formulations with a simple probabilistic model, leading to scalings over the
form $d=O(\sqrt{n})$ for the affine invariant case and $d=O(n)$ for the sparse
case, where $n$ is the number of examples and $d$ the ambient dimension, and
finally, (e) we propose an efficient iterative algorithm with running-time
complexity proportional to $O(nd^2)$, improving on earlier algorithms which had
quadratic complexity in the number of examples.
| Nicolas Flammarion and Balamurugan Palaniappan and Francis Bach | null | 1608.08052 | null | null |
Wasserstein Discriminant Analysis | stat.ML cs.LG | Wasserstein Discriminant Analysis (WDA) is a new supervised method that can
improve classification of high-dimensional data by computing a suitable linear
map onto a lower dimensional subspace. Following the blueprint of classical
Linear Discriminant Analysis (LDA), WDA selects the projection matrix that
maximizes the ratio of two quantities: the dispersion of projected points
coming from different classes, divided by the dispersion of projected points
coming from the same class. To quantify dispersion, WDA uses regularized
Wasserstein distances, rather than cross-variance measures which have been
usually considered, notably in LDA. Thanks to the the underlying principles of
optimal transport, WDA is able to capture both global (at distribution scale)
and local (at samples scale) interactions between classes. Regularized
Wasserstein distances can be computed using the Sinkhorn matrix scaling
algorithm; We show that the optimization of WDA can be tackled using automatic
differentiation of Sinkhorn iterations. Numerical experiments show promising
results both in terms of prediction and visualization on toy examples and real
life datasets such as MNIST and on deep features obtained from a subset of the
Caltech dataset.
| R\'emi Flamary, Marco Cuturi, Nicolas Courty, Alain Rakotomamonjy | 10.1007/s10994-018-5717-1 | 1608.08063 | null | null |
Data Poisoning Attacks on Factorization-Based Collaborative Filtering | cs.LG cs.CR cs.IR | Recommendation and collaborative filtering systems are important in modern
information and e-commerce applications. As these systems are becoming
increasingly popular in the industry, their outputs could affect business
decision making, introducing incentives for an adversarial party to compromise
the availability or integrity of such systems. We introduce a data poisoning
attack on collaborative filtering systems. We demonstrate how a powerful
attacker with full knowledge of the learner can generate malicious data so as
to maximize his/her malicious objectives, while at the same time mimicking
normal user behavior to avoid being detected. While the complete knowledge
assumption seems extreme, it enables a robust assessment of the vulnerability
of collaborative filtering schemes to highly motivated attacks. We present
efficient solutions for two popular factorization-based collaborative filtering
algorithms: the \emph{alternative minimization} formulation and the
\emph{nuclear norm minimization} method. Finally, we test the effectiveness of
our proposed algorithms on real-world data and discuss potential defensive
strategies.
| Bo Li, Yining Wang, Aarti Singh, Yevgeniy Vorobeychik | null | 1608.08182 | null | null |
Why does deep and cheap learning work so well? | cond-mat.dis-nn cs.LG cs.NE stat.ML | We show how the success of deep learning could depend not only on mathematics
but also on physics: although well-known mathematical theorems guarantee that
neural networks can approximate arbitrary functions well, the class of
functions of practical interest can frequently be approximated through "cheap
learning" with exponentially fewer parameters than generic ones. We explore how
properties frequently encountered in physics such as symmetry, locality,
compositionality, and polynomial log-probability translate into exceptionally
simple neural networks. We further argue that when the statistical process
generating the data is of a certain hierarchical form prevalent in physics and
machine-learning, a deep neural network can be more efficient than a shallow
one. We formalize these claims using information theory and discuss the
relation to the renormalization group. We prove various "no-flattening
theorems" showing when efficient linear deep networks cannot be accurately
approximated by shallow ones without efficiency loss, for example, we show that
$n$ variables cannot be multiplied using fewer than 2^n neurons in a single
hidden layer.
| Henry W. Lin (Harvard), Max Tegmark (MIT), David Rolnick (MIT) | 10.1007/s10955-017-1836-5 | 1608.08225 | null | null |
Visualizing and Understanding Sum-Product Networks | cs.LG stat.ML | Sum-Product Networks (SPNs) are recently introduced deep tractable
probabilistic models by which several kinds of inference queries can be
answered exactly and in a tractable time. Up to now, they have been largely
used as black box density estimators, assessed only by comparing their
likelihood scores only. In this paper we explore and exploit the inner
representations learned by SPNs. We do this with a threefold aim: first we want
to get a better understanding of the inner workings of SPNs; secondly, we seek
additional ways to evaluate one SPN model and compare it against other
probabilistic models, providing diagnostic tools to practitioners; lastly, we
want to empirically evaluate how good and meaningful the extracted
representations are, as in a classic Representation Learning framework. In
order to do so we revise their interpretation as deep neural networks and we
propose to exploit several visualization techniques on their node activations
and network outputs under different types of inference queries. To investigate
these models as feature extractors, we plug some SPNs, learned in a greedy
unsupervised fashion on image datasets, in supervised classification learning
tasks. We extract several embedding types from node activations by filtering
nodes by their type, by their associated feature abstraction level and by their
scope. In a thorough empirical comparison we prove them to be competitive
against those generated from popular feature extractors as Restricted Boltzmann
Machines. Finally, we investigate embeddings generated from random
probabilistic marginal queries as means to compare other tractable
probabilistic models on a common ground, extending our experiments to Mixtures
of Trees.
| Antonio Vergari and Nicola Di Mauro and Floriana Esposito | 10.1007/s10994-018-5760-y | 1608.08266 | null | null |
Data Dependent Convergence for Distributed Stochastic Optimization | math.OC cs.LG stat.ML | In this dissertation we propose alternative analysis of distributed
stochastic gradient descent (SGD) algorithms that rely on spectral properties
of the data covariance. As a consequence we can relate questions pertaining to
speedups and convergence rates for distributed SGD to the data distribution
instead of the regularity properties of the objective functions. More precisely
we show that this rate depends on the spectral norm of the sample covariance
matrix. An estimate of this norm can provide practitioners with guidance
towards a potential gain in algorithm performance. For example many sparse
datasets with low spectral norm prove to be amenable to gains in distributed
settings. Towards establishing this data dependence we first study a
distributed consensus-based SGD algorithm and show that the rate of convergence
involves the spectral norm of the sample covariance matrix when the underlying
data is assumed to be independent and identically distributed (homogenous).
This dependence allows us to identify network regimes that prove to be
beneficial for datasets with low sample covariance spectral norm. Existing
consensus based analyses prove to be sub-optimal in the homogenous setting. Our
analysis method also allows us to find data-dependent convergence rates as we
limit the amount of communication. Spreading a fixed amount of data across more
nodes slows convergence; in the asymptotic regime we show that adding more
machines can help when minimizing twice-differentiable losses. Since the
mini-batch results don't follow from the consensus results we propose a
different data dependent analysis thereby providing theoretical validation for
why certain datasets are more amenable to mini-batching. We also provide
empirical evidence for results in this thesis.
| Avleen S. Bijral | null | 1608.08337 | null | null |
Multi-Label Classification Method Based on Extreme Learning Machines | cs.LG cs.AI cs.NE | In this paper, an Extreme Learning Machine (ELM) based technique for
Multi-label classification problems is proposed and discussed. In multi-label
classification, each of the input data samples belongs to one or more than one
class labels. The traditional binary and multi-class classification problems
are the subset of the multi-label problem with the number of labels
corresponding to each sample limited to one. The proposed ELM based multi-label
classification technique is evaluated with six different benchmark multi-label
datasets from different domains such as multimedia, text and biology. A
detailed comparison of the results is made by comparing the proposed method
with the results from nine state of the arts techniques for five different
evaluation metrics. The nine methods are chosen from different categories of
multi-label methods. The comparative results shows that the proposed Extreme
Learning Machine based multi-label classification technique is a better
alternative than the existing state of the art methods for multi-label
problems.
| Rajasekar Venkatesan, Meng Joo Er | 10.1109/ICARCV.2014.7064375 | 1608.08435 | null | null |
Applying Naive Bayes Classification to Google Play Apps Categorization | cs.LG cs.IR | There are over one million apps on Google Play Store and over half a million
publishers. Having such a huge number of apps and developers can pose a
challenge to app users and new publishers on the store. Discovering apps can be
challenging if apps are not correctly published in the right category, and, in
turn, reduce earnings for app developers. Additionally, with over 41 categories
on Google Play Store, deciding on the right category to publish an app can be
challenging for developers due to the number of categories they have to choose
from. Machine Learning has been very useful, especially in classification
problems such sentiment analysis, document classification and spam detection.
These strategies can also be applied to app categorization on Google Play Store
to suggest appropriate categories for app publishers using details from their
application.
In this project, we built two variations of the Naive Bayes classifier using
open metadata from top developer apps on Google Play Store in other to classify
new apps on the store. These classifiers are then evaluated using various
evaluation methods and their results compared against each other. The results
show that the Naive Bayes algorithm performs well for our classification
problem and can potentially automate app categorization for Android app
publishers on Google Play Store
| Babatunde Olabenjo | null | 1608.08574 | null | null |
What makes ImageNet good for transfer learning? | cs.CV cs.AI cs.LG | The tremendous success of ImageNet-trained deep features on a wide range of
transfer tasks begs the question: what are the properties of the ImageNet
dataset that are critical for learning good, general-purpose features? This
work provides an empirical investigation of various facets of this question: Is
more pre-training data always better? How does feature quality depend on the
number of training examples per class? Does adding more object classes improve
performance? For the same data budget, how should the data be split into
classes? Is fine-grained recognition necessary for learning good features?
Given the same number of training classes, is it better to have coarse classes
or fine-grained classes? Which is better: more classes or more examples per
class? To answer these and related questions, we pre-trained CNN features on
various subsets of the ImageNet dataset and evaluated transfer performance on
PASCAL detection, PASCAL action classification, and SUN scene classification
tasks. Our overall findings suggest that most changes in the choice of
pre-training data long thought to be critical do not significantly affect
transfer performance.? Given the same number of training classes, is it better
to have coarse classes or fine-grained classes? Which is better: more classes
or more examples per class?
| Minyoung Huh, Pulkit Agrawal, Alexei A. Efros | null | 1608.08614 | null | null |
Pruning Filters for Efficient ConvNets | cs.CV cs.LG | The success of CNNs in various applications is accompanied by a significant
increase in the computation and parameter storage costs. Recent efforts toward
reducing these overheads involve pruning and compressing the weights of various
layers without hurting original accuracy. However, magnitude-based pruning of
weights reduces a significant number of parameters from the fully connected
layers and may not adequately reduce the computation costs in the convolutional
layers due to irregular sparsity in the pruned networks. We present an
acceleration method for CNNs, where we prune filters from CNNs that are
identified as having a small effect on the output accuracy. By removing whole
filters in the network together with their connecting feature maps, the
computation costs are reduced significantly. In contrast to pruning weights,
this approach does not result in sparse connectivity patterns. Hence, it does
not need the support of sparse convolution libraries and can work with existing
efficient BLAS libraries for dense matrix multiplications. We show that even
simple filter pruning techniques can reduce inference costs for VGG-16 by up to
34% and ResNet-110 by up to 38% on CIFAR10 while regaining close to the
original accuracy by retraining the networks.
| Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, Hans Peter Graf | null | 1608.0871 | null | null |
Measuring Machine Intelligence Through Visual Question Answering | cs.AI cs.CL cs.CV cs.LG | As machines have become more intelligent, there has been a renewed interest
in methods for measuring their intelligence. A common approach is to propose
tasks for which a human excels, but one which machines find difficult. However,
an ideal task should also be easy to evaluate and not be easily gameable. We
begin with a case study exploring the recently popular task of image captioning
and its limitations as a task for measuring machine intelligence. An
alternative and more promising task is Visual Question Answering that tests a
machine's ability to reason about language and vision. We describe a dataset
unprecedented in size created for the task that contains over 760,000 human
generated questions about images. Using around 10 million human generated
answers, machines may be easily evaluated.
| C. Lawrence Zitnick, Aishwarya Agrawal, Stanislaw Antol, Margaret
Mitchell, Dhruv Batra, Devi Parikh | null | 1608.08716 | null | null |
hi-RF: Incremental Learning Random Forest for large-scale multi-class
Data Classification | cs.LG stat.ML | In recent years, dynamically growing data and incrementally growing number of
classes pose new challenges to large-scale data classification research. Most
traditional methods struggle to balance the precision and computational burden
when data and its number of classes increased. However, some methods are with
weak precision, and the others are time-consuming. In this paper, we propose an
incremental learning method, namely, heterogeneous incremental Nearest Class
Mean Random Forest (hi-RF), to handle this issue. It is a heterogeneous method
that either replaces trees or updates trees leaves in the random forest
adaptively, to reduce the computational time in comparable performance, when
data of new classes arrive. Specifically, to keep the accuracy, one proportion
of trees are replaced by new NCM decision trees; to reduce the computational
load, the rest trees are updated their leaves probabilities only. Most of all,
out-of-bag estimation and out-of-bag boosting are proposed to balance the
accuracy and the computational efficiency. Fair experiments were conducted and
demonstrated its comparable precision with much less computational time.
| Tingting Xie, Yuxing Peng, Changjian Wang | null | 1608.08761 | null | null |
A Mathematical Framework for Feature Selection from Real-World Data with
Non-Linear Observations | stat.ML cs.LG math.ST stat.TH | In this paper, we study the challenge of feature selection based on a
relatively small collection of sample pairs $\{(x_i, y_i)\}_{1 \leq i \leq m}$.
The observations $y_i \in \mathbb{R}$ are thereby supposed to follow a noisy
single-index model, depending on a certain set of signal variables. A major
difficulty is that these variables usually cannot be observed directly, but
rather arise as hidden factors in the actual data vectors $x_i \in
\mathbb{R}^d$ (feature variables). We will prove that a successful variable
selection is still possible in this setup, even when the applied estimator does
not have any knowledge of the underlying model parameters and only takes the
'raw' samples $\{(x_i, y_i)\}_{1 \leq i \leq m}$ as input. The model
assumptions of our results will be fairly general, allowing for non-linear
observations, arbitrary convex signal structures as well as strictly convex
loss functions. This is particularly appealing for practical purposes, since in
many applications, already standard methods, e.g., the Lasso or logistic
regression, yield surprisingly good outcomes. Apart from a general discussion
of the practical scope of our theoretical findings, we will also derive a
rigorous guarantee for a specific real-world problem, namely sparse feature
extraction from (proteomics-based) mass spectrometry data.
| Martin Genzel and Gitta Kutyniok | null | 1608.08852 | null | null |
A High Speed Multi-label Classifier based on Extreme Learning Machines | cs.LG cs.AI cs.NE | In this paper a high speed neural network classifier based on extreme
learning machines for multi-label classification problem is proposed and
dis-cussed. Multi-label classification is a superset of traditional binary and
multi-class classification problems. The proposed work extends the extreme
learning machine technique to adapt to the multi-label problems. As opposed to
the single-label problem, both the number of labels the sample belongs to, and
each of those target labels are to be identified for multi-label classification
resulting in in-creased complexity. The proposed high speed multi-label
classifier is applied to six benchmark datasets comprising of different
application areas such as multi-media, text and biology. The training time and
testing time of the classifier are compared with those of the state-of-the-arts
methods. Experimental studies show that for all the six datasets, our proposed
technique have faster execution speed and better performance, thereby
outperforming all the existing multi-label clas-sification methods.
| Meng Joo Er, Rajasekar Venkatesan and Ning Wang | 10.1007/978-3-319-28373-9_37 | 1608.08898 | null | null |
A Novel Online Real-time Classifier for Multi-label Data Streams | cs.LG cs.AI cs.NE | In this paper, a novel extreme learning machine based online multi-label
classifier for real-time data streams is proposed. Multi-label classification
is one of the actively researched machine learning paradigm that has gained
much attention in the recent years due to its rapidly increasing real world
applications. In contrast to traditional binary and multi-class classification,
multi-label classification involves association of each of the input samples
with a set of target labels simultaneously. There are no real-time online
neural network based multi-label classifier available in the literature. In
this paper, we exploit the inherent nature of high speed exhibited by the
extreme learning machines to develop a novel online real-time classifier for
multi-label data streams. The developed classifier is experimented with
datasets from different application domains for consistency, performance and
speed. The experimental studies show that the proposed method outperforms the
existing state-of-the-art techniques in terms of speed and accuracy and can
classify multi-label data streams in real-time.
| Rajasekar Venkatesan, Meng Joo Er, Shiqian Wu, Mahardhika Pratama | null | 1608.08905 | null | null |
Recursive Partitioning for Personalization using Observational Data | stat.ML cs.LG | We study the problem of learning to choose from m discrete treatment options
(e.g., news item or medical drug) the one with best causal effect for a
particular instance (e.g., user or patient) where the training data consists of
passive observations of covariates, treatment, and the outcome of the
treatment. The standard approach to this problem is regress and compare: split
the training data by treatment, fit a regression model in each split, and, for
a new instance, predict all m outcomes and pick the best. By reformulating the
problem as a single learning task rather than m separate ones, we propose a new
approach based on recursively partitioning the data into regimes where
different treatments are optimal. We extend this approach to an optimal
partitioning approach that finds a globally optimal partition, achieving a
compact, interpretable, and impactful personalization model. We develop new
tools for validating and evaluating personalization models on observational
data and use these to demonstrate the power of our novel approaches in a
personalized medicine and a job training application.
| Nathan Kallus | null | 1608.08925 | null | null |
Hash2Vec, Feature Hashing for Word Embeddings | cs.CL cs.IR cs.LG | In this paper we propose the application of feature hashing to create word
embeddings for natural language processing. Feature hashing has been used
successfully to create document vectors in related tasks like document
classification. In this work we show that feature hashing can be applied to
obtain word embeddings in linear time with the size of the data. The results
show that this algorithm, that does not need training, is able to capture the
semantic meaning of words. We compare the results against GloVe showing that
they are similar. As far as we know this is the first application of feature
hashing to the word embeddings problem and the results indicate this is a
scalable technique with practical results for NLP applications.
| Luis Argerich, Joaqu\'in Torr\'e Zaffaroni, Mat\'ias J Cano | null | 1608.0894 | null | null |
Robustness of classifiers: from adversarial to random noise | cs.LG cs.CV stat.ML | Several recent works have shown that state-of-the-art classifiers are
vulnerable to worst-case (i.e., adversarial) perturbations of the datapoints.
On the other hand, it has been empirically observed that these same classifiers
are relatively robust to random noise. In this paper, we propose to study a
\textit{semi-random} noise regime that generalizes both the random and
worst-case noise regimes. We propose the first quantitative analysis of the
robustness of nonlinear classifiers in this general noise regime. We establish
precise theoretical bounds on the robustness of classifiers in this general
regime, which depend on the curvature of the classifier's decision boundary.
Our bounds confirm and quantify the empirical observations that classifiers
satisfying curvature constraints are robust to random noise. Moreover, we
quantify the robustness of classifiers in terms of the subspace dimension in
the semi-random noise regime, and show that our bounds remarkably interpolate
between the worst-case and random noise regimes. We perform experiments and
show that the derived bounds provide very accurate estimates when applied to
various state-of-the-art deep neural networks and datasets. This result
suggests bounds on the curvature of the classifiers' decision boundaries that
we support experimentally, and more generally offers important insights onto
the geometry of high dimensional classification problems.
| Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard | null | 1608.08967 | null | null |
Towards Transparent AI Systems: Interpreting Visual Question Answering
Models | cs.CV cs.AI cs.CL cs.LG | Deep neural networks have shown striking progress and obtained
state-of-the-art results in many AI research fields in the recent years.
However, it is often unsatisfying to not know why they predict what they do. In
this paper, we address the problem of interpreting Visual Question Answering
(VQA) models. Specifically, we are interested in finding what part of the input
(pixels in images or words in questions) the VQA model focuses on while
answering the question. To tackle this problem, we use two visualization
techniques -- guided backpropagation and occlusion -- to find important words
in the question and important regions in the image. We then present qualitative
and quantitative analyses of these importance maps. We found that even without
explicit attention mechanisms, VQA models may sometimes be implicitly attending
to relevant regions in the image, and often to appropriate words in the
question.
| Yash Goyal, Akrit Mohapatra, Devi Parikh, Dhruv Batra | null | 1608.08974 | null | null |
Towards Competitive Classifiers for Unbalanced Classification Problems:
A Study on the Performance Scores | stat.ML cs.LG | Although a great methodological effort has been invested in proposing
competitive solutions to the class-imbalance problem, little effort has been
made in pursuing a theoretical understanding of this matter.
In order to shed some light on this topic, we perform, through a novel
framework, an exhaustive analysis of the adequateness of the most commonly used
performance scores to assess this complex scenario. We conclude that using
unweighted H\"older means with exponent $p \leq 1$ to average the recalls of
all the classes produces adequate scores which are capable of determining
whether a classifier is competitive.
Then, we review the major solutions presented in the class-imbalance
literature. Since any learning task can be defined as an optimisation problem
where a loss function, usually connected to a particular score, is minimised,
our goal, here, is to find whether the learning tasks found in the literature
are also oriented to maximise the previously detected adequate scores. We
conclude that they usually maximise the unweighted H\"older mean with $p = 1$
(a-mean).
Finally, we provide bounds on the values of the studied performance scores
which guarantee a classifier with a higher recall than the random classifier in
each and every class.
| Jonathan Ortigosa-Hern\'andez, I\~naki Inza, Jose A. Lozano | null | 1608.08984 | null | null |
Learning Syntactic Program Transformations from Examples | cs.SE cs.LG cs.PL | IDEs, such as Visual Studio, automate common transformations, such as Rename
and Extract Method refactorings. However, extending these catalogs of
transformations is complex and time-consuming. A similar phenomenon appears in
intelligent tutoring systems where instructors have to write cumbersome code
transformations that describe "common faults" to fix similar student
submissions to programming assignments. We present REFAZER, a technique for
automatically generating program transformations. REFAZER builds on the
observation that code edits performed by developers can be used as examples for
learning transformations. Example edits may share the same structure but
involve different variables and subexpressions, which must be generalized in a
transformation at the right level of abstraction. To learn transformations,
REFAZER leverages state-of-the-art programming-by-example methodology using the
following key components: (a) a novel domain-specific language (DSL) for
describing program transformations, (b) domain-specific deductive algorithms
for synthesizing transformations in the DSL, and (c) functions for ranking the
synthesized transformations. We instantiate and evaluate REFAZER in two
domains. First, given examples of edits used by students to fix incorrect
programming assignment submissions, we learn transformations that can fix other
students' submissions with similar faults. In our evaluation conducted on 4
programming tasks performed by 720 students, our technique helped to fix
incorrect submissions for 87% of the students. In the second domain, we use
repetitive edits applied by developers to the same project to synthesize a
program transformation that applies these edits to other locations in the code.
In our evaluation conducted on 59 scenarios of repetitive edits taken from 3 C#
open-source projects, REFAZER learns the intended program transformation in 83%
of the cases.
| Reudismam Rolim, Gustavo Soares, Loris D'Antoni, Oleksandr Polozov,
Sumit Gulwani, Rohit Gheyi, Ryo Suzuki, Bjoern Hartmann | null | 1608.09 | null | null |
A Tutorial on Online Supervised Learning with Applications to Node
Classification in Social Networks | cs.LG stat.ML | We revisit the elegant observation of T. Cover '65 which, perhaps, is not as
well-known to the broader community as it should be. The first goal of the
tutorial is to explain---through the prism of this elementary result---how to
solve certain sequence prediction problems by modeling sets of solutions rather
than the unknown data-generating mechanism. We extend Cover's observation in
several directions and focus on computational aspects of the proposed
algorithms. The applicability of the methods is illustrated on several
examples, including node classification in a network.
The second aim of this tutorial is to demonstrate the following phenomenon:
it is possible to predict as well as a combinatorial "benchmark" for which we
have a certain multiplicative approximation algorithm, even if the exact
computation of the benchmark given all the data is NP-hard. The proposed
prediction methods, therefore, circumvent some of the computational
difficulties associated with finding the best model given the data. These
difficulties arise rather quickly when one attempts to develop a probabilistic
model for graph-based or other problems with a combinatorial structure.
| Alexander Rakhlin and Karthik Sridharan | null | 1608.09014 | null | null |
Neural Network Architecture Optimization through Submodularity and
Supermodularity | stat.ML cs.LG | Deep learning models' architectures, including depth and width, are key
factors influencing models' performance, such as test accuracy and computation
time. This paper solves two problems: given computation time budget, choose an
architecture to maximize accuracy, and given accuracy requirement, choose an
architecture to minimize computation time. We convert this architecture
optimization into a subset selection problem. With accuracy's submodularity and
computation time's supermodularity, we propose efficient greedy optimization
algorithms. The experiments demonstrate our algorithm's ability to find more
accurate models or faster models. By analyzing architecture evolution with
growing time budget, we discuss relationships among accuracy, time and
architecture, and give suggestions on neural network architecture design.
| Junqi Jin, Ziang Yan, Kun Fu, Nan Jiang, Changshui Zhang | null | 1609.00074 | null | null |
A Novel Progressive Learning Technique for Multi-class Classification | cs.LG cs.AI cs.NE | In this paper, a progressive learning technique for multi-class
classification is proposed. This newly developed learning technique is
independent of the number of class constraints and it can learn new classes
while still retaining the knowledge of previous classes. Whenever a new class
(non-native to the knowledge learnt thus far) is encountered, the neural
network structure gets remodeled automatically by facilitating new neurons and
interconnections, and the parameters are calculated in such a way that it
retains the knowledge learnt thus far. This technique is suitable for
real-world applications where the number of classes is often unknown and online
learning from real-time data is required. The consistency and the complexity of
the progressive learning technique are analyzed. Several standard datasets are
used to evaluate the performance of the developed technique. A comparative
study shows that the developed technique is superior.
| Rajasekar Venkatesan, Meng Joo Er | null | 1609.00085 | null | null |
A novel online multi-label classifier for high-speed streaming data
applications | cs.LG cs.AI cs.NE | In this paper, a high-speed online neural network classifier based on extreme
learning machines for multi-label classification is proposed. In multi-label
classification, each of the input data sample belongs to one or more than one
of the target labels. The traditional binary and multi-class classification
where each sample belongs to only one target class forms the subset of
multi-label classification. Multi-label classification problems are far more
complex than binary and multi-class classification problems, as both the number
of target labels and each of the target labels corresponding to each of the
input samples are to be identified. The proposed work exploits the high-speed
nature of the extreme learning machines to achieve real-time multi-label
classification of streaming data. A new threshold-based online sequential
learning algorithm is proposed for high speed and streaming data classification
of multi-label problems. The proposed method is experimented with six different
datasets from different application domains such as multimedia, text, and
biology. The hamming loss, accuracy, training time and testing time of the
proposed technique is compared with nine different state-of-the-art methods.
Experimental studies shows that the proposed technique outperforms the existing
multi-label classifiers in terms of performance and speed.
| Rajasekar Venkatesan, Meng Joo Er, Mihika Dave, Mahardhika Pratama,
Shiqian Wu | 10.1007/s12530-016-9162-8 | 1609.00086 | null | null |
Neural Coarse-Graining: Extracting slowly-varying latent degrees of
freedom with neural networks | cs.AI cs.LG stat.ML | We present a loss function for neural networks that encompasses an idea of
trivial versus non-trivial predictions, such that the network jointly
determines its own prediction goals and learns to satisfy them. This permits
the network to choose sub-sets of a problem which are most amenable to its
abilities to focus on solving, while discarding 'distracting' elements that
interfere with its learning. To do this, the network first transforms the raw
data into a higher-level categorical representation, and then trains a
predictor from that new time series to its future. To prevent a trivial
solution of mapping the signal to zero, we introduce a measure of
non-triviality via a contrast between the prediction error of the learned model
with a naive model of the overall signal statistics. The transform can learn to
discard uninformative and unpredictable components of the signal in favor of
the features which are both highly predictive and highly predictable. This
creates a coarse-grained model of the time-series dynamics, focusing on
predicting the slowly varying latent parameters which control the statistics of
the time-series, rather than predicting the fast details directly. The result
is a semi-supervised algorithm which is capable of extracting latent
parameters, segmenting sections of time-series with differing statistics, and
building a higher-level representation of the underlying dynamics from
unlabeled data.
| Nicholas Guttenberg, Martin Biehl, Ryota Kanai | null | 1609.00116 | null | null |
Reward Augmented Maximum Likelihood for Neural Structured Prediction | cs.LG | A key problem in structured output prediction is direct optimization of the
task reward function that matters for test evaluation. This paper presents a
simple and computationally efficient approach to incorporate task reward into a
maximum likelihood framework. By establishing a link between the log-likelihood
and expected reward objectives, we show that an optimal regularized expected
reward is achieved when the conditional distribution of the outputs given the
inputs is proportional to their exponentiated scaled rewards. Accordingly, we
present a framework to smooth the predictive probability of the outputs using
their corresponding rewards. We optimize the conditional log-probability of
augmented outputs that are sampled proportionally to their exponentiated scaled
rewards. Experiments on neural sequence to sequence models for speech
recognition and machine translation show notable improvements over a maximum
likelihood baseline by using reward augmented maximum likelihood (RAML), where
the rewards are defined as the negative edit distance between the outputs and
the ground truth labels.
| Mohammad Norouzi, Samy Bengio, Zhifeng Chen, Navdeep Jaitly, Mike
Schuster, Yonghui Wu, Dale Schuurmans | null | 1609.0015 | null | null |
Employing traditional machine learning algorithms for big data streams
analysis: the case of object trajectory prediction | cs.LG | In this paper, we model the trajectory of sea vessels and provide a service
that predicts in near-real time the position of any given vessel in 4', 10',
20' and 40' time intervals. We explore the necessary tradeoffs between
accuracy, performance and resource utilization are explored given the large
volume and update rates of input data. We start with building models based on
well-established machine learning algorithms using static datasets and
multi-scan training approaches and identify the best candidate to be used in
implementing a single-pass predictive approach, under real-time constraints.
The results are measured in terms of accuracy and performance and are compared
against the baseline kinematic equations. Results show that it is possible to
efficiently model the trajectory of multiple vessels using a single model,
which is trained and evaluated using an adequately large, static dataset, thus
achieving a significant gain in terms of resource usage while not compromising
accuracy.
| Angelos Valsamis, Konstantinos Tserpes, Dimitrios Zissis, Dimosthenis
Anagnostopoulos, Theodora Varvarigou | 10.1016/j.jss.2016.06.016 | 1609.00203 | null | null |
Ternary Neural Networks for Resource-Efficient AI Applications | cs.LG cs.AI cs.NE | The computation and storage requirements for Deep Neural Networks (DNNs) are
usually high. This issue limits their deployability on ubiquitous computing
devices such as smart phones, wearables and autonomous drones. In this paper,
we propose ternary neural networks (TNNs) in order to make deep learning more
resource-efficient. We train these TNNs using a teacher-student approach based
on a novel, layer-wise greedy methodology. Thanks to our two-stage training
procedure, the teacher network is still able to use state-of-the-art methods
such as dropout and batch normalization to increase accuracy and reduce
training time. Using only ternary weights and activations, the student ternary
network learns to mimic the behavior of its teacher network without using any
multiplication. Unlike its -1,1 binary counterparts, a ternary neural network
inherently prunes the smaller weights by setting them to zero during training.
This makes them sparser and thus more energy-efficient. We design a
purpose-built hardware architecture for TNNs and implement it on FPGA and ASIC.
We evaluate TNNs on several benchmark datasets and demonstrate up to 3.1x
better energy efficiency with respect to the state of the art while also
improving accuracy.
| Hande Alemdar and Vincent Leroy and Adrien Prost-Boucle and
Fr\'ed\'eric P\'etrot | null | 1609.00222 | null | null |
Testing $k$-Monotonicity | cs.DS cs.DM cs.LG | A Boolean $k$-monotone function defined over a finite poset domain ${\cal D}$
alternates between the values $0$ and $1$ at most $k$ times on any ascending
chain in ${\cal D}$. Therefore, $k$-monotone functions are natural
generalizations of the classical monotone functions, which are the $1$-monotone
functions. Motivated by the recent interest in $k$-monotone functions in the
context of circuit complexity and learning theory, and by the central role that
monotonicity testing plays in the context of property testing, we initiate a
systematic study of $k$-monotone functions, in the property testing model. In
this model, the goal is to distinguish functions that are $k$-monotone (or are
close to being $k$-monotone) from functions that are far from being
$k$-monotone. Our results include the following:
- We demonstrate a separation between testing $k$-monotonicity and testing
monotonicity, on the hypercube domain $\{0,1\}^d$, for $k\geq 3$;
- We demonstrate a separation between testing and learning on $\{0,1\}^d$,
for $k=\omega(\log d)$: testing $k$-monotonicity can be performed with
$2^{O(\sqrt d \cdot \log d\cdot \log{1/\varepsilon})}$ queries, while learning
$k$-monotone functions requires $2^{\Omega(k\cdot \sqrt
d\cdot{1/\varepsilon})}$ queries (Blais et al. (RANDOM 2015)).
- We present a tolerant test for functions $f\colon[n]^d\to \{0,1\}$ with
complexity independent of $n$, which makes progress on a problem left open by
Berman et al. (STOC 2014).
Our techniques exploit the testing-by-learning paradigm, use novel
applications of Fourier analysis on the grid $[n]^d$, and draw connections to
distribution testing techniques.
| Cl\'ement L. Canonne, Elena Grigorescu, Siyao Guo, Akash Kumar, Karl
Wimmer | null | 1609.00265 | null | null |
A Unified View of Multi-Label Performance Measures | cs.LG | Multi-label classification deals with the problem where each instance is
associated with multiple class labels. Because evaluation in multi-label
classification is more complicated than single-label setting, a number of
performance measures have been proposed. It is noticed that an algorithm
usually performs differently on different measures. Therefore, it is important
to understand which algorithms perform well on which measure(s) and why. In
this paper, we propose a unified margin view to revisit eleven performance
measures in multi-label classification. In particular, we define label-wise
margin and instance-wise margin, and prove that through maximizing these
margins, different corresponding performance measures will be optimized. Based
on the defined margins, a max-margin approach called LIMO is designed and
empirical results verify our theoretical findings.
| Xi-Zhu Wu and Zhi-Hua Zhou | null | 1609.00288 | null | null |
Least Ambiguous Set-Valued Classifiers with Bounded Error Levels | stat.ME cs.LG stat.ML | In most classification tasks there are observations that are ambiguous and
therefore difficult to correctly label. Set-valued classifiers output sets of
plausible labels rather than a single label, thereby giving a more appropriate
and informative treatment to the labeling of ambiguous instances. We introduce
a framework for multiclass set-valued classification, where the classifiers
guarantee user-defined levels of coverage or confidence (the probability that
the true label is contained in the set) while minimizing the ambiguity (the
expected size of the output). We first derive oracle classifiers assuming the
true distribution to be known. We show that the oracle classifiers are obtained
from level sets of the functions that define the conditional probability of
each class. Then we develop estimators with good asymptotic and finite sample
properties. The proposed estimators build on existing single-label classifiers.
The optimal classifier can sometimes output the empty set, but we provide two
solutions to fix this issue that are suitable for various practical needs.
| Mauricio Sadinle, Jing Lei, Larry Wasserman | 10.1080/01621459.2017.1395341 | 1609.00451 | null | null |
A deep learning model for estimating story points | cs.SE cs.LG stat.ML | Although there has been substantial research in software analytics for effort
estimation in traditional software projects, little work has been done for
estimation in agile projects, especially estimating user stories or issues.
Story points are the most common unit of measure used for estimating the effort
involved in implementing a user story or resolving an issue. In this paper, we
offer for the \emph{first} time a comprehensive dataset for story points-based
estimation that contains 23,313 issues from 16 open source projects. We also
propose a prediction model for estimating story points based on a novel
combination of two powerful deep learning architectures: long short-term memory
and recurrent highway network. Our prediction system is \emph{end-to-end}
trainable from raw input data to prediction outcomes without any manual feature
engineering. An empirical evaluation demonstrates that our approach
consistently outperforms three common effort estimation baselines and two
alternatives in both Mean Absolute Error and the Standardized Accuracy.
| Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Trang Pham, Aditya
Ghose and Tim Menzies | null | 1609.00489 | null | null |
Doubly stochastic large scale kernel learning with the empirical kernel
map | cs.LG | With the rise of big data sets, the popularity of kernel methods declined and
neural networks took over again. The main problem with kernel methods is that
the kernel matrix grows quadratically with the number of data points. Most
attempts to scale up kernel methods solve this problem by discarding data
points or basis functions of some approximation of the kernel map. Here we
present a simple yet effective alternative for scaling up kernel methods that
takes into account the entire data set via doubly stochastic optimization of
the emprical kernel map. The algorithm is straightforward to implement, in
particular in parallel execution settings; it leverages the full power and
versatility of classical kernel functions without the need to explicitly
formulate a kernel map approximation. We provide empirical evidence that the
algorithm works on large data sets.
| Nikolaas Steenbergen, Sebastian Schelter, Felix Bie{\ss}mann | null | 1609.00585 | null | null |
SEBOOST - Boosting Stochastic Learning Using Subspace Optimization
Techniques | cs.CV cs.LG stat.ML | We present SEBOOST, a technique for boosting the performance of existing
stochastic optimization methods. SEBOOST applies a secondary optimization
process in the subspace spanned by the last steps and descent directions. The
method was inspired by the SESOP optimization method for large-scale problems,
and has been adapted for the stochastic learning framework. It can be applied
on top of any existing optimization method with no need to tweak the internal
algorithm. We show that the method is able to boost the performance of
different algorithms, and make them more robust to changes in their
hyper-parameters. As the boosting steps of SEBOOST are applied between large
sets of descent steps, the additional subspace optimization hardly increases
the overall computational burden. We introduce two hyper-parameters that
control the balance between the baseline method and the secondary optimization
process. The method was evaluated on several deep learning tasks, demonstrating
promising results.
| Elad Richardson, Rom Herskovitz, Boris Ginsburg, Michael Zibulevsky | null | 1609.00629 | null | null |
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep
Learning Model | q-bio.BM cs.LG q-bio.QM stat.ML | Recently exciting progress has been made on protein contact prediction, but
the predicted contacts for proteins without many sequence homologs is still of
low quality and not very useful for de novo structure prediction. This paper
presents a new deep learning method that predicts contacts by integrating both
evolutionary coupling (EC) and sequence conservation information through an
ultra-deep neural network formed by two deep residual networks. This deep
neural network allows us to model very complex sequence-contact relationship as
well as long-range inter-contact correlation. Our method greatly outperforms
existing contact prediction methods and leads to much more accurate
contact-assisted protein folding. Tested on three datasets of 579 proteins, the
average top L long-range prediction accuracy obtained our method, the
representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21
and 0.30, respectively; the average top L/10 long-range accuracy of our method,
CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding
using our predicted contacts as restraints can yield correct folds (i.e.,
TMscore>0.6) for 203 test proteins, while that using MetaPSICOV- and
CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively.
Further, our contact-assisted models have much better quality than
template-based models. Using our predicted contacts as restraints, we can (ab
initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast,
when the training proteins of our method are used as templates, homology
modeling can only do so for 10 of them. One interesting finding is that even if
we do not train our prediction models with any membrane proteins, our method
works very well on membrane protein prediction. Finally, in recent blind CAMEO
benchmark our method successfully folded 5 test proteins with a novel fold.
| Sheng Wang, Siqi Sun, Zhen Li, Renyu Zhang and Jinbo Xu | 10.1371/journal.pcbi.1005324 | 1609.0068 | null | null |
Single photon in hierarchical architecture for physical reinforcement
learning: Photon intelligence | cs.LG physics.optics quant-ph | Understanding and using natural processes for intelligent functionalities,
referred to as natural intelligence, has recently attracted interest from a
variety of fields, including post-silicon computing for artificial intelligence
and decision making in the behavioural sciences. In a past study, we
successfully used the wave-particle duality of single photons to solve the
two-armed bandit problem, which constitutes the foundation of reinforcement
learning and decision making. In this study, we propose and confirm a
hierarchical architecture for single-photon-based reinforcement learning and
decision making that verifies the scalability of the principle. Specifically,
the four-armed bandit problem is solved given zero prior knowledge in a
two-layer hierarchical architecture, where polarization is autonomously adapted
in order to effect adequate decision making using single-photon measurements.
In the hierarchical structure, the notion of layer-dependent decisions emerges.
The optimal solutions in the coarse layer and in the fine layer, however,
conflict with each other in some contradictive problems. We show that while
what we call a tournament strategy resolves such contradictions, the
probabilistic nature of single photons allows for the direct location of the
optimal solution even for contradictive problems, hence manifesting the
exploration ability of single photons. This study provides insights into photon
intelligence in hierarchical architectures for future artificial intelligence
as well as the potential of natural processes for intelligent functionalities.
| Makoto Naruse, Martin Berthel, Aur\'elien Drezet, Serge Huant,
Hirokazu Hori, Song-Ju Kim | null | 1609.00686 | null | null |
Convolutional Neural Networks for Text Categorization: Shallow
Word-level vs. Deep Character-level | cs.CL cs.LG stat.ML | This paper reports the performances of shallow word-level convolutional
neural networks (CNN), our earlier work (2015), on the eight datasets with
relatively large training data that were used for testing the very deep
character-level CNN in Conneau et al. (2016). Our findings are as follows. The
shallow word-level CNNs achieve better error rates than the error rates
reported in Conneau et al., though the results should be interpreted with some
consideration due to the unique pre-processing of Conneau et al. The shallow
word-level CNN uses more parameters and therefore requires more storage than
the deep character-level CNN; however, the shallow word-level CNN computes much
faster.
| Rie Johnson and Tong Zhang | null | 1609.00718 | null | null |
Towards End-to-End Reinforcement Learning of Dialogue Agents for
Information Access | cs.CL cs.LG | This paper proposes KB-InfoBot -- a multi-turn dialogue agent which helps
users search Knowledge Bases (KBs) without composing complicated queries. Such
goal-oriented dialogue agents typically need to interact with an external
database to access real-world knowledge. Previous systems achieved this by
issuing a symbolic query to the KB to retrieve entries based on their
attributes. However, such symbolic operations break the differentiability of
the system and prevent end-to-end training of neural dialogue agents. In this
paper, we address this limitation by replacing symbolic queries with an induced
"soft" posterior distribution over the KB that indicates which entities the
user is interested in. Integrating the soft retrieval process with a
reinforcement learner leads to higher task success rate and reward in both
simulations and against real users. We also present a fully neural end-to-end
agent, trained entirely from user feedback, and discuss its application towards
personalized dialogue agents. The source code is available at
https://github.com/MiuLab/KB-InfoBot.
| Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen,
Faisal Ahmed, Li Deng | null | 1609.00777 | null | null |
Randomized Prediction Games for Adversarial Machine Learning | cs.LG cs.GT | In spam and malware detection, attackers exploit randomization to obfuscate
malicious data and increase their chances of evading detection at test time;
e.g., malware code is typically obfuscated using random strings or byte
sequences to hide known exploits. Interestingly, randomization has also been
proposed to improve security of learning algorithms against evasion attacks, as
it results in hiding information about the classifier to the attacker. Recent
work has proposed game-theoretical formulations to learn secure classifiers, by
simulating different evasion attacks and modifying the classification function
accordingly. However, both the classification function and the simulated data
manipulations have been modeled in a deterministic manner, without accounting
for any form of randomization. In this work, we overcome this limitation by
proposing a randomized prediction game, namely, a non-cooperative
game-theoretic formulation in which the classifier and the attacker make
randomized strategy selections according to some probability distribution
defined over the respective strategy set. We show that our approach allows one
to improve the trade-off between attack detection and false alarms with respect
to state-of-the-art secure classifiers, even against attacks that are different
from those hypothesized during design, on application examples including
handwritten digit recognition, spam and malware detection.
| Samuel Rota Bul\`o and Battista Biggio and Ignazio Pillai and Marcello
Pelillo and Fabio Roli | 10.1109/TNNLS.2016.2593488 | 1609.00804 | null | null |
An Online Universal Classifier for Binary, Multi-class and Multi-label
Classification | cs.LG cs.AI cs.NE | Classification involves the learning of the mapping function that associates
input samples to corresponding target label. There are two major categories of
classification problems: Single-label classification and Multi-label
classification. Traditional binary and multi-class classifications are
sub-categories of single-label classification. Several classifiers are
developed for binary, multi-class and multi-label classification problems, but
there are no classifiers available in the literature capable of performing all
three types of classification. In this paper, a novel online universal
classifier capable of performing all the three types of classification is
proposed. Being a high speed online classifier, the proposed technique can be
applied to streaming data applications. The performance of the developed
classifier is evaluated using datasets from binary, multi-class and multi-label
problems. The results obtained are compared with state-of-the-art techniques
from each of the classification types.
| Meng Joo Er, Rajasekar Venkatesan, Ning Wang | null | 1609.00843 | null | null |
Graph-Based Active Learning: A New Look at Expected Error Minimization | stat.ML cs.LG | In graph-based active learning, algorithms based on expected error
minimization (EEM) have been popular and yield good empirical performance. The
exact computation of EEM optimally balances exploration and exploitation. In
practice, however, EEM-based algorithms employ various approximations due to
the computational hardness of exact EEM. This can result in a lack of either
exploration or exploitation, which can negatively impact the effectiveness of
active learning. We propose a new algorithm TSA (Two-Step Approximation) that
balances between exploration and exploitation efficiently while enjoying the
same computational complexity as existing approximations. Finally, we
empirically show the value of balancing between exploration and exploitation in
both toy and real-world datasets where our method outperforms several
state-of-the-art methods.
| Kwang-Sung Jun and Robert Nowak | null | 1609.00845 | null | null |
A Probabilistic Optimum-Path Forest Classifier for Binary Classification
Problems | cs.CV cs.LG stat.ML | Probabilistic-driven classification techniques extend the role of traditional
approaches that output labels (usually integer numbers) only. Such techniques
are more fruitful when dealing with problems where one is not interested in
recognition/identification only, but also into monitoring the behavior of
consumers and/or machines, for instance. Therefore, by means of probability
estimates, one can take decisions to work better in a number of scenarios. In
this paper, we propose a probabilistic-based Optimum Path Forest (OPF)
classifier to handle with binary classification problems, and we show it can be
more accurate than naive OPF in a number of datasets. In addition to being just
more accurate or not, probabilistic OPF turns to be another useful tool to the
scientific community.
| Silas E. N. Fernandes, Danillo R. Pereira, Caio C. O. Ramos, Andre N.
Souza and Joao P. Papa | null | 1609.00878 | null | null |
High Dimensional Human Guided Machine Learning | cs.AI cs.LG stat.ML | Have you ever looked at a machine learning classification model and thought,
I could have made that? Well, that is what we test in this project, comparing
XGBoost trained on human engineered features to training directly on data. The
human engineered features do not outperform XGBoost trained di- rectly on the
data, but they are comparable. This project con- tributes a novel method for
utilizing human created classifi- cation models on high dimensional datasets.
| Eric Holloway and Robert Marks II | null | 1609.00904 | null | null |
Decoding visual stimuli in human brain by using Anatomical Pattern
Analysis on fMRI images | stat.ML cs.LG q-bio.NC | A universal unanswered question in neuroscience and machine learning is
whether computers can decode the patterns of the human brain. Multi-Voxels
Pattern Analysis (MVPA) is a critical tool for addressing this question.
However, there are two challenges in the previous MVPA methods, which include
decreasing sparsity and noises in the extracted features and increasing the
performance of prediction. In overcoming mentioned challenges, this paper
proposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in the
human brain. This framework develops a novel anatomical feature extraction
method and a new imbalance AdaBoost algorithm for binary classification.
Further, it utilizes an Error-Correcting Output Codes (ECOC) method for
multi-class prediction. APA can automatically detect active regions for each
category of the visual stimuli. Moreover, it enables us to combine homogeneous
datasets for applying advanced classification. Experimental studies on 4 visual
categories (words, consonants, objects and scrambled photos) demonstrate that
the proposed approach achieves superior performance to state-of-the-art
methods.
| Muhammad Yousefnezhad and Daoqiang Zhang | null | 1609.00921 | null | null |
Spectral learning of dynamic systems from nonequilibrium data | cs.LG cs.AI cs.SY math.PR physics.data-an | Observable operator models (OOMs) and related models are one of the most
important and powerful tools for modeling and analyzing stochastic systems.
They exactly describe dynamics of finite-rank systems and can be efficiently
and consistently estimated through spectral learning under the assumption of
identically distributed data. In this paper, we investigate the properties of
spectral learning without this assumption due to the requirements of analyzing
large-time scale systems, and show that the equilibrium dynamics of a system
can be extracted from nonequilibrium observation data by imposing an
equilibrium constraint. In addition, we propose a binless extension of spectral
learning for continuous data. In comparison with the other continuous-valued
spectral algorithms, the binless algorithm can achieve consistent estimation of
equilibrium dynamics with only linear complexity.
| Hao Wu and Frank No\'e | null | 1609.00932 | null | null |
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