title
stringlengths 5
246
| categories
stringlengths 5
94
⌀ | abstract
stringlengths 54
5.03k
| authors
stringlengths 0
6.72k
| doi
stringlengths 12
54
⌀ | id
stringlengths 6
10
⌀ | year
float64 2.02k
2.02k
⌀ | venue
stringclasses 13
values |
|---|---|---|---|---|---|---|---|
Safe Reinforcement Learning via Shielding | cs.LO cs.AI cs.LG | Reinforcement learning algorithms discover policies that maximize reward, but
do not necessarily guarantee safety during learning or execution phases. We
introduce a new approach to learn optimal policies while enforcing properties
expressed in temporal logic. To this end, given the temporal logic
specification that is to be obeyed by the learning system, we propose to
synthesize a reactive system called a shield. The shield is introduced in the
traditional learning process in two alternative ways, depending on the location
at which the shield is implemented. In the first one, the shield acts each time
the learning agent is about to make a decision and provides a list of safe
actions. In the second way, the shield is introduced after the learning agent.
The shield monitors the actions from the learner and corrects them only if the
chosen action causes a violation of the specification. We discuss which
requirements a shield must meet to preserve the convergence guarantees of the
learner. Finally, we demonstrate the versatility of our approach on several
challenging reinforcement learning scenarios.
| Mohammed Alshiekh, Roderick Bloem, Ruediger Ehlers, Bettina
K\"onighofer, Scott Niekum, Ufuk Topcu | null | 1708.08611 | null | null |
Performance Analysis of Open Source Machine Learning Frameworks for
Various Parameters in Single-Threaded and Multi-Threaded Modes | cs.LG cs.CV cs.DC cs.PF | The basic features of some of the most versatile and popular open source
frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are
considered and compared. Their comparative analysis was performed and
conclusions were made as to the advantages and disadvantages of these
platforms. The performance tests for the de facto standard MNIST data set were
carried out on H2O framework for deep learning algorithms designed for CPU and
GPU platforms for single-threaded and multithreaded modes of operation Also, we
present the results of testing neural networks architectures on H2O platform
for various activation functions, stopping metrics, and other parameters of
machine learning algorithm. It was demonstrated for the use case of MNIST
database of handwritten digits in single-threaded mode that blind selection of
these parameters can hugely increase (by 2-3 orders) the runtime without the
significant increase of precision. This result can have crucial influence for
optimization of available and new machine learning methods, especially for
image recognition problems.
| Yuriy Kochura, Sergii Stirenko, Oleg Alienin, Michail Novotarskiy, and
Yuri Gordienko | 10.1007/978-3-319-70581-1_17 | 1708.0867 | null | null |
Towards Poisoning of Deep Learning Algorithms with Back-gradient
Optimization | cs.LG | A number of online services nowadays rely upon machine learning to extract
valuable information from data collected in the wild. This exposes learning
algorithms to the threat of data poisoning, i.e., a coordinate attack in which
a fraction of the training data is controlled by the attacker and manipulated
to subvert the learning process. To date, these attacks have been devised only
against a limited class of binary learning algorithms, due to the inherent
complexity of the gradient-based procedure used to optimize the poisoning
points (a.k.a. adversarial training examples). In this work, we rst extend the
de nition of poisoning attacks to multiclass problems. We then propose a novel
poisoning algorithm based on the idea of back-gradient optimization, i.e., to
compute the gradient of interest through automatic di erentiation, while also
reversing the learning procedure to drastically reduce the attack complexity.
Compared to current poisoning strategies, our approach is able to target a
wider class of learning algorithms, trained with gradient- based procedures,
including neural networks and deep learning architectures. We empirically
evaluate its e ectiveness on several application examples, including spam
ltering, malware detection, and handwritten digit recognition. We nally show
that, similarly to adversarial test examples, adversarial training examples can
also be transferred across di erent learning algorithms.
| Luis Mu\~noz-Gonz\'alez, Battista Biggio, Ambra Demontis, Andrea
Paudice, Vasin Wongrassamee, Emil C. Lupu, Fabio Roli | null | 1708.08689 | null | null |
Natasha 2: Faster Non-Convex Optimization Than SGD | math.OC cs.DS cs.LG cs.NE stat.ML | We design a stochastic algorithm to train any smooth neural network to
$\varepsilon$-approximate local minima, using $O(\varepsilon^{-3.25})$
backpropagations. The best result was essentially $O(\varepsilon^{-4})$ by SGD.
More broadly, it finds $\varepsilon$-approximate local minima of any smooth
nonconvex function in rate $O(\varepsilon^{-3.25})$, with only oracle access to
stochastic gradients.
| Zeyuan Allen-Zhu | null | 1708.08694 | null | null |
Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary
Learning | cs.CV cs.LG stat.ML | The recently proposed Multi-Layer Convolutional Sparse Coding (ML-CSC) model,
consisting of a cascade of convolutional sparse layers, provides a new
interpretation of Convolutional Neural Networks (CNNs). Under this framework,
the computation of the forward pass in a CNN is equivalent to a pursuit
algorithm aiming to estimate the nested sparse representation vectors -- or
feature maps -- from a given input signal. Despite having served as a pivotal
connection between CNNs and sparse modeling, a deeper understanding of the
ML-CSC is still lacking: there are no pursuit algorithms that can serve this
model exactly, nor are there conditions to guarantee a non-empty model. While
one can easily obtain signals that approximately satisfy the ML-CSC
constraints, it remains unclear how to simply sample from the model and, more
importantly, how one can train the convolutional filters from real data.
In this work, we propose a sound pursuit algorithm for the ML-CSC model by
adopting a projection approach. We provide new and improved bounds on the
stability of the solution of such pursuit and we analyze different practical
alternatives to implement this in practice. We show that the training of the
filters is essential to allow for non-trivial signals in the model, and we
derive an online algorithm to learn the dictionaries from real data,
effectively resulting in cascaded sparse convolutional layers. Last, but not
least, we demonstrate the applicability of the ML-CSC model for several
applications in an unsupervised setting, providing competitive results. Our
work represents a bridge between matrix factorization, sparse dictionary
learning and sparse auto-encoders, and we analyze these connections in detail.
| Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad | 10.1109/TSP.2018.2846226 | 1708.08705 | null | null |
Machine Learning Approach for Detection of nonTor Traffic | cs.CR cs.LG | Intrusion detection has attracted a considerable interest from researchers
and industries. After many years of research the community still faces the
problem of building reliable and efficient intrusion detection systems (IDS)
capable of handling large quantities of data with changing patterns in real
time situations. The Tor network is popular in providing privacy and security
to end user by anonymising the identity of internet users connecting through a
series of tunnels and nodes. This work focuses on the classification of Tor
traffic and nonTor traffic to expose the activities within Tor traffic that
minimizes the protection of users. A study to compare the reliability and
efficiency of Artificial Neural Network and Support vector machine in detecting
nonTor traffic in UNB-CIC Tor Network Traffic dataset is presented in this
paper. The results are analysed based on the overall accuracy, detection rate
and false positive rate of the two algorithms. Experimental results show that
both algorithms could detect nonTor traffic in the dataset. A hybrid Artificial
neural network proved a better classifier than SVM in detecting nonTor traffic
in UNB-CIC Tor Network Traffic dataset.
| Elike Hodo and Xavier Bellekens and Ephraim Iorkyase and Andrew
Hamilton and Christos Tachtatzis and Robert Atkinson | 10.3390/info9090231 | 1708.08725 | null | null |
Multi-view Low-rank Sparse Subspace Clustering | cs.CV cs.LG math.OC stat.ML | Most existing approaches address multi-view subspace clustering problem by
constructing the affinity matrix on each view separately and afterwards propose
how to extend spectral clustering algorithm to handle multi-view data. This
paper presents an approach to multi-view subspace clustering that learns a
joint subspace representation by constructing affinity matrix shared among all
views. Relying on the importance of both low-rank and sparsity constraints in
the construction of the affinity matrix, we introduce the objective that
balances between the agreement across different views, while at the same time
encourages sparsity and low-rankness of the solution. Related low-rank and
sparsity constrained optimization problem is for each view solved using the
alternating direction method of multipliers. Furthermore, we extend our
approach to cluster data drawn from nonlinear subspaces by solving the
corresponding problem in a reproducing kernel Hilbert space. The proposed
algorithm outperforms state-of-the-art multi-view subspace clustering
algorithms on one synthetic and four real-world datasets.
| Maria Brbic and Ivica Kopriva | 10.1016/j.patcog.2017.08.024 | 1708.08732 | null | null |
Multi-Stage Feature Selection Based Intelligent Classifier for
Classification of Incipient Stage Fire in Building | cs.CY cs.LG | In this study, an early fire detection algorithm has been proposed based on
low cost array sensing system, utilizing gas sensors, dust particles and
ambient sensors such as temperature and humidity sensor. The odor or
smell-print emanated from various fire sources and building construction
materials at early stage are measured. For this purpose, odor profile data from
five common fire sources and three common building construction materials were
used to develop the classification model. Normalized feature extractions of the
smell print data were performed before subjected to prediction classifier.
These features represent the odor signals in the time domain. The obtained
features undergo the proposed multi-stage feature selection technique and
lastly, further reduced by Principal Component Analysis (PCA), a dimension
reduction technique. The hybrid PCA-PNN based approach has been applied on
different datasets from in-house developed system and the portable electronic
nose unit. Experimental classification results show that the dimension
reduction process performed by PCA has improved the classification accuracy and
provided high reliability, regardless of ambient temperature and humidity
variation, baseline sensor drift, the different gas concentration level and
exposure towards different heating temperature range.
| Allan Melvin Andrew, Ammar Zakaria, Shaharil Mad Saad and Ali Yeon Md
Shakaff | 10.3390/s16010031 | 1708.0875 | null | null |
Multi-task Neural Networks for Personalized Pain Recognition from
Physiological Signals | cs.CY cs.LG q-bio.NC | Pain is a complex and subjective experience that poses a number of
measurement challenges. While self-report by the patient is viewed as the gold
standard of pain assessment, this approach fails when patients cannot verbally
communicate pain intensity or lack normal mental abilities. Here, we present a
pain intensity measurement method based on physiological signals. Specifically,
we implement a multi-task learning approach based on neural networks that
accounts for individual differences in pain responses while still leveraging
data from across the population. We test our method in a dataset containing
multi-modal physiological responses to nociceptive pain.
| Daniel Lopez-Martinez, Rosalind Picard | null | 1708.08755 | null | null |
Anomaly Detection: Review and preliminary Entropy method tests | cs.LG | Anomalies are strange data points; they usually represent an unusual
occurrence. Anomaly detection is presented from the perspective of Wireless
sensor networks. Different approaches have been taken in the past, as we will
see, not only to identify outliers, but also to establish the statistical
properties of the different methods. The usual goal is to show that the
approach is asymptotically efficient and that the metric used is unbiased or
maybe biased.
This project is based on a work done by [1]. The approach is based on the
principle that the entropy of the data is increased when an anomalous data
point is measured. The entropy of the data set is thus to be estimated. In this
report however, preliminary efforts at confirming the results of [1] is
presented. To estimate the entropy of the dataset, since no parametric form is
assumed, the probability density function of the data set is first estimated
using data split method. This estimated pdf value is then plugged-in to the
entropy estimation formula to estimate the entropy of the dataset. The data
(test signal) used in this report is Gaussian distributed with zero mean and
variance 4. Results of pdf estimation using the k-nearest neighbour method
using the entire dataset, and a data-split method are presented and compared
based on how well they approximate the probability density function of a
Gaussian with similar mean and variance. The number of nearest neighbours
chosen for the purpose of this report is 8. This is arbitrary, but is
reasonable since the number of anomalies introduced is expected to be less than
this upon data-split. The data-split method is preferred and rightly so.
| Pelumi Oluwasanya | null | 1708.08813 | null | null |
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields | cs.LG cs.GT stat.ML | Generative adversarial networks (GANs) evolved into one of the most
successful unsupervised techniques for generating realistic images. Even though
it has recently been shown that GAN training converges, GAN models often end up
in local Nash equilibria that are associated with mode collapse or otherwise
fail to model the target distribution. We introduce Coulomb GANs, which pose
the GAN learning problem as a potential field of charged particles, where
generated samples are attracted to training set samples but repel each other.
The discriminator learns a potential field while the generator decreases the
energy by moving its samples along the vector (force) field determined by the
gradient of the potential field. Through decreasing the energy, the GAN model
learns to generate samples according to the whole target distribution and does
not only cover some of its modes. We prove that Coulomb GANs possess only one
Nash equilibrium which is optimal in the sense that the model distribution
equals the target distribution. We show the efficacy of Coulomb GANs on a
variety of image datasets. On LSUN and celebA, Coulomb GANs set a new state of
the art and produce a previously unseen variety of different samples.
| Thomas Unterthiner, Bernhard Nessler, Calvin Seward, G\"unter
Klambauer, Martin Heusel, Hubert Ramsauer, Sepp Hochreiter | null | 1708.08819 | null | null |
Gradual Learning of Recurrent Neural Networks | stat.ML cs.IT cs.LG math.IT | Recurrent Neural Networks (RNNs) achieve state-of-the-art results in many
sequence-to-sequence modeling tasks. However, RNNs are difficult to train and
tend to suffer from overfitting. Motivated by the Data Processing Inequality
(DPI), we formulate the multi-layered network as a Markov chain, introducing a
training method that comprises training the network gradually and using
layer-wise gradient clipping. We found that applying our methods, combined with
previously introduced regularization and optimization methods, resulted in
improvements in state-of-the-art architectures operating in language modeling
tasks.
| Ziv Aharoni, Gal Rattner, Haim Permuter | null | 1708.08863 | null | null |
CirCNN: Accelerating and Compressing Deep Neural Networks Using
Block-CirculantWeight Matrices | cs.CV cs.AI cs.LG stat.ML | Large-scale deep neural networks (DNNs) are both compute and memory
intensive. As the size of DNNs continues to grow, it is critical to improve the
energy efficiency and performance while maintaining accuracy. For DNNs, the
model size is an important factor affecting performance, scalability and energy
efficiency. Weight pruning achieves good compression ratios but suffers from
three drawbacks: 1) the irregular network structure after pruning; 2) the
increased training complexity; and 3) the lack of rigorous guarantee of
compression ratio and inference accuracy. To overcome these limitations, this
paper proposes CirCNN, a principled approach to represent weights and process
neural networks using block-circulant matrices. CirCNN utilizes the Fast
Fourier Transform (FFT)-based fast multiplication, simultaneously reducing the
computational complexity (both in inference and training) from O(n2) to
O(nlogn) and the storage complexity from O(n2) to O(n), with negligible
accuracy loss. Compared to other approaches, CirCNN is distinct due to its
mathematical rigor: it can converge to the same effectiveness as DNNs without
compression. The CirCNN architecture, a universal DNN inference engine that can
be implemented on various hardware/software platforms with configurable network
architecture. To demonstrate the performance and energy efficiency, we test
CirCNN in FPGA, ASIC and embedded processors. Our results show that CirCNN
architecture achieves very high energy efficiency and performance with a small
hardware footprint. Based on the FPGA implementation and ASIC synthesis
results, CirCNN achieves 6-102X energy efficiency improvements compared with
the best state-of-the-art results.
| Caiwen Ding, Siyu Liao, Yanzhi Wang, Zhe Li, Ning Liu, Youwei Zhuo,
Chao Wang, Xuehai Qian, Yu Bai, Geng Yuan, Xiaolong Ma, Yipeng Zhang, Jian
Tang, Qinru Qiu, Xue Lin, Bo Yuan | 10.1145/3123939.3124552 | 1708.08917 | null | null |
Limiting the Reconstruction Capability of Generative Neural Network
using Negative Learning | cs.CV cs.AI cs.LG | Generative models are widely used for unsupervised learning with various
applications, including data compression and signal restoration. Training
methods for such systems focus on the generality of the network given limited
amount of training data. A less researched type of techniques concerns
generation of only a single type of input. This is useful for applications such
as constraint handling, noise reduction and anomaly detection. In this paper we
present a technique to limit the generative capability of the network using
negative learning. The proposed method searches the solution in the gradient
direction for the desired input and in the opposite direction for the undesired
input. One of the application can be anomaly detection where the undesired
inputs are the anomalous data. In the results section we demonstrate the
features of the algorithm using MNIST handwritten digit dataset and latter
apply the technique to a real-world obstacle detection problem. The results
clearly show that the proposed learning technique can significantly improve the
performance for anomaly detection.
| Asim Munawar, Phongtharin Vinayavekhin and Giovanni De Magistris | null | 1708.08985 | null | null |
Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable
Sensors | cs.CV cs.LG | Human activity recognition (HAR) has become a popular topic in research
because of its wide application. With the development of deep learning, new
ideas have appeared to address HAR problems. Here, a deep network architecture
using residual bidirectional long short-term memory (LSTM) cells is proposed.
The advantages of the new network include that a bidirectional connection can
concatenate the positive time direction (forward state) and the negative time
direction (backward state). Second, residual connections between stacked cells
act as highways for gradients, which can pass underlying information directly
to the upper layer, effectively avoiding the gradient vanishing problem.
Generally, the proposed network shows improvements on both the temporal (using
bidirectional cells) and the spatial (residual connections stacked deeply)
dimensions, aiming to enhance the recognition rate. When tested with the
Opportunity data set and the public domain UCI data set, the accuracy was
increased by 4.78% and 3.68%, respectively, compared with previously reported
results. Finally, the confusion matrix of the public domain UCI data set was
analyzed.
| Yu Zhao, Rennong Yang, Guillaume Chevalier, Maoguo Gong | null | 1708.08989 | null | null |
Clustering Patients with Tensor Decomposition | stat.ML cs.LG | In this paper we present a method for the unsupervised clustering of
high-dimensional binary data, with a special focus on electronic healthcare
records. We present a robust and efficient heuristic to face this problem using
tensor decomposition. We present the reasons why this approach is preferable
for tasks such as clustering patient records, to more commonly used
distance-based methods. We run the algorithm on two datasets of healthcare
records, obtaining clinically meaningful results.
| Matteo Ruffini, Ricard Gavald\`a, Esther Lim\'on | null | 1708.08994 | null | null |
Deep Convolutional Neural Networks for Raman Spectrum Recognition: A
Unified Solution | cs.LG stat.ML | Machine learning methods have found many applications in Raman spectroscopy,
especially for the identification of chemical species. However, almost all of
these methods require non-trivial preprocessing such as baseline correction
and/or PCA as an essential step. Here we describe our unified solution for the
identification of chemical species in which a convolutional neural network is
trained to automatically identify substances according to their Raman spectrum
without the need of ad-hoc preprocessing steps. We evaluated our approach using
the RRUFF spectral database, comprising mineral sample data. Superior
classification performance is demonstrated compared with other frequently used
machine learning algorithms including the popular support vector machine.
| Jinchao Liu, Margarita Osadchy, Lorna Ashton, Michael Foster,
Christopher J. Solomon, Stuart J. Gibson | 10.1039/C7AN01371J | 1708.09022 | null | null |
Unsupervised Terminological Ontology Learning based on Hierarchical
Topic Modeling | cs.CL cs.IR cs.LG | In this paper, we present hierarchical relationbased latent Dirichlet
allocation (hrLDA), a data-driven hierarchical topic model for extracting
terminological ontologies from a large number of heterogeneous documents. In
contrast to traditional topic models, hrLDA relies on noun phrases instead of
unigrams, considers syntax and document structures, and enriches topic
hierarchies with topic relations. Through a series of experiments, we
demonstrate the superiority of hrLDA over existing topic models, especially for
building hierarchies. Furthermore, we illustrate the robustness of hrLDA in the
settings of noisy data sets, which are likely to occur in many practical
scenarios. Our ontology evaluation results show that ontologies extracted from
hrLDA are very competitive with the ontologies created by domain experts.
| Xiaofeng Zhu, Diego Klabjan, Patrick Bless | 10.1109/IRI.2017.18 | 1708.09025 | null | null |
Practical Attacks Against Graph-based Clustering | cs.CR cs.LG | Graph modeling allows numerous security problems to be tackled in a general
way, however, little work has been done to understand their ability to
withstand adversarial attacks. We design and evaluate two novel graph attacks
against a state-of-the-art network-level, graph-based detection system. Our
work highlights areas in adversarial machine learning that have not yet been
addressed, specifically: graph-based clustering techniques, and a global
feature space where realistic attackers without perfect knowledge must be
accounted for (by the defenders) in order to be practical. Even though less
informed attackers can evade graph clustering with low cost, we show that some
practical defenses are possible.
| Yizheng Chen, Yacin Nadji, Athanasios Kountouras, Fabian Monrose,
Roberto Perdisci, Manos Antonakakis, Nikolaos Vasiloglou | 10.1145/3133956.3134083 | 1708.09056 | null | null |
Block-Simultaneous Direction Method of Multipliers: A proximal
primal-dual splitting algorithm for nonconvex problems with multiple
constraints | math.OC cs.CV cs.LG | We introduce a generalization of the linearized Alternating Direction Method
of Multipliers to optimize a real-valued function $f$ of multiple arguments
with potentially multiple constraints $g_\circ$ on each of them. The function
$f$ may be nonconvex as long as it is convex in every argument, while the
constraints $g_\circ$ need to be convex but not smooth. If $f$ is smooth, the
proposed Block-Simultaneous Direction Method of Multipliers (bSDMM) can be
interpreted as a proximal analog to inexact coordinate descent methods under
constraints. Unlike alternative approaches for joint solvers of
multiple-constraint problems, we do not require linear operators $L$ of a
constraint function $g(L\ \cdot)$ to be invertible or linked between each
other. bSDMM is well-suited for a range of optimization problems, in particular
for data analysis, where $f$ is the likelihood function of a model and $L$
could be a transformation matrix describing e.g. finite differences or basis
transforms. We apply bSDMM to the Non-negative Matrix Factorization task of a
hyperspectral unmixing problem and demonstrate convergence and effectiveness of
multiple constraints on both matrix factors. The algorithms are implemented in
python and released as an open-source package.
| Fred Moolekamp and Peter Melchior | 10.1007/s11081-018-9380-y | 1708.09066 | null | null |
Interpretable Categorization of Heterogeneous Time Series Data | cs.LG | Understanding heterogeneous multivariate time series data is important in
many applications ranging from smart homes to aviation. Learning models of
heterogeneous multivariate time series that are also human-interpretable is
challenging and not adequately addressed by the existing literature. We propose
grammar-based decision trees (GBDTs) and an algorithm for learning them. GBDTs
extend decision trees with a grammar framework. Logical expressions derived
from a context-free grammar are used for branching in place of simple
thresholds on attributes. The added expressivity enables support for a wide
range of data types while retaining the interpretability of decision trees. In
particular, when a grammar based on temporal logic is used, we show that GBDTs
can be used for the interpretable classi cation of high-dimensional and
heterogeneous time series data. Furthermore, we show how GBDTs can also be used
for categorization, which is a combination of clustering and generating
interpretable explanations for each cluster. We apply GBDTs to analyze the
classic Australian Sign Language dataset as well as data on near mid-air
collisions (NMACs). The NMAC data comes from aircraft simulations used in the
development of the next-generation Airborne Collision Avoidance System (ACAS
X).
| Ritchie Lee, Mykel J. Kochenderfer, Ole J. Mengshoel and Joshua
Silbermann | null | 1708.09121 | null | null |
Tensor Networks for Dimensionality Reduction and Large-Scale
Optimizations. Part 2 Applications and Future Perspectives | cs.NA cs.LG | Part 2 of this monograph builds on the introduction to tensor networks and
their operations presented in Part 1. It focuses on tensor network models for
super-compressed higher-order representation of data/parameters and related
cost functions, while providing an outline of their applications in machine
learning and data analytics. A particular emphasis is on the tensor train (TT)
and Hierarchical Tucker (HT) decompositions, and their physically meaningful
interpretations which reflect the scalability of the tensor network approach.
Through a graphical approach, we also elucidate how, by virtue of the
underlying low-rank tensor approximations and sophisticated contractions of
core tensors, tensor networks have the ability to perform distributed
computations on otherwise prohibitively large volumes of data/parameters,
thereby alleviating or even eliminating the curse of dimensionality. The
usefulness of this concept is illustrated over a number of applied areas,
including generalized regression and classification (support tensor machines,
canonical correlation analysis, higher order partial least squares),
generalized eigenvalue decomposition, Riemannian optimization, and in the
optimization of deep neural networks. Part 1 and Part 2 of this work can be
used either as stand-alone separate texts, or indeed as a conjoint
comprehensive review of the exciting field of low-rank tensor networks and
tensor decompositions.
| A. Cichocki, A-H. Phan, Q. Zhao, N. Lee, I.V. Oseledets, M. Sugiyama,
D. Mandic | 10.1561/2200000067 | 1708.09165 | null | null |
THAP: A Matlab Toolkit for Learning with Hawkes Processes | stat.ML cs.LG | As a powerful tool of asynchronous event sequence analysis, point processes
have been studied for a long time and achieved numerous successes in different
fields. Among various point process models, Hawkes process and its variants
attract many researchers in statistics and computer science these years because
they capture the self- and mutually-triggering patterns between different
events in complicated sequences explicitly and quantitatively and are broadly
applicable to many practical problems. In this paper, we describe an
open-source toolkit implementing many learning algorithms and analysis tools
for Hawkes process model and its variants. Our toolkit systematically
summarizes recent state-of-the-art algorithms as well as most classic
algorithms of Hawkes processes, which is beneficial for both academical
education and research. Source code can be downloaded from
https://github.com/HongtengXu/Hawkes-Process-Toolkit.
| Hongteng Xu and Hongyuan Zha | null | 1708.09252 | null | null |
Efficient Convolutional Network Learning using Parametric Log based
Dual-Tree Wavelet ScatterNet | cs.LG stat.ML | We propose a DTCWT ScatterNet Convolutional Neural Network (DTSCNN) formed by
replacing the first few layers of a CNN network with a parametric log based
DTCWT ScatterNet. The ScatterNet extracts edge based invariant representations
that are used by the later layers of the CNN to learn high-level features. This
improves the training of the network as the later layers can learn more complex
patterns from the start of learning because the edge representations are
already present. The efficient learning of the DTSCNN network is demonstrated
on CIFAR-10 and Caltech-101 datasets. The generic nature of the ScatterNet
front-end is shown by an equivalent performance to pre-trained CNN front-ends.
A comparison with the state-of-the-art on CIFAR-10 and Caltech-101 datasets is
also presented.
| Amarjot Singh and Nick Kingsbury | null | 1708.09259 | null | null |
Optimal and Learning Control for Autonomous Robots | cs.SY cs.LG cs.RO math.OC | Optimal and Learning Control for Autonomous Robots has been taught in the
Robotics, Systems and Controls Masters at ETH Zurich with the aim to teach
optimal control and reinforcement learning for closed loop control problems
from a unified point of view. The starting point is the formulation of of an
optimal control problem and deriving the different types of solutions and
algorithms from there. These lecture notes aim at supporting this unified view
with a unified notation wherever possible, and a bit of a translation help to
compare the terminology and notation in the different fields. The course
assumes basic knowledge of Control Theory, Linear Algebra and Stochastic
Calculus.
| Jonas Buchli, Farbod Farshidian, Alexander Winkler, Timothy Sandy,
Markus Giftthaler | null | 1708.09342 | null | null |
Machine Learning Topological Invariants with Neural Networks | cond-mat.mes-hall cond-mat.dis-nn cond-mat.str-el cs.AI cs.LG | In this Letter we supervisedly train neural networks to distinguish different
topological phases in the context of topological band insulators. After
training with Hamiltonians of one-dimensional insulators with chiral symmetry,
the neural network can predict their topological winding numbers with nearly
100% accuracy, even for Hamiltonians with larger winding numbers that are not
included in the training data. These results show a remarkable success that the
neural network can capture the global and nonlinear topological features of
quantum phases from local inputs. By opening up the neural network, we confirm
that the network does learn the discrete version of the winding number formula.
We also make a couple of remarks regarding the role of the symmetry and the
opposite effect of regularization techniques when applying machine learning to
physical systems.
| Pengfei Zhang, Huitao Shen, Hui Zhai | 10.1103/PhysRevLett.120.066401 | 1708.09401 | null | null |
Incorporating Feedback into Tree-based Anomaly Detection | cs.LG cs.AI stat.ML | Anomaly detectors are often used to produce a ranked list of statistical
anomalies, which are examined by human analysts in order to extract the actual
anomalies of interest. Unfortunately, in realworld applications, this process
can be exceedingly difficult for the analyst since a large fraction of
high-ranking anomalies are false positives and not interesting from the
application perspective. In this paper, we aim to make the analyst's job easier
by allowing for analyst feedback during the investigation process. Ideally, the
feedback influences the ranking of the anomaly detector in a way that reduces
the number of false positives that must be examined before discovering the
anomalies of interest. In particular, we introduce a novel technique for
incorporating simple binary feedback into tree-based anomaly detectors. We
focus on the Isolation Forest algorithm as a representative tree-based anomaly
detector, and show that we can significantly improve its performance by
incorporating feedback, when compared with the baseline algorithm that does not
incorporate feedback. Our technique is simple and scales well as the size of
the data increases, which makes it suitable for interactive discovery of
anomalies in large datasets.
| Shubhomoy Das, Weng-Keen Wong, Alan Fern, Thomas G. Dietterich, Md
Amran Siddiqui | null | 1708.09441 | null | null |
A Compressive Sensing Approach to Community Detection with Applications | cs.IT cs.LG math.IT stat.ML | The community detection problem for graphs asks one to partition the n
vertices V of a graph G into k communities, or clusters, such that there are
many intracluster edges and few intercluster edges. Of course this is
equivalent to finding a permutation matrix P such that, if A denotes the
adjacency matrix of G, then PAP^T is approximately block diagonal. As there are
k^n possible partitions of n vertices into k subsets, directly determining the
optimal clustering is clearly infeasible. Instead one seeks to solve a more
tractable approximation to the clustering problem. In this paper we reformulate
the community detection problem via sparse solution of a linear system
associated with the Laplacian of a graph G and then develop a two-stage
approach based on a thresholding technique and a compressive sensing algorithm
to find a sparse solution which corresponds to the community containing a
vertex of interest in G. Crucially, our approach results in an algorithm which
is able to find a single cluster of size n_0 in O(nlog(n)n_0) operations and
all k clusters in fewer than O(n^2ln(n)) operations. This is a marked
improvement over the classic spectral clustering algorithm, which is unable to
find a single cluster at a time and takes approximately O(n^3) operations to
find all k clusters. Moreover, we are able to provide robust guarantees of
success for the case where G is drawn at random from the Stochastic Block
Model, a popular model for graphs with clusters. Extensive numerical results
are also provided, showing the efficacy of our algorithm on both synthetic and
real-world data sets.
| Ming-Jun Lai and Daniel Mckenzie | null | 1708.09477 | null | null |
Leveraging Deep Neural Network Activation Entropy to cope with Unseen
Data in Speech Recognition | cs.LG cs.CL stat.ML | Unseen data conditions can inflict serious performance degradation on systems
relying on supervised machine learning algorithms. Because data can often be
unseen, and because traditional machine learning algorithms are trained in a
supervised manner, unsupervised adaptation techniques must be used to adapt the
model to the unseen data conditions. However, unsupervised adaptation is often
challenging, as one must generate some hypothesis given a model and then use
that hypothesis to bootstrap the model to the unseen data conditions.
Unfortunately, reliability of such hypotheses is often poor, given the mismatch
between the training and testing datasets. In such cases, a model hypothesis
confidence measure enables performing data selection for the model adaptation.
Underlying this approach is the fact that for unseen data conditions, data
variability is introduced to the model, which the model propagates to its
output decision, impacting decision reliability. In a fully connected network,
this data variability is propagated as distortions from one layer to the next.
This work aims to estimate the propagation of such distortion in the form of
network activation entropy, which is measured over a short- time running window
on the activation from each neuron of a given hidden layer, and these
measurements are then used to compute summary entropy. This work demonstrates
that such an entropy measure can help to select data for unsupervised model
adaptation, resulting in performance gains in speech recognition tasks. Results
from standard benchmark speech recognition tasks show that the proposed
approach can alleviate the performance degradation experienced under unseen
data conditions by iteratively adapting the model to the unseen datas acoustic
condition.
| Vikramjit Mitra and Horacio Franco | null | 1708.09516 | null | null |
Resilient Autonomous Control of Distributed Multi-agent Systems in
Contested Environments | cs.MA cs.LG cs.SY | An autonomous and resilient controller is proposed for leader-follower
multi-agent systems under uncertainties and cyber-physical attacks. The leader
is assumed non-autonomous with a nonzero control input, which allows changing
the team behavior or mission in response to environmental changes. A resilient
learning-based control protocol is presented to find optimal solutions to the
synchronization problem in the presence of attacks and system dynamic
uncertainties. An observer-based distributed H_infinity controller is first
designed to prevent propagating the effects of attacks on sensors and actuators
throughout the network, as well as to attenuate the effect of these attacks on
the compromised agent itself. Non-homogeneous game algebraic Riccati equations
are derived to solve the H_infinity optimal synchronization problem and
off-policy reinforcement learning is utilized to learn their solution without
requiring any knowledge of the agent's dynamics. A trust-confidence based
distributed control protocol is then proposed to mitigate attacks that hijack
the entire node and attacks on communication links. A confidence value is
defined for each agent based solely on its local evidence. The proposed
resilient reinforcement learning algorithm employs the confidence value of each
agent to indicate the trustworthiness of its own information and broadcast it
to its neighbors to put weights on the data they receive from it during and
after learning. If the confidence value of an agent is low, it employs a trust
mechanism to identify compromised agents and remove the data it receives from
them from the learning process. Simulation results are provided to show the
effectiveness of the proposed approach.
| Rohollah Moghadam and Hamidreza Modares | null | 1708.0963 | null | null |
Design and Analysis of the NIPS 2016 Review Process | cs.DL cs.LG cs.SI stat.ML | Neural Information Processing Systems (NIPS) is a top-tier annual conference
in machine learning. The 2016 edition of the conference comprised more than
2,400 paper submissions, 3,000 reviewers, and 8,000 attendees. This represents
a growth of nearly 40% in terms of submissions, 96% in terms of reviewers, and
over 100% in terms of attendees as compared to the previous year. The massive
scale as well as rapid growth of the conference calls for a thorough quality
assessment of the peer-review process and novel means of improvement. In this
paper, we analyze several aspects of the data collected during the review
process, including an experiment investigating the efficacy of collecting
ordinal rankings from reviewers. Our goal is to check the soundness of the
review process, and provide insights that may be useful in the design of the
review process of subsequent conferences.
| Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon,
Ulrike von Luxburg | null | 1708.09794 | null | null |
Efficient tracking of a growing number of experts | stat.ML cs.LG | We consider a variation on the problem of prediction with expert advice,
where new forecasters that were unknown until then may appear at each round. As
often in prediction with expert advice, designing an algorithm that achieves
near-optimal regret guarantees is straightforward, using aggregation of
experts. However, when the comparison class is sufficiently rich, for instance
when the best expert and the set of experts itself changes over time, such
strategies naively require to maintain a prohibitive number of weights
(typically exponential with the time horizon). By contrast, designing
strategies that both achieve a near-optimal regret and maintain a reasonable
number of weights is highly non-trivial. We consider three increasingly
challenging objectives (simple regret, shifting regret and sparse shifting
regret) that extend existing notions defined for a fixed expert ensemble; in
each case, we design strategies that achieve tight regret bounds, adaptive to
the parameters of the comparison class, while being computationally
inexpensive. Moreover, our algorithms are anytime, agnostic to the number of
incoming experts and completely parameter-free. Such remarkable results are
made possible thanks to two simple but highly effective recipes: first the
"abstention trick" that comes from the specialist framework and enables to
handle the least challenging notions of regret, but is limited when addressing
more sophisticated objectives. Second, the "muting trick" that we introduce to
give more flexibility. We show how to combine these two tricks in order to
handle the most challenging class of comparison strategies.
| Jaouad Mourtada and Odalric-Ambrym Maillard | null | 1708.09811 | null | null |
A State-Space Approach to Dynamic Nonnegative Matrix Factorization | cs.LG stat.ML | Nonnegative matrix factorization (NMF) has been actively investigated and
used in a wide range of problems in the past decade. A significant amount of
attention has been given to develop NMF algorithms that are suitable to model
time series with strong temporal dependencies. In this paper, we propose a
novel state-space approach to perform dynamic NMF (D-NMF). In the proposed
probabilistic framework, the NMF coefficients act as the state variables and
their dynamics are modeled using a multi-lag nonnegative vector autoregressive
(N-VAR) model within the process equation. We use expectation maximization and
propose a maximum-likelihood estimation framework to estimate the basis matrix
and the N-VAR model parameters. Interestingly, the N-VAR model parameters are
obtained by simply applying NMF. Moreover, we derive a maximum a posteriori
estimate of the state variables (i.e., the NMF coefficients) that is based on a
prediction step and an update step, similarly to the Kalman filter. We
illustrate the benefits of the proposed approach using different numerical
simulations where D-NMF significantly outperforms its static counterpart.
Experimental results for three different applications show that the proposed
approach outperforms two state-of-the-art NMF approaches that exploit temporal
dependencies, namely a nonnegative hidden Markov model and a frame stacking
approach, while it requires less memory and computational power.
| Nasser Mohammadiha, Paris Smaragdis, Ghazaleh Panahandeh, Simon Doclo | 10.1109/TSP.2014.2385655 | 1709.00025 | null | null |
Glyph-aware Embedding of Chinese Characters | cs.CL cs.LG | Given the advantage and recent success of English character-level and
subword-unit models in several NLP tasks, we consider the equivalent modeling
problem for Chinese. Chinese script is logographic and many Chinese logograms
are composed of common substructures that provide semantic, phonetic and
syntactic hints. In this work, we propose to explicitly incorporate the visual
appearance of a character's glyph in its representation, resulting in a novel
glyph-aware embedding of Chinese characters. Being inspired by the success of
convolutional neural networks in computer vision, we use them to incorporate
the spatio-structural patterns of Chinese glyphs as rendered in raw pixels. In
the context of two basic Chinese NLP tasks of language modeling and word
segmentation, the model learns to represent each character's task-relevant
semantic and syntactic information in the character-level embedding.
| Falcon Z. Dai and Zheng Cai | null | 1709.00028 | null | null |
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and
Land Cover Classification | cs.CV cs.LG | In this paper, we address the challenge of land use and land cover
classification using Sentinel-2 satellite images. The Sentinel-2 satellite
images are openly and freely accessible provided in the Earth observation
program Copernicus. We present a novel dataset based on Sentinel-2 satellite
images covering 13 spectral bands and consisting out of 10 classes with in
total 27,000 labeled and geo-referenced images. We provide benchmarks for this
novel dataset with its spectral bands using state-of-the-art deep Convolutional
Neural Network (CNNs). With the proposed novel dataset, we achieved an overall
classification accuracy of 98.57%. The resulting classification system opens a
gate towards a number of Earth observation applications. We demonstrate how
this classification system can be used for detecting land use and land cover
changes and how it can assist in improving geographical maps. The
geo-referenced dataset EuroSAT is made publicly available at
https://github.com/phelber/eurosat.
| Patrick Helber, Benjamin Bischke, Andreas Dengel, Damian Borth | null | 1709.00029 | null | null |
On Security and Sparsity of Linear Classifiers for Adversarial Settings | cs.LG cs.CR | Machine-learning techniques are widely used in security-related applications,
like spam and malware detection. However, in such settings, they have been
shown to be vulnerable to adversarial attacks, including the deliberate
manipulation of data at test time to evade detection. In this work, we focus on
the vulnerability of linear classifiers to evasion attacks. This can be
considered a relevant problem, as linear classifiers have been increasingly
used in embedded systems and mobile devices for their low processing time and
memory requirements. We exploit recent findings in robust optimization to
investigate the link between regularization and security of linear classifiers,
depending on the type of attack. We also analyze the relationship between the
sparsity of feature weights, which is desirable for reducing processing cost,
and the security of linear classifiers. We further propose a novel octagonal
regularizer that allows us to achieve a proper trade-off between them. Finally,
we empirically show how this regularizer can improve classifier security and
sparsity in real-world application examples including spam and malware
detection.
| Ambra Demontis, Paolo Russu, Battista Biggio, Giorgio Fumera, Fabio
Roli | 10.1007/978-3-319-49055-7_29 | 1709.00045 | null | null |
The Role of Minimal Complexity Functions in Unsupervised Learning of
Semantic Mappings | cs.LG | We discuss the feasibility of the following learning problem: given unmatched
samples from two domains and nothing else, learn a mapping between the two,
which preserves semantics. Due to the lack of paired samples and without any
definition of the semantic information, the problem might seem ill-posed.
Specifically, in typical cases, it seems possible to build infinitely many
alternative mappings from every target mapping. This apparent ambiguity stands
in sharp contrast to the recent empirical success in solving this problem.
We identify the abstract notion of aligning two domains in a semantic way
with concrete terms of minimal relative complexity. A theoretical framework for
measuring the complexity of compositions of functions is developed in order to
show that it is reasonable to expect the minimal complexity mapping to be
unique. The measured complexity used is directly related to the depth of the
neural networks being learned and a semantically aligned mapping could then be
captured simply by learning using architectures that are not much bigger than
the minimal architecture.
Various predictions are made based on the hypothesis that semantic alignment
can be captured by the minimal mapping. These are verified extensively. In
addition, a new mapping algorithm is proposed and shown to lead to better
mapping results.
| Tomer Galanti, Lior Wolf and Sagie Benaim | null | 1709.00074 | null | null |
First and Second Order Methods for Online Convolutional Dictionary
Learning | cs.LG cs.CV eess.IV math.OC stat.ML | Convolutional sparse representations are a form of sparse representation with
a structured, translation invariant dictionary. Most convolutional dictionary
learning algorithms to date operate in batch mode, requiring simultaneous
access to all training images during the learning process, which results in
very high memory usage and severely limits the training data that can be used.
Very recently, however, a number of authors have considered the design of
online convolutional dictionary learning algorithms that offer far better
scaling of memory and computational cost with training set size than batch
methods. This paper extends our prior work, improving a number of aspects of
our previous algorithm; proposing an entirely new one, with better performance,
and that supports the inclusion of a spatial mask for learning from incomplete
data; and providing a rigorous theoretical analysis of these methods.
| Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg, Wotao Yin | 10.1137/17M1145689 | 1709.00106 | null | null |
Low Permutation-rank Matrices: Structural Properties and Noisy
Completion | stat.ML cs.IT cs.LG math.IT | We consider the problem of noisy matrix completion, in which the goal is to
reconstruct a structured matrix whose entries are partially observed in noise.
Standard approaches to this underdetermined inverse problem are based on
assuming that the underlying matrix has low rank, or is well-approximated by a
low rank matrix. In this paper, we propose a richer model based on what we term
the "permutation-rank" of a matrix. We first describe how the classical
non-negative rank model enforces restrictions that may be undesirable in
practice, and how and these restrictions can be avoided by using the richer
permutation-rank model. Second, we establish the minimax rates of estimation
under the new permutation-based model, and prove that surprisingly, the minimax
rates are equivalent up to logarithmic factors to those for estimation under
the typical low rank model. Third, we analyze a computationally efficient
singular-value-thresholding algorithm, known to be optimal for the low-rank
setting, and show that it also simultaneously yields a consistent estimator for
the low-permutation rank setting. Finally, we present various structural
results characterizing the uniqueness of the permutation-rank decomposition,
and characterizing convex approximations of the permutation-rank polytope.
| Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright | null | 1709.00127 | null | null |
Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel | stat.ML cs.LG | Support vector data description (SVDD) is a machine learning technique that
is used for single-class classification and outlier detection. The idea of SVDD
is to find a set of support vectors that defines a boundary around data. When
dealing with online or large data, existing batch SVDD methods have to be rerun
in each iteration. We propose an incremental learning algorithm for SVDD that
uses the Gaussian kernel. This algorithm builds on the observation that all
support vectors on the boundary have the same distance to the center of sphere
in a higher-dimensional feature space as mapped by the Gaussian kernel
function. Each iteration involves only the existing support vectors and the new
data point. Moreover, the algorithm is based solely on matrix manipulations;
the support vectors and their corresponding Lagrange multiplier $\alpha_i$'s
are automatically selected and determined in each iteration. It can be seen
that the complexity of our algorithm in each iteration is only $O(k^2)$, where
$k$ is the number of support vectors. Experimental results on some real data
sets indicate that FISVDD demonstrates significant gains in efficiency with
almost no loss in either outlier detection accuracy or objective function
value.
| Hansi Jiang, Haoyu Wang, Wenhao Hu, Deovrat Kakde and Arin Chaudhuri | null | 1709.00139 | null | null |
Learning what to read: Focused machine reading | cs.AI cs.CL cs.IR cs.LG | Recent efforts in bioinformatics have achieved tremendous progress in the
machine reading of biomedical literature, and the assembly of the extracted
biochemical interactions into large-scale models such as protein signaling
pathways. However, batch machine reading of literature at today's scale (PubMed
alone indexes over 1 million papers per year) is unfeasible due to both cost
and processing overhead. In this work, we introduce a focused reading approach
to guide the machine reading of biomedical literature towards what literature
should be read to answer a biomedical query as efficiently as possible. We
introduce a family of algorithms for focused reading, including an intuitive,
strong baseline, and a second approach which uses a reinforcement learning (RL)
framework that learns when to explore (widen the search) or exploit (narrow
it). We demonstrate that the RL approach is capable of answering more queries
than the baseline, while being more efficient, i.e., reading fewer documents.
| Enrique Noriega-Atala, Marco A. Valenzuela-Escarcega, Clayton T.
Morrison, Mihai Surdeanu | null | 1709.00149 | null | null |
Order-Planning Neural Text Generation From Structured Data | cs.CL cs.AI cs.IR cs.LG | Generating texts from structured data (e.g., a table) is important for
various natural language processing tasks such as question answering and dialog
systems. In recent studies, researchers use neural language models and
encoder-decoder frameworks for table-to-text generation. However, these neural
network-based approaches do not model the order of contents during text
generation. When a human writes a summary based on a given table, he or she
would probably consider the content order before wording. In a biography, for
example, the nationality of a person is typically mentioned before occupation
in a biography. In this paper, we propose an order-planning text generation
model to capture the relationship between different fields and use such
relationship to make the generated text more fluent and smooth. We conducted
experiments on the WikiBio dataset and achieve significantly higher performance
than previous methods in terms of BLEU, ROUGE, and NIST scores.
| Lei Sha, Lili Mou, Tianyu Liu, Pascal Poupart, Sujian Li, Baobao
Chang, Zhifang Sui | null | 1709.00155 | null | null |
A Two-Step Disentanglement Method | cs.LG stat.ML | We address the problem of disentanglement of factors that generate a given
data into those that are correlated with the labeling and those that are not.
Our solution is simpler than previous solutions and employs adversarial
training. First, the part of the data that is correlated with the labels is
extracted by training a classifier. Then, the other part is extracted such that
it enables the reconstruction of the original data but does not contain label
information. The utility of the new method is demonstrated on visual datasets
as well as on financial data. Our code is available at
https://github.com/naamahadad/A-Two-Step-Disentanglement-Method
| Naama Hadad, Lior Wolf, Moni Shahar | null | 1709.00199 | null | null |
Learning Multi-item Auctions with (or without) Samples | cs.GT cs.DS cs.LG | We provide algorithms that learn simple auctions whose revenue is
approximately optimal in multi-item multi-bidder settings, for a wide range of
valuations including unit-demand, additive, constrained additive, XOS, and
subadditive. We obtain our learning results in two settings. The first is the
commonly studied setting where sample access to the bidders' distributions over
valuations is given, for both regular distributions and arbitrary distributions
with bounded support. Our algorithms require polynomially many samples in the
number of items and bidders. The second is a more general max-min learning
setting that we introduce, where we are given "approximate distributions," and
we seek to compute an auction whose revenue is approximately optimal
simultaneously for all "true distributions" that are close to the given ones.
These results are more general in that they imply the sample-based results, and
are also applicable in settings where we have no sample access to the
underlying distributions but have estimated them indirectly via market research
or by observation of previously run, potentially non-truthful auctions.
Our results hold for valuation distributions satisfying the standard (and
necessary) independence-across-items property. They also generalize and improve
upon recent works, which have provided algorithms that learn approximately
optimal auctions in more restricted settings with additive, subadditive and
unit-demand valuations using sample access to distributions. We generalize
these results to the complete unit-demand, additive, and XOS setting, to i.i.d.
subadditive bidders, and to the max-min setting.
Our results are enabled by new uniform convergence bounds for hypotheses
classes under product measures. Our bounds result in exponential savings in
sample complexity compared to bounds derived by bounding the VC dimension, and
are of independent interest.
| Yang Cai, Constantinos Daskalakis | null | 1709.00228 | null | null |
Telepath: Understanding Users from a Human Vision Perspective in
Large-Scale Recommender Systems | cs.IR cs.CV cs.LG | Designing an e-commerce recommender system that serves hundreds of millions
of active users is a daunting challenge. From a human vision perspective,
there're two key factors that affect users' behaviors: items' attractiveness
and their matching degree with users' interests. This paper proposes Telepath,
a vision-based bionic recommender system model, which understands users from
such perspective. Telepath is a combination of a convolutional neural network
(CNN), a recurrent neural network (RNN) and deep neural networks (DNNs). Its
CNN subnetwork simulates the human vision system to extract key visual signals
of items' attractiveness and generate corresponding activations. Its RNN and
DNN subnetworks simulate cerebral cortex to understand users' interest based on
the activations generated from browsed items. In practice, the Telepath model
has been launched to JD's recommender system and advertising system. For one of
the major item recommendation blocks on the JD app, click-through rate (CTR),
gross merchandise value (GMV) and orders have increased 1.59%, 8.16% and 8.71%
respectively. For several major ads publishers of JD demand-side platform, CTR,
GMV and return on investment have increased 6.58%, 61.72% and 65.57%
respectively by the first launch, and further increased 2.95%, 41.75% and
41.37% respectively by the second launch.
| Yu Wang, Jixing Xu, Aohan Wu, Mantian Li, Yang He, Jinghe Hu, Weipeng
P. Yan | null | 1709.003 | null | null |
MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre
Broadcast Challenge | cs.CL cs.LG cs.SD | In order to successfully annotate the Arabic speech con- tent found in
open-domain media broadcasts, it is essential to be able to process a diverse
set of Arabic dialects. For the 2017 Multi-Genre Broadcast challenge (MGB-3)
there were two possible tasks: Arabic speech recognition, and Arabic Dialect
Identification (ADI). In this paper, we describe our efforts to create an ADI
system for the MGB-3 challenge, with the goal of distinguishing amongst four
major Arabic dialects, as well as Modern Standard Arabic. Our research fo-
cused on dialect variability and domain mismatches between the training and
test domain. In order to achieve a robust ADI system, we explored both Siamese
neural network models to learn similarity and dissimilarities among Arabic
dialects, as well as i-vector post-processing to adapt domain mismatches. Both
Acoustic and linguistic features were used for the final MGB-3 submissions,
with the best primary system achieving 75% accuracy on the official 10hr test
set.
| Suwon Shon, Ahmed Ali and James Glass | null | 1709.00387 | null | null |
PassGAN: A Deep Learning Approach for Password Guessing | cs.CR cs.LG stat.ML | State-of-the-art password guessing tools, such as HashCat and John the
Ripper, enable users to check billions of passwords per second against password
hashes. In addition to performing straightforward dictionary attacks, these
tools can expand password dictionaries using password generation rules, such as
concatenation of words (e.g., "password123456") and leet speak (e.g.,
"password" becomes "p4s5w0rd"). Although these rules work well in practice,
expanding them to model further passwords is a laborious task that requires
specialized expertise. To address this issue, in this paper we introduce
PassGAN, a novel approach that replaces human-generated password rules with
theory-grounded machine learning algorithms. Instead of relying on manual
password analysis, PassGAN uses a Generative Adversarial Network (GAN) to
autonomously learn the distribution of real passwords from actual password
leaks, and to generate high-quality password guesses. Our experiments show that
this approach is very promising. When we evaluated PassGAN on two large
password datasets, we were able to surpass rule-based and state-of-the-art
machine learning password guessing tools. However, in contrast with the other
tools, PassGAN achieved this result without any a-priori knowledge on passwords
or common password structures. Additionally, when we combined the output of
PassGAN with the output of HashCat, we were able to match 51%-73% more
passwords than with HashCat alone. This is remarkable, because it shows that
PassGAN can autonomously extract a considerable number of password properties
that current state-of-the art rules do not encode.
| Briland Hitaj, Paolo Gasti, Giuseppe Ateniese, Fernando Perez-Cruz | null | 1709.0044 | null | null |
Mean Actor Critic | stat.ML cs.AI cs.LG | We propose a new algorithm, Mean Actor-Critic (MAC), for discrete-action
continuous-state reinforcement learning. MAC is a policy gradient algorithm
that uses the agent's explicit representation of all action values to estimate
the gradient of the policy, rather than using only the actions that were
actually executed. We prove that this approach reduces variance in the policy
gradient estimate relative to traditional actor-critic methods. We show
empirical results on two control domains and on six Atari games, where MAC is
competitive with state-of-the-art policy search algorithms.
| Cameron Allen, Kavosh Asadi, Melrose Roderick, Abdel-rahman Mohamed,
George Konidaris, Michael Littman | null | 1709.00503 | null | null |
Training Shallow and Thin Networks for Acceleration via Knowledge
Distillation with Conditional Adversarial Networks | cs.LG cs.AI cs.CV | There is an increasing interest on accelerating neural networks for real-time
applications. We study the student-teacher strategy, in which a small and fast
student network is trained with the auxiliary information learned from a large
and accurate teacher network. We propose to use conditional adversarial
networks to learn the loss function to transfer knowledge from teacher to
student. The proposed method is particularly effective for relatively small
student networks. Moreover, experimental results show the effect of network
size when the modern networks are used as student. We empirically study the
trade-off between inference time and classification accuracy, and provide
suggestions on choosing a proper student network.
| Zheng Xu, Yen-Chang Hsu, Jiawei Huang | null | 1709.00513 | null | null |
Communication-efficient Algorithm for Distributed Sparse Learning via
Two-way Truncation | stat.ML cs.LG math.OC | We propose a communicationally and computationally efficient algorithm for
high-dimensional distributed sparse learning. At each iteration, local machines
compute the gradient on local data and the master machine solves one shifted
$l_1$ regularized minimization problem. The communication cost is reduced from
constant times of the dimension number for the state-of-the-art algorithm to
constant times of the sparsity number via Two-way Truncation procedure.
Theoretically, we prove that the estimation error of the proposed algorithm
decreases exponentially and matches that of the centralized method under mild
assumptions. Extensive experiments on both simulated data and real data verify
that the proposed algorithm is efficient and has performance comparable with
the centralized method on solving high-dimensional sparse learning problems.
| Jineng Ren and Jarvis Haupt | null | 1709.00537 | null | null |
Patterns versus Characters in Subword-aware Neural Language Modeling | cs.CL cs.LG | Words in some natural languages can have a composite structure. Elements of
this structure include the root (that could also be composite), prefixes and
suffixes with which various nuances and relations to other words can be
expressed. Thus, in order to build a proper word representation one must take
into account its internal structure. From a corpus of texts we extract a set of
frequent subwords and from the latter set we select patterns, i.e. subwords
which encapsulate information on character $n$-gram regularities. The selection
is made using the pattern-based Conditional Random Field model with $l_1$
regularization. Further, for every word we construct a new sequence over an
alphabet of patterns. The new alphabet's symbols confine a local statistical
context stronger than the characters, therefore they allow better
representations in ${\mathbb{R}}^n$ and are better building blocks for word
representation. In the task of subword-aware language modeling, pattern-based
models outperform character-based analogues by 2-20 perplexity points. Also, a
recurrent neural network in which a word is represented as a sum of embeddings
of its patterns is on par with a competitive and significantly more
sophisticated character-based convolutional architecture.
| Rustem Takhanov and Zhenisbek Assylbekov | null | 1709.00541 | null | null |
XFlow: Cross-modal Deep Neural Networks for Audiovisual Classification | stat.ML cs.AI cs.CV cs.LG | In recent years, there have been numerous developments towards solving
multimodal tasks, aiming to learn a stronger representation than through a
single modality. Certain aspects of the data can be particularly useful in this
case - for example, correlations in the space or time domain across modalities
- but should be wisely exploited in order to benefit from their full predictive
potential. We propose two deep learning architectures with multimodal
cross-connections that allow for dataflow between several feature extractors
(XFlow). Our models derive more interpretable features and achieve better
performances than models which do not exchange representations, usefully
exploiting correlations between audio and visual data, which have a different
dimensionality and are nontrivially exchangeable. Our work improves on existing
multimodal deep learning algorithms in two essential ways: (1) it presents a
novel method for performing cross-modality (before features are learned from
individual modalities) and (2) extends the previously proposed
cross-connections which only transfer information between streams that process
compatible data. Illustrating some of the representations learned by the
connections, we analyse their contribution to the increase in discrimination
ability and reveal their compatibility with a lip-reading network intermediate
representation. We provide the research community with Digits, a new dataset
consisting of three data types extracted from videos of people saying the
digits 0-9. Results show that both cross-modal architectures outperform their
baselines (by up to 11.5%) when evaluated on the AVletters, CUAVE and Digits
datasets, achieving state-of-the-art results.
| C\u{a}t\u{a}lina Cangea, Petar Veli\v{c}kovi\'c, Pietro Li\`o | null | 1709.00572 | null | null |
Grasping the Finer Point: A Supervised Similarity Network for Metaphor
Detection | cs.CL cs.LG cs.NE | The ubiquity of metaphor in our everyday communication makes it an important
problem for natural language understanding. Yet, the majority of metaphor
processing systems to date rely on hand-engineered features and there is still
no consensus in the field as to which features are optimal for this task. In
this paper, we present the first deep learning architecture designed to capture
metaphorical composition. Our results demonstrate that it outperforms the
existing approaches in the metaphor identification task.
| Marek Rei, Luana Bulat, Douwe Kiela, Ekaterina Shutova | null | 1709.00575 | null | null |
Deep Learning-Guided Image Reconstruction from Incomplete Data | cs.CV cs.LG | An approach to incorporate deep learning within an iterative image
reconstruction framework to reconstruct images from severely incomplete
measurement data is presented. Specifically, we utilize a convolutional neural
network (CNN) as a quasi-projection operator within a least squares
minimization procedure. The CNN is trained to encode high level information
about the class of images being imaged; this information is utilized to
mitigate artifacts in intermediate images produced by use of an iterative
method. The structure of the method was inspired by the proximal gradient
descent method, where the proximal operator is replaced by a deep CNN and the
gradient descent step is generalized by use of a linear reconstruction
operator. It is demonstrated that this approach improves image quality for
several cases of limited-view image reconstruction and that using a CNN in an
iterative method increases performance compared to conventional image
reconstruction approaches. We test our method on several limited-view image
reconstruction problems. Qualitative and quantitative results demonstrate
state-of-the-art performance.
| Brendan Kelly, Thomas P. Matthews, Mark A. Anastasio | null | 1709.00584 | null | null |
First-Order Adaptive Sample Size Methods to Reduce Complexity of
Empirical Risk Minimization | cs.LG math.OC | This paper studies empirical risk minimization (ERM) problems for large-scale
datasets and incorporates the idea of adaptive sample size methods to improve
the guaranteed convergence bounds for first-order stochastic and deterministic
methods. In contrast to traditional methods that attempt to solve the ERM
problem corresponding to the full dataset directly, adaptive sample size
schemes start with a small number of samples and solve the corresponding ERM
problem to its statistical accuracy. The sample size is then grown
geometrically -- e.g., scaling by a factor of two -- and use the solution of
the previous ERM as a warm start for the new ERM. Theoretical analyses show
that the use of adaptive sample size methods reduces the overall computational
cost of achieving the statistical accuracy of the whole dataset for a broad
range of deterministic and stochastic first-order methods. The gains are
specific to the choice of method. When particularized to, e.g., accelerated
gradient descent and stochastic variance reduce gradient, the computational
cost advantage is a logarithm of the number of training samples. Numerical
experiments on various datasets confirm theoretical claims and showcase the
gains of using the proposed adaptive sample size scheme.
| Aryan Mokhtari and Alejandro Ribeiro | null | 1709.00599 | null | null |
Security Evaluation of Pattern Classifiers under Attack | cs.LG cs.CR | Pattern classification systems are commonly used in adversarial applications,
like biometric authentication, network intrusion detection, and spam filtering,
in which data can be purposely manipulated by humans to undermine their
operation. As this adversarial scenario is not taken into account by classical
design methods, pattern classification systems may exhibit vulnerabilities,
whose exploitation may severely affect their performance, and consequently
limit their practical utility. Extending pattern classification theory and
design methods to adversarial settings is thus a novel and very relevant
research direction, which has not yet been pursued in a systematic way. In this
paper, we address one of the main open issues: evaluating at design phase the
security of pattern classifiers, namely, the performance degradation under
potential attacks they may incur during operation. We propose a framework for
empirical evaluation of classifier security that formalizes and generalizes the
main ideas proposed in the literature, and give examples of its use in three
real applications. Reported results show that security evaluation can provide a
more complete understanding of the classifier's behavior in adversarial
environments, and lead to better design choices.
| Battista Biggio, Giorgio Fumera, Fabio Roli | 10.1109/TKDE.2013.57 | 1709.00609 | null | null |
On Identifiability of Nonnegative Matrix Factorization | cs.LG stat.ML | In this letter, we propose a new identification criterion that guarantees the
recovery of the low-rank latent factors in the nonnegative matrix factorization
(NMF) model, under mild conditions. Specifically, using the proposed criterion,
it suffices to identify the latent factors if the rows of one factor are
\emph{sufficiently scattered} over the nonnegative orthant, while no structural
assumption is imposed on the other factor except being full-rank. This is by
far the mildest condition under which the latent factors are provably
identifiable from the NMF model.
| Xiao Fu and Kejun Huang and Nicholas D. Sidiropoulos | 10.1109/LSP.2018.2789405 | 1709.00614 | null | null |
Fast Image Processing with Fully-Convolutional Networks | cs.CV cs.GR cs.LG | We present an approach to accelerating a wide variety of image processing
operators. Our approach uses a fully-convolutional network that is trained on
input-output pairs that demonstrate the operator's action. After training, the
original operator need not be run at all. The trained network operates at full
resolution and runs in constant time. We investigate the effect of network
architecture on approximation accuracy, runtime, and memory footprint, and
identify a specific architecture that balances these considerations. We
evaluate the presented approach on ten advanced image processing operators,
including multiple variational models, multiscale tone and detail manipulation,
photographic style transfer, nonlocal dehazing, and nonphotorealistic
stylization. All operators are approximated by the same model. Experiments
demonstrate that the presented approach is significantly more accurate than
prior approximation schemes. It increases approximation accuracy as measured by
PSNR across the evaluated operators by 8.5 dB on the MIT-Adobe dataset (from
27.5 to 36 dB) and reduces DSSIM by a multiplicative factor of 3 compared to
the most accurate prior approximation scheme, while being the fastest. We show
that our models generalize across datasets and across resolutions, and
investigate a number of extensions of the presented approach. The results are
shown in the supplementary video at https://youtu.be/eQyfHgLx8Dc
| Qifeng Chen, Jia Xu, Vladlen Koltun | null | 1709.00643 | null | null |
From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search
Approach | cs.IR cs.AI cs.LG | One key challenge in talent search is to translate complex criteria of a
hiring position into a search query, while it is relatively easy for a searcher
to list examples of suitable candidates for a given position. To improve search
efficiency, we propose the next generation of talent search at LinkedIn, also
referred to as Search By Ideal Candidates. In this system, a searcher provides
one or several ideal candidates as the input to hire for a given position. The
system then generates a query based on the ideal candidates and uses it to
retrieve and rank results. Shifting from the traditional Query-By-Keyword to
this new Query-By-Example system poses a number of challenges: How to generate
a query that best describes the candidates? When moving to a completely
different paradigm, how does one leverage previous product logs to learn
ranking models and/or evaluate the new system with no existing usage logs?
Finally, given the different nature between the two search paradigms, the
ranking features typically used for Query-By-Keyword systems might not be
optimal for Query-By-Example. This paper describes our approach to solving
these challenges. We present experimental results confirming the effectiveness
of the proposed solution, particularly on query building and search ranking
tasks. As of writing this paper, the new system has been available to all
LinkedIn members.
| Viet Ha-Thuc, Yan Yan, Xianren Wu, Vijay Dialani, Abhishek Gupta,
Shakti Sinha | 10.1145/3132847.3132869 | 1709.00653 | null | null |
SamBaTen: Sampling-based Batch Incremental Tensor Decomposition | stat.ML cs.LG | Tensor decompositions are invaluable tools in analyzing multimodal datasets.
In many real-world scenarios, such datasets are far from being static, to the
contrary they tend to grow over time. For instance, in an online social network
setting, as we observe new interactions over time, our dataset gets updated in
its "time" mode. How can we maintain a valid and accurate tensor decomposition
of such a dynamically evolving multimodal dataset, without having to re-compute
the entire decomposition after every single update? In this paper we introduce
SaMbaTen, a Sampling-based Batch Incremental Tensor Decomposition algorithm,
which incrementally maintains the decomposition given new updates to the tensor
dataset. SaMbaTen is able to scale to datasets that the state-of-the-art in
incremental tensor decomposition is unable to operate on, due to its ability to
effectively summarize the existing tensor and the incoming updates, and perform
all computations in the reduced summary space. We extensively evaluate SaMbaTen
using synthetic and real datasets. Indicatively, SaMbaTen achieves comparable
accuracy to state-of-the-art incremental and non-incremental techniques, while
being 25-30 times faster. Furthermore, SaMbaTen scales to very large sparse and
dense dynamically evolving tensors of dimensions up to 100K x 100K x 100K where
state-of-the-art incremental approaches were not able to operate.
| Ekta Gujral, Ravdeep Pasricha, Evangelos E. Papalexakis | null | 1709.00668 | null | null |
Understanding the Logical and Semantic Structure of Large Documents | cs.CL cs.IR cs.LG | Current language understanding approaches focus on small documents, such as
newswire articles, blog posts, product reviews and discussion forum entries.
Understanding and extracting information from large documents like legal
briefs, proposals, technical manuals and research articles is still a
challenging task. We describe a framework that can analyze a large document and
help people to know where a particular information is in that document. We aim
to automatically identify and classify semantic sections of documents and
assign consistent and human-understandable labels to similar sections across
documents. A key contribution of our research is modeling the logical and
semantic structure of an electronic document. We apply machine learning
techniques, including deep learning, in our prototype system. We also make
available a dataset of information about a collection of scholarly articles
from the arXiv eprints collection that includes a wide range of metadata for
each article, including a table of contents, section labels, section
summarizations and more. We hope that this dataset will be a useful resource
for the machine learning and NLP communities in information retrieval,
content-based question answering and language modeling.
| Muhammad Mahbubur Rahman, Tim Finin | null | 1709.0077 | null | null |
Semi-supervised Learning with Deep Generative Models for Asset Failure
Prediction | cs.LG | This work presents a novel semi-supervised learning approach for data-driven
modeling of asset failures when health status is only partially known in
historical data. We combine a generative model parameterized by deep neural
networks with non-linear embedding technique. It allows us to build prognostic
models with the limited amount of health status information for the precise
prediction of future asset reliability. The proposed method is evaluated on a
publicly available dataset for remaining useful life (RUL) estimation, which
shows significant improvement even when a fraction of the data with known
health status is as sparse as 1% of the total. Our study suggests that the
non-linear embedding based on a deep generative model can efficiently
regularize a complex model with deep architectures while achieving high
prediction accuracy that is far less sensitive to the availability of health
status information.
| Andre S. Yoon, Taehoon Lee, Yongsub Lim, Deokwoo Jung, Philgyun Kang,
Dongwon Kim, Keuntae Park, Yongjin Choi | null | 1709.00845 | null | null |
Neural Networks for Safety-Critical Applications - Challenges,
Experiments and Perspectives | cs.SE cs.LG | We propose a methodology for designing dependable Artificial Neural Networks
(ANN) by extending the concepts of understandability, correctness, and validity
that are crucial ingredients in existing certification standards. We apply the
concept in a concrete case study in designing a high-way ANN-based motion
predictor to guarantee safety properties such as impossibility for the ego
vehicle to suggest moving to the right lane if there exists another vehicle on
its right.
| Chih-Hong Cheng, Frederik Diehl, Yassine Hamza, Gereon Hinz, Georg
N\"uhrenberg, Markus Rickert, Harald Ruess, Michael Troung-Le | null | 1709.00911 | null | null |
Learning Word Embeddings from the Portuguese Twitter Stream: A Study of
some Practical Aspects | cs.CL cs.LG | This paper describes a preliminary study for producing and distributing a
large-scale database of embeddings from the Portuguese Twitter stream. We start
by experimenting with a relatively small sample and focusing on three
challenges: volume of training data, vocabulary size and intrinsic evaluation
metrics. Using a single GPU, we were able to scale up vocabulary size from 2048
words embedded and 500K training examples to 32768 words over 10M training
examples while keeping a stable validation loss and approximately linear trend
on training time per epoch. We also observed that using less than 50\% of the
available training examples for each vocabulary size might result in
overfitting. Results on intrinsic evaluation show promising performance for a
vocabulary size of 32768 words. Nevertheless, intrinsic evaluation metrics
suffer from over-sensitivity to their corresponding cosine similarity
thresholds, indicating that a wider range of metrics need to be developed to
track progress.
| Pedro Saleiro, Lu\'is Sarmento, Eduarda Mendes Rodrigues, Carlos
Soares, Eug\'enio Oliveira | null | 1709.00947 | null | null |
Learning Implicit Generative Models Using Differentiable Graph Tests | stat.ML cs.LG | Recently, there has been a growing interest in the problem of learning rich
implicit models - those from which we can sample, but can not evaluate their
density. These models apply some parametric function, such as a deep network,
to a base measure, and are learned end-to-end using stochastic optimization.
One strategy of devising a loss function is through the statistics of two
sample tests - if we can fool a statistical test, the learned distribution
should be a good model of the true data. However, not all tests can easily fit
into this framework, as they might not be differentiable with respect to the
data points, and hence with respect to the parameters of the implicit model.
Motivated by this problem, in this paper we show how two such classical tests,
the Friedman-Rafsky and k-nearest neighbour tests, can be effectively smoothed
using ideas from undirected graphical models - the matrix tree theorem and
cardinality potentials. Moreover, as we show experimentally, smoothing can
significantly increase the power of the test, which might of of independent
interest. Finally, we apply our method to learn implicit models.
| Josip Djolonga, Andreas Krause | null | 1709.01006 | null | null |
A hierarchical loss and its problems when classifying non-hierarchically | cs.LG cs.CV stat.ML | Failing to distinguish between a sheepdog and a skyscraper should be worse
and penalized more than failing to distinguish between a sheepdog and a poodle;
after all, sheepdogs and poodles are both breeds of dogs. However, existing
metrics of failure (so-called "loss" or "win") used in textual or visual
classification/recognition via neural networks seldom leverage a-priori
information, such as a sheepdog being more similar to a poodle than to a
skyscraper. We define a metric that, inter alia, can penalize failure to
distinguish between a sheepdog and a skyscraper more than failure to
distinguish between a sheepdog and a poodle. Unlike previously employed
possibilities, this metric is based on an ultrametric tree associated with any
given tree organization into a semantically meaningful hierarchy of a
classifier's classes. An ultrametric tree is a tree with a so-called
ultrametric distance metric such that all leaves are at the same distance from
the root. Unfortunately, extensive numerical experiments indicate that the
standard practice of training neural networks via stochastic gradient descent
with random starting points often drives down the hierarchical loss nearly as
much when minimizing the standard cross-entropy loss as when trying to minimize
the hierarchical loss directly. Thus, this hierarchical loss is unreliable as
an objective for plain, randomly started stochastic gradient descent to
minimize; the main value of the hierarchical loss may be merely as a meaningful
metric of success of a classifier.
| Cinna Wu, Mark Tygert, and Yann LeCun | null | 1709.01062 | null | null |
Predicting Remaining Useful Life using Time Series Embeddings based on
Recurrent Neural Networks | cs.LG | We consider the problem of estimating the remaining useful life (RUL) of a
system or a machine from sensor data. Many approaches for RUL estimation based
on sensor data make assumptions about how machines degrade. Additionally,
sensor data from machines is noisy and often suffers from missing values in
many practical settings. We propose Embed-RUL: a novel approach for RUL
estimation from sensor data that does not rely on any degradation-trend
assumptions, is robust to noise, and handles missing values. Embed-RUL utilizes
a sequence-to-sequence model based on Recurrent Neural Networks (RNNs) to
generate embeddings for multivariate time series subsequences. The embeddings
for normal and degraded machines tend to be different, and are therefore found
to be useful for RUL estimation. We show that the embeddings capture the
overall pattern in the time series while filtering out the noise, so that the
embeddings of two machines with similar operational behavior are close to each
other, even when their sensor readings have significant and varying levels of
noise content. We perform experiments on publicly available turbofan engine
dataset and a proprietary real-world dataset, and demonstrate that Embed-RUL
outperforms the previously reported state-of-the-art on several metrics.
| Narendhar Gugulothu, Vishnu TV, Pankaj Malhotra, Lovekesh Vig, Puneet
Agarwal, Gautam Shroff | null | 1709.01073 | null | null |
Learning mutational graphs of individual tumour evolution from
single-cell and multi-region sequencing data | q-bio.GN cs.LG | Background. A large number of algorithms is being developed to reconstruct
evolutionary models of individual tumours from genome sequencing data. Most
methods can analyze multiple samples collected either through bulk multi-region
sequencing experiments or the sequencing of individual cancer cells. However,
rarely the same method can support both data types.
Results. We introduce TRaIT, a computational framework to infer mutational
graphs that model the accumulation of multiple types of somatic alterations
driving tumour evolution. Compared to other tools, TRaIT supports multi-region
and single-cell sequencing data within the same statistical framework, and
delivers expressive models that capture many complex evolutionary phenomena.
TRaIT improves accuracy, robustness to data-specific errors and computational
complexity compared to competing methods.
Conclusions. We show that the application of TRaIT to single-cell and
multi-region cancer datasets can produce accurate and reliable models of
single-tumour evolution, quantify the extent of intra-tumour heterogeneity and
generate new testable experimental hypotheses.
| Daniele Ramazzotti and Alex Graudenzi and Luca De Sano and Marco
Antoniotti and Giulio Caravagna | null | 1709.01076 | null | null |
WRPN: Wide Reduced-Precision Networks | cs.CV cs.LG cs.NE | For computer vision applications, prior works have shown the efficacy of
reducing numeric precision of model parameters (network weights) in deep neural
networks. Activation maps, however, occupy a large memory footprint during both
the training and inference step when using mini-batches of inputs. One way to
reduce this large memory footprint is to reduce the precision of activations.
However, past works have shown that reducing the precision of activations hurts
model accuracy. We study schemes to train networks from scratch using
reduced-precision activations without hurting accuracy. We reduce the precision
of activation maps (along with model parameters) and increase the number of
filter maps in a layer, and find that this scheme matches or surpasses the
accuracy of the baseline full-precision network. As a result, one can
significantly improve the execution efficiency (e.g. reduce dynamic memory
footprint, memory bandwidth and computational energy) and speed up the training
and inference process with appropriate hardware support. We call our scheme
WRPN - wide reduced-precision networks. We report results and show that WRPN
scheme is better than previously reported accuracies on ILSVRC-12 dataset while
being computationally less expensive compared to previously reported
reduced-precision networks.
| Asit Mishra, Eriko Nurvitadhi, Jeffrey J Cook and Debbie Marr | null | 1709.01134 | null | null |
Information Theoretic Analysis of DNN-HMM Acoustic Modeling | cs.SD cs.CL cs.LG | We propose an information theoretic framework for quantitative assessment of
acoustic modeling for hidden Markov model (HMM) based automatic speech
recognition (ASR). Acoustic modeling yields the probabilities of HMM sub-word
states for a short temporal window of speech acoustic features. We cast ASR as
a communication channel where the input sub-word probabilities convey the
information about the output HMM state sequence. The quality of the acoustic
model is thus quantified in terms of the information transmitted through this
channel. The process of inferring the most likely HMM state sequence from the
sub-word probabilities is known as decoding. HMM based decoding assumes that an
acoustic model yields accurate state-level probabilities and the data
distribution given the underlying hidden state is independent of any other
state in the sequence. We quantify 1) the acoustic model accuracy and 2) its
robustness to mismatch between data and the HMM conditional independence
assumption in terms of some mutual information quantities. In this context,
exploiting deep neural network (DNN) posterior probabilities leads to a simple
and straightforward analysis framework to assess shortcomings of the acoustic
model for HMM based decoding. This analysis enables us to evaluate the Gaussian
mixture acoustic model (GMM) and the importance of many hidden layers in DNNs
without any need of explicit speech recognition. In addition, it sheds light on
the contribution of low-dimensional models to enhance acoustic modeling for
better compliance with the HMM based decoding requirements.
| Pranay Dighe, Afsaneh Asaei, Herv\'e Bourlard | null | 1709.01144 | null | null |
Balancing Interpretability and Predictive Accuracy for Unsupervised
Tensor Mining | stat.ML cs.LG | The PARAFAC tensor decomposition has enjoyed an increasing success in
exploratory multi-aspect data mining scenarios. A major challenge remains the
estimation of the number of latent factors (i.e., the rank) of the
decomposition, which yields high-quality, interpretable results. Previously, we
have proposed an automated tensor mining method which leverages a well-known
quality heuristic from the field of Chemometrics, the Core Consistency
Diagnostic (CORCONDIA), in order to automatically determine the rank for the
PARAFAC decomposition. In this work we set out to explore the trade-off between
1) the interpretability/quality of the results (as expressed by CORCONDIA), and
2) the predictive accuracy of the results, in order to further improve the rank
estimation quality. Our preliminary results indicate that striking a good
balance in that trade-off benefits rank estimation.
| Ishmam Zabir, Evangelos E. Papalexakis | null | 1709.01147 | null | null |
Random Subspace with Trees for Feature Selection Under Memory
Constraints | stat.ML cs.LG | Dealing with datasets of very high dimension is a major challenge in machine
learning. In this paper, we consider the problem of feature selection in
applications where the memory is not large enough to contain all features. In
this setting, we propose a novel tree-based feature selection approach that
builds a sequence of randomized trees on small subsamples of variables mixing
both variables already identified as relevant by previous models and variables
randomly selected among the other variables. As our main contribution, we
provide an in-depth theoretical analysis of this method in infinite sample
setting. In particular, we study its soundness with respect to common
definitions of feature relevance and its convergence speed under various
variable dependance scenarios. We also provide some preliminary empirical
results highlighting the potential of the approach.
| Antonio Sutera, C\'elia Ch\^atel, Gilles Louppe, Louis Wehenkel,
Pierre Geurts | null | 1709.01177 | null | null |
ALICE: Towards Understanding Adversarial Learning for Joint Distribution
Matching | stat.ML cs.AI cs.CV cs.LG cs.NE | We investigate the non-identifiability issues associated with bidirectional
adversarial training for joint distribution matching. Within a framework of
conditional entropy, we propose both adversarial and non-adversarial approaches
to learn desirable matched joint distributions for unsupervised and supervised
tasks. We unify a broad family of adversarial models as joint distribution
matching problems. Our approach stabilizes learning of unsupervised
bidirectional adversarial learning methods. Further, we introduce an extension
for semi-supervised learning tasks. Theoretical results are validated in
synthetic data and real-world applications.
| Chunyuan Li, Hao Liu, Changyou Chen, Yunchen Pu, Liqun Chen, Ricardo
Henao, Lawrence Carin | null | 1709.01215 | null | null |
On the Suboptimality of Proximal Gradient Descent for $\ell^{0}$ Sparse
Approximation | math.OC cs.LG | We study the proximal gradient descent (PGD) method for $\ell^{0}$ sparse
approximation problem as well as its accelerated optimization with randomized
algorithms in this paper. We first offer theoretical analysis of PGD showing
the bounded gap between the sub-optimal solution by PGD and the globally
optimal solution for the $\ell^{0}$ sparse approximation problem under
conditions weaker than Restricted Isometry Property widely used in compressive
sensing literature. Moreover, we propose randomized algorithms to accelerate
the optimization by PGD using randomized low rank matrix approximation
(PGD-RMA) and randomized dimension reduction (PGD-RDR). Our randomized
algorithms substantially reduces the computation cost of the original PGD for
the $\ell^{0}$ sparse approximation problem, and the resultant sub-optimal
solution still enjoys provable suboptimality, namely, the sub-optimal solution
to the reduced problem still has bounded gap to the globally optimal solution
to the original problem.
| Yingzhen Yang, Jiashi Feng, Nebojsa Jojic, Jianchao Yang, Thomas S.
Huang | null | 1709.0123 | null | null |
Discriminative Similarity for Clustering and Semi-Supervised Learning | stat.ML cs.LG | Similarity-based clustering and semi-supervised learning methods separate the
data into clusters or classes according to the pairwise similarity between the
data, and the pairwise similarity is crucial for their performance. In this
paper, we propose a novel discriminative similarity learning framework which
learns discriminative similarity for either data clustering or semi-supervised
learning. The proposed framework learns classifier from each hypothetical
labeling, and searches for the optimal labeling by minimizing the
generalization error of the learned classifiers associated with the
hypothetical labeling. Kernel classifier is employed in our framework. By
generalization analysis via Rademacher complexity, the generalization error
bound for the kernel classifier learned from hypothetical labeling is expressed
as the sum of pairwise similarity between the data from different classes,
parameterized by the weights of the kernel classifier. Such pairwise similarity
serves as the discriminative similarity for the purpose of clustering and
semi-supervised learning, and discriminative similarity with similar form can
also be induced by the integrated squared error bound for kernel density
classification. Based on the discriminative similarity induced by the kernel
classifier, we propose new clustering and semi-supervised learning methods.
| Yingzhen Yang, Feng Liang, Nebojsa Jojic, Shuicheng Yan, Jiashi Feng,
Thomas S. Huang | null | 1709.01231 | null | null |
Newton-type Methods for Inference in Higher-Order Markov Random Fields | cs.CV cs.LG cs.NA | Linear programming relaxations are central to {\sc map} inference in discrete
Markov Random Fields. The ability to properly solve the Lagrangian dual is a
critical component of such methods. In this paper, we study the benefit of
using Newton-type methods to solve the Lagrangian dual of a smooth version of
the problem. We investigate their ability to achieve superior convergence
behavior and to better handle the ill-conditioned nature of the formulation, as
compared to first order methods. We show that it is indeed possible to
efficiently apply a trust region Newton method for a broad range of {\sc map}
inference problems. In this paper we propose a provably convergent and
efficient framework that includes (i) excellent compromise between
computational complexity and precision concerning the Hessian matrix
construction, (ii) a damping strategy that aids efficient optimization, (iii) a
truncation strategy coupled with a generic pre-conditioner for Conjugate
Gradients, (iv) efficient sum-product computation for sparse clique potentials.
Results for higher-order Markov Random Fields demonstrate the potential of this
approach.
| Hariprasad Kannan, Nikos Komodakis, Nikos Paragios | null | 1709.01237 | null | null |
Inhomogeneous Hypergraph Clustering with Applications | cs.LG stat.ML | Hypergraph partitioning is an important problem in machine learning, computer
vision and network analytics. A widely used method for hypergraph partitioning
relies on minimizing a normalized sum of the costs of partitioning hyperedges
across clusters. Algorithmic solutions based on this approach assume that
different partitions of a hyperedge incur the same cost. However, this
assumption fails to leverage the fact that different subsets of vertices within
the same hyperedge may have different structural importance. We hence propose a
new hypergraph clustering technique, termed inhomogeneous hypergraph
partitioning, which assigns different costs to different hyperedge cuts. We
prove that inhomogeneous partitioning produces a quadratic approximation to the
optimal solution if the inhomogeneous costs satisfy submodularity constraints.
Moreover, we demonstrate that inhomogenous partitioning offers significant
performance improvements in applications such as structure learning of
rankings, subspace segmentation and motif clustering.
| Pan Li, Olgica Milenkovic | null | 1709.01249 | null | null |
Tensor Representation in High-Frequency Financial Data for Price Change
Prediction | cs.CE cs.LG cs.NA q-fin.TR | Nowadays, with the availability of massive amount of trade data collected,
the dynamics of the financial markets pose both a challenge and an opportunity
for high frequency traders. In order to take advantage of the rapid, subtle
movement of assets in High Frequency Trading (HFT), an automatic algorithm to
analyze and detect patterns of price change based on transaction records must
be available. The multichannel, time-series representation of financial data
naturally suggests tensor-based learning algorithms. In this work, we
investigate the effectiveness of two multilinear methods for the mid-price
prediction problem against other existing methods. The experiments in a large
scale dataset which contains more than 4 millions limit orders show that by
utilizing tensor representation, multilinear models outperform vector-based
approaches and other competing ones.
| Dat Thanh Tran, Martin Magris, Juho Kanniainen, Moncef Gabbouj,
Alexandros Iosifidis | 10.1109/SSCI.2017.8280812 | 1709.01268 | null | null |
Spectral Mixture Kernels for Multi-Output Gaussian Processes | stat.ML cs.LG | Early approaches to multiple-output Gaussian processes (MOGPs) relied on
linear combinations of independent, latent, single-output Gaussian processes
(GPs). This resulted in cross-covariance functions with limited parametric
interpretation, thus conflicting with the ability of single-output GPs to
understand lengthscales, frequencies and magnitudes to name a few. On the
contrary, current approaches to MOGP are able to better interpret the
relationship between different channels by directly modelling the
cross-covariances as a spectral mixture kernel with a phase shift. We extend
this rationale and propose a parametric family of complex-valued cross-spectral
densities and then build on Cram\'er's Theorem (the multivariate version of
Bochner's Theorem) to provide a principled approach to design multivariate
covariance functions. The so-constructed kernels are able to model delays among
channels in addition to phase differences and are thus more expressive than
previous methods, while also providing full parametric interpretation of the
relationship across channels. The proposed method is first validated on
synthetic data and then compared to existing MOGP methods on two real-world
examples.
| Gabriel Parra and Felipe Tobar | null | 1709.01298 | null | null |
Boosting the kernelized shapelets: Theory and algorithms for local
features | cs.LG | We consider binary classification problems using local features of objects.
One of motivating applications is time-series classification, where features
reflecting some local closeness measure between a time series and a pattern
sequence called shapelet are useful. Despite the empirical success of such
approaches using local features, the generalization ability of resulting
hypotheses is not fully understood and previous work relies on a bunch of
heuristics. In this paper, we formulate a class of hypotheses using local
features, where the richness of features is controlled by kernels. We derive
generalization bounds of sparse ensembles over the class which is exponentially
better than a standard analysis in terms of the number of possible local
features. The resulting optimization problem is well suited to the boosting
approach and the weak learning problem is formulated as a DC program, for which
practical algorithms exist. In preliminary experiments on time-series data
sets, our method achieves competitive accuracy with the state-of-the-art
algorithms with small parameter-tuning cost.
| Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi
Bannai, Akiko Takeda | null | 1709.013 | null | null |
Recovery Conditions and Sampling Strategies for Network Lasso | stat.ML cs.LG | The network Lasso is a recently proposed convex optimization method for
machine learning from massive network structured datasets, i.e., big data over
networks. It is a variant of the well-known least absolute shrinkage and
selection operator (Lasso), which is underlying many methods in learning and
signal processing involving sparse models. Highly scalable implementations of
the network Lasso can be obtained by state-of-the art proximal methods, e.g.,
the alternating direction method of multipliers (ADMM). By generalizing the
concept of the compatibility condition put forward by van de Geer and Buehlmann
as a powerful tool for the analysis of plain Lasso, we derive a sufficient
condition, i.e., the network compatibility condition, on the underlying network
topology such that network Lasso accurately learns a clustered underlying graph
signal. This network compatibility condition relates the location of the
sampled nodes with the clustering structure of the network. In particular, the
NCC informs the choice of which nodes to sample, or in machine learning terms,
which data points provide most information if labeled.
| Alexandru Mara and Alexander Jung | null | 1709.01402 | null | null |
Deep learning: Technical introduction | stat.ML cs.LG | This note presents in a technical though hopefully pedagogical way the three
most common forms of neural network architectures: Feedforward, Convolutional
and Recurrent. For each network, their fundamental building blocks are
detailed. The forward pass and the update rules for the backpropagation
algorithm are then derived in full.
| Thomas Epelbaum | null | 1709.01412 | null | null |
Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D
Convolutional Neural Networks | cs.CV cs.LG | Automatic analysis of the video is one of most complex problems in the fields
of computer vision and machine learning. A significant part of this research
deals with (human) activity recognition (HAR) since humans, and the activities
that they perform, generate most of the video semantics. Video-based HAR has
applications in various domains, but one of the most important and challenging
is HAR in sports videos. Some of the major issues include high inter- and
intra-class variations, large class imbalance, the presence of both group
actions and single player actions, and recognizing simultaneous actions, i.e.,
the multi-label learning problem. Keeping in mind these challenges and the
recent success of CNNs in solving various computer vision problems, in this
work, we implement a 3D CNN based multi-label deep HAR system for multi-label
class-imbalanced action recognition in hockey videos. We test our system for
two different scenarios: an ensemble of $k$ binary networks vs. a single
$k$-output network, on a publicly available dataset. We also compare our
results with the system that was originally designed for the chosen dataset.
Experimental results show that the proposed approach performs better than the
existing solution.
| Konstantin Sozykin, Stanislav Protasov, Adil Khan, Rasheed Hussain,
Jooyoung Lee | null | 1709.01421 | null | null |
A Maximal Heterogeneity Based Clustering Approach for Obtaining Samples | cs.LG | Medical and social sciences demand sampling techniques which are robust,
reliable, replicable and have the least dissimilarity between the samples
obtained. Majority of the applications of sampling use randomized sampling,
albeit with stratification where applicable. The randomized technique is not
consistent, and may provide different samples each time, and the different
samples themselves may not be similar to each other. In this paper, we
introduce a novel non-statistical no-replacement sampling technique called
Wobbly Center Algorithm, which relies on building clusters iteratively based on
maximizing the heterogeneity inside each cluster. The algorithm works on the
principle of stepwise building of clusters by finding the points with the
maximal distance from the cluster center. The obtained results are validated
statistically using Analysis of Variance tests by comparing the samples
obtained to check if they are representative of each other. The obtained
results generated from running the Wobbly Center algorithm on benchmark
datasets when compared against other sampling algorithms indicate the
superiority of the Wobbly Center Algorithm.
| Megha Mishra, Chandrasekaran Anirudh Bhardwaj, and Kalyani Desikan | null | 1709.01423 | null | null |
Stochastic Gradient Descent: Going As Fast As Possible But Not Faster | stat.ML cs.LG cs.NE | When applied to training deep neural networks, stochastic gradient descent
(SGD) often incurs steady progression phases, interrupted by catastrophic
episodes in which loss and gradient norm explode. A possible mitigation of such
events is to slow down the learning process. This paper presents a novel
approach to control the SGD learning rate, that uses two statistical tests. The
first one, aimed at fast learning, compares the momentum of the normalized
gradient vectors to that of random unit vectors and accordingly gracefully
increases or decreases the learning rate. The second one is a change point
detection test, aimed at the detection of catastrophic learning episodes; upon
its triggering the learning rate is instantly halved. Both abilities of
speeding up and slowing down the learning rate allows the proposed approach,
called SALeRA, to learn as fast as possible but not faster. Experiments on
standard benchmarks show that SALeRA performs well in practice, and compares
favorably to the state of the art.
| Alice Schoenauer-Sebag, Marc Schoenauer and Mich\`ele Sebag | null | 1709.01427 | null | null |
A Generic Approach for Escaping Saddle points | cs.LG cs.AI | A central challenge to using first-order methods for optimizing nonconvex
problems is the presence of saddle points. First-order methods often get stuck
at saddle points, greatly deteriorating their performance. Typically, to escape
from saddles one has to use second-order methods. However, most works on
second-order methods rely extensively on expensive Hessian-based computations,
making them impractical in large-scale settings. To tackle this challenge, we
introduce a generic framework that minimizes Hessian based computations while
at the same time provably converging to second-order critical points. Our
framework carefully alternates between a first-order and a second-order
subroutine, using the latter only close to saddle points, and yields
convergence results competitive to the state-of-the-art. Empirical results
suggest that our strategy also enjoys a good practical performance.
| Sashank J Reddi, Manzil Zaheer, Suvrit Sra, Barnabas Poczos, Francis
Bach, Ruslan Salakhutdinov, Alexander J Smola | null | 1709.01434 | null | null |
A Statistical Approach to Increase Classification Accuracy in Supervised
Learning Algorithms | cs.LG stat.ML | Probabilistic mixture models have been widely used for different machine
learning and pattern recognition tasks such as clustering, dimensionality
reduction, and classification. In this paper, we focus on trying to solve the
most common challenges related to supervised learning algorithms by using
mixture probability distribution functions. With this modeling strategy, we
identify sub-labels and generate synthetic data in order to reach better
classification accuracy. It means we focus on increasing the training data
synthetically to increase the classification accuracy.
| Gustavo A Valencia-Zapata, Daniel Mejia, Gerhard Klimeck, Michael
Zentner, and Okan Ersoy | null | 1709.01439 | null | null |
Learning the PE Header, Malware Detection with Minimal Domain Knowledge | stat.ML cs.LG | Many efforts have been made to use various forms of domain knowledge in
malware detection. Currently there exist two common approaches to malware
detection without domain knowledge, namely byte n-grams and strings. In this
work we explore the feasibility of applying neural networks to malware
detection and feature learning. We do this by restricting ourselves to a
minimal amount of domain knowledge in order to extract a portion of the
Portable Executable (PE) header. By doing this we show that neural networks can
learn from raw bytes without explicit feature construction, and perform even
better than a domain knowledge approach that parses the PE header into explicit
features.
| Edward Raff, Jared Sylvester, Charles Nicholas | 10.1145/3128572.3140442 | 1709.01471 | null | null |
Fine-tuning deep CNN models on specific MS COCO categories | cs.CV cs.AI cs.LG | Fine-tuning of a deep convolutional neural network (CNN) is often desired.
This paper provides an overview of our publicly available py-faster-rcnn-ft
software library that can be used to fine-tune the VGG_CNN_M_1024 model on
custom subsets of the Microsoft Common Objects in Context (MS COCO) dataset.
For example, we improved the procedure so that the user does not have to look
for suitable image files in the dataset by hand which can then be used in the
demo program. Our implementation randomly selects images that contain at least
one object of the categories on which the model is fine-tuned.
| Daniel Sonntag, Michael Barz, Jan Zacharias, Sven Stauden, Vahid
Rahmani, \'Aron F\'othi, Andr\'as L\H{o}rincz | null | 1709.01476 | null | null |
Linking Generative Adversarial Learning and Binary Classification | cs.LG cs.AI stat.ML | In this note, we point out a basic link between generative adversarial (GA)
training and binary classification -- any powerful discriminator essentially
computes an (f-)divergence between real and generated samples. The result,
repeatedly re-derived in decision theory, has implications for GA Networks
(GANs), providing an alternative perspective on training f-GANs by designing
the discriminator loss function.
| Akshay Balsubramani | null | 1709.01509 | null | null |
Interacting Attention-gated Recurrent Networks for Recommendation | cs.IR cs.AI cs.LG cs.SI | Capturing the temporal dynamics of user preferences over items is important
for recommendation. Existing methods mainly assume that all time steps in
user-item interaction history are equally relevant to recommendation, which
however does not apply in real-world scenarios where user-item interactions can
often happen accidentally. More importantly, they learn user and item dynamics
separately, thus failing to capture their joint effects on user-item
interactions. To better model user and item dynamics, we present the
Interacting Attention-gated Recurrent Network (IARN) which adopts the attention
model to measure the relevance of each time step. In particular, we propose a
novel attention scheme to learn the attention scores of user and item history
in an interacting way, thus to account for the dependencies between user and
item dynamics in shaping user-item interactions. By doing so, IARN can
selectively memorize different time steps of a user's history when predicting
her preferences over different items. Our model can therefore provide
meaningful interpretations for recommendation results, which could be further
enhanced by auxiliary features. Extensive validation on real-world datasets
shows that IARN consistently outperforms state-of-the-art methods.
| Wenjie Pei, Jie Yang, Zhu Sun, Jie Zhang, Alessandro Bozzon, David
M.J. Tax | null | 1709.01532 | null | null |
Sequence Prediction with Neural Segmental Models | cs.CL cs.LG cs.SD | Segments that span contiguous parts of inputs, such as phonemes in speech,
named entities in sentences, actions in videos, occur frequently in sequence
prediction problems. Segmental models, a class of models that explicitly
hypothesizes segments, have allowed the exploration of rich segment features
for sequence prediction. However, segmental models suffer from slow decoding,
hampering the use of computationally expensive features.
In this thesis, we introduce discriminative segmental cascades, a multi-pass
inference framework that allows us to improve accuracy by adding higher-order
features and neural segmental features while maintaining efficiency. We also
show that instead of including more features to obtain better accuracy,
segmental cascades can be used to speed up training and decoding.
Segmental models, similarly to conventional speech recognizers, are typically
trained in multiple stages. In the first stage, a frame classifier is trained
with manual alignments, and then in the second stage, segmental models are
trained with manual alignments and the out- puts of the frame classifier.
However, obtaining manual alignments are time-consuming and expensive. We
explore end-to-end training for segmental models with various loss functions,
and show how end-to-end training with marginal log loss can eliminate the need
for detailed manual alignments.
We draw the connections between the marginal log loss and a popular
end-to-end training approach called connectionist temporal classification. We
present a unifying framework for various end-to-end graph search-based models,
such as hidden Markov models, connectionist temporal classification, and
segmental models. Finally, we discuss possible extensions of segmental models
to large-vocabulary sequence prediction tasks.
| Hao Tang | null | 1709.01572 | null | null |
Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario | cs.IR cs.LG stat.ML | Item cold-start is a classical issue in recommender systems that affects
anime and manga recommendations as well. This problem can be framed as follows:
how to predict whether a user will like a manga that received few ratings from
the community? Content-based techniques can alleviate this issue but require
extra information, that is usually expensive to gather. In this paper, we use a
deep learning technique, Illustration2Vec, to easily extract tag information
from the manga and anime posters (e.g., sword, or ponytail). We propose BALSE
(Blended Alternate Least Squares with Explanation), a new model for
collaborative filtering, that benefits from this extra information to recommend
mangas. We show, using real data from an online manga recommender system called
Mangaki, that our model improves substantially the quality of recommendations,
especially for less-known manga, and is able to provide an interpretation of
the taste of the users.
| Jill-J\^enn Vie, Florian Yger, Ryan Lahfa, Basile Clement, K\'evin
Cocchi, Thomas Chalumeau and Hisashi Kashima | null | 1709.01584 | null | null |
Privacy Risk in Machine Learning: Analyzing the Connection to
Overfitting | cs.CR cs.LG stat.ML | Machine learning algorithms, when applied to sensitive data, pose a distinct
threat to privacy. A growing body of prior work demonstrates that models
produced by these algorithms may leak specific private information in the
training data to an attacker, either through the models' structure or their
observable behavior. However, the underlying cause of this privacy risk is not
well understood beyond a handful of anecdotal accounts that suggest overfitting
and influence might play a role.
This paper examines the effect that overfitting and influence have on the
ability of an attacker to learn information about the training data from
machine learning models, either through training set membership inference or
attribute inference attacks. Using both formal and empirical analyses, we
illustrate a clear relationship between these factors and the privacy risk that
arises in several popular machine learning algorithms. We find that overfitting
is sufficient to allow an attacker to perform membership inference and, when
the target attribute meets certain conditions about its influence, attribute
inference attacks. Interestingly, our formal analysis also shows that
overfitting is not necessary for these attacks and begins to shed light on what
other factors may be in play. Finally, we explore the connection between
membership inference and attribute inference, showing that there are deep
connections between the two that lead to effective new attacks.
| Samuel Yeom, Irene Giacomelli, Matt Fredrikson, Somesh Jha | null | 1709.01604 | null | null |
Deep Learning Techniques for Music Generation -- A Survey | cs.SD cs.LG | This paper is a survey and an analysis of different ways of using deep
learning (deep artificial neural networks) to generate musical content. We
propose a methodology based on five dimensions for our analysis:
Objective - What musical content is to be generated? Examples are: melody,
polyphony, accompaniment or counterpoint. - For what destination and for what
use? To be performed by a human(s) (in the case of a musical score), or by a
machine (in the case of an audio file).
Representation - What are the concepts to be manipulated? Examples are:
waveform, spectrogram, note, chord, meter and beat. - What format is to be
used? Examples are: MIDI, piano roll or text. - How will the representation be
encoded? Examples are: scalar, one-hot or many-hot.
Architecture - What type(s) of deep neural network is (are) to be used?
Examples are: feedforward network, recurrent network, autoencoder or generative
adversarial networks.
Challenge - What are the limitations and open challenges? Examples are:
variability, interactivity and creativity.
Strategy - How do we model and control the process of generation? Examples
are: single-step feedforward, iterative feedforward, sampling or input
manipulation.
For each dimension, we conduct a comparative analysis of various models and
techniques and we propose some tentative multidimensional typology. This
typology is bottom-up, based on the analysis of many existing deep-learning
based systems for music generation selected from the relevant literature. These
systems are described and are used to exemplify the various choices of
objective, representation, architecture, challenge and strategy. The last
section includes some discussion and some prospects.
| Jean-Pierre Briot, Ga\"etan Hadjeres and Fran\c{c}ois-David Pachet | null | 1709.0162 | null | null |
Learning to Compose Domain-Specific Transformations for Data
Augmentation | stat.ML cs.CV cs.LG | Data augmentation is a ubiquitous technique for increasing the size of
labeled training sets by leveraging task-specific data transformations that
preserve class labels. While it is often easy for domain experts to specify
individual transformations, constructing and tuning the more sophisticated
compositions typically needed to achieve state-of-the-art results is a
time-consuming manual task in practice. We propose a method for automating this
process by learning a generative sequence model over user-specified
transformation functions using a generative adversarial approach. Our method
can make use of arbitrary, non-deterministic transformation functions, is
robust to misspecified user input, and is trained on unlabeled data. The
learned transformation model can then be used to perform data augmentation for
any end discriminative model. In our experiments, we show the efficacy of our
approach on both image and text datasets, achieving improvements of 4.0
accuracy points on CIFAR-10, 1.4 F1 points on the ACE relation extraction task,
and 3.4 accuracy points when using domain-specific transformation operations on
a medical imaging dataset as compared to standard heuristic augmentation
approaches.
| Alexander J. Ratner, Henry R. Ehrenberg, Zeshan Hussain, Jared
Dunnmon, Christopher R\'e | null | 1709.01643 | null | null |
Boosting Deep Learning Risk Prediction with Generative Adversarial
Networks for Electronic Health Records | cs.LG stat.ML | The rapid growth of Electronic Health Records (EHRs), as well as the
accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting
widespread interests and attentions. Recent progress in the design and
applications of deep learning methods has shown promising results and is
forcing massive changes in healthcare academia and industry, but most of these
methods rely on massive labeled data. In this work, we propose a general deep
learning framework which is able to boost risk prediction performance with
limited EHR data. Our model takes a modified generative adversarial network
namely ehrGAN, which can provide plausible labeled EHR data by mimicking real
patient records, to augment the training dataset in a semi-supervised learning
manner. We use this generative model together with a convolutional neural
network (CNN) based prediction model to improve the onset prediction
performance. Experiments on two real healthcare datasets demonstrate that our
proposed framework produces realistic data samples and achieves significant
improvements on classification tasks with the generated data over several
stat-of-the-art baselines.
| Zhengping Che, Yu Cheng, Shuangfei Zhai, Zhaonan Sun, Yan Liu | null | 1709.01648 | null | null |
Unsupervised Generative Modeling Using Matrix Product States | cond-mat.stat-mech cs.LG quant-ph stat.ML | Generative modeling, which learns joint probability distribution from data
and generates samples according to it, is an important task in machine learning
and artificial intelligence. Inspired by probabilistic interpretation of
quantum physics, we propose a generative model using matrix product states,
which is a tensor network originally proposed for describing (particularly
one-dimensional) entangled quantum states. Our model enjoys efficient learning
analogous to the density matrix renormalization group method, which allows
dynamically adjusting dimensions of the tensors and offers an efficient direct
sampling approach for generative tasks. We apply our method to generative
modeling of several standard datasets including the Bars and Stripes, random
binary patterns and the MNIST handwritten digits to illustrate the abilities,
features and drawbacks of our model over popular generative models such as
Hopfield model, Boltzmann machines and generative adversarial networks. Our
work sheds light on many interesting directions of future exploration on the
development of quantum-inspired algorithms for unsupervised machine learning,
which are promisingly possible to be realized on quantum devices.
| Zhao-Yu Han, Jun Wang, Heng Fan, Lei Wang and Pan Zhang | 10.1103/PhysRevX.8.031012 | 1709.01662 | null | null |
Throughput Optimal Decentralized Scheduling of Multi-Hop Networks with
End-to-End Deadline Constraints: II Wireless Networks with Interference | cs.NI cs.LG cs.NE cs.SY | Consider a multihop wireless network serving multiple flows in which wireless
link interference constraints are described by a link interference graph. For
such a network, we design routing-scheduling policies that maximize the
end-to-end timely throughput of the network. Timely throughput of a flow $f$ is
defined as the average rate at which packets of flow $f$ reach their
destination node $d_f$ within their deadline.
Our policy has several surprising characteristics. Firstly, we show that the
optimal routing-scheduling decision for an individual packet that is present at
a wireless node $i\in V$ is solely a function of its location, and "age". Thus,
a wireless node $i$ does not require the knowledge of the "global" network
state in order to maximize the timely throughput. We notice that in comparison,
under the backpressure routing policy, a node $i$ requires only the knowledge
of its neighbours queue lengths in order to guarantee maximal stability, and
hence is decentralized. The key difference arises due to the fact that in our
set-up the packets loose their utility once their "age" has crossed their
deadline, thus making the task of optimizing timely throughput much more
challenging than that of ensuring network stability. Of course, due to this key
difference, the decision process involved in maximizing the timely throughput
is also much more complex than that involved in ensuring network-wide queue
stabilization. In view of this, our results are somewhat surprising.
| Rahul Singh, P.R. Kumar, and Eytan Modiano | null | 1709.01672 | null | null |
Probabilistic Rule Realization and Selection | cs.LG stat.ML | Abstraction and realization are bilateral processes that are key in deriving
intelligence and creativity. In many domains, the two processes are approached
through rules: high-level principles that reveal invariances within similar yet
diverse examples. Under a probabilistic setting for discrete input spaces, we
focus on the rule realization problem which generates input sample
distributions that follow the given rules. More ambitiously, we go beyond a
mechanical realization that takes whatever is given, but instead ask for
proactively selecting reasonable rules to realize. This goal is demanding in
practice, since the initial rule set may not always be consistent and thus
intelligent compromises are needed. We formulate both rule realization and
selection as two strongly connected components within a single and symmetric
bi-convex problem, and derive an efficient algorithm that works at large scale.
Taking music compositional rules as the main example throughout the paper, we
demonstrate our model's efficiency in not only music realization (composition)
but also music interpretation and understanding (analysis).
| Haizi Yu, Tianxi Li, Lav R. Varshney | null | 1709.01674 | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.