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Affinity CNN: Learning Pixel-Centric Pairwise Relations for
Figure/Ground Embedding | cs.CV cs.LG cs.NE | Spectral embedding provides a framework for solving perceptual organization
problems, including image segmentation and figure/ground organization. From an
affinity matrix describing pairwise relationships between pixels, it clusters
pixels into regions, and, using a complex-valued extension, orders pixels
according to layer. We train a convolutional neural network (CNN) to directly
predict the pairwise relationships that define this affinity matrix. Spectral
embedding then resolves these predictions into a globally-consistent
segmentation and figure/ground organization of the scene. Experiments
demonstrate significant benefit to this direct coupling compared to prior works
which use explicit intermediate stages, such as edge detection, on the pathway
from image to affinities. Our results suggest spectral embedding as a powerful
alternative to the conditional random field (CRF)-based globalization schemes
typically coupled to deep neural networks.
| Michael Maire, Takuya Narihira, Stella X. Yu | null | 1512.02767 | null | null |
Bigger Buffer k-d Trees on Multi-Many-Core Systems | cs.DC cs.DS cs.LG | A buffer k-d tree is a k-d tree variant for massively-parallel nearest
neighbor search. While providing valuable speed-ups on modern many-core devices
in case both a large number of reference and query points are given, buffer k-d
trees are limited by the amount of points that can fit on a single device. In
this work, we show how to modify the original data structure and the associated
workflow to make the overall approach capable of dealing with massive data
sets. We further provide a simple yet efficient way of using multiple devices
given in a single workstation. The applicability of the modified framework is
demonstrated in the context of astronomy, a field that is faced with huge
amounts of data.
| Fabian Gieseke and Cosmin Eugen Oancea and Ashish Mahabal and
Christian Igel and Tom Heskes | null | 1512.02831 | null | null |
Multi-Player Bandits -- a Musical Chairs Approach | cs.LG stat.ML | We consider a variant of the stochastic multi-armed bandit problem, where
multiple players simultaneously choose from the same set of arms and may
collide, receiving no reward. This setting has been motivated by problems
arising in cognitive radio networks, and is especially challenging under the
realistic assumption that communication between players is limited. We provide
a communication-free algorithm (Musical Chairs) which attains constant regret
with high probability, as well as a sublinear-regret, communication-free
algorithm (Dynamic Musical Chairs) for the more difficult setting of players
dynamically entering and leaving throughout the game. Moreover, both algorithms
do not require prior knowledge of the number of players. To the best of our
knowledge, these are the first communication-free algorithms with these types
of formal guarantees. We also rigorously compare our algorithms to previous
works, and complement our theoretical findings with experiments.
| Jonathan Rosenski, Ohad Shamir, Liran Szlak | null | 1512.02866 | null | null |
Where You Are Is Who You Are: User Identification by Matching Statistics | cs.LG cs.CR cs.SI stat.AP stat.ML | Most users of online services have unique behavioral or usage patterns. These
behavioral patterns can be exploited to identify and track users by using only
the observed patterns in the behavior. We study the task of identifying users
from statistics of their behavioral patterns. Specifically, we focus on the
setting in which we are given histograms of users' data collected during two
different experiments. We assume that, in the first dataset, the users'
identities are anonymized or hidden and that, in the second dataset, their
identities are known. We study the task of identifying the users by matching
the histograms of their data in the first dataset with the histograms from the
second dataset. In recent works, the optimal algorithm for this user
identification task is introduced. In this paper, we evaluate the effectiveness
of this method on three different types of datasets and in multiple scenarios.
Using datasets such as call data records, web browsing histories, and GPS
trajectories, we show that a large fraction of users can be easily identified
given only histograms of their data; hence these histograms can act as users'
fingerprints. We also verify that simultaneous identification of users achieves
better performance compared to one-by-one user identification. We show that
using the optimal method for identification gives higher identification
accuracy than heuristics-based approaches in practical scenarios. The accuracy
obtained under this optimal method can thus be used to quantify the maximum
level of user identification that is possible in such settings. We show that
the key factors affecting the accuracy of the optimal identification algorithm
are the duration of the data collection, the number of users in the anonymized
dataset, and the resolution of the dataset. We analyze the effectiveness of
k-anonymization in resisting user identification attacks on these datasets.
| Farid M. Naini, Jayakrishnan Unnikrishnan, Patrick Thiran, Martin
Vetterli | 10.1109/TIFS.2015.2498131 | 1512.02896 | null | null |
Efficient Distributed SGD with Variance Reduction | cs.LG cs.DC math.OC stat.ML | Stochastic Gradient Descent (SGD) has become one of the most popular
optimization methods for training machine learning models on massive datasets.
However, SGD suffers from two main drawbacks: (i) The noisy gradient updates
have high variance, which slows down convergence as the iterates approach the
optimum, and (ii) SGD scales poorly in distributed settings, typically
experiencing rapidly decreasing marginal benefits as the number of workers
increases. In this paper, we propose a highly parallel method, CentralVR, that
uses error corrections to reduce the variance of SGD gradient updates, and
scales linearly with the number of worker nodes. CentralVR enjoys low iteration
complexity, provably linear convergence rates, and exhibits linear performance
gains up to hundreds of cores for massive datasets. We compare CentralVR to
state-of-the-art parallel stochastic optimization methods on a variety of
models and datasets, and find that our proposed methods exhibit stronger
scaling than other SGD variants.
| Soham De and Tom Goldstein | null | 1512.02970 | null | null |
Partial Reinitialisation for Optimisers | stat.ML cs.LG cs.NE math.OC | Heuristic optimisers which search for an optimal configuration of variables
relative to an objective function often get stuck in local optima where the
algorithm is unable to find further improvement. The standard approach to
circumvent this problem involves periodically restarting the algorithm from
random initial configurations when no further improvement can be found. We
propose a method of partial reinitialization, whereby, in an attempt to find a
better solution, only sub-sets of variables are re-initialised rather than the
whole configuration. Much of the information gained from previous runs is hence
retained. This leads to significant improvements in the quality of the solution
found in a given time for a variety of optimisation problems in machine
learning.
| Ilia Zintchenko, Matthew Hastings, Nathan Wiebe, Ethan Brown, Matthias
Troyer | null | 1512.03025 | null | null |
Gated networks: an inventory | cs.LG | Gated networks are networks that contain gating connections, in which the
outputs of at least two neurons are multiplied. Initially, gated networks were
used to learn relationships between two input sources, such as pixels from two
images. More recently, they have been applied to learning activity recognition
or multi-modal representations. The aims of this paper are threefold: 1) to
explain the basic computations in gated networks to the non-expert, while
adopting a standpoint that insists on their symmetric nature. 2) to serve as a
quick reference guide to the recent literature, by providing an inventory of
applications of these networks, as well as recent extensions to the basic
architecture. 3) to suggest future research directions and applications.
| Olivier Sigaud and Cl\'ement Masson and David Filliat and Freek Stulp | null | 1512.03201 | null | null |
Norm-Free Radon-Nikodym Approach to Machine Learning | cs.LG stat.ML | For Machine Learning (ML) classification problem, where a vector of
$\mathbf{x}$--observations (values of attributes) is mapped to a single $y$
value (class label), a generalized Radon--Nikodym type of solution is proposed.
Quantum--mechanics --like probability states $\psi^2(\mathbf{x})$ are
considered and "Cluster Centers", corresponding to the extremums of
$<y\psi^2(\mathbf{x})>/<\psi^2(\mathbf{x})>$, are found from generalized
eigenvalues problem. The eigenvalues give possible $y^{[i]}$ outcomes and
corresponding to them eigenvectors $\psi^{[i]}(\mathbf{x})$ define "Cluster
Centers". The projection of a $\psi$ state, localized at given $\mathbf{x}$ to
classify, on these eigenvectors define the probability of $y^{[i]}$ outcome,
thus avoiding using a norm ($L^2$ or other types), required for "quality
criteria" in a typical Machine Learning technique. A coverage of each `Cluster
Center" is calculated, what potentially allows to separate system properties
(described by $y^{[i]}$ outcomes) and system testing conditions (described by
$C^{[i]}$ coverage). As an example of such application $y$ distribution
estimator is proposed in a form of pairs $(y^{[i]},C^{[i]})$, that can be
considered as Gauss quadratures generalization. This estimator allows to
perform $y$ probability distribution estimation in a strongly non--Gaussian
case.
| Vladislav Gennadievich Malyshkin | null | 1512.03219 | null | null |
Convolutional Monte Carlo Rollouts in Go | cs.LG cs.AI | In this work, we present a MCTS-based Go-playing program which uses
convolutional networks in all parts. Our method performs MCTS in batches,
explores the Monte Carlo search tree using Thompson sampling and a
convolutional network, and evaluates convnet-based rollouts on the GPU. We
achieve strong win rates against open source Go programs and attain competitive
results against state of the art convolutional net-based Go-playing programs.
| Peter H. Jin and Kurt Keutzer | null | 1512.03375 | null | null |
Boosted Sparse Non-linear Distance Metric Learning | stat.ML cs.LG | This paper proposes a boosting-based solution addressing metric learning
problems for high-dimensional data. Distance measures have been used as natural
measures of (dis)similarity and served as the foundation of various learning
methods. The efficiency of distance-based learning methods heavily depends on
the chosen distance metric. With increasing dimensionality and complexity of
data, however, traditional metric learning methods suffer from poor scalability
and the limitation due to linearity as the true signals are usually embedded
within a low-dimensional nonlinear subspace. In this paper, we propose a
nonlinear sparse metric learning algorithm via boosting. We restructure a
global optimization problem into a forward stage-wise learning of weak learners
based on a rank-one decomposition of the weight matrix in the Mahalanobis
distance metric. A gradient boosting algorithm is devised to obtain a sparse
rank-one update of the weight matrix at each step. Nonlinear features are
learned by a hierarchical expansion of interactions incorporated within the
boosting algorithm. Meanwhile, an early stopping rule is imposed to control the
overall complexity of the learned metric. As a result, our approach guarantees
three desirable properties of the final metric: positive semi-definiteness, low
rank and element-wise sparsity. Numerical experiments show that our learning
model compares favorably with the state-of-the-art methods in the current
literature of metric learning.
| Yuting Ma, Tian Zheng | null | 1512.03396 | null | null |
Predicting proximity with ambient mobile sensors for non-invasive health
diagnostics | cs.CY cs.LG | Modern smart phones are becoming helpful in the areas of Internet-Of-Things
(IoT) and ambient health intelligence. By learning data from several mobile
sensors, we detect nearness of the human body to a mobile device in a
three-dimensional space with no physical contact with the device for
non-invasive health diagnostics. We show that the human body generates wave
patterns that interact with other naturally occurring ambient signals that
could be measured by mobile sensors, such as, temperature, humidity, magnetic
field, acceleration, gravity, and light. This interaction consequentially
alters the patterns of the naturally occurring signals, and thus, exhibits
characteristics that could be learned to predict the nearness of the human body
to a mobile device, hence provide diagnostic information for medical
practitioners. Our prediction technique achieved 88.75% accuracy and 88.3%
specificity.
| Sylvester Olubolu Orimaye, Foo Chuan Leong, Chen Hui Lee, Eddy Cheng
Han Ng | 10.1109/MICC.2015.7725398 | 1512.03423 | null | null |
A Unified Approach to Error Bounds for Structured Convex Optimization
Problems | math.OC cs.LG math.NA stat.ML | Error bounds, which refer to inequalities that bound the distance of vectors
in a test set to a given set by a residual function, have proven to be
extremely useful in analyzing the convergence rates of a host of iterative
methods for solving optimization problems. In this paper, we present a new
framework for establishing error bounds for a class of structured convex
optimization problems, in which the objective function is the sum of a smooth
convex function and a general closed proper convex function. Such a class
encapsulates not only fairly general constrained minimization problems but also
various regularized loss minimization formulations in machine learning, signal
processing, and statistics. Using our framework, we show that a number of
existing error bound results can be recovered in a unified and transparent
manner. To further demonstrate the power of our framework, we apply it to a
class of nuclear-norm regularized loss minimization problems and establish a
new error bound for this class under a strict complementarity-type regularity
condition. We then complement this result by constructing an example to show
that the said error bound could fail to hold without the regularity condition.
Consequently, we obtain a rather complete answer to a question raised by Tseng.
We believe that our approach will find further applications in the study of
error bounds for structured convex optimization problems.
| Zirui Zhou, Anthony Man-Cho So | null | 1512.03518 | null | null |
Distilling Knowledge from Deep Networks with Applications to Healthcare
Domain | stat.ML cs.LG | Exponential growth in Electronic Healthcare Records (EHR) has resulted in new
opportunities and urgent needs for discovery of meaningful data-driven
representations and patterns of diseases in Computational Phenotyping research.
Deep Learning models have shown superior performance for robust prediction in
computational phenotyping tasks, but suffer from the issue of model
interpretability which is crucial for clinicians involved in decision-making.
In this paper, we introduce a novel knowledge-distillation approach called
Interpretable Mimic Learning, to learn interpretable phenotype features for
making robust prediction while mimicking the performance of deep learning
models. Our framework uses Gradient Boosting Trees to learn interpretable
features from deep learning models such as Stacked Denoising Autoencoder and
Long Short-Term Memory. Exhaustive experiments on a real-world clinical
time-series dataset show that our method obtains similar or better performance
than the deep learning models, and it provides interpretable phenotypes for
clinical decision making.
| Zhengping Che, Sanjay Purushotham, Robinder Khemani, Yan Liu | null | 1512.03542 | null | null |
Words are not Equal: Graded Weighting Model for building Composite
Document Vectors | cs.CL cs.LG cs.NE | Despite the success of distributional semantics, composing phrases from word
vectors remains an important challenge. Several methods have been tried for
benchmark tasks such as sentiment classification, including word vector
averaging, matrix-vector approaches based on parsing, and on-the-fly learning
of paragraph vectors. Most models usually omit stop words from the composition.
Instead of such an yes-no decision, we consider several graded schemes where
words are weighted according to their discriminatory relevance with respect to
its use in the document (e.g., idf). Some of these methods (particularly
tf-idf) are seen to result in a significant improvement in performance over
prior state of the art. Further, combining such approaches into an ensemble
based on alternate classifiers such as the RNN model, results in an 1.6%
performance improvement on the standard IMDB movie review dataset, and a 7.01%
improvement on Amazon product reviews. Since these are language free models and
can be obtained in an unsupervised manner, they are of interest also for
under-resourced languages such as Hindi as well and many more languages. We
demonstrate the language free aspects by showing a gain of 12% for two review
datasets over earlier results, and also release a new larger dataset for future
testing (Singh,2015).
| Pranjal Singh, Amitabha Mukerjee | null | 1512.03549 | null | null |
Efficient Deep Feature Learning and Extraction via StochasticNets | cs.LG stat.ML | Deep neural networks are a powerful tool for feature learning and extraction
given their ability to model high-level abstractions in highly complex data.
One area worth exploring in feature learning and extraction using deep neural
networks is efficient neural connectivity formation for faster feature learning
and extraction. Motivated by findings of stochastic synaptic connectivity
formation in the brain as well as the brain's uncanny ability to efficiently
represent information, we propose the efficient learning and extraction of
features via StochasticNets, where sparsely-connected deep neural networks can
be formed via stochastic connectivity between neurons. To evaluate the
feasibility of such a deep neural network architecture for feature learning and
extraction, we train deep convolutional StochasticNets to learn abstract
features using the CIFAR-10 dataset, and extract the learned features from
images to perform classification on the SVHN and STL-10 datasets. Experimental
results show that features learned using deep convolutional StochasticNets,
with fewer neural connections than conventional deep convolutional neural
networks, can allow for better or comparable classification accuracy than
conventional deep neural networks: relative test error decrease of ~4.5% for
classification on the STL-10 dataset and ~1% for classification on the SVHN
dataset. Furthermore, it was shown that the deep features extracted using deep
convolutional StochasticNets can provide comparable classification accuracy
even when only 10% of the training data is used for feature learning. Finally,
it was also shown that significant gains in feature extraction speed can be
achieved in embedded applications using StochasticNets. As such, StochasticNets
allow for faster feature learning and extraction performance while facilitate
for better or comparable accuracy performances.
| Mohammad Javad Shafiee, Parthipan Siva, Paul Fieguth, and Alexander
Wong | null | 1512.03844 | null | null |
Active Sampler: Light-weight Accelerator for Complex Data Analytics at
Scale | cs.DB cs.LG stat.ML | Recent years have witnessed amazing outcomes from "Big Models" trained by
"Big Data". Most popular algorithms for model training are iterative. Due to
the surging volumes of data, we can usually afford to process only a fraction
of the training data in each iteration. Typically, the data are either
uniformly sampled or sequentially accessed.
In this paper, we study how the data access pattern can affect model
training. We propose an Active Sampler algorithm, where training data with more
"learning value" to the model are sampled more frequently. The goal is to focus
training effort on valuable instances near the classification boundaries,
rather than evident cases, noisy data or outliers. We show the correctness and
optimality of Active Sampler in theory, and then develop a light-weight
vectorized implementation. Active Sampler is orthogonal to most approaches
optimizing the efficiency of large-scale data analytics, and can be applied to
most analytics models trained by stochastic gradient descent (SGD) algorithm.
Extensive experimental evaluations demonstrate that Active Sampler can speed up
the training procedure of SVM, feature selection and deep learning, for
comparable training quality by 1.6-2.2x.
| Jinyang Gao, H.V.Jagadish, Beng Chin Ooi | null | 1512.03880 | null | null |
Quantum assisted Gaussian process regression | quant-ph cs.LG stat.ML | Gaussian processes (GP) are a widely used model for regression problems in
supervised machine learning. Implementation of GP regression typically requires
$O(n^3)$ logic gates. We show that the quantum linear systems algorithm [Harrow
et al., Phys. Rev. Lett. 103, 150502 (2009)] can be applied to Gaussian process
regression (GPR), leading to an exponential reduction in computation time in
some instances. We show that even in some cases not ideally suited to the
quantum linear systems algorithm, a polynomial increase in efficiency still
occurs.
| Zhikuan Zhao, Jack K. Fitzsimons and Joseph F. Fitzsimons | 10.1103/PhysRevA.99.052331 | 1512.03929 | null | null |
Active Distance-Based Clustering using K-medoids | cs.LG | k-medoids algorithm is a partitional, centroid-based clustering algorithm
which uses pairwise distances of data points and tries to directly decompose
the dataset with $n$ points into a set of $k$ disjoint clusters. However,
k-medoids itself requires all distances between data points that are not so
easy to get in many applications. In this paper, we introduce a new method
which requires only a small proportion of the whole set of distances and makes
an effort to estimate an upper-bound for unknown distances using the inquired
ones. This algorithm makes use of the triangle inequality to calculate an
upper-bound estimation of the unknown distances. Our method is built upon a
recursive approach to cluster objects and to choose some points actively from
each bunch of data and acquire the distances between these prominent points
from oracle. Experimental results show that the proposed method using only a
small subset of the distances can find proper clustering on many real-world and
synthetic datasets.
| Mehrdad Ghadiri, Amin Aghaee, Mahdieh Soleymani Baghshah | null | 1512.03953 | null | null |
The Power of Depth for Feedforward Neural Networks | cs.LG cs.NE stat.ML | We show that there is a simple (approximately radial) function on $\reals^d$,
expressible by a small 3-layer feedforward neural networks, which cannot be
approximated by any 2-layer network, to more than a certain constant accuracy,
unless its width is exponential in the dimension. The result holds for
virtually all known activation functions, including rectified linear units,
sigmoids and thresholds, and formally demonstrates that depth -- even if
increased by 1 -- can be exponentially more valuable than width for standard
feedforward neural networks. Moreover, compared to related results in the
context of Boolean functions, our result requires fewer assumptions, and the
proof techniques and construction are very different.
| Ronen Eldan and Ohad Shamir | null | 1512.03965 | null | null |
Quantum Privacy-Preserving Data Mining | quant-ph cs.CR cs.DB cs.LG | Data mining is a key technology in big data analytics and it can discover
understandable knowledge (patterns) hidden in large data sets. Association rule
is one of the most useful knowledge patterns, and a large number of algorithms
have been developed in the data mining literature to generate association rules
corresponding to different problems and situations. Privacy becomes a vital
issue when data mining is used to sensitive data sets like medical records,
commercial data sets and national security. In this Letter, we present a
quantum protocol for mining association rules on vertically partitioned
databases. The quantum protocol can improve the privacy level preserved by
known classical protocols and at the same time it can exponentially reduce the
computational complexity and communication cost.
| Shenggang Ying, Mingsheng Ying and Yuan Feng | null | 1512.04009 | null | null |
L1-Regularized Distributed Optimization: A Communication-Efficient
Primal-Dual Framework | cs.LG | Despite the importance of sparsity in many large-scale applications, there
are few methods for distributed optimization of sparsity-inducing objectives.
In this paper, we present a communication-efficient framework for
L1-regularized optimization in the distributed environment. By viewing
classical objectives in a more general primal-dual setting, we develop a new
class of methods that can be efficiently distributed and applied to common
sparsity-inducing models, such as Lasso, sparse logistic regression, and
elastic net-regularized problems. We provide theoretical convergence guarantees
for our framework, and demonstrate its efficiency and flexibility with a
thorough experimental comparison on Amazon EC2. Our proposed framework yields
speedups of up to 50x as compared to current state-of-the-art methods for
distributed L1-regularized optimization.
| Virginia Smith, Simone Forte, Michael I. Jordan, Martin Jaggi | null | 1512.04011 | null | null |
Tracking Idea Flows between Social Groups | cs.SI cs.LG | In many applications, ideas that are described by a set of words often flow
between different groups. To facilitate users in analyzing the flow, we present
a method to model the flow behaviors that aims at identifying the lead-lag
relationships between word clusters of different user groups. In particular, an
improved Bayesian conditional cointegration based on dynamic time warping is
employed to learn links between words in different groups. A tensor-based
technique is developed to cluster these linked words into different clusters
(ideas) and track the flow of ideas. The main feature of the tensor
representation is that we introduce two additional dimensions to represent both
time and lead-lag relationships. Experiments on both synthetic and real
datasets show that our method is more effective than methods based on
traditional clustering techniques and achieves better accuracy. A case study
was conducted to demonstrate the usefulness of our method in helping users
understand the flow of ideas between different user groups on social media
| Yangxin Zhong, Shixia Liu, Xiting Wang, Jiannan Xiao, and Yangqiu Song | null | 1512.04036 | null | null |
Distributed Optimization with Arbitrary Local Solvers | cs.LG math.OC | With the growth of data and necessity for distributed optimization methods,
solvers that work well on a single machine must be re-designed to leverage
distributed computation. Recent work in this area has been limited by focusing
heavily on developing highly specific methods for the distributed environment.
These special-purpose methods are often unable to fully leverage the
competitive performance of their well-tuned and customized single machine
counterparts. Further, they are unable to easily integrate improvements that
continue to be made to single machine methods. To this end, we present a
framework for distributed optimization that both allows the flexibility of
arbitrary solvers to be used on each (single) machine locally, and yet
maintains competitive performance against other state-of-the-art
special-purpose distributed methods. We give strong primal-dual convergence
rate guarantees for our framework that hold for arbitrary local solvers. We
demonstrate the impact of local solver selection both theoretically and in an
extensive experimental comparison. Finally, we provide thorough implementation
details for our framework, highlighting areas for practical performance gains.
| Chenxin Ma, Jakub Kone\v{c}n\'y, Martin Jaggi, Virginia Smith, Michael
I. Jordan, Peter Richt\'arik and Martin Tak\'a\v{c} | null | 1512.04039 | null | null |
Big Data Scaling through Metric Mapping: Exploiting the Remarkable
Simplicity of Very High Dimensional Spaces using Correspondence Analysis | stat.ML cs.LG | We present new findings in regard to data analysis in very high dimensional
spaces. We use dimensionalities up to around one million. A particular benefit
of Correspondence Analysis is its suitability for carrying out an orthonormal
mapping, or scaling, of power law distributed data. Power law distributed data
are found in many domains. Correspondence factor analysis provides a latent
semantic or principal axes mapping. Our experiments use data from digital
chemistry and finance, and other statistically generated data.
| Fionn Murtagh | null | 1512.04052 | null | null |
True Online Temporal-Difference Learning | cs.AI cs.LG | The temporal-difference methods TD($\lambda$) and Sarsa($\lambda$) form a
core part of modern reinforcement learning. Their appeal comes from their good
performance, low computational cost, and their simple interpretation, given by
their forward view. Recently, new versions of these methods were introduced,
called true online TD($\lambda$) and true online Sarsa($\lambda$), respectively
(van Seijen & Sutton, 2014). These new versions maintain an exact equivalence
with the forward view at all times, whereas the traditional versions only
approximate it for small step-sizes. We hypothesize that these true online
methods not only have better theoretical properties, but also dominate the
regular methods empirically. In this article, we put this hypothesis to the
test by performing an extensive empirical comparison. Specifically, we compare
the performance of true online TD($\lambda$)/Sarsa($\lambda$) with regular
TD($\lambda$)/Sarsa($\lambda$) on random MRPs, a real-world myoelectric
prosthetic arm, and a domain from the Arcade Learning Environment. We use
linear function approximation with tabular, binary, and non-binary features.
Our results suggest that the true online methods indeed dominate the regular
methods. Across all domains/representations the learning speed of the true
online methods are often better, but never worse than that of the regular
methods. An additional advantage is that no choice between traces has to be
made for the true online methods. Besides the empirical results, we provide an
in-depth analysis of the theory behind true online temporal-difference
learning. In addition, we show that new true online temporal-difference methods
can be derived by making changes to the online forward view and then rewriting
the update equations.
| Harm van Seijen and A. Rupam Mahmood and Patrick M. Pilarski and
Marlos C. Machado and Richard S. Sutton | null | 1512.04087 | null | null |
Stack Exchange Tagger | cs.CL cs.LG | The goal of our project is to develop an accurate tagger for questions posted
on Stack Exchange. Our problem is an instance of the more general problem of
developing accurate classifiers for large scale text datasets. We are tackling
the multilabel classification problem where each item (in this case, question)
can belong to multiple classes (in this case, tags). We are predicting the tags
(or keywords) for a particular Stack Exchange post given only the question text
and the title of the post. In the process, we compare the performance of
Support Vector Classification (SVC) for different kernel functions, loss
function, etc. We found linear SVC with Crammer Singer technique produces best
results.
| Sanket Mehta, Shagun Sodhani | null | 1512.04092 | null | null |
Policy Gradient Methods for Off-policy Control | cs.AI cs.LG | Off-policy learning refers to the problem of learning the value function of a
way of behaving, or policy, while following a different policy. Gradient-based
off-policy learning algorithms, such as GTD and TDC/GQ, converge even when
using function approximation and incremental updates. However, they have been
developed for the case of a fixed behavior policy. In control problems, one
would like to adapt the behavior policy over time to become more greedy with
respect to the existing value function. In this paper, we present the first
gradient-based learning algorithms for this problem, which rely on the
framework of policy gradient in order to modify the behavior policy. We present
derivations of the algorithms, a convergence theorem, and empirical evidence
showing that they compare favorably to existing approaches.
| Lucas Lehnert and Doina Precup | null | 1512.04105 | null | null |
Fighting Bandits with a New Kind of Smoothness | cs.LG cs.GT stat.ML | We define a novel family of algorithms for the adversarial multi-armed bandit
problem, and provide a simple analysis technique based on convex smoothing. We
prove two main results. First, we show that regularization via the
\emph{Tsallis entropy}, which includes EXP3 as a special case, achieves the
$\Theta(\sqrt{TN})$ minimax regret. Second, we show that a wide class of
perturbation methods achieve a near-optimal regret as low as $O(\sqrt{TN \log
N})$ if the perturbation distribution has a bounded hazard rate. For example,
the Gumbel, Weibull, Frechet, Pareto, and Gamma distributions all satisfy this
key property.
| Jacob Abernethy, Chansoo Lee, Ambuj Tewari | null | 1512.04152 | null | null |
Preconditioned Stochastic Gradient Descent | stat.ML cs.LG | Stochastic gradient descent (SGD) still is the workhorse for many practical
problems. However, it converges slow, and can be difficult to tune. It is
possible to precondition SGD to accelerate its convergence remarkably. But many
attempts in this direction either aim at solving specialized problems, or
result in significantly more complicated methods than SGD. This paper proposes
a new method to estimate a preconditioner such that the amplitudes of
perturbations of preconditioned stochastic gradient match that of the
perturbations of parameters to be optimized in a way comparable to Newton
method for deterministic optimization. Unlike the preconditioners based on
secant equation fitting as done in deterministic quasi-Newton methods, which
assume positive definite Hessian and approximate its inverse, the new
preconditioner works equally well for both convex and non-convex optimizations
with exact or noisy gradients. When stochastic gradient is used, it can
naturally damp the gradient noise to stabilize SGD. Efficient preconditioner
estimation methods are developed, and with reasonable simplifications, they are
applicable to large scaled problems. Experimental results demonstrate that
equipped with the new preconditioner, without any tuning effort, preconditioned
SGD can efficiently solve many challenging problems like the training of a deep
neural network or a recurrent neural network requiring extremely long term
memories.
| Xi-Lin Li | 10.1109/TNNLS.2017.2672978 | 1512.04202 | null | null |
Small-footprint Deep Neural Networks with Highway Connections for Speech
Recognition | cs.CL cs.LG cs.NE | For speech recognition, deep neural networks (DNNs) have significantly
improved the recognition accuracy in most of benchmark datasets and application
domains. However, compared to the conventional Gaussian mixture models,
DNN-based acoustic models usually have much larger number of model parameters,
making it challenging for their applications in resource constrained platforms,
e.g., mobile devices. In this paper, we study the application of the recently
proposed highway network to train small-footprint DNNs, which are {\it thinner}
and {\it deeper}, and have significantly smaller number of model parameters
compared to conventional DNNs. We investigated this approach on the AMI meeting
speech transcription corpus which has around 70 hours of audio data. The
highway neural networks constantly outperformed their plain DNN counterparts,
and the number of model parameters can be reduced significantly without
sacrificing the recognition accuracy.
| Liang Lu and Steve Renals | null | 1512.04280 | null | null |
Origami: A 803 GOp/s/W Convolutional Network Accelerator | cs.CV cs.AI cs.LG cs.NE | An ever increasing number of computer vision and image/video processing
challenges are being approached using deep convolutional neural networks,
obtaining state-of-the-art results in object recognition and detection,
semantic segmentation, action recognition, optical flow and superresolution.
Hardware acceleration of these algorithms is essential to adopt these
improvements in embedded and mobile computer vision systems. We present a new
architecture, design and implementation as well as the first reported silicon
measurements of such an accelerator, outperforming previous work in terms of
power-, area- and I/O-efficiency. The manufactured device provides up to 196
GOp/s on 3.09 mm^2 of silicon in UMC 65nm technology and can achieve a power
efficiency of 803 GOp/s/W. The massively reduced bandwidth requirements make it
the first architecture scalable to TOp/s performance.
| Lukas Cavigelli, Luca Benini | 10.1109/TCSVT.2016.2592330 | 1512.04295 | null | null |
Automatic Incident Classification for Big Traffic Data by Adaptive
Boosting SVM | cs.LG | Modern cities experience heavy traffic flows and congestions regularly across
space and time. Monitoring traffic situations becomes an important challenge
for the Traffic Control and Surveillance Systems (TCSS). In advanced TCSS, it
is helpful to automatically detect and classify different traffic incidents
such as severity of congestion, abnormal driving pattern, abrupt or illegal
stop on road, etc. Although most TCSS are equipped with basic incident
detection algorithms, they are however crude to be really useful as an
automated tool for further classification. In literature, there is a lack of
research for Automated Incident Classification (AIC). Therefore, a novel AIC
method is proposed in this paper to tackle such challenges. In the proposed
method, traffic signals are firstly extracted from captured videos and
converted as spatial-temporal (ST) signals. Based on the characteristics of the
ST signals, a set of realistic simulation data are generated to construct an
extended big traffic database to cover a variety of traffic situations. Next, a
Mean-Shift filter is introduced to suppress the effect of noise and extract
significant features from the ST signals. The extracted features are then
associated with various types of traffic data: one normal type (inliers) and
multiple abnormal types (outliers). For the classification, an adaptive
boosting classifier is trained to detect outliers in traffic data
automatically. Further, a Support Vector Machine (SVM) based method is adopted
to train the model for identifying the categories of outliers. In short, this
hybrid approach is called an Adaptive Boosting Support Vector Machines (AB-SVM)
method. Experimental results show that the proposed AB-SVM method achieves a
satisfied result with more than 92% classification accuracy on average.
| Li-Li Wang, Henry Y.T. Ngan, Nelson H.C. Yung | null | 1512.04392 | null | null |
We Are Humor Beings: Understanding and Predicting Visual Humor | cs.CV cs.CL cs.LG | Humor is an integral part of human lives. Despite being tremendously
impactful, it is perhaps surprising that we do not have a detailed
understanding of humor yet. As interactions between humans and AI systems
increase, it is imperative that these systems are taught to understand
subtleties of human expressions such as humor. In this work, we are interested
in the question - what content in a scene causes it to be funny? As a first
step towards understanding visual humor, we analyze the humor manifested in
abstract scenes and design computational models for them. We collect two
datasets of abstract scenes that facilitate the study of humor at both the
scene-level and the object-level. We analyze the funny scenes and explore the
different types of humor depicted in them via human studies. We model two tasks
that we believe demonstrate an understanding of some aspects of visual humor.
The tasks involve predicting the funniness of a scene and altering the
funniness of a scene. We show that our models perform well quantitatively, and
qualitatively through human studies. Our datasets are publicly available.
| Arjun Chandrasekaran, Ashwin K. Vijayakumar, Stanislaw Antol, Mohit
Bansal, Dhruv Batra, C. Lawrence Zitnick and Devi Parikh | null | 1512.04407 | null | null |
Near-Optimal Bounds for Binary Embeddings of Arbitrary Sets | cs.LG cs.DS math.FA | We study embedding a subset $K$ of the unit sphere to the Hamming cube
$\{-1,+1\}^m$. We characterize the tradeoff between distortion and sample
complexity $m$ in terms of the Gaussian width $\omega(K)$ of the set. For
subspaces and several structured sets we show that Gaussian maps provide the
optimal tradeoff $m\sim \delta^{-2}\omega^2(K)$, in particular for $\delta$
distortion one needs $m\approx\delta^{-2}{d}$ where $d$ is the subspace
dimension. For general sets, we provide sharp characterizations which reduces
to $m\approx{\delta^{-4}}{\omega^2(K)}$ after simplification. We provide
improved results for local embedding of points that are in close proximity of
each other which is related to locality sensitive hashing. We also discuss
faster binary embedding where one takes advantage of an initial sketching
procedure based on Fast Johnson-Lindenstauss Transform. Finally, we list
several numerical observations and discuss open problems.
| Samet Oymak, Ben Recht | null | 1512.04433 | null | null |
Memory-based control with recurrent neural networks | cs.LG | Partially observed control problems are a challenging aspect of reinforcement
learning. We extend two related, model-free algorithms for continuous control
-- deterministic policy gradient and stochastic value gradient -- to solve
partially observed domains using recurrent neural networks trained with
backpropagation through time.
We demonstrate that this approach, coupled with long-short term memory is
able to solve a variety of physical control problems exhibiting an assortment
of memory requirements. These include the short-term integration of information
from noisy sensors and the identification of system parameters, as well as
long-term memory problems that require preserving information over many time
steps. We also demonstrate success on a combined exploration and memory problem
in the form of a simplified version of the well-known Morris water maze task.
Finally, we show that our approach can deal with high-dimensional observations
by learning directly from pixels.
We find that recurrent deterministic and stochastic policies are able to
learn similarly good solutions to these tasks, including the water maze where
the agent must learn effective search strategies.
| Nicolas Heess, Jonathan J Hunt, Timothy P Lillicrap, David Silver | null | 1512.04455 | null | null |
Semisupervised Autoencoder for Sentiment Analysis | cs.LG | In this paper, we investigate the usage of autoencoders in modeling textual
data. Traditional autoencoders suffer from at least two aspects: scalability
with the high dimensionality of vocabulary size and dealing with
task-irrelevant words. We address this problem by introducing supervision via
the loss function of autoencoders. In particular, we first train a linear
classifier on the labeled data, then define a loss for the autoencoder with the
weights learned from the linear classifier. To reduce the bias brought by one
single classifier, we define a posterior probability distribution on the
weights of the classifier, and derive the marginalized loss of the autoencoder
with Laplace approximation. We show that our choice of loss function can be
rationalized from the perspective of Bregman Divergence, which justifies the
soundness of our model. We evaluate the effectiveness of our model on six
sentiment analysis datasets, and show that our model significantly outperforms
all the competing methods with respect to classification accuracy. We also show
that our model is able to take advantage of unlabeled dataset and get improved
performance. We further show that our model successfully learns highly
discriminative feature maps, which explains its superior performance.
| Shuangfei Zhai, Zhongfei Zhang | null | 1512.04466 | null | null |
\"Uber die Klassifizierung von Knoten in dynamischen Netzwerken mit
Inhalt | cs.LG | This paper explains the DYCOS-Algorithm as it was introduced in by Aggarwal
and Li in 2011. It operates on graphs whichs nodes are partially labeled and
automatically adds missing labels to nodes. To do so, the DYCOS algorithm makes
use of the structure of the graph as well as content which is assigned to the
node. Aggarwal and Li measured in an experimental analysis that DYCOS adds the
missing labels to a Graph with 19396 nodes of which 14814 are labeled and
another Graph with 806635 nodes of which 18999 are labeld on one core of an
Intel Xeon 2.5 GHz CPU with 32 G RAM within less than a minute. Additionally,
extensions of the DYCOS algorithm are proposed.
-----
In dieser Arbeit wird der DYCOS-Algorithmus, wie er 2011 von Aggarwal und Li
vorgestellt wurde, erkl\"art. Er arbeitet auf Graphen, deren Knoten teilweise
mit Beschriftungen versehen sind und erg\"anzt automatisch Beschriftungen f\"ur
Knoten, die bisher noch keine Beschriftung haben. Dieser Vorgang wird
"Klassifizierung" genannt. Dazu verwendet er die Struktur des Graphen sowie
textuelle Informationen, die den Knoten zugeordnet sind. Die von Aggarwal und
Li beschriebene experimentelle Analyse ergab, dass er auch auf dynamischen
Graphen mit 19396 bzw. 806635 Knoten, von denen nur 14814 bzw. 18999
beschriftet waren, innerhalb von weniger als einer Minute auf einem Kern einer
Intel Xeon 2.5 GHz CPU mit 32 G RAM ausgef\"uhrt werden kann. Zus\"atzlich wird
die Ver\"offentlichung von Aggarwal und Li kritisch er\"ortert und und es
werden m\"ogliche Erweiterungen des DYCOS-Algorithmus vorgeschlagen.
| Martin Thoma | null | 1512.04469 | null | null |
Dropout Training of Matrix Factorization and Autoencoder for Link
Prediction in Sparse Graphs | cs.LG | Matrix factorization (MF) and Autoencoder (AE) are among the most successful
approaches of unsupervised learning. While MF based models have been
extensively exploited in the graph modeling and link prediction literature, the
AE family has not gained much attention. In this paper we investigate both MF
and AE's application to the link prediction problem in sparse graphs. We show
the connection between AE and MF from the perspective of multiview learning,
and further propose MF+AE: a model training MF and AE jointly with shared
parameters. We apply dropout to training both the MF and AE parts, and show
that it can significantly prevent overfitting by acting as an adaptive
regularization. We conduct experiments on six real world sparse graph datasets,
and show that MF+AE consistently outperforms the competing methods, especially
on datasets that demonstrate strong non-cohesive structures.
| Shuangfei Zhai, Zhongfei Zhang | null | 1512.04483 | null | null |
On non-iterative training of a neural classifier | cs.CV cs.LG cs.NE | Recently an algorithm, was discovered, which separates points in n-dimension
by planes in such a manner that no two points are left un-separated by at least
one plane{[}1-3{]}. By using this new algorithm we show that there are two ways
of classification by a neural network, for a large dimension feature space,
both of which are non-iterative and deterministic. To demonstrate the power of
both these methods we apply them exhaustively to the classical pattern
recognition problem: The Fisher-Anderson's, IRIS flower data set and present
the results.
It is expected these methods will now be widely used for the training of
neural networks for Deep Learning not only because of their non-iterative and
deterministic nature but also because of their efficiency and speed and will
supersede other classification methods which are iterative in nature and rely
on error minimization.
| K.Eswaran and K.Damodhar Rao | null | 1512.04509 | null | null |
Relaxed Linearized Algorithms for Faster X-Ray CT Image Reconstruction | math.OC cs.LG stat.ML | Statistical image reconstruction (SIR) methods are studied extensively for
X-ray computed tomography (CT) due to the potential of acquiring CT scans with
reduced X-ray dose while maintaining image quality. However, the longer
reconstruction time of SIR methods hinders their use in X-ray CT in practice.
To accelerate statistical methods, many optimization techniques have been
investigated. Over-relaxation is a common technique to speed up convergence of
iterative algorithms. For instance, using a relaxation parameter that is close
to two in alternating direction method of multipliers (ADMM) has been shown to
speed up convergence significantly. This paper proposes a relaxed linearized
augmented Lagrangian (AL) method that shows theoretical faster convergence rate
with over-relaxation and applies the proposed relaxed linearized AL method to
X-ray CT image reconstruction problems. Experimental results with both
simulated and real CT scan data show that the proposed relaxed algorithm (with
ordered-subsets [OS] acceleration) is about twice as fast as the existing
unrelaxed fast algorithms, with negligible computation and memory overhead.
| Hung Nien and Jeffrey A. Fessler | 10.1109/TMI.2015.2508780 | 1512.04564 | null | null |
Learning optimal nonlinearities for iterative thresholding algorithms | cs.LG stat.ML | Iterative shrinkage/thresholding algorithm (ISTA) is a well-studied method
for finding sparse solutions to ill-posed inverse problems. In this letter, we
present a data-driven scheme for learning optimal thresholding functions for
ISTA. The proposed scheme is obtained by relating iterations of ISTA to layers
of a simple deep neural network (DNN) and developing a corresponding error
backpropagation algorithm that allows to fine-tune the thresholding functions.
Simulations on sparse statistical signals illustrate potential gains in
estimation quality due to the proposed data adaptive ISTA.
| Ulugbek S. Kamilov and Hassan Mansour | 10.1109/LSP.2016.2548245 | 1512.04754 | null | null |
From One Point to A Manifold: Knowledge Graph Embedding For Precise Link
Prediction | cs.AI cs.LG | Knowledge graph embedding aims at offering a numerical knowledge
representation paradigm by transforming the entities and relations into
continuous vector space. However, existing methods could not characterize the
knowledge graph in a fine degree to make a precise prediction. There are two
reasons: being an ill-posed algebraic system and applying an overstrict
geometric form. As precise prediction is critical, we propose an manifold-based
embedding principle (\textbf{ManifoldE}) which could be treated as a well-posed
algebraic system that expands the position of golden triples from one point in
current models to a manifold in ours. Extensive experiments show that the
proposed models achieve substantial improvements against the state-of-the-art
baselines especially for the precise prediction task, and yet maintain high
efficiency.
| Han Xiao, Minlie Huang, Xiaoyan Zhu | null | 1512.04792 | null | null |
Causal and anti-causal learning in pattern recognition for neuroimaging | stat.ML cs.LG q-bio.NC stat.ME | Pattern recognition in neuroimaging distinguishes between two types of
models: encoding- and decoding models. This distinction is based on the insight
that brain state features, that are found to be relevant in an experimental
paradigm, carry a different meaning in encoding- than in decoding models. In
this paper, we argue that this distinction is not sufficient: Relevant features
in encoding- and decoding models carry a different meaning depending on whether
they represent causal- or anti-causal relations. We provide a theoretical
justification for this argument and conclude that causal inference is essential
for interpretation in neuroimaging.
| Sebastian Weichwald, Bernhard Sch\"olkopf, Tonio Ball, Moritz
Grosse-Wentrup | 10.1109/PRNI.2014.6858551 | 1512.04808 | null | null |
Feature-Level Domain Adaptation | stat.ML cs.LG | Domain adaptation is the supervised learning setting in which the training
and test data are sampled from different distributions: training data is
sampled from a source domain, whilst test data is sampled from a target domain.
This paper proposes and studies an approach, called feature-level domain
adaptation (FLDA), that models the dependence between the two domains by means
of a feature-level transfer model that is trained to describe the transfer from
source to target domain. Subsequently, we train a domain-adapted classifier by
minimizing the expected loss under the resulting transfer model. For linear
classifiers and a large family of loss functions and transfer models, this
expected loss can be computed or approximated analytically, and minimized
efficiently. Our empirical evaluation of FLDA focuses on problems comprising
binary and count data in which the transfer can be naturally modeled via a
dropout distribution, which allows the classifier to adapt to differences in
the marginal probability of features in the source and the target domain. Our
experiments on several real-world problems show that FLDA performs on par with
state-of-the-art domain-adaptation techniques.
| Wouter M. Kouw, Jesse H. Krijthe, Marco Loog and Laurens J.P. van der
Maaten | null | 1512.04829 | null | null |
Data Driven Resource Allocation for Distributed Learning | cs.LG cs.DS stat.ML | In distributed machine learning, data is dispatched to multiple machines for
processing. Motivated by the fact that similar data points often belong to the
same or similar classes, and more generally, classification rules of high
accuracy tend to be "locally simple but globally complex" (Vapnik & Bottou
1993), we propose data dependent dispatching that takes advantage of such
structure. We present an in-depth analysis of this model, providing new
algorithms with provable worst-case guarantees, analysis proving existing
scalable heuristics perform well in natural non worst-case conditions, and
techniques for extending a dispatching rule from a small sample to the entire
distribution. We overcome novel technical challenges to satisfy important
conditions for accurate distributed learning, including fault tolerance and
balancedness. We empirically compare our approach with baselines based on
random partitioning, balanced partition trees, and locality sensitive hashing,
showing that we achieve significantly higher accuracy on both synthetic and
real world image and advertising datasets. We also demonstrate that our
technique strongly scales with the available computing power.
| Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria
Florina Balcan, Alex Smola | null | 1512.04848 | null | null |
Energy-Efficient Classification for Anomaly Detection: The Wireless
Channel as a Helper | cs.IT cs.LG math.IT | Anomaly detection has various applications including condition monitoring and
fault diagnosis. The objective is to sense the environment, learn the normal
system state, and then periodically classify whether the instantaneous state
deviates from the normal one or not. A flexible and cost-effective way of
monitoring a system state is to use a wireless sensor network. In the
traditional approach, the sensors encode their observations and transmit them
to a fusion center by means of some interference avoiding channel access
method. The fusion center then decodes all the data and classifies the
corresponding system state. As this approach can be highly inefficient in terms
of energy consumption, in this paper we propose a transmission scheme that
exploits interference for carrying out the anomaly detection directly in the
air. In other words, the wireless channel helps the fusion center to retrieve
the sought classification outcome immediately from the channel output. To
achieve this, the chosen learning model is linear support vector machines.
After discussing the proposed scheme and proving its reliability, we present
numerical examples demonstrating that the scheme reduces the energy consumption
for anomaly detection by up to 53% compared to a strategy that uses time
division multiple-access.
| Kiril Ralinovski, Mario Goldenbaum, and S{\l}awomir Sta\'nczak | null | 1512.04857 | null | null |
Increasing the Action Gap: New Operators for Reinforcement Learning | cs.AI cs.LG | This paper introduces new optimality-preserving operators on Q-functions. We
first describe an operator for tabular representations, the consistent Bellman
operator, which incorporates a notion of local policy consistency. We show that
this local consistency leads to an increase in the action gap at each state;
increasing this gap, we argue, mitigates the undesirable effects of
approximation and estimation errors on the induced greedy policies. This
operator can also be applied to discretized continuous space and time problems,
and we provide empirical results evidencing superior performance in this
context. Extending the idea of a locally consistent operator, we then derive
sufficient conditions for an operator to preserve optimality, leading to a
family of operators which includes our consistent Bellman operator. As
corollaries we provide a proof of optimality for Baird's advantage learning
algorithm and derive other gap-increasing operators with interesting
properties. We conclude with an empirical study on 60 Atari 2600 games
illustrating the strong potential of these new operators.
| Marc G. Bellemare, Georg Ostrovski, Arthur Guez, Philip S. Thomas and
R\'emi Munos | null | 1512.04860 | null | null |
Strategies for Training Large Vocabulary Neural Language Models | cs.CL cs.LG | Training neural network language models over large vocabularies is still
computationally very costly compared to count-based models such as Kneser-Ney.
At the same time, neural language models are gaining popularity for many
applications such as speech recognition and machine translation whose success
depends on scalability. We present a systematic comparison of strategies to
represent and train large vocabularies, including softmax, hierarchical
softmax, target sampling, noise contrastive estimation and self normalization.
We further extend self normalization to be a proper estimator of likelihood and
introduce an efficient variant of softmax. We evaluate each method on three
popular benchmarks, examining performance on rare words, the speed/accuracy
trade-off and complementarity to Kneser-Ney.
| Welin Chen and David Grangier and Michael Auli | null | 1512.04906 | null | null |
A Light Touch for Heavily Constrained SGD | cs.LG | Minimizing empirical risk subject to a set of constraints can be a useful
strategy for learning restricted classes of functions, such as monotonic
functions, submodular functions, classifiers that guarantee a certain class
label for some subset of examples, etc. However, these restrictions may result
in a very large number of constraints. Projected stochastic gradient descent
(SGD) is often the default choice for large-scale optimization in machine
learning, but requires a projection after each update. For heavily-constrained
objectives, we propose an efficient extension of SGD that stays close to the
feasible region while only applying constraints probabilistically at each
iteration. Theoretical analysis shows a compelling trade-off between
per-iteration work and the number of iterations needed on problems with a large
number of constraints.
| Andrew Cotter, Maya Gupta, Jan Pfeifer | null | 1512.04960 | null | null |
Streaming Kernel Principal Component Analysis | cs.DS cs.LG stat.ML | Kernel principal component analysis (KPCA) provides a concise set of basis
vectors which capture non-linear structures within large data sets, and is a
central tool in data analysis and learning. To allow for non-linear relations,
typically a full $n \times n$ kernel matrix is constructed over $n$ data
points, but this requires too much space and time for large values of $n$.
Techniques such as the Nystr\"om method and random feature maps can help
towards this goal, but they do not explicitly maintain the basis vectors in a
stream and take more space than desired. We propose a new approach for
streaming KPCA which maintains a small set of basis elements in a stream,
requiring space only logarithmic in $n$, and also improves the dependence on
the error parameter. Our technique combines together random feature maps with
recent advances in matrix sketching, it has guaranteed spectral norm error
bounds with respect to the original kernel matrix, and it compares favorably in
practice to state-of-the-art approaches.
| Mina Ghashami, Daniel Perry, Jeff M. Phillips | null | 1512.05059 | null | null |
DNA-Level Splice Junction Prediction using Deep Recurrent Neural
Networks | cs.LG q-bio.GN | A eukaryotic gene consists of multiple exons (protein coding regions) and
introns (non-coding regions), and a splice junction refers to the boundary
between a pair of exon and intron. Precise identification of spice junctions on
a gene is important for deciphering its primary structure, function, and
interaction. Experimental techniques for determining exon/intron boundaries
include RNA-seq, which is often accompanied by computational approaches.
Canonical splicing signals are known, but computational junction prediction
still remains challenging because of a large number of false positives and
other complications. In this paper, we exploit deep recurrent neural networks
(RNNs) to model DNA sequences and to detect splice junctions thereon. We test
various RNN units and architectures including long short-term memory units,
gated recurrent units, and recently proposed iRNN for in-depth design space
exploration. According to our experimental results, the proposed approach
significantly outperforms not only conventional machine learning-based methods
but also a recent state-of-the-art deep belief network-based technique in terms
of prediction accuracy.
| Byunghan Lee, Taehoon Lee, Byunggook Na, Sungroh Yoon | null | 1512.05135 | null | null |
Learning Games and Rademacher Observations Losses | cs.LG | It has recently been shown that supervised learning with the popular logistic
loss is equivalent to optimizing the exponential loss over sufficient
statistics about the class: Rademacher observations (rados). We first show that
this unexpected equivalence can actually be generalized to other example / rado
losses, with necessary and sufficient conditions for the equivalence,
exemplified on four losses that bear popular names in various fields:
exponential (boosting), mean-variance (finance), Linear Hinge (on-line
learning), ReLU (deep learning), and unhinged (statistics). Second, we show
that the generalization unveils a surprising new connection to regularized
learning, and in particular a sufficient condition under which regularizing the
loss over examples is equivalent to regularizing the rados (with Minkowski
sums) in the equivalent rado loss. This brings simple and powerful rado-based
learning algorithms for sparsity-controlling regularization, that we exemplify
on a boosting algorithm for the regularized exponential rado-loss, which
formally boosts over four types of regularization, including the popular ridge
and lasso, and the recently coined slope --- we obtain the first proven
boosting algorithm for this last regularization. Through our first contribution
on the equivalence of rado and example-based losses, Omega-R.AdaBoost~appears
to be an efficient proxy to boost the regularized logistic loss over examples
using whichever of the four regularizers. Experiments display that
regularization consistently improves performances of rado-based learning, and
may challenge or beat the state of the art of example-based learning even when
learning over small sets of rados. Finally, we connect regularization to
differential privacy, and display how tiny budgets can be afforded on big
domains while beating (protected) example-based learning.
| Richard Nock | null | 1512.05244 | null | null |
Blockout: Dynamic Model Selection for Hierarchical Deep Networks | cs.CV cs.LG | Most deep architectures for image classification--even those that are trained
to classify a large number of diverse categories--learn shared image
representations with a single model. Intuitively, however, categories that are
more similar should share more information than those that are very different.
While hierarchical deep networks address this problem by learning separate
features for subsets of related categories, current implementations require
simplified models using fixed architectures specified via heuristic clustering
methods. Instead, we propose Blockout, a method for regularization and model
selection that simultaneously learns both the model architecture and
parameters. A generalization of Dropout, our approach gives a novel
parametrization of hierarchical architectures that allows for structure
learning via back-propagation. To demonstrate its utility, we evaluate Blockout
on the CIFAR and ImageNet datasets, demonstrating improved classification
accuracy, better regularization performance, faster training, and the clear
emergence of hierarchical network structures.
| Calvin Murdock, Zhen Li, Howard Zhou, Tom Duerig | 10.1109/CVPR.2016.283 | 1512.05246 | null | null |
Feature Representation for ICU Mortality | cs.AI cs.LG stat.ML | Good predictors of ICU Mortality have the potential to identify high-risk
patients earlier, improve ICU resource allocation, or create more accurate
population-level risk models. Machine learning practitioners typically make
choices about how to represent features in a particular model, but these
choices are seldom evaluated quantitatively. This study compares the
performance of different representations of clinical event data from MIMIC II
in a logistic regression model to predict 36-hour ICU mortality. The most
common representations are linear (normalized counts) and binary (yes/no).
These, along with a new representation termed "hill", are compared using both
L1 and L2 regularization. Results indicate that the introduced "hill"
representation outperforms both the binary and linear representations, the hill
representation thus has the potential to improve existing models of ICU
mortality.
| Harini Suresh | null | 1512.05294 | null | null |
Unsupervised Feature Construction for Improving Data Representation and
Semantics | cs.AI cs.LG | Feature-based format is the main data representation format used by machine
learning algorithms. When the features do not properly describe the initial
data, performance starts to degrade. Some algorithms address this problem by
internally changing the representation space, but the newly-constructed
features are rarely comprehensible. We seek to construct, in an unsupervised
way, new features that are more appropriate for describing a given dataset and,
at the same time, comprehensible for a human user. We propose two algorithms
that construct the new features as conjunctions of the initial primitive
features or their negations. The generated feature sets have reduced
correlations between features and succeed in catching some of the hidden
relations between individuals in a dataset. For example, a feature like $sky
\wedge \neg building \wedge panorama$ would be true for non-urban images and is
more informative than simple features expressing the presence or the absence of
an object. The notion of Pareto optimality is used to evaluate feature sets and
to obtain a balance between total correlation and the complexity of the
resulted feature set. Statistical hypothesis testing is used in order to
automatically determine the values of the parameters used for constructing a
data-dependent feature set. We experimentally show that our approaches achieve
the construction of informative feature sets for multiple datasets.
| Marian-Andrei Rizoiu, Julien Velcin, St\'ephane Lallich | 10.1007/s10844-013-0235-x | 1512.05467 | null | null |
An Empirical Comparison of Neural Architectures for Reinforcement
Learning in Partially Observable Environments | cs.NE cs.AI cs.LG | This paper explores the performance of fitted neural Q iteration for
reinforcement learning in several partially observable environments, using
three recurrent neural network architectures: Long Short-Term Memory, Gated
Recurrent Unit and MUT1, a recurrent neural architecture evolved from a pool of
several thousands candidate architectures. A variant of fitted Q iteration,
based on Advantage values instead of Q values, is also explored. The results
show that GRU performs significantly better than LSTM and MUT1 for most of the
problems considered, requiring less training episodes and less CPU time before
learning a very good policy. Advantage learning also tends to produce better
results.
| Denis Steckelmacher and Peter Vrancx | null | 1512.05509 | null | null |
Deep-Spying: Spying using Smartwatch and Deep Learning | cs.CR cs.CY cs.LG | Wearable technologies are today on the rise, becoming more common and broadly
available to mainstream users. In fact, wristband and armband devices such as
smartwatches and fitness trackers already took an important place in the
consumer electronics market and are becoming ubiquitous. By their very nature
of being wearable, these devices, however, provide a new pervasive attack
surface threatening users privacy, among others.
In the meantime, advances in machine learning are providing unprecedented
possibilities to process complex data efficiently. Allowing patterns to emerge
from high dimensional unavoidably noisy data.
The goal of this work is to raise awareness about the potential risks related
to motion sensors built-in wearable devices and to demonstrate abuse
opportunities leveraged by advanced neural network architectures.
The LSTM-based implementation presented in this research can perform
touchlogging and keylogging on 12-keys keypads with above-average accuracy even
when confronted with raw unprocessed data. Thus demonstrating that deep neural
networks are capable of making keystroke inference attacks based on motion
sensors easier to achieve by removing the need for non-trivial pre-processing
pipelines and carefully engineered feature extraction strategies. Our results
suggest that the complete technological ecosystem of a user can be compromised
when a wearable wristband device is worn.
| Tony Beltramelli, Sebastian Risi | null | 1512.05616 | null | null |
Probabilistic Programming with Gaussian Process Memoization | cs.LG cs.AI stat.ML | Gaussian Processes (GPs) are widely used tools in statistics, machine
learning, robotics, computer vision, and scientific computation. However,
despite their popularity, they can be difficult to apply; all but the simplest
classification or regression applications require specification and inference
over complex covariance functions that do not admit simple analytical
posteriors. This paper shows how to embed Gaussian processes in any
higher-order probabilistic programming language, using an idiom based on
memoization, and demonstrates its utility by implementing and extending classic
and state-of-the-art GP applications. The interface to Gaussian processes,
called gpmem, takes an arbitrary real-valued computational process as input and
returns a statistical emulator that automatically improve as the original
process is invoked and its input-output behavior is recorded. The flexibility
of gpmem is illustrated via three applications: (i) robust GP regression with
hierarchical hyper-parameter learning, (ii) discovering symbolic expressions
from time-series data by fully Bayesian structure learning over kernels
generated by a stochastic grammar, and (iii) a bandit formulation of Bayesian
optimization with automatic inference and action selection. All applications
share a single 50-line Python library and require fewer than 20 lines of
probabilistic code each.
| Ulrich Schaechtle, Ben Zinberg, Alexey Radul, Kostas Stathis and
Vikash K. Mansinghka | null | 1512.05665 | null | null |
A Survey of Available Corpora for Building Data-Driven Dialogue Systems | cs.CL cs.AI cs.HC cs.LG stat.ML | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective.
| Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin,
Joelle Pineau | null | 1512.05742 | null | null |
Successive Ray Refinement and Its Application to Coordinate Descent for
LASSO | cs.LG | Coordinate descent is one of the most popular approaches for solving Lasso
and its extensions due to its simplicity and efficiency. When applying
coordinate descent to solving Lasso, we update one coordinate at a time while
fixing the remaining coordinates. Such an update, which is usually easy to
compute, greedily decreases the objective function value. In this paper, we aim
to improve its computational efficiency by reducing the number of coordinate
descent iterations. To this end, we propose a novel technique called Successive
Ray Refinement (SRR). SRR makes use of the following ray continuation property
on the successive iterations: for a particular coordinate, the value obtained
in the next iteration almost always lies on a ray that starts at its previous
iteration and passes through the current iteration. Motivated by this
ray-continuation property, we propose that coordinate descent be performed not
directly on the previous iteration but on a refined search point that has the
following properties: on one hand, it lies on a ray that starts at a history
solution and passes through the previous iteration, and on the other hand, it
achieves the minimum objective function value among all the points on the ray.
We propose two schemes for defining the search point and show that the refined
search point can be efficiently obtained. Empirical results for real and
synthetic data sets show that the proposed SRR can significantly reduce the
number of coordinate descent iterations, especially for small Lasso
regularization parameters.
| Jun Liu, Zheng Zhao, Ruiwen Zhang | null | 1512.05808 | null | null |
Relay Backpropagation for Effective Learning of Deep Convolutional
Neural Networks | cs.CV cs.LG | Learning deeper convolutional neural networks becomes a tendency in recent
years. However, many empirical evidences suggest that performance improvement
cannot be gained by simply stacking more layers. In this paper, we consider the
issue from an information theoretical perspective, and propose a novel method
Relay Backpropagation, that encourages the propagation of effective information
through the network in training stage. By virtue of the method, we achieved the
first place in ILSVRC 2015 Scene Classification Challenge. Extensive
experiments on two challenging large scale datasets demonstrate the
effectiveness of our method is not restricted to a specific dataset or network
architecture. Our models will be available to the research community later.
| Li Shen and Zhouchen Lin and Qingming Huang | null | 1512.05830 | null | null |
Deep Poisson Factorization Machines: factor analysis for mapping
behaviors in journalist ecosystem | cs.CY cs.LG stat.ML | Newsroom in online ecosystem is difficult to untangle. With prevalence of
social media, interactions between journalists and individuals become visible,
but lack of understanding to inner processing of information feedback loop in
public sphere leave most journalists baffled. Can we provide an organized view
to characterize journalist behaviors on individual level to know better of the
ecosystem? To this end, I propose Poisson Factorization Machine (PFM), a
Bayesian analogue to matrix factorization that assumes Poisson distribution for
generative process. The model generalizes recent studies on Poisson Matrix
Factorization to account temporal interaction which involves tensor-like
structure, and label information. Two inference procedures are designed, one
based on batch variational EM and another stochastic variational inference
scheme that efficiently scales with data size. An important novelty in this
note is that I show how to stack layers of PFM to introduce a deep
architecture. This work discusses some potential results applying the model and
explains how such latent factors may be useful for analyzing latent behaviors
for data exploration.
| Pau Perng-Hwa Kung | null | 1512.05840 | null | null |
Complexity and Approximation of the Fuzzy K-Means Problem | cs.LG cs.DS | The fuzzy $K$-means problem is a generalization of the classical $K$-means
problem to soft clusterings, i.e. clusterings where each points belongs to each
cluster to some degree. Although popular in practice, prior to this work the
fuzzy $K$-means problem has not been studied from a complexity theoretic or
algorithmic perspective. We show that optimal solutions for fuzzy $K$-means
cannot, in general, be expressed by radicals over the input points.
Surprisingly, this already holds for very simple inputs in one-dimensional
space. Hence, one cannot expect to compute optimal solutions exactly. We give
the first $(1+\epsilon)$-approximation algorithms for the fuzzy $K$-means
problem. First, we present a deterministic approximation algorithm whose
runtime is polynomial in $N$ and linear in the dimension $D$ of the input set,
given that $K$ is constant, i.e. a polynomial time approximation algorithm
given a fixed $K$. We achieve this result by showing that for each soft
clustering there exists a hard clustering with comparable properties. Second,
by using techniques known from coreset constructions for the $K$-means problem,
we develop a deterministic approximation algorithm that runs in time almost
linear in $N$ but exponential in the dimension $D$. We complement these results
with a randomized algorithm which imposes some natural restrictions on the
input set and whose runtime is comparable to some of the most efficient
approximation algorithms for $K$-means, i.e. linear in the number of points and
the dimension, but exponential in the number of clusters.
| Johannes Bl\"omer, Sascha Brauer, and Kathrin Bujna | null | 1512.05947 | null | null |
Asymptotic Behavior of Mean Partitions in Consensus Clustering | cs.LG stat.ML | Although consistency is a minimum requirement of any estimator, little is
known about consistency of the mean partition approach in consensus clustering.
This contribution studies the asymptotic behavior of mean partitions. We show
that under normal assumptions, the mean partition approach is consistent and
asymptotic normal. To derive both results, we represent partitions as points of
some geometric space, called orbit space. Then we draw on results from the
theory of Fr\'echet means and stochastic programming. The asymptotic properties
hold for continuous extensions of standard cluster criteria (indices). The
results justify consensus clustering using finite but sufficiently large sample
sizes. Furthermore, the orbit space framework provides a mathematical
foundation for studying further statistical, geometrical, and analytical
properties of sets of partitions.
| Brijnesh Jain | null | 1512.06061 | null | null |
Discriminative Subnetworks with Regularized Spectral Learning for
Global-state Network Data | cs.LG | Data mining practitioners are facing challenges from data with network
structure. In this paper, we address a specific class of global-state networks
which comprises of a set of network instances sharing a similar structure yet
having different values at local nodes. Each instance is associated with a
global state which indicates the occurrence of an event. The objective is to
uncover a small set of discriminative subnetworks that can optimally classify
global network values. Unlike most existing studies which explore an
exponential subnetwork space, we address this difficult problem by adopting a
space transformation approach. Specifically, we present an algorithm that
optimizes a constrained dual-objective function to learn a low-dimensional
subspace that is capable of discriminating networks labelled by different
global states, while reconciling with common network topology sharing across
instances. Our algorithm takes an appealing approach from spectral graph
learning and we show that the globally optimum solution can be achieved via
matrix eigen-decomposition.
| Xuan Hong Dang, Ambuj K. Singh, Petko Bogdanov, Hongyuan You and
Bayyuan Hsu | null | 1512.06173 | null | null |
Poseidon: A System Architecture for Efficient GPU-based Deep Learning on
Multiple Machines | cs.LG cs.CV cs.DC | Deep learning (DL) has achieved notable successes in many machine learning
tasks. A number of frameworks have been developed to expedite the process of
designing and training deep neural networks (DNNs), such as Caffe, Torch and
Theano. Currently they can harness multiple GPUs on a single machine, but are
unable to use GPUs that are distributed across multiple machines; as even
average-sized DNNs can take days to train on a single GPU with 100s of GBs to
TBs of data, distributed GPUs present a prime opportunity for scaling up DL.
However, the limited bandwidth available on commodity Ethernet networks
presents a bottleneck to distributed GPU training, and prevents its trivial
realization.
To investigate how to adapt existing frameworks to efficiently support
distributed GPUs, we propose Poseidon, a scalable system architecture for
distributed inter-machine communication in existing DL frameworks. We integrate
Poseidon with Caffe and evaluate its performance at training DNNs for object
recognition. Poseidon features three key contributions that accelerate DNN
training on clusters: (1) a three-level hybrid architecture that allows
Poseidon to support both CPU-only and GPU-equipped clusters, (2) a distributed
wait-free backpropagation (DWBP) algorithm to improve GPU utilization and to
balance communication, and (3) a structure-aware communication protocol (SACP)
to minimize communication overheads. We empirically show that Poseidon
converges to same objectives as a single machine, and achieves state-of-art
training speedup across multiple models and well-established datasets using a
commodity GPU cluster of 8 nodes (e.g. 4.5x speedup on AlexNet, 4x on
GoogLeNet, 4x on CIFAR-10). On the much larger ImageNet22K dataset, Poseidon
with 8 nodes achieves better speedup and competitive accuracy to recent
CPU-based distributed systems such as Adam and Le et al., which use 10s to
1000s of nodes.
| Hao Zhang, Zhiting Hu, Jinliang Wei, Pengtao Xie, Gunhee Kim, Qirong
Ho and Eric Xing | null | 1512.06216 | null | null |
A new robust adaptive algorithm for underwater acoustic channel
equalization | cs.SD cs.IT cs.LG math.IT | We introduce a novel family of adaptive robust equalizers for highly
challenging underwater acoustic (UWA) channel equalization. Since the
underwater environment is highly non-stationary and subjected to impulsive
noise, we use adaptive filtering techniques based on a relative logarithmic
cost function inspired by the competitive methods from the online learning
literature. To improve the convergence performance of the conventional linear
equalization methods, while mitigating the stability issues, we intrinsically
combine different norms of the error in the cost function, using logarithmic
functions. Hence, we achieve a comparable convergence performance to least mean
fourth (LMF) equalizer, while significantly enhancing the stability performance
in such an adverse communication medium. We demonstrate the performance of our
algorithms through highly realistic experiments performed on accurately
simulated underwater acoustic channels.
| Dariush Kari and Muhammed Omer Sayin and Suleyman Serdar Kozat | null | 1512.06222 | null | null |
Using machine learning for medium frequency derivative portfolio trading | q-fin.TR cs.LG stat.ML | We use machine learning for designing a medium frequency trading strategy for
a portfolio of 5 year and 10 year US Treasury note futures. We formulate this
as a classification problem where we predict the weekly direction of movement
of the portfolio using features extracted from a deep belief network trained on
technical indicators of the portfolio constituents. The experimentation shows
that the resulting pipeline is effective in making a profitable trade.
| Abhijit Sharang and Chetan Rao | null | 1512.06228 | null | null |
The Limitations of Optimization from Samples | cs.DS cs.DM cs.LG | In this paper we consider the following question: can we optimize objective
functions from the training data we use to learn them? We formalize this
question through a novel framework we call optimization from samples (OPS). In
OPS, we are given sampled values of a function drawn from some distribution and
the objective is to optimize the function under some constraint.
While there are interesting classes of functions that can be optimized from
samples, our main result is an impossibility. We show that there are classes of
functions which are statistically learnable and optimizable, but for which no
reasonable approximation for optimization from samples is achievable. In
particular, our main result shows that there is no constant factor
approximation for maximizing coverage functions under a cardinality constraint
using polynomially-many samples drawn from any distribution.
We also show tight approximation guarantees for maximization under a
cardinality constraint of several interesting classes of functions including
unit-demand, additive, and general monotone submodular functions, as well as a
constant factor approximation for monotone submodular functions with bounded
curvature.
| Eric Balkanski, Aviad Rubinstein, Yaron Singer | null | 1512.06238 | null | null |
A Mathematical Theory of Deep Convolutional Neural Networks for Feature
Extraction | cs.IT cs.AI cs.LG math.FA math.IT stat.ML | Deep convolutional neural networks have led to breakthrough results in
numerous practical machine learning tasks such as classification of images in
the ImageNet data set, control-policy-learning to play Atari games or the board
game Go, and image captioning. Many of these applications first perform feature
extraction and then feed the results thereof into a trainable classifier. The
mathematical analysis of deep convolutional neural networks for feature
extraction was initiated by Mallat, 2012. Specifically, Mallat considered
so-called scattering networks based on a wavelet transform followed by the
modulus non-linearity in each network layer, and proved translation invariance
(asymptotically in the wavelet scale parameter) and deformation stability of
the corresponding feature extractor. This paper complements Mallat's results by
developing a theory that encompasses general convolutional transforms, or in
more technical parlance, general semi-discrete frames (including
Weyl-Heisenberg filters, curvelets, shearlets, ridgelets, wavelets, and learned
filters), general Lipschitz-continuous non-linearities (e.g., rectified linear
units, shifted logistic sigmoids, hyperbolic tangents, and modulus functions),
and general Lipschitz-continuous pooling operators emulating, e.g.,
sub-sampling and averaging. In addition, all of these elements can be different
in different network layers. For the resulting feature extractor we prove a
translation invariance result of vertical nature in the sense of the features
becoming progressively more translation-invariant with increasing network
depth, and we establish deformation sensitivity bounds that apply to signal
classes such as, e.g., band-limited functions, cartoon functions, and Lipschitz
functions.
| Thomas Wiatowski and Helmut B\"olcskei | null | 1512.06293 | null | null |
Kernel principal component analysis network for image classification | cs.LG cs.CV | In order to classify the nonlinear feature with linear classifier and improve
the classification accuracy, a deep learning network named kernel principal
component analysis network (KPCANet) is proposed. First, mapping the data into
higher space with kernel principal component analysis to make the data linearly
separable. Then building a two-layer KPCANet to obtain the principal components
of image. Finally, classifying the principal components with linearly
classifier. Experimental results show that the proposed KPCANet is effective in
face recognition, object recognition and hand-writing digits recognition, it
also outperforms principal component analysis network (PCANet) generally as
well. Besides, KPCANet is invariant to illumination and stable to occlusion and
slight deformation.
| Dan Wu, Jiasong Wu, Rui Zeng, Longyu Jiang, Lotfi Senhadji, Huazhong
Shu | null | 1512.06337 | null | null |
Revisiting Differentially Private Regression: Lessons From Learning
Theory and their Consequences | cs.CR cs.DB cs.LG | Private regression has received attention from both database and security
communities. Recent work by Fredrikson et al. (USENIX Security 2014) analyzed
the functional mechanism (Zhang et al. VLDB 2012) for training linear
regression models over medical data. Unfortunately, they found that model
accuracy is already unacceptable with differential privacy when $\varepsilon =
5$. We address this issue, presenting an explicit connection between
differential privacy and stable learning theory through which a substantially
better privacy/utility tradeoff can be obtained. Perhaps more importantly, our
theory reveals that the most basic mechanism in differential privacy, output
perturbation, can be used to obtain a better tradeoff for all
convex-Lipschitz-bounded learning tasks. Since output perturbation is simple to
implement, it means that our approach is potentially widely applicable in
practice. We go on to apply it on the same medical data as used by Fredrikson
et al. Encouragingly, we achieve accurate models even for $\varepsilon = 0.1$.
In the last part of this paper, we study the impact of our improved
differentially private mechanisms on model inversion attacks, a privacy attack
introduced by Fredrikson et al. We observe that the improved tradeoff makes the
resulting differentially private model more susceptible to inversion attacks.
We analyze this phenomenon formally.
| Xi Wu, Matthew Fredrikson, Wentao Wu, Somesh Jha, Jeffrey F. Naughton | null | 1512.06388 | null | null |
Behavioral Modeling for Churn Prediction: Early Indicators and Accurate
Predictors of Custom Defection and Loyalty | cs.LG | Churn prediction, or the task of identifying customers who are likely to
discontinue use of a service, is an important and lucrative concern of firms in
many different industries. As these firms collect an increasing amount of
large-scale, heterogeneous data on the characteristics and behaviors of
customers, new methods become possible for predicting churn. In this paper, we
present a unified analytic framework for detecting the early warning signs of
churn, and assigning a "Churn Score" to each customer that indicates the
likelihood that the particular individual will churn within a predefined amount
of time. This framework employs a brute force approach to feature engineering,
then winnows the set of relevant attributes via feature selection, before
feeding the final feature-set into a suite of supervised learning algorithms.
Using several terabytes of data from a large mobile phone network, our method
identifies several intuitive - and a few surprising - early warning signs of
churn, and our best model predicts whether a subscriber will churn with 89.4%
accuracy.
| Muhammad R. Khan, Johua Manoj, Anikate Singh, Joshua Blumenstock | 10.1109/BigDataCongress.2015.107 | 1512.06430 | null | null |
ATD: Anomalous Topic Discovery in High Dimensional Discrete Data | stat.ML cs.LG | We propose an algorithm for detecting patterns exhibited by anomalous
clusters in high dimensional discrete data. Unlike most anomaly detection (AD)
methods, which detect individual anomalies, our proposed method detects groups
(clusters) of anomalies; i.e. sets of points which collectively exhibit
abnormal patterns. In many applications this can lead to better understanding
of the nature of the atypical behavior and to identifying the sources of the
anomalies. Moreover, we consider the case where the atypical patterns exhibit
on only a small (salient) subset of the very high dimensional feature space.
Individual AD techniques and techniques that detect anomalies using all the
features typically fail to detect such anomalies, but our method can detect
such instances collectively, discover the shared anomalous patterns exhibited
by them, and identify the subsets of salient features. In this paper, we focus
on detecting anomalous topics in a batch of text documents, developing our
algorithm based on topic models. Results of our experiments show that our
method can accurately detect anomalous topics and salient features (words)
under each such topic in a synthetic data set and two real-world text corpora
and achieves better performance compared to both standard group AD and
individual AD techniques. All required code to reproduce our experiments is
available from https://github.com/hsoleimani/ATD
| Hossein Soleimani, David J. Miller | 10.1109/TKDE.2016.2561288 | 1512.06452 | null | null |
Backward and Forward Language Modeling for Constrained Sentence
Generation | cs.CL cs.LG cs.NE | Recent language models, especially those based on recurrent neural networks
(RNNs), make it possible to generate natural language from a learned
probability. Language generation has wide applications including machine
translation, summarization, question answering, conversation systems, etc.
Existing methods typically learn a joint probability of words conditioned on
additional information, which is (either statically or dynamically) fed to
RNN's hidden layer. In many applications, we are likely to impose hard
constraints on the generated texts, i.e., a particular word must appear in the
sentence. Unfortunately, existing approaches could not solve this problem. In
this paper, we propose a novel backward and forward language model. Provided a
specific word, we use RNNs to generate previous words and future words, either
simultaneously or asynchronously, resulting in two model variants. In this way,
the given word could appear at any position in the sentence. Experimental
results show that the generated texts are comparable to sequential LMs in
quality.
| Lili Mou, Rui Yan, Ge Li, Lu Zhang, Zhi Jin | null | 1512.06612 | null | null |
Deep Learning for Surface Material Classification Using Haptic And
Visual Information | cs.RO cs.CV cs.LG | When a user scratches a hand-held rigid tool across an object surface, an
acceleration signal can be captured, which carries relevant information about
the surface. More importantly, such a haptic signal is complementary to the
visual appearance of the surface, which suggests the combination of both
modalities for the recognition of the surface material. In this paper, we
present a novel deep learning method dealing with the surface material
classification problem based on a Fully Convolutional Network (FCN), which
takes as input the aforementioned acceleration signal and a corresponding image
of the surface texture. Compared to previous surface material classification
solutions, which rely on a careful design of hand-crafted domain-specific
features, our method automatically extracts discriminative features utilizing
the advanced deep learning methodologies. Experiments performed on the TUM
surface material database demonstrate that our method achieves state-of-the-art
classification accuracy robustly and efficiently.
| Haitian Zheng, Lu Fang, Mengqi Ji, Matti Strese, Yigitcan Ozer,
Eckehard Steinbach | null | 1512.06658 | null | null |
Multilinear Subspace Clustering | cs.IT cs.CV cs.LG math.IT stat.ML | In this paper we present a new model and an algorithm for unsupervised
clustering of 2-D data such as images. We assume that the data comes from a
union of multilinear subspaces (UOMS) model, which is a specific structured
case of the much studied union of subspaces (UOS) model. For segmentation under
this model, we develop Multilinear Subspace Clustering (MSC) algorithm and
evaluate its performance on the YaleB and Olivietti image data sets. We show
that MSC is highly competitive with existing algorithms employing the UOS model
in terms of clustering performance while enjoying improvement in computational
complexity.
| Eric Kernfeld, Nathan Majumder, Shuchin Aeron, Misha Kilmer | null | 1512.06730 | null | null |
GraphConnect: A Regularization Framework for Neural Networks | cs.CV cs.LG cs.NE | Deep neural networks have proved very successful in domains where large
training sets are available, but when the number of training samples is small,
their performance suffers from overfitting. Prior methods of reducing
overfitting such as weight decay, Dropout and DropConnect are data-independent.
This paper proposes a new method, GraphConnect, that is data-dependent, and is
motivated by the observation that data of interest lie close to a manifold. The
new method encourages the relationships between the learned decisions to
resemble a graph representing the manifold structure. Essentially GraphConnect
is designed to learn attributes that are present in data samples in contrast to
weight decay, Dropout and DropConnect which are simply designed to make it more
difficult to fit to random error or noise. Empirical Rademacher complexity is
used to connect the generalization error of the neural network to spectral
properties of the graph learned from the input data. This framework is used to
show that GraphConnect is superior to weight decay. Experimental results on
several benchmark datasets validate the theoretical analysis, and show that
when the number of training samples is small, GraphConnect is able to
significantly improve performance over weight decay.
| Jiaji Huang, Qiang Qiu, Robert Calderbank, Guillermo Sapiro | null | 1512.06757 | null | null |
Predicting the Co-Evolution of Event and Knowledge Graphs | cs.LG | Embedding learning, a.k.a. representation learning, has been shown to be able
to model large-scale semantic knowledge graphs. A key concept is a mapping of
the knowledge graph to a tensor representation whose entries are predicted by
models using latent representations of generalized entities. Knowledge graphs
are typically treated as static: A knowledge graph grows more links when more
facts become available but the ground truth values associated with links is
considered time invariant. In this paper we address the issue of knowledge
graphs where triple states depend on time. We assume that changes in the
knowledge graph always arrive in form of events, in the sense that the events
are the gateway to the knowledge graph. We train an event prediction model
which uses both knowledge graph background information and information on
recent events. By predicting future events, we also predict likely changes in
the knowledge graph and thus obtain a model for the evolution of the knowledge
graph as well. Our experiments demonstrate that our approach performs well in a
clinical application, a recommendation engine and a sensor network application.
| Crist\'obal Esteban and Volker Tresp and Yinchong Yang and Stephan
Baier and Denis Krompa{\ss} | null | 1512.06900 | null | null |
A C++ library for Multimodal Deep Learning | cs.LG | MDL, Multimodal Deep Learning Library, is a deep learning framework that
supports multiple models, and this document explains its philosophy and
functionality. MDL runs on Linux, Mac, and Unix platforms. It depends on
OpenCV.
| Jian Jin | null | 1512.06927 | null | null |
On the Differential Privacy of Bayesian Inference | cs.AI cs.CR cs.LG math.ST stat.ML stat.TH | We study how to communicate findings of Bayesian inference to third parties,
while preserving the strong guarantee of differential privacy. Our main
contributions are four different algorithms for private Bayesian inference on
proba-bilistic graphical models. These include two mechanisms for adding noise
to the Bayesian updates, either directly to the posterior parameters, or to
their Fourier transform so as to preserve update consistency. We also utilise a
recently introduced posterior sampling mechanism, for which we prove bounds for
the specific but general case of discrete Bayesian networks; and we introduce a
maximum-a-posteriori private mechanism. Our analysis includes utility and
privacy bounds, with a novel focus on the influence of graph structure on
privacy. Worked examples and experiments with Bayesian na{\"i}ve Bayes and
Bayesian linear regression illustrate the application of our mechanisms.
| Zuhe Zhang, Benjamin Rubinstein, Christos Dimitrakakis | null | 1512.06992 | null | null |
FAASTA: A fast solver for total-variation regularization of
ill-conditioned problems with application to brain imaging | q-bio.NC cs.LG stat.CO stat.ML | The total variation (TV) penalty, as many other analysis-sparsity problems,
does not lead to separable factors or a proximal operatorwith a closed-form
expression, such as soft thresholding for the $\ell\_1$ penalty. As a result,
in a variational formulation of an inverse problem or statisticallearning
estimation, it leads to challenging non-smooth optimization problemsthat are
often solved with elaborate single-step first-order methods. When thedata-fit
term arises from empirical measurements, as in brain imaging, it isoften very
ill-conditioned and without simple structure. In this situation, in proximal
splitting methods, the computation cost of thegradient step can easily dominate
each iteration. Thus it is beneficialto minimize the number of gradient
steps.We present fAASTA, a variant of FISTA, that relies on an internal solver
forthe TV proximal operator, and refines its tolerance to balance
computationalcost of the gradient and the proximal steps. We give benchmarks
andillustrations on "brain decoding": recovering brain maps from
noisymeasurements to predict observed behavior. The algorithm as well as
theempirical study of convergence speed are valuable for any non-exact
proximaloperator, in particular analysis-sparsity problems.
| Ga\"el Varoquaux (PARIETAL), Michael Eickenberg (PARIETAL), Elvis
Dohmatob (PARIETAL), Bertand Thirion (PARIETAL) | null | 1512.06999 | null | null |
Implementation of deep learning algorithm for automatic detection of
brain tumors using intraoperative IR-thermal mapping data | cs.CV cs.LG q-bio.QM stat.ML | The efficiency of deep machine learning for automatic delineation of tumor
areas has been demonstrated for intraoperative neuronavigation using active
IR-mapping with the use of the cold test. The proposed approach employs a
matrix IR-imager to remotely register the space-time distribution of surface
temperature pattern, which is determined by the dynamics of local cerebral
blood flow. The advantages of this technique are non-invasiveness, zero risks
for the health of patients and medical staff, low implementation and
operational costs, ease and speed of use. Traditional IR-diagnostic technique
has a crucial limitation - it involves a diagnostician who determines the
boundaries of tumor areas, which gives rise to considerable uncertainty, which
can lead to diagnosis errors that are difficult to control. The current study
demonstrates that implementing deep learning algorithms allows to eliminate the
explained drawback.
| A.V. Makarenko, M.G. Volovik | null | 1512.07041 | null | null |
Move from Perturbed scheme to exponential weighting average | cs.LG | In an online decision problem, one makes decisions often with a pool of
decision sequence called experts but without knowledge of the future. After
each step, one pays a cost based on the decision and observed rate. One
reasonal goal would be to perform as well as the best expert in the pool. The
modern and well-known way to attain this goal is the algorithm of exponential
weighting. However, recently, another algorithm called follow the perturbed
leader is developed and achieved about the same performance. In our work, we
first show the properties shared in common by the two algorithms which explain
the similarities on the performance. Next we will show that for a specific
perturbation, the two algorithms are identical. Finally, we show with some
examples that follow-the-leader style algorithms extend naturally to a large
class of structured online problems for which the exponential algorithms are
inefficient.
| Chunyang Xiao | null | 1512.07074 | null | null |
Recent Advances in Convolutional Neural Networks | cs.CV cs.LG cs.NE | In the last few years, deep learning has led to very good performance on a
variety of problems, such as visual recognition, speech recognition and natural
language processing. Among different types of deep neural networks,
convolutional neural networks have been most extensively studied. Leveraging on
the rapid growth in the amount of the annotated data and the great improvements
in the strengths of graphics processor units, the research on convolutional
neural networks has been emerged swiftly and achieved state-of-the-art results
on various tasks. In this paper, we provide a broad survey of the recent
advances in convolutional neural networks. We detailize the improvements of CNN
on different aspects, including layer design, activation function, loss
function, regularization, optimization and fast computation. Besides, we also
introduce various applications of convolutional neural networks in computer
vision, speech and natural language processing.
| Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy,
Bing Shuai, Ting Liu, Xingxing Wang, Li Wang, Gang Wang, Jianfei Cai, Tsuhan
Chen | null | 1512.07108 | null | null |
Refined Error Bounds for Several Learning Algorithms | cs.LG math.ST stat.ML stat.TH | This article studies the achievable guarantees on the error rates of certain
learning algorithms, with particular focus on refining logarithmic factors.
Many of the results are based on a general technique for obtaining bounds on
the error rates of sample-consistent classifiers with monotonic error regions,
in the realizable case. We prove bounds of this type expressed in terms of
either the VC dimension or the sample compression size. This general technique
also enables us to derive several new bounds on the error rates of general
sample-consistent learning algorithms, as well as refined bounds on the label
complexity of the CAL active learning algorithm. Additionally, we establish a
simple necessary and sufficient condition for the existence of a
distribution-free bound on the error rates of all sample-consistent learning
rules, converging at a rate inversely proportional to the sample size. We also
study learning in the presence of classification noise, deriving a new excess
error rate guarantee for general VC classes under Tsybakov's noise condition,
and establishing a simple and general necessary and sufficient condition for
the minimax excess risk under bounded noise to converge at a rate inversely
proportional to the sample size.
| Steve Hanneke | null | 1512.07146 | null | null |
Feature Selection for Classification under Anonymity Constraint | cs.LG cs.CR | Over the last decade, proliferation of various online platforms and their
increasing adoption by billions of users have heightened the privacy risk of a
user enormously. In fact, security researchers have shown that sparse microdata
containing information about online activities of a user although anonymous,
can still be used to disclose the identity of the user by cross-referencing the
data with other data sources. To preserve the privacy of a user, in existing
works several methods (k-anonymity, l-diversity, differential privacy) are
proposed that ensure a dataset which is meant to share or publish bears small
identity disclosure risk. However, the majority of these methods modify the
data in isolation, without considering their utility in subsequent knowledge
discovery tasks, which makes these datasets less informative. In this work, we
consider labeled data that are generally used for classification, and propose
two methods for feature selection considering two goals: first, on the reduced
feature set the data has small disclosure risk, and second, the utility of the
data is preserved for performing a classification task. Experimental results on
various real-world datasets show that the method is effective and useful in
practice.
| Baichuan Zhang, Noman Mohammed, Vachik Dave, Mohammad Al Hasan | null | 1512.07158 | null | null |
Latent Variable Modeling with Diversity-Inducing Mutual Angular
Regularization | cs.LG stat.ML | Latent Variable Models (LVMs) are a large family of machine learning models
providing a principled and effective way to extract underlying patterns,
structure and knowledge from observed data. Due to the dramatic growth of
volume and complexity of data, several new challenges have emerged and cannot
be effectively addressed by existing LVMs: (1) How to capture long-tail
patterns that carry crucial information when the popularity of patterns is
distributed in a power-law fashion? (2) How to reduce model complexity and
computational cost without compromising the modeling power of LVMs? (3) How to
improve the interpretability and reduce the redundancy of discovered patterns?
To addresses the three challenges discussed above, we develop a novel
regularization technique for LVMs, which controls the geometry of the latent
space during learning to enable the learned latent components of LVMs to be
diverse in the sense that they are favored to be mutually different from each
other, to accomplish long-tail coverage, low redundancy, and better
interpretability. We propose a mutual angular regularizer (MAR) to encourage
the components in LVMs to have larger mutual angles. The MAR is non-convex and
non-smooth, entailing great challenges for optimization. To cope with this
issue, we derive a smooth lower bound of the MAR and optimize the lower bound
instead. We show that the monotonicity of the lower bound is closely aligned
with the MAR to qualify the lower bound as a desirable surrogate of the MAR.
Using neural network (NN) as an instance, we analyze how the MAR affects the
generalization performance of NN. On two popular latent variable models ---
restricted Boltzmann machine and distance metric learning, we demonstrate that
MAR can effectively capture long-tail patterns, reduce model complexity without
sacrificing expressivity and improve interpretability.
| Pengtao Xie, Yuntian Deng, Eric Xing | null | 1512.07336 | null | null |
A Deep Generative Deconvolutional Image Model | cs.CV cs.LG stat.ML | A deep generative model is developed for representation and analysis of
images, based on a hierarchical convolutional dictionary-learning framework.
Stochastic {\em unpooling} is employed to link consecutive layers in the model,
yielding top-down image generation. A Bayesian support vector machine is linked
to the top-layer features, yielding max-margin discrimination. Deep
deconvolutional inference is employed when testing, to infer the latent
features, and the top-layer features are connected with the max-margin
classifier for discrimination tasks. The model is efficiently trained using a
Monte Carlo expectation-maximization (MCEM) algorithm, with implementation on
graphical processor units (GPUs) for efficient large-scale learning, and fast
testing. Excellent results are obtained on several benchmark datasets,
including ImageNet, demonstrating that the proposed model achieves results that
are highly competitive with similarly sized convolutional neural networks.
| Yunchen Pu, Xin Yuan, Andrew Stevens, Chunyuan Li, Lawrence Carin | null | 1512.07344 | null | null |
Adaptive Algorithms for Online Convex Optimization with Long-term
Constraints | stat.ML cs.LG math.OC | We present an adaptive online gradient descent algorithm to solve online
convex optimization problems with long-term constraints , which are constraints
that need to be satisfied when accumulated over a finite number of rounds T ,
but can be violated in intermediate rounds. For some user-defined trade-off
parameter $\beta$ $\in$ (0, 1), the proposed algorithm achieves cumulative
regret bounds of O(T^max{$\beta$,1--$\beta$}) and O(T^(1--$\beta$/2)) for the
loss and the constraint violations respectively. Our results hold for convex
losses and can handle arbitrary convex constraints without requiring knowledge
of the number of rounds in advance. Our contributions improve over the best
known cumulative regret bounds by Mahdavi, et al. (2012) that are respectively
O(T^1/2) and O(T^3/4) for general convex domains, and respectively O(T^2/3) and
O(T^2/3) when further restricting to polyhedral domains. We supplement the
analysis with experiments validating the performance of our algorithm in
practice.
| Rodolphe Jenatton, Jim Huang, C\'edric Archambeau | null | 1512.07422 | null | null |
Adaptive Ensemble Learning with Confidence Bounds | cs.LG stat.ML | Extracting actionable intelligence from distributed, heterogeneous,
correlated and high-dimensional data sources requires run-time processing and
learning both locally and globally. In the last decade, a large number of
meta-learning techniques have been proposed in which local learners make online
predictions based on their locally-collected data instances, and feed these
predictions to an ensemble learner, which fuses them and issues a global
prediction. However, most of these works do not provide performance guarantees
or, when they do, these guarantees are asymptotic. None of these existing works
provide confidence estimates about the issued predictions or rate of learning
guarantees for the ensemble learner. In this paper, we provide a systematic
ensemble learning method called Hedged Bandits, which comes with both long run
(asymptotic) and short run (rate of learning) performance guarantees. Moreover,
our approach yields performance guarantees with respect to the optimal local
prediction strategy, and is also able to adapt its predictions in a data-driven
manner. We illustrate the performance of Hedged Bandits in the context of
medical informatics and show that it outperforms numerous online and offline
ensemble learning methods.
| Cem Tekin, Jinsung Yoon, Mihaela van der Schaar | null | 1512.07446 | null | null |
A Latent-Variable Lattice Model | cs.LG cs.CV stat.ML | Markov random field (MRF) learning is intractable, and its approximation
algorithms are computationally expensive. We target a small subset of MRF that
is used frequently in computer vision. We characterize this subset with three
concepts: Lattice, Homogeneity, and Inertia; and design a non-markov model as
an alternative. Our goal is robust learning from small datasets. Our learning
algorithm uses vector quantization and, at time complexity O(U log U) for a
dataset of U pixels, is much faster than that of general-purpose MRF.
| Rajasekaran Masatran | null | 1512.07587 | null | null |
Satisficing in multi-armed bandit problems | cs.LG math.OC stat.ML | Satisficing is a relaxation of maximizing and allows for less risky decision
making in the face of uncertainty. We propose two sets of satisficing
objectives for the multi-armed bandit problem, where the objective is to
achieve reward-based decision-making performance above a given threshold. We
show that these new problems are equivalent to various standard multi-armed
bandit problems with maximizing objectives and use the equivalence to find
bounds on performance. The different objectives can result in qualitatively
different behavior; for example, agents explore their options continually in
one case and only a finite number of times in another. For the case of Gaussian
rewards we show an additional equivalence between the two sets of satisficing
objectives that allows algorithms developed for one set to be applied to the
other. We then develop variants of the Upper Credible Limit (UCL) algorithm
that solve the problems with satisficing objectives and show that these
modified UCL algorithms achieve efficient satisficing performance.
| Paul Reverdy and Vaibhav Srivastava and Naomi Ehrich Leonard | null | 1512.07638 | null | null |
The Max $K$-Armed Bandit: PAC Lower Bounds and Efficient Algorithms | stat.ML cs.AI cs.LG | We consider the Max $K$-Armed Bandit problem, where a learning agent is faced
with several stochastic arms, each a source of i.i.d. rewards of unknown
distribution. At each time step the agent chooses an arm, and observes the
reward of the obtained sample. Each sample is considered here as a separate
item with the reward designating its value, and the goal is to find an item
with the highest possible value. Our basic assumption is a known lower bound on
the {\em tail function} of the reward distributions. Under the PAC framework,
we provide a lower bound on the sample complexity of any
$(\epsilon,\delta)$-correct algorithm, and propose an algorithm that attains
this bound up to logarithmic factors. We analyze the robustness of the proposed
algorithm and in addition, we compare the performance of this algorithm to the
variant in which the arms are not distinguishable by the agent and are chosen
randomly at each stage. Interestingly, when the maximal rewards of the arms
happen to be similar, the latter approach may provide better performance.
| Yahel David and Nahum Shimkin | null | 1512.07650 | null | null |
Deep Reinforcement Learning in Large Discrete Action Spaces | cs.AI cs.LG cs.NE stat.ML | Being able to reason in an environment with a large number of discrete
actions is essential to bringing reinforcement learning to a larger class of
problems. Recommender systems, industrial plants and language models are only
some of the many real-world tasks involving large numbers of discrete actions
for which current methods are difficult or even often impossible to apply. An
ability to generalize over the set of actions as well as sub-linear complexity
relative to the size of the set are both necessary to handle such tasks.
Current approaches are not able to provide both of these, which motivates the
work in this paper. Our proposed approach leverages prior information about the
actions to embed them in a continuous space upon which it can generalize.
Additionally, approximate nearest-neighbor methods allow for logarithmic-time
lookup complexity relative to the number of actions, which is necessary for
time-wise tractable training. This combined approach allows reinforcement
learning methods to be applied to large-scale learning problems previously
intractable with current methods. We demonstrate our algorithm's abilities on a
series of tasks having up to one million actions.
| Gabriel Dulac-Arnold and Richard Evans and Hado van Hasselt and Peter
Sunehag and Timothy Lillicrap and Jonathan Hunt and Timothy Mann and
Theophane Weber and Thomas Degris and Ben Coppin | null | 1512.07679 | null | null |
Fast Parallel SVM using Data Augmentation | cs.LG | As one of the most popular classifiers, linear SVMs still have challenges in
dealing with very large-scale problems, even though linear or sub-linear
algorithms have been developed recently on single machines. Parallel computing
methods have been developed for learning large-scale SVMs. However, existing
methods rely on solving local sub-optimization problems. In this paper, we
develop a novel parallel algorithm for learning large-scale linear SVM. Our
approach is based on a data augmentation equivalent formulation, which casts
the problem of learning SVM as a Bayesian inference problem, for which we can
develop very efficient parallel sampling methods. We provide empirical results
for this parallel sampling SVM, and provide extensions for SVR, non-linear
kernels, and provide a parallel implementation of the Crammer and Singer model.
This approach is very promising in its own right, and further is a very useful
technique to parallelize a broader family of general maximum-margin models.
| Hugh Perkins, Minjie Xu, Jun Zhu, Bo Zhang | null | 1512.07716 | null | null |
Real-Time Audio-to-Score Alignment of Music Performances Containing
Errors and Arbitrary Repeats and Skips | cs.SD cs.LG cs.MM | This paper discusses real-time alignment of audio signals of music
performance to the corresponding score (a.k.a. score following) which can
handle tempo changes, errors and arbitrary repeats and/or skips (repeats/skips)
in performances. This type of score following is particularly useful in
automatic accompaniment for practices and rehearsals, where errors and
repeats/skips are often made. Simple extensions of the algorithms previously
proposed in the literature are not applicable in these situations for scores of
practical length due to the problem of large computational complexity. To cope
with this problem, we present two hidden Markov models of monophonic
performance with errors and arbitrary repeats/skips, and derive efficient
score-following algorithms with an assumption that the prior probability
distributions of score positions before and after repeats/skips are independent
from each other. We confirmed real-time operation of the algorithms with music
scores of practical length (around 10000 notes) on a modern laptop and their
tracking ability to the input performance within 0.7 s on average after
repeats/skips in clarinet performance data. Further improvements and extension
for polyphonic signals are also discussed.
| Tomohiko Nakamura, Eita Nakamura and Shigeki Sagayama | 10.1109/TASLP.2015.2507862 | 1512.07748 | null | null |
The Lov\'asz Hinge: A Novel Convex Surrogate for Submodular Losses | stat.ML cs.LG | Learning with non-modular losses is an important problem when sets of
predictions are made simultaneously. The main tools for constructing convex
surrogate loss functions for set prediction are margin rescaling and slack
rescaling. In this work, we show that these strategies lead to tight convex
surrogates iff the underlying loss function is increasing in the number of
incorrect predictions. However, gradient or cutting-plane computation for these
functions is NP-hard for non-supermodular loss functions. We propose instead a
novel surrogate loss function for submodular losses, the Lov\'asz hinge, which
leads to O(p log p) complexity with O(p) oracle accesses to the loss function
to compute a gradient or cutting-plane. We prove that the Lov\'asz hinge is
convex and yields an extension. As a result, we have developed the first
tractable convex surrogates in the literature for submodular losses. We
demonstrate the utility of this novel convex surrogate through several set
prediction tasks, including on the PASCAL VOC and Microsoft COCO datasets.
| Jiaqian Yu (CVC, GALEN), Matthew Blaschko | null | 1512.07797 | null | null |
Visualizations Relevant to The User By Multi-View Latent Variable
Factorization | cs.LG cs.IR | A main goal of data visualization is to find, from among all the available
alternatives, mappings to the 2D/3D display which are relevant to the user.
Assuming user interaction data, or other auxiliary data about the items or
their relationships, the goal is to identify which aspects in the primary data
support the user\'s input and, equally importantly, which aspects of the
user\'s potentially noisy input have support in the primary data. For solving
the problem, we introduce a multi-view embedding in which a latent
factorization identifies which aspects in the two data views (primary data and
user data) are related and which are specific to only one of them. The
factorization is a generative model in which the display is parameterized as a
part of the factorization and the other factors explain away the aspects not
expressible in a two-dimensional display. Functioning of the model is
demonstrated on several data sets.
| Seppo Virtanen, Homayun Afrabandpey, Samuel Kaski | 10.1109/ICASSP.2016.7472120 | 1512.07807 | null | null |
Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown
Stimulus Dynamics | cs.LG stat.ML | In order to interact intelligently with objects in the world, animals must
first transform neural population responses into estimates of the dynamic,
unknown stimuli which caused them. The Bayesian solution to this problem is
known as a Bayes filter, which applies Bayes' rule to combine population
responses with the predictions of an internal model. In this paper we present a
method for learning to approximate a Bayes filter when the stimulus dynamics
are unknown. To do this we use the inferential properties of probabilistic
population codes to compute Bayes' rule, and train a neural network to compute
approximate predictions by the method of maximum likelihood. In particular, we
perform stochastic gradient descent on the negative log-likelihood with a novel
approximation of the gradient. We demonstrate our methods on a finite-state, a
linear, and a nonlinear filtering problem, and show how the hidden layer of the
neural network develops tuning curves which are consistent with findings in
experimental neuroscience.
| Sacha Sokoloski | null | 1512.07839 | null | null |
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