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Applications of Online Deep Learning for Crisis Response Using Social
Media Information | cs.CL cs.CY cs.LG | During natural or man-made disasters, humanitarian response organizations
look for useful information to support their decision-making processes. Social
media platforms such as Twitter have been considered as a vital source of
useful information for disaster response and management. Despite advances in
natural language processing techniques, processing short and informal Twitter
messages is a challenging task. In this paper, we propose to use Deep Neural
Network (DNN) to address two types of information needs of response
organizations: 1) identifying informative tweets and 2) classifying them into
topical classes. DNNs use distributed representation of words and learn the
representation as well as higher level features automatically for the
classification task. We propose a new online algorithm based on stochastic
gradient descent to train DNNs in an online fashion during disaster situations.
We test our models using a crisis-related real-world Twitter dataset.
| Dat Tien Nguyen, Shafiq Joty, Muhammad Imran, Hassan Sajjad, Prasenjit
Mitra | null | 1610.0103 | null | null |
Tutorial on Answering Questions about Images with Deep Learning | cs.CV cs.AI cs.CL cs.LG cs.NE | Together with the development of more accurate methods in Computer Vision and
Natural Language Understanding, holistic architectures that answer on questions
about the content of real-world images have emerged. In this tutorial, we build
a neural-based approach to answer questions about images. We base our tutorial
on two datasets: (mostly on) DAQUAR, and (a bit on) VQA. With small tweaks the
models that we present here can achieve a competitive performance on both
datasets, in fact, they are among the best methods that use a combination of
LSTM with a global, full frame CNN representation of an image. We hope that
after reading this tutorial, the reader will be able to use Deep Learning
frameworks, such as Keras and introduced Kraino, to build various architectures
that will lead to a further performance improvement on this challenging task.
| Mateusz Malinowski and Mario Fritz | null | 1610.01076 | null | null |
FLOCK: Combating Astroturfing on Livestreaming Platforms | cs.SI cs.LG | Livestreaming platforms have become increasingly popular in recent years as a
means of sharing and advertising creative content. Popular content streamers
who attract large viewership to their live broadcasts can earn a living by
means of ad revenue, donations and channel subscriptions. Unfortunately, this
incentivized popularity has simultaneously resulted in incentive for fraudsters
to provide services to astroturf, or artificially inflate viewership metrics by
providing fake "live" views to customers. Our work provides a number of major
contributions: (a) formulation: we are the first to introduce and characterize
the viewbot fraud problem in livestreaming platforms, (b) methodology: we
propose FLOCK, a principled and unsupervised method which efficiently and
effectively identifies botted broadcasts and their constituent botted views,
and (c) practicality: our approach achieves over 98% precision in identifying
botted broadcasts and over 90% precision/recall against sizable synthetically
generated viewbot attacks on a real-world livestreaming workload of over 16
million views and 92 thousand broadcasts. FLOCK successfully operates on larger
datasets in practice and is regularly used at a large, undisclosed
livestreaming corporation.
| Neil Shah | null | 1610.01096 | null | null |
A SMART Stochastic Algorithm for Nonconvex Optimization with
Applications to Robust Machine Learning | stat.ML cs.LG math.OC | In this paper, we show how to transform any optimization problem that arises
from fitting a machine learning model into one that (1) detects and removes
contaminated data from the training set while (2) simultaneously fitting the
trimmed model on the uncontaminated data that remains. To solve the resulting
nonconvex optimization problem, we introduce a fast stochastic
proximal-gradient algorithm that incorporates prior knowledge through nonsmooth
regularization. For datasets of size $n$, our approach requires
$O(n^{2/3}/\varepsilon)$ gradient evaluations to reach $\varepsilon$-accuracy
and, when a certain error bound holds, the complexity improves to $O(\kappa
n^{2/3}\log(1/\varepsilon))$. These rates are $n^{1/3}$ times better than those
achieved by typical, full gradient methods.
| Aleksandr Aravkin and Damek Davis | null | 1610.01101 | null | null |
Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning
with Stochastic Initial States | cs.LG cs.RO | Autonomous learning of robotic skills can allow general-purpose robots to
learn wide behavioral repertoires without requiring extensive manual
engineering. However, robotic skill learning methods typically make one of
several trade-offs to enable practical real-world learning, such as requiring
manually designed policy or value function representations, initialization from
human-provided demonstrations, instrumentation of the training environment, or
extremely long training times. In this paper, we propose a new reinforcement
learning algorithm for learning manipulation skills that can train
general-purpose neural network policies with minimal human engineering, while
still allowing for fast, efficient learning in stochastic environments. Our
approach builds on the guided policy search (GPS) algorithm, which transforms
the reinforcement learning problem into supervised learning from a
computational teacher (without human demonstrations). In contrast to prior GPS
methods, which require a consistent set of initial states to which the system
must be reset after each episode, our approach can handle randomized initial
states, allowing it to be used in environments where deterministic resets are
impossible. We compare our method to existing policy search techniques in
simulation, showing that it can train high-dimensional neural network policies
with the same sample efficiency as prior GPS methods, and present real-world
results on a PR2 robotic manipulator.
| William Montgomery, Anurag Ajay, Chelsea Finn, Pieter Abbeel, Sergey
Levine | null | 1610.01112 | null | null |
A Non-generative Framework and Convex Relaxations for Unsupervised
Learning | cs.LG cs.DS stat.ML | We give a novel formal theoretical framework for unsupervised learning with
two distinctive characteristics. First, it does not assume any generative model
and based on a worst-case performance metric. Second, it is comparative, namely
performance is measured with respect to a given hypothesis class. This allows
to avoid known computational hardness results and improper algorithms based on
convex relaxations. We show how several families of unsupervised learning
models, which were previously only analyzed under probabilistic assumptions and
are otherwise provably intractable, can be efficiently learned in our framework
by convex optimization.
| Elad Hazan, Tengyu Ma | null | 1610.01132 | null | null |
Error bounds for approximations with deep ReLU networks | cs.LG cs.NE | We study expressive power of shallow and deep neural networks with piece-wise
linear activation functions. We establish new rigorous upper and lower bounds
for the network complexity in the setting of approximations in Sobolev spaces.
In particular, we prove that deep ReLU networks more efficiently approximate
smooth functions than shallow networks. In the case of approximations of 1D
Lipschitz functions we describe adaptive depth-6 network architectures more
efficient than the standard shallow architecture.
| Dmitry Yarotsky | null | 1610.01145 | null | null |
A Tour of TensorFlow | cs.LG | Deep learning is a branch of artificial intelligence employing deep neural
network architectures that has significantly advanced the state-of-the-art in
computer vision, speech recognition, natural language processing and other
domains. In November 2015, Google released $\textit{TensorFlow}$, an open
source deep learning software library for defining, training and deploying
machine learning models. In this paper, we review TensorFlow and put it in
context of modern deep learning concepts and software. We discuss its basic
computational paradigms and distributed execution model, its programming
interface as well as accompanying visualization toolkits. We then compare
TensorFlow to alternative libraries such as Theano, Torch or Caffe on a
qualitative as well as quantitative basis and finally comment on observed
use-cases of TensorFlow in academia and industry.
| Peter Goldsborough | null | 1610.01178 | null | null |
A Survey of Multi-View Representation Learning | cs.LG cs.CV cs.IR | Recently, multi-view representation learning has become a rapidly growing
direction in machine learning and data mining areas. This paper introduces two
categories for multi-view representation learning: multi-view representation
alignment and multi-view representation fusion. Consequently, we first review
the representative methods and theories of multi-view representation learning
based on the perspective of alignment, such as correlation-based alignment.
Representative examples are canonical correlation analysis (CCA) and its
several extensions. Then from the perspective of representation fusion we
investigate the advancement of multi-view representation learning that ranges
from generative methods including multi-modal topic learning, multi-view sparse
coding, and multi-view latent space Markov networks, to neural network-based
methods including multi-modal autoencoders, multi-view convolutional neural
networks, and multi-modal recurrent neural networks. Further, we also
investigate several important applications of multi-view representation
learning. Overall, this survey aims to provide an insightful overview of
theoretical foundation and state-of-the-art developments in the field of
multi-view representation learning and to help researchers find the most
appropriate tools for particular applications.
| Yingming Li, Ming Yang, and Zhongfei Zhang | 10.1109/TKDE.2018.2872063 | 1610.01206 | null | null |
Ensemble Validation: Selectivity has a Price, but Variety is Free | stat.ML cs.LG | Suppose some classifiers are selected from a set of hypothesis classifiers to
form an equally-weighted ensemble that selects a member classifier at random
for each input example. Then the ensemble has an error bound consisting of the
average error bound for the member classifiers, a term for selectivity that
varies from zero (if all hypothesis classifiers are selected) to a standard
uniform error bound (if only a single classifier is selected), and small
constants. There is no penalty for using a richer hypothesis set if the same
fraction of the hypothesis classifiers are selected for the ensemble.
| Eric Bax and Farshad Kooti | null | 1610.01234 | null | null |
Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for
Urban Autonomy | cs.RO cs.AI cs.CV cs.LG | We present a weakly-supervised approach to segmenting proposed drivable paths
in images with the goal of autonomous driving in complex urban environments.
Using recorded routes from a data collection vehicle, our proposed method
generates vast quantities of labelled images containing proposed paths and
obstacles without requiring manual annotation, which we then use to train a
deep semantic segmentation network. With the trained network we can segment
proposed paths and obstacles at run-time using a vehicle equipped with only a
monocular camera without relying on explicit modelling of road or lane
markings. We evaluate our method on the large-scale KITTI and Oxford RobotCar
datasets and demonstrate reliable path proposal and obstacle segmentation in a
wide variety of environments under a range of lighting, weather and traffic
conditions. We illustrate how the method can generalise to multiple path
proposals at intersections and outline plans to incorporate the system into a
framework for autonomous urban driving.
| Dan Barnes, Will Maddern and Ingmar Posner | null | 1610.01238 | null | null |
Adversary Resistant Deep Neural Networks with an Application to Malware
Detection | cs.LG | Beyond its highly publicized victories in Go, there have been numerous
successful applications of deep learning in information retrieval, computer
vision and speech recognition. In cybersecurity, an increasing number of
companies have become excited about the potential of deep learning, and have
started to use it for various security incidents, the most popular being
malware detection. These companies assert that deep learning (DL) could help
turn the tide in the battle against malware infections. However, deep neural
networks (DNNs) are vulnerable to adversarial samples, a flaw that plagues most
if not all statistical learning models. Recent research has demonstrated that
those with malicious intent can easily circumvent deep learning-powered malware
detection by exploiting this flaw.
In order to address this problem, previous work has developed various defense
mechanisms that either augmenting training data or enhance model's complexity.
However, after a thorough analysis of the fundamental flaw in DNNs, we discover
that the effectiveness of current defenses is limited and, more importantly,
cannot provide theoretical guarantees as to their robustness against
adversarial sampled-based attacks. As such, we propose a new adversary
resistant technique that obstructs attackers from constructing impactful
adversarial samples by randomly nullifying features within samples. In this
work, we evaluate our proposed technique against a real world dataset with
14,679 malware variants and 17,399 benign programs. We theoretically validate
the robustness of our technique, and empirically show that our technique
significantly boosts DNN robustness to adversarial samples while maintaining
high accuracy in classification. To demonstrate the general applicability of
our proposed method, we also conduct experiments using the MNIST and CIFAR-10
datasets, generally used in image recognition research.
| Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Alexander G. Ororbia II,
Xinyu Xing, C. Lee Giles, Xue Liu | null | 1610.01239 | null | null |
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles | cs.LG cs.AI cs.RO | Sample complexity and safety are major challenges when learning policies with
reinforcement learning for real-world tasks, especially when the policies are
represented using rich function approximators like deep neural networks.
Model-based methods where the real-world target domain is approximated using a
simulated source domain provide an avenue to tackle the above challenges by
augmenting real data with simulated data. However, discrepancies between the
simulated source domain and the target domain pose a challenge for simulated
training. We introduce the EPOpt algorithm, which uses an ensemble of simulated
source domains and a form of adversarial training to learn policies that are
robust and generalize to a broad range of possible target domains, including
unmodeled effects. Further, the probability distribution over source domains in
the ensemble can be adapted using data from target domain and approximate
Bayesian methods, to progressively make it a better approximation. Thus,
learning on a model ensemble, along with source domain adaptation, provides the
benefit of both robustness and learning/adaptation.
| Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, Sergey
Levine | null | 1610.01283 | null | null |
Domain Adaptation with Soft-margin multiple feature-kernel learning
beats Deep Learning for surveillance face recognition | cs.CV cs.AI cs.HC cs.LG | Face recognition (FR) is the most preferred mode for biometric-based
surveillance, due to its passive nature of detecting subjects, amongst all
different types of biometric traits. FR under surveillance scenario does not
give satisfactory performance due to low contrast, noise and poor illumination
conditions on probes, as compared to the training samples. A state-of-the-art
technology, Deep Learning, even fails to perform well in these scenarios. We
propose a novel soft-margin based learning method for multiple feature-kernel
combinations, followed by feature transformed using Domain Adaptation, which
outperforms many recent state-of-the-art techniques, when tested using three
real-world surveillance face datasets.
| Samik Banerjee, Sukhendu Das | null | 1610.01374 | null | null |
Decentralized Topic Modelling with Latent Dirichlet Allocation | stat.ML cs.LG | Privacy preserving networks can be modelled as decentralized networks (e.g.,
sensors, connected objects, smartphones), where communication between nodes of
the network is not controlled by an all-knowing, central node. For this type of
networks, the main issue is to gather/learn global information on the network
(e.g., by optimizing a global cost function) while keeping the (sensitive)
information at each node. In this work, we focus on text information that
agents do not want to share (e.g., text messages, emails, confidential
reports). We use recent advances on decentralized optimization and topic models
to infer topics from a graph with limited communication. We propose a method to
adapt latent Dirichlet allocation (LDA) model to decentralized optimization and
show on synthetic data that we still recover similar parameters and similar
performance at each node than with stochastic methods accessing to the whole
information in the graph.
| Igor Colin, Christophe Dupuy | null | 1610.01417 | null | null |
$\ell_1$ Regularized Gradient Temporal-Difference Learning | cs.AI cs.LG | In this paper, we study the Temporal Difference (TD) learning with linear
value function approximation. It is well known that most TD learning algorithms
are unstable with linear function approximation and off-policy learning. Recent
development of Gradient TD (GTD) algorithms has addressed this problem
successfully. However, the success of GTD algorithms requires a set of well
chosen features, which are not always available. When the number of features is
huge, the GTD algorithms might face the problem of overfitting and being
computationally expensive. To cope with this difficulty, regularization
techniques, in particular $\ell_1$ regularization, have attracted significant
attentions in developing TD learning algorithms. The present work combines the
GTD algorithms with $\ell_1$ regularization. We propose a family of $\ell_1$
regularized GTD algorithms, which employ the well known soft thresholding
operator. We investigate convergence properties of the proposed algorithms, and
depict their performance with several numerical experiments.
| Dominik Meyer, Hao Shen, Klaus Diepold | null | 1610.01476 | null | null |
Conversational Recommendation System with Unsupervised Learning | cs.CL cs.IR cs.LG | We will demonstrate a conversational products recommendation agent. This
system shows how we combine research in personalized recommendation systems
with research in dialogue systems to build a virtual sales agent. Based on new
deep learning technologies we developed, the virtual agent is capable of
learning how to interact with users, how to answer user questions, what is the
next question to ask, and what to recommend when chatting with a human user.
Normally a descent conversational agent for a particular domain requires tens
of thousands of hand labeled conversational data or hand written rules. This is
a major barrier when launching a conversation agent for a new domain. We will
explore and demonstrate the effectiveness of the learning solution even when
there is no hand written rules or hand labeled training data.
| Yueming Sun, Yi Zhang, Yunfei Chen, Roger Jin | null | 1610.01546 | null | null |
Learning Protein Dynamics with Metastable Switching Systems | stat.ML cs.LG | We introduce a machine learning approach for extracting fine-grained
representations of protein evolution from molecular dynamics datasets.
Metastable switching linear dynamical systems extend standard switching models
with a physically-inspired stability constraint. This constraint enables the
learning of nuanced representations of protein dynamics that closely match
physical reality. We derive an EM algorithm for learning, where the E-step
extends the forward-backward algorithm for HMMs and the M-step requires the
solution of large biconvex optimization problems. We construct an approximate
semidefinite program solver based on the Frank-Wolfe algorithm and use it to
solve the M-step. We apply our EM algorithm to learn accurate dynamics from
large simulation datasets for the opioid peptide met-enkephalin and the
proto-oncogene Src-kinase. Our learned models demonstrate significant
improvements in temporal coherence over HMMs and standard switching models for
met-enkephalin, and sample transition paths (possibly useful in rational drug
design) for Src-kinase.
| Bharath Ramsundar and Vijay S. Pande | null | 1610.01642 | null | null |
Understanding intermediate layers using linear classifier probes | stat.ML cs.LG | Neural network models have a reputation for being black boxes. We propose to
monitor the features at every layer of a model and measure how suitable they
are for classification. We use linear classifiers, which we refer to as
"probes", trained entirely independently of the model itself.
This helps us better understand the roles and dynamics of the intermediate
layers. We demonstrate how this can be used to develop a better intuition about
models and to diagnose potential problems.
We apply this technique to the popular models Inception v3 and Resnet-50.
Among other things, we observe experimentally that the linear separability of
features increase monotonically along the depth of the model.
| Guillaume Alain and Yoshua Bengio | null | 1610.01644 | null | null |
Generalized Inverse Classification | cs.LG stat.ML | Inverse classification is the process of perturbing an instance in a
meaningful way such that it is more likely to conform to a specific class.
Historical methods that address such a problem are often framed to leverage
only a single classifier, or specific set of classifiers. These works are often
accompanied by naive assumptions. In this work we propose generalized inverse
classification (GIC), which avoids restricting the classification model that
can be used. We incorporate this formulation into a refined framework in which
GIC takes place. Under this framework, GIC operates on features that are
immediately actionable. Each change incurs an individual cost, either linear or
non-linear. Such changes are subjected to occur within a specified level of
cumulative change (budget). Furthermore, our framework incorporates the
estimation of features that change as a consequence of direct actions taken
(indirectly changeable features). To solve such a problem, we propose three
real-valued heuristic-based methods and two sensitivity analysis-based
comparison methods, each of which is evaluated on two freely available
real-world datasets. Our results demonstrate the validity and benefits of our
formulation, framework, and methods.
| Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson,
Jeffrey Ohlmann | 10.1137/1.9781611974973.19 | 1610.01675 | null | null |
Automatic Sleep Stage Scoring with Single-Channel EEG Using
Convolutional Neural Networks | stat.ML cs.LG | We used convolutional neural networks (CNNs) for automatic sleep stage
scoring based on single-channel electroencephalography (EEG) to learn
task-specific filters for classification without using prior domain knowledge.
We used an openly available dataset from 20 healthy young adults for evaluation
and applied 20-fold cross-validation. We used class-balanced random sampling
within the stochastic gradient descent (SGD) optimization of the CNN to avoid
skewed performance in favor of the most represented sleep stages. We achieved
high mean F1-score (81%, range 79-83%), mean accuracy across individual sleep
stages (82%, range 80-84%) and overall accuracy (74%, range 71-76%) over all
subjects. By analyzing and visualizing the filters that our CNN learns, we
found that rules learned by the filters correspond to sleep scoring criteria in
the American Academy of Sleep Medicine (AASM) manual that human experts follow.
Our method's performance is balanced across classes and our results are
comparable to state-of-the-art methods with hand-engineered features. We show
that, without using prior domain knowledge, a CNN can automatically learn to
distinguish among different normal sleep stages.
| Orestis Tsinalis, Paul M. Matthews, Yike Guo, Stefanos Zafeiriou | null | 1610.01683 | null | null |
Supervision via Competition: Robot Adversaries for Learning Tasks | cs.RO cs.CV cs.LG | There has been a recent paradigm shift in robotics to data-driven learning
for planning and control. Due to large number of experiences required for
training, most of these approaches use a self-supervised paradigm: using
sensors to measure success/failure. However, in most cases, these sensors
provide weak supervision at best. In this work, we propose an adversarial
learning framework that pits an adversary against the robot learning the task.
In an effort to defeat the adversary, the original robot learns to perform the
task with more robustness leading to overall improved performance. We show that
this adversarial framework forces the the robot to learn a better grasping
model in order to overcome the adversary. By grasping 82% of presented novel
objects compared to 68% without an adversary, we demonstrate the utility of
creating adversaries. We also demonstrate via experiments that having robots in
adversarial setting might be a better learning strategy as compared to having
collaborative multiple robots.
| Lerrel Pinto, James Davidson and Abhinav Gupta | null | 1610.01685 | null | null |
Sampled Fictitious Play is Hannan Consistent | cs.GT cs.LG stat.ML | Fictitious play is a simple and widely studied adaptive heuristic for playing
repeated games. It is well known that fictitious play fails to be Hannan
consistent. Several variants of fictitious play including regret matching,
generalized regret matching and smooth fictitious play, are known to be Hannan
consistent. In this note, we consider sampled fictitious play: at each round,
the player samples past times and plays the best response to previous moves of
other players at the sampled time points. We show that sampled fictitious play,
using Bernoulli sampling, is Hannan consistent. Unlike several existing Hannan
consistency proofs that rely on concentration of measure results, ours instead
uses anti-concentration results from Littlewood-Offord theory.
| Zifan Li, Ambuj Tewari | null | 1610.01687 | null | null |
Low-tubal-rank Tensor Completion using Alternating Minimization | cs.LG cs.IT math.IT | The low-tubal-rank tensor model has been recently proposed for real-world
multidimensional data. In this paper, we study the low-tubal-rank tensor
completion problem, i.e., to recover a third-order tensor by observing a subset
of its elements selected uniformly at random. We propose a fast iterative
algorithm, called {\em Tubal-Alt-Min}, that is inspired by a similar approach
for low-rank matrix completion. The unknown low-tubal-rank tensor is
represented as the product of two much smaller tensors with the low-tubal-rank
property being automatically incorporated, and Tubal-Alt-Min alternates between
estimating those two tensors using tensor least squares minimization. First, we
note that tensor least squares minimization is different from its matrix
counterpart and nontrivial as the circular convolution operator of the
low-tubal-rank tensor model is intertwined with the sub-sampling operator.
Second, the theoretical performance guarantee is challenging since
Tubal-Alt-Min is iterative and nonconvex in nature. We prove that 1)
Tubal-Alt-Min guarantees exponential convergence to the global optima, and 2)
for an $n \times n \times k$ tensor with tubal-rank $r \ll n$, the required
sampling complexity is $O(nr^2k \log^3 n)$ and the computational complexity is
$O(n^2rk^2 \log^2 n)$. Third, on both synthetic data and real-world video data,
evaluation results show that compared with tensor-nuclear norm minimization
(TNN-ADMM), Tubal-Alt-Min improves the recovery error dramatically (by orders
of magnitude). It is estimated that Tubal-Alt-Min converges at an exponential
rate $10^{-0.4423 \text{Iter}}$ where $\text{Iter}$ denotes the number of
iterations, which is much faster than TNN-ADMM's $10^{-0.0332 \text{Iter}}$,
and the running time can be accelerated by more than $5$ times for a $200
\times 200 \times 20$ tensor.
| Xiao-Yang Liu and Shuchin Aeron and Vaneet Aggarwal and Xiaodong Wang | 10.1117/12.2224039 | 1610.0169 | null | null |
A Methodology for Customizing Clinical Tests for Esophageal Cancer based
on Patient Preferences | cs.LG stat.ML | Tests for Esophageal cancer can be expensive, uncomfortable and can have side
effects. For many patients, we can predict non-existence of disease with 100%
certainty, just using demographics, lifestyle, and medical history information.
Our objective is to devise a general methodology for customizing tests using
user preferences so that expensive or uncomfortable tests can be avoided. We
propose to use classifiers trained from electronic health records (EHR) for
selection of tests. The key idea is to design classifiers with 100% false
normal rates, possibly at the cost higher false abnormals. We compare Naive
Bayes classification (NB), Random Forests (RF), Support Vector Machines (SVM)
and Logistic Regression (LR), and find kernel Logistic regression to be most
suitable for the task. We propose an algorithm for finding the best probability
threshold for kernel LR, based on test set accuracy. Using the proposed
algorithm, we describe schemes for selecting tests, which appear as features in
the automatic classification algorithm, using preferences on costs and
discomfort of the users. We test our methodology with EHRs collected for more
than 3000 patients, as a part of project carried out by a reputed hospital in
Mumbai, India. Kernel SVM and kernel LR with a polynomial kernel of degree 3,
yields an accuracy of 99.8% and sensitivity 100%, without the MP features, i.e.
using only clinical tests. We demonstrate our test selection algorithm using
two case studies, one using cost of clinical tests, and other using
"discomfort" values for clinical tests. We compute the test sets corresponding
to the lowest false abnormals for each criterion described above, using
exhaustive enumeration of 15 clinical tests. The sets turn out to different,
substantiating our claim that one can customize test sets based on user
preferences.
| Asis Roy, Sourangshu Bhattacharya, Kalyan Guin | null | 1610.01712 | null | null |
Combining Generative and Discriminative Neural Networks for Sleep Stages
Classification | cs.LG cs.NE | Sleep stages pattern provides important clues in diagnosing the presence of
sleep disorder. By analyzing sleep stages pattern and extracting its features
from EEG, EOG, and EMG signals, we can classify sleep stages. This study
presents a novel classification model for predicting sleep stages with a high
accuracy. The main idea is to combine the generative capability of Deep Belief
Network (DBN) with a discriminative ability and sequence pattern recognizing
capability of Long Short-term Memory (LSTM). We use DBN that is treated as an
automatic higher level features generator. The input to DBN is 28 "handcrafted"
features as used in previous sleep stages studies. We compared our method with
other techniques which combined DBN with Hidden Markov Model (HMM).In this
study, we exploit the sequence or time series characteristics of sleep dataset.
To the best of our knowledge, most of the present sleep analysis from
polysomnogram relies only on single instanced label (nonsequence) for
classification. In this study, we used two datasets: an open data set that is
treated as a benchmark; the other dataset is our sleep stages dataset
(available for download) to verify the results further. Our experiments showed
that the combination of DBN with LSTM gives better overall accuracy 98.75\%
(Fscore=0.9875) for benchmark dataset and 98.94\% (Fscore=0.9894) for MKG
dataset. This result is better than the state of the art of sleep stages
classification that was 91.31\%.
| Endang Purnama Giri, Mohamad Ivan Fanany, Aniati Murni Arymurthy | null | 1610.01741 | null | null |
Ischemic Stroke Identification Based on EEG and EOG using 1D
Convolutional Neural Network and Batch Normalization | cs.LG cs.NE | In 2015, stroke was the number one cause of death in Indonesia. The majority
type of stroke is ischemic. The standard tool for diagnosing stroke is CT-Scan.
For developing countries like Indonesia, the availability of CT-Scan is very
limited and still relatively expensive. Because of the availability, another
device that potential to diagnose stroke in Indonesia is EEG. Ischemic stroke
occurs because of obstruction that can make the cerebral blood flow (CBF) on a
person with stroke has become lower than CBF on a normal person (control) so
that the EEG signal have a deceleration. On this study, we perform the ability
of 1D Convolutional Neural Network (1DCNN) to construct classification model
that can distinguish the EEG and EOG stroke data from EEG and EOG control data.
To accelerate training process our model we use Batch Normalization. Involving
62 person data object and from leave one out the scenario with five times
repetition of measurement we obtain the average of accuracy 0.86 (F-Score
0.861) only at 200 epoch. This result is better than all over shallow and
popular classifiers as the comparator (the best result of accuracy 0.69 and
F-Score 0.72 ). The feature used in our study were only 24 handcrafted feature
with simple feature extraction process.
| Endang Purnama Giri, Mohamad Ivan Fanany, Aniati Murni Arymurthy | null | 1610.01757 | null | null |
A New Data Representation Based on Training Data Characteristics to
Extract Drug Named-Entity in Medical Text | cs.CL cs.AI cs.LG cs.NE | One essential task in information extraction from the medical corpus is drug
name recognition. Compared with text sources come from other domains, the
medical text is special and has unique characteristics. In addition, the
medical text mining poses more challenges, e.g., more unstructured text, the
fast growing of new terms addition, a wide range of name variation for the same
drug. The mining is even more challenging due to the lack of labeled dataset
sources and external knowledge, as well as multiple token representations for a
single drug name that is more common in the real application setting. Although
many approaches have been proposed to overwhelm the task, some problems
remained with poor F-score performance (less than 0.75). This paper presents a
new treatment in data representation techniques to overcome some of those
challenges. We propose three data representation techniques based on the
characteristics of word distribution and word similarities as a result of word
embedding training. The first technique is evaluated with the standard NN
model, i.e., MLP (Multi-Layer Perceptrons). The second technique involves two
deep network classifiers, i.e., DBN (Deep Belief Networks), and SAE (Stacked
Denoising Encoders). The third technique represents the sentence as a sequence
that is evaluated with a recurrent NN model, i.e., LSTM (Long Short Term
Memory). In extracting the drug name entities, the third technique gives the
best F-score performance compared to the state of the art, with its average
F-score being 0.8645.
| Sadikin Mujiono, Mohamad Ivan Fanany, Chan Basaruddin | null | 1610.01891 | null | null |
Adaptive Online Sequential ELM for Concept Drift Tackling | cs.AI cs.LG cs.NE | A machine learning method needs to adapt to over time changes in the
environment. Such changes are known as concept drift. In this paper, we propose
concept drift tackling method as an enhancement of Online Sequential Extreme
Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by
adding adaptive capability for classification and regression problem. The
scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme
that works well to handle real drift, virtual drift, and hybrid drift. The
AOS-ELM also works well for sudden drift and recurrent context change type. The
scheme is a simple unified method implemented in simple lines of code. We
evaluated AOS-ELM on regression and classification problem by using concept
drift public data set (SEA and STAGGER) and other public data sets such as
MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value
compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice
does not need hidden nodes increase, we address some issues related to the
increasing of the hidden nodes such as error condition and rank values. We
propose taking the rank of the pseudoinverse matrix as an indicator parameter
to detect underfitting condition.
| Arif Budiman, Mohamad Ivan Fanany, Chan Basaruddin | 10.1155/2016/8091267 | 1610.01922 | null | null |
Using Non-invertible Data Transformations to Build Adversarial-Robust
Neural Networks | cs.LG | Deep neural networks have proven to be quite effective in a wide variety of
machine learning tasks, ranging from improved speech recognition systems to
advancing the development of autonomous vehicles. However, despite their
superior performance in many applications, these models have been recently
shown to be susceptible to a particular type of attack possible through the
generation of particular synthetic examples referred to as adversarial samples.
These samples are constructed by manipulating real examples from the training
data distribution in order to "fool" the original neural model, resulting in
misclassification (with high confidence) of previously correctly classified
samples. Addressing this weakness is of utmost importance if deep neural
architectures are to be applied to critical applications, such as those in the
domain of cybersecurity. In this paper, we present an analysis of this
fundamental flaw lurking in all neural architectures to uncover limitations of
previously proposed defense mechanisms. More importantly, we present a unifying
framework for protecting deep neural models using a non-invertible data
transformation--developing two adversary-resilient architectures utilizing both
linear and nonlinear dimensionality reduction. Empirical results indicate that
our framework provides better robustness compared to state-of-art solutions
while having negligible degradation in accuracy.
| Qinglong Wang, Wenbo Guo, Alexander G. Ororbia II, Xinyu Xing, Lin
Lin, C. Lee Giles, Xue Liu, Peng Liu, Gang Xiong | null | 1610.01934 | null | null |
Sequence-based Sleep Stage Classification using Conditional Neural
Fields | cs.NE cs.LG | Sleep signals from a polysomnographic database are sequences in nature.
Commonly employed analysis and classification methods, however, ignored this
fact and treated the sleep signals as non-sequence data. Treating the sleep
signals as sequences, this paper compared two powerful unsupervised feature
extractors and three sequence-based classifiers regarding accuracy and
computational (training and testing) time after 10-folds cross-validation. The
compared feature extractors are Deep Belief Networks (DBN) and Fuzzy C-Means
(FCM) clustering. Whereas the compared sequence-based classifiers are Hidden
Markov Models (HMM), Conditional Random Fields (CRF) and its variants, i.e.,
Hidden-state CRF (HCRF) and Latent-Dynamic CRF (LDCRF); and Conditional Neural
Fields (CNF) and its variant (LDCNF). In this study, we use two datasets. The
first dataset is an open (public) polysomnographic dataset downloadable from
the Internet, while the second dataset is our polysomnographic dataset (also
available for download). For the first dataset, the combination of FCM and CNF
gives the highest accuracy (96.75\%) with relatively short training time (0.33
hours). For the second dataset, the combination of DBN and CRF gives the
accuracy of 99.96\% but with 1.02 hours training time, whereas the combination
of DBN and CNF gives slightly less accuracy (99.69\%) but also less computation
time (0.89 hours).
| Intan Nurma Yulita, Mohamad Ivan Fanany, Aniati Murni Arymurthy | null | 1610.01935 | null | null |
Connecting Generative Adversarial Networks and Actor-Critic Methods | cs.LG stat.ML | Both generative adversarial networks (GAN) in unsupervised learning and
actor-critic methods in reinforcement learning (RL) have gained a reputation
for being difficult to optimize. Practitioners in both fields have amassed a
large number of strategies to mitigate these instabilities and improve
training. Here we show that GANs can be viewed as actor-critic methods in an
environment where the actor cannot affect the reward. We review the strategies
for stabilizing training for each class of models, both those that generalize
between the two and those that are particular to that model. We also review a
number of extensions to GANs and RL algorithms with even more complicated
information flow. We hope that by highlighting this formal connection we will
encourage both GAN and RL communities to develop general, scalable, and stable
algorithms for multilevel optimization with deep networks, and to draw
inspiration across communities.
| David Pfau and Oriol Vinyals | null | 1610.01945 | null | null |
Efficient L1-Norm Principal-Component Analysis via Bit Flipping | cs.DS cs.LG stat.ML | It was shown recently that the $K$ L1-norm principal components (L1-PCs) of a
real-valued data matrix $\mathbf X \in \mathbb R^{D \times N}$ ($N$ data
samples of $D$ dimensions) can be exactly calculated with cost
$\mathcal{O}(2^{NK})$ or, when advantageous, $\mathcal{O}(N^{dK - K + 1})$
where $d=\mathrm{rank}(\mathbf X)$, $K<d$ [1],[2]. In applications where
$\mathbf X$ is large (e.g., "big" data of large $N$ and/or "heavy" data of
large $d$), these costs are prohibitive. In this work, we present a novel
suboptimal algorithm for the calculation of the $K < d$ L1-PCs of $\mathbf X$
of cost $\mathcal O(ND \mathrm{min} \{ N,D\} + N^2(K^4 + dK^2) + dNK^3)$, which
is comparable to that of standard (L2-norm) PC analysis. Our theoretical and
experimental studies show that the proposed algorithm calculates the exact
optimal L1-PCs with high frequency and achieves higher value in the L1-PC
optimization metric than any known alternative algorithm of comparable
computational cost. The superiority of the calculated L1-PCs over standard
L2-PCs (singular vectors) in characterizing potentially faulty
data/measurements is demonstrated with experiments on data dimensionality
reduction and disease diagnosis from genomic data.
| Panos P. Markopoulos, Sandipan Kundu, Shubham Chamadia, Dimitris A.
Pados | 10.1109/TSP.2017.2708023 | 1610.01959 | null | null |
Polynomial-time Tensor Decompositions with Sum-of-Squares | cs.DS cs.LG | We give new algorithms based on the sum-of-squares method for tensor
decomposition. Our results improve the best known running times from
quasi-polynomial to polynomial for several problems, including decomposing
random overcomplete 3-tensors and learning overcomplete dictionaries with
constant relative sparsity. We also give the first robust analysis for
decomposing overcomplete 4-tensors in the smoothed analysis model. A key
ingredient of our analysis is to establish small spectral gaps in moment
matrices derived from solutions to sum-of-squares relaxations. To enable this
analysis we augment sum-of-squares relaxations with spectral analogs of maximum
entropy constraints.
| Tengyu Ma, Jonathan Shi, David Steurer | null | 1610.0198 | null | null |
Active exploration in parameterized reinforcement learning | cs.LG | Online model-free reinforcement learning (RL) methods with continuous actions
are playing a prominent role when dealing with real-world applications such as
Robotics. However, when confronted to non-stationary environments, these
methods crucially rely on an exploration-exploitation trade-off which is rarely
dynamically and automatically adjusted to changes in the environment. Here we
propose an active exploration algorithm for RL in structured (parameterized)
continuous action space. This framework deals with a set of discrete actions,
each of which is parameterized with continuous variables. Discrete exploration
is controlled through a Boltzmann softmax function with an inverse temperature
$\beta$ parameter. In parallel, a Gaussian exploration is applied to the
continuous action parameters. We apply a meta-learning algorithm based on the
comparison between variations of short-term and long-term reward running
averages to simultaneously tune $\beta$ and the width of the Gaussian
distribution from which continuous action parameters are drawn. When applied to
a simple virtual human-robot interaction task, we show that this algorithm
outperforms continuous parameterized RL both without active exploration and
with active exploration based on uncertainty variations measured by a
Kalman-Q-learning algorithm.
| Mehdi Khamassi, Costas Tzafestas | null | 1610.01986 | null | null |
Regularized Dynamic Boltzmann Machine with Delay Pruning for
Unsupervised Learning of Temporal Sequences | cs.LG cs.NE stat.ML | We introduce Delay Pruning, a simple yet powerful technique to regularize
dynamic Boltzmann machines (DyBM). The recently introduced DyBM provides a
particularly structured Boltzmann machine, as a generative model of a
multi-dimensional time-series. This Boltzmann machine can have infinitely many
layers of units but allows exact inference and learning based on its
biologically motivated structure. DyBM uses the idea of conduction delays in
the form of fixed length first-in first-out (FIFO) queues, with a neuron
connected to another via this FIFO queue, and spikes from a pre-synaptic neuron
travel along the queue to the post-synaptic neuron with a constant period of
delay. Here, we present Delay Pruning as a mechanism to prune the lengths of
the FIFO queues (making them zero) by setting some delay lengths to one with a
fixed probability, and finally selecting the best performing model with fixed
delays. The uniqueness of structure and a non-sampling based learning rule in
DyBM, make the application of previously proposed regularization techniques
like Dropout or DropConnect difficult, leading to poor generalization. First,
we evaluate the performance of Delay Pruning to let DyBM learn a
multidimensional temporal sequence generated by a Markov chain. Finally, we
show the effectiveness of delay pruning in learning high dimensional sequences
using the moving MNIST dataset, and compare it with Dropout and DropConnect
methods.
| Sakyasingha Dasgupta and Takayuki Yoshizumi and Takayuki Osogami | null | 1610.01989 | null | null |
Stochastic Games for Smart Grid Energy Management with Prospect
Prosumers | cs.GT cs.IT cs.LG cs.SY math.IT | In this paper, the problem of smart grid energy management under stochastic
dynamics is investigated. In the considered model, at the demand side, it is
assumed that customers can act as prosumers who own renewable energy sources
and can both produce and consume energy. Due to the coupling between the
prosumers' decisions and the stochastic nature of renewable energy, the
interaction among prosumers is formulated as a stochastic game, in which each
prosumer seeks to maximize its payoff, in terms of revenues, by controlling its
energy consumption and demand. In particular, the subjective behavior of
prosumers is explicitly reflected into their payoff functions using prospect
theory, a powerful framework that allows modeling real-life human choices. For
this prospect-based stochastic game, it is shown that there always exists a
stationary Nash equilibrium where the prosumers' trading policies in the
equilibrium are independent of the time and their histories of the play.
Moreover, a novel distributed algorithm with no information sharing among
prosumers is proposed and shown to converge to an $\epsilon$-Nash equilibrium.
On the other hand, at the supply side, the interaction between the utility
company and the prosumers is formulated as an online optimization problem in
which the utility company's goal is to learn its optimal energy allocation
rules. For this case, it is shown that such an optimization problem admits a
no-regret algorithm meaning that regardless of the actual outcome of the game
among the prosumers, the utility company can follow a strategy that mitigates
its allocation costs as if it knew the entire demand market a priori.
Simulation results show the convergence of the proposed algorithms to their
predicted outcomes and present new insights resulting from prospect theory that
contribute toward more efficient energy management in the smart grids.
| Seyed Rasoul Etesami, Walid Saad, Narayan Mandayam, and H. Vincent
Poor | null | 1610.02067 | null | null |
An efficient high-probability algorithm for Linear Bandits | cs.DS cs.LG | For the linear bandit problem, we extend the analysis of algorithm CombEXP
from [R. Combes, M. S. Talebi Mazraeh Shahi, A. Proutiere, and M. Lelarge.
Combinatorial bandits revisited. In C. Cortes, N. D. Lawrence, D. D. Lee, M.
Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing
Systems 28, pages 2116--2124. Curran Associates, Inc., 2015. URL
http://papers.nips.cc/paper/5831-combinatorial-bandits-revisited.pdf] to the
high-probability case against adaptive adversaries, allowing actions to come
from an arbitrary polytope. We prove a high-probability regret of
\(O(T^{2/3})\) for time horizon \(T\). While this bound is weaker than the
optimal \(O(\sqrt{T})\) bound achieved by GeometricHedge in [P. L. Bartlett, V.
Dani, T. Hayes, S. Kakade, A. Rakhlin, and A. Tewari. High-probability regret
bounds for bandit online linear optimization. In 21th Annual Conference on
Learning Theory (COLT 2008), July 2008.
http://eprints.qut.edu.au/45706/1/30-Bartlett.pdf], CombEXP is computationally
efficient, requiring only an efficient linear optimization oracle over the
convex hull of the actions.
| G\'abor Braun and Sebastian Pokutta | null | 1610.02072 | null | null |
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding | cs.LG cs.DS | Parallel implementations of stochastic gradient descent (SGD) have received
significant research attention, thanks to excellent scalability properties of
this algorithm, and to its efficiency in the context of training deep neural
networks. A fundamental barrier for parallelizing large-scale SGD is the fact
that the cost of communicating the gradient updates between nodes can be very
large. Consequently, lossy compression heuristics have been proposed, by which
nodes only communicate quantized gradients. Although effective in practice,
these heuristics do not always provably converge, and it is not clear whether
they are optimal.
In this paper, we propose Quantized SGD (QSGD), a family of compression
schemes which allow the compression of gradient updates at each node, while
guaranteeing convergence under standard assumptions. QSGD allows the user to
trade off compression and convergence time: it can communicate a sublinear
number of bits per iteration in the model dimension, and can achieve
asymptotically optimal communication cost. We complement our theoretical
results with empirical data, showing that QSGD can significantly reduce
communication cost, while being competitive with standard uncompressed
techniques on a variety of real tasks.
In particular, experiments show that gradient quantization applied to
training of deep neural networks for image classification and automated speech
recognition can lead to significant reductions in communication cost, and
end-to-end training time. For instance, on 16 GPUs, we are able to train a
ResNet-152 network on ImageNet 1.8x faster to full accuracy. Of note, we show
that there exist generic parameter settings under which all known network
architectures preserve or slightly improve their full accuracy when using
quantization.
| Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, Milan Vojnovic | null | 1610.02132 | null | null |
A Baseline for Detecting Misclassified and Out-of-Distribution Examples
in Neural Networks | cs.NE cs.CV cs.LG | We consider the two related problems of detecting if an example is
misclassified or out-of-distribution. We present a simple baseline that
utilizes probabilities from softmax distributions. Correctly classified
examples tend to have greater maximum softmax probabilities than erroneously
classified and out-of-distribution examples, allowing for their detection. We
assess performance by defining several tasks in computer vision, natural
language processing, and automatic speech recognition, showing the
effectiveness of this baseline across all. We then show the baseline can
sometimes be surpassed, demonstrating the room for future research on these
underexplored detection tasks.
| Dan Hendrycks and Kevin Gimpel | null | 1610.02136 | null | null |
Stochastic Averaging for Constrained Optimization with Application to
Online Resource Allocation | math.OC cs.DC cs.LG stat.ML | Existing approaches to resource allocation for nowadays stochastic networks
are challenged to meet fast convergence and tolerable delay requirements. The
present paper leverages online learning advances to facilitate stochastic
resource allocation tasks. By recognizing the central role of Lagrange
multipliers, the underlying constrained optimization problem is formulated as a
machine learning task involving both training and operational modes, with the
goal of learning the sought multipliers in a fast and efficient manner. To this
end, an order-optimal offline learning approach is developed first for batch
training, and it is then generalized to the online setting with a procedure
termed learn-and-adapt. The novel resource allocation protocol permeates
benefits of stochastic approximation and statistical learning to obtain
low-complexity online updates with learning errors close to the statistical
accuracy limits, while still preserving adaptation performance, which in the
stochastic network optimization context guarantees queue stability. Analysis
and simulated tests demonstrate that the proposed data-driven approach improves
the delay and convergence performance of existing resource allocation schemes.
| Tianyi Chen, Aryan Mokhtari, Xin Wang, Alejandro Ribeiro, and Georgios
B. Giannakis | 10.1109/TSP.2017.2679690 | 1610.02143 | null | null |
Deep Reinforcement Learning From Raw Pixels in Doom | cs.LG cs.AI | Using current reinforcement learning methods, it has recently become possible
to learn to play unknown 3D games from raw pixels. In this work, we study the
challenges that arise in such complex environments, and summarize current
methods to approach these. We choose a task within the Doom game, that has not
been approached yet. The goal for the agent is to fight enemies in a 3D world
consisting of five rooms. We train the DQN and LSTM-A3C algorithms on this
task. Results show that both algorithms learn sensible policies, but fail to
achieve high scores given the amount of training. We provide insights into the
learned behavior, which can serve as a valuable starting point for further
research in the Doom domain.
| Danijar Hafner | null | 1610.02164 | null | null |
Temporal Ensembling for Semi-Supervised Learning | cs.NE cs.LG | In this paper, we present a simple and efficient method for training deep
neural networks in a semi-supervised setting where only a small portion of
training data is labeled. We introduce self-ensembling, where we form a
consensus prediction of the unknown labels using the outputs of the
network-in-training on different epochs, and most importantly, under different
regularization and input augmentation conditions. This ensemble prediction can
be expected to be a better predictor for the unknown labels than the output of
the network at the most recent training epoch, and can thus be used as a target
for training. Using our method, we set new records for two standard
semi-supervised learning benchmarks, reducing the (non-augmented)
classification error rate from 18.44% to 7.05% in SVHN with 500 labels and from
18.63% to 16.55% in CIFAR-10 with 4000 labels, and further to 5.12% and 12.16%
by enabling the standard augmentations. We additionally obtain a clear
improvement in CIFAR-100 classification accuracy by using random images from
the Tiny Images dataset as unlabeled extra inputs during training. Finally, we
demonstrate good tolerance to incorrect labels.
| Samuli Laine, Timo Aila | null | 1610.02242 | null | null |
Near-Data Processing for Differentiable Machine Learning Models | cs.AR cs.DC cs.LG | Near-data processing (NDP) refers to augmenting memory or storage with
processing power. Despite its potential for acceleration computing and reducing
power requirements, only limited progress has been made in popularizing NDP for
various reasons. Recently, two major changes have occurred that have ignited
renewed interest and caused a resurgence of NDP. The first is the success of
machine learning (ML), which often demands a great deal of computation for
training, requiring frequent transfers of big data. The second is the
popularity of NAND flash-based solid-state drives (SSDs) containing multicore
processors that can accommodate extra computation for data processing. In this
paper, we evaluate the potential of NDP for ML using a new SSD platform that
allows us to simulate instorage processing (ISP) of ML workloads. Our platform
(named ISP-ML) is a full-fledged simulator of a realistic multi-channel SSD
that can execute various ML algorithms using data stored in the SSD. To conduct
a thorough performance analysis and an in-depth comparison with alternative
techniques, we focus on a specific algorithm: stochastic gradient descent
(SGD), which is the de facto standard for training differentiable models such
as logistic regression and neural networks. We implement and compare three SGD
variants (synchronous, Downpour, and elastic averaging) using ISP-ML,
exploiting the multiple NAND channels to parallelize SGD. In addition, we
compare the performance of ISP and that of conventional in-host processing,
revealing the advantages of ISP. Based on the advantages and limitations
identified through our experiments, we further discuss directions for future
research on ISP for accelerating ML.
| Hyeokjun Choe, Seil Lee, Hyunha Nam, Seongsik Park, Seijoon Kim,
Eui-Young Chung, Sungroh Yoon | null | 1610.02273 | null | null |
Effective Classification of MicroRNA Precursors Using Combinatorial
Feature Mining and AdaBoost Algorithms | q-bio.GN cs.CE cs.LG | MicroRNAs (miRNAs) are non-coding RNAs with approximately 22 nucleotides (nt)
that are derived from precursor molecules. These precursor molecules or
pre-miRNAs often fold into stem-loop hairpin structures. However, a large
number of sequences with pre-miRNA-like hairpins can be found in genomes. It is
a challenge to distinguish the real pre-miRNAs from other hairpin sequences
with similar stem-loops (referred to as pseudo pre-miRNAs). Several
computational methods have been developed to tackle this challenge. In this
paper we propose a new method, called MirID, for identifying and classifying
microRNA precursors. We collect 74 features from the sequences and secondary
structures of pre-miRNAs; some of these features are taken from our previous
studies on non-coding RNA prediction while others were suggested in the
literature. We develop a combinatorial feature mining algorithm to identify
suitable feature sets. These feature sets are then used to train support vector
machines to obtain classification models, based on which classifier ensemble is
constructed. Finally we use an AdaBoost algorithm to further enhance the
accuracy of the classifier ensemble. Experimental results on a variety of
species demonstrate the good performance of the proposed method, and its
superiority over existing tools.
| Ling Zhong and Jason T. L. Wang | null | 1610.02281 | null | null |
Adaptive Convolutional ELM For Concept Drift Handling in Online Stream
Data | cs.AI cs.LG cs.NE | In big data era, the data continuously generated and its distribution may
keep changes overtime. These challenges in online stream of data are known as
concept drift. In this paper, we proposed the Adaptive Convolutional ELM method
(ACNNELM) as enhancement of Convolutional Neural Network (CNN) with a hybrid
Extreme Learning Machine (ELM) model plus adaptive capability. This method is
aimed for concept drift handling. We enhanced the CNN as convolutional
hiererchical features representation learner combined with Elastic ELM
(E$^2$LM) as a parallel supervised classifier. We propose an Adaptive OS-ELM
(AOS-ELM) for concept drift adaptability in classifier level (named ACNNELM-1)
and matrices concatenation ensembles for concept drift adaptability in ensemble
level (named ACNNELM-2). Our proposed Adaptive CNNELM is flexible that works
well in classifier level and ensemble level while most current methods only
proposed to work on either one of the levels.
We verified our method in extended MNIST data set and not MNIST data set. We
set the experiment to simulate virtual drift, real drift, and hybrid drift
event and we demonstrated how our CNNELM adaptability works. Our proposed
method works well and gives better accuracy, computation scalability, and
concept drifts adaptability compared to the regular ELM and CNN. Further
researches are still required to study the optimum parameters and to use more
varied image data set.
| Arif Budiman, Mohamad Ivan Fanany, Chan Basaruddin | null | 1610.02348 | null | null |
Distributed Averaging CNN-ELM for Big Data | cs.LG cs.CV cs.DC | Increasing the scalability of machine learning to handle big volume of data
is a challenging task. The scale up approach has some limitations. In this
paper, we proposed a scale out approach for CNN-ELM based on MapReduce on
classifier level. Map process is the CNN-ELM training for certain partition of
data. It involves many CNN-ELM models that can be trained asynchronously.
Reduce process is the averaging of all CNN-ELM weights as final training
result. This approach can save a lot of training time than single CNN-ELM
models trained alone. This approach also increased the scalability of machine
learning by combining scale out and scale up approaches. We verified our method
in extended MNIST data set and not-MNIST data set experiment. However, it has
some drawbacks by additional iteration learning parameters that need to be
carefully taken and training data distribution that need to be carefully
selected. Further researches to use more complex image data set are required.
| Arif Budiman, Mohamad Ivan Fanany, Chan Basaruddin | null | 1610.02373 | null | null |
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based
Localization | cs.CV cs.AI cs.LG | We propose a technique for producing "visual explanations" for decisions from
a large class of CNN-based models, making them more transparent. Our approach -
Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of
any target concept, flowing into the final convolutional layer to produce a
coarse localization map highlighting important regions in the image for
predicting the concept. Grad-CAM is applicable to a wide variety of CNN
model-families: (1) CNNs with fully-connected layers, (2) CNNs used for
structured outputs, (3) CNNs used in tasks with multimodal inputs or
reinforcement learning, without any architectural changes or re-training. We
combine Grad-CAM with fine-grained visualizations to create a high-resolution
class-discriminative visualization and apply it to off-the-shelf image
classification, captioning, and visual question answering (VQA) models,
including ResNet-based architectures. In the context of image classification
models, our visualizations (a) lend insights into their failure modes, (b) are
robust to adversarial images, (c) outperform previous methods on localization,
(d) are more faithful to the underlying model and (e) help achieve
generalization by identifying dataset bias. For captioning and VQA, we show
that even non-attention based models can localize inputs. We devise a way to
identify important neurons through Grad-CAM and combine it with neuron names to
provide textual explanations for model decisions. Finally, we design and
conduct human studies to measure if Grad-CAM helps users establish appropriate
trust in predictions from models and show that Grad-CAM helps untrained users
successfully discern a 'stronger' nodel from a 'weaker' one even when both make
identical predictions. Our code is available at
https://github.com/ramprs/grad-cam/, along with a demo at
http://gradcam.cloudcv.org, and a video at youtu.be/COjUB9Izk6E.
| Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna
Vedantam, Devi Parikh, Dhruv Batra | 10.1007/s11263-019-01228-7 | 1610.02391 | null | null |
Equality of Opportunity in Supervised Learning | cs.LG | We propose a criterion for discrimination against a specified sensitive
attribute in supervised learning, where the goal is to predict some target
based on available features. Assuming data about the predictor, target, and
membership in the protected group are available, we show how to optimally
adjust any learned predictor so as to remove discrimination according to our
definition. Our framework also improves incentives by shifting the cost of poor
classification from disadvantaged groups to the decision maker, who can respond
by improving the classification accuracy.
In line with other studies, our notion is oblivious: it depends only on the
joint statistics of the predictor, the target and the protected attribute, but
not on interpretation of individualfeatures. We study the inherent limits of
defining and identifying biases based on such oblivious measures, outlining
what can and cannot be inferred from different oblivious tests.
We illustrate our notion using a case study of FICO credit scores.
| Moritz Hardt and Eric Price and Nathan Srebro | null | 1610.02413 | null | null |
Automatic chemical design using a data-driven continuous representation
of molecules | cs.LG physics.chem-ph | We report a method to convert discrete representations of molecules to and
from a multidimensional continuous representation. This model allows us to
generate new molecules for efficient exploration and optimization through
open-ended spaces of chemical compounds. A deep neural network was trained on
hundreds of thousands of existing chemical structures to construct three
coupled functions: an encoder, a decoder and a predictor. The encoder converts
the discrete representation of a molecule into a real-valued continuous vector,
and the decoder converts these continuous vectors back to discrete molecular
representations. The predictor estimates chemical properties from the latent
continuous vector representation of the molecule. Continuous representations
allow us to automatically generate novel chemical structures by performing
simple operations in the latent space, such as decoding random vectors,
perturbing known chemical structures, or interpolating between molecules.
Continuous representations also allow the use of powerful gradient-based
optimization to efficiently guide the search for optimized functional
compounds. We demonstrate our method in the domain of drug-like molecules and
also in the set of molecules with fewer that nine heavy atoms.
| Rafael G\'omez-Bombarelli, Jennifer N. Wei, David Duvenaud, Jos\'e
Miguel Hern\'andez-Lobato, Benjam\'in S\'anchez-Lengeling, Dennis Sheberla,
Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams and Al\'an
Aspuru-Guzik | 10.1021/acscentsci.7b00572 | 1610.02415 | null | null |
Boost K-Means | cs.LG cs.CV cs.DB | Due to its simplicity and versatility, k-means remains popular since it was
proposed three decades ago. The performance of k-means has been enhanced from
different perspectives over the years. Unfortunately, a good trade-off between
quality and efficiency is hardly reached. In this paper, a novel k-means
variant is presented. Different from most of k-means variants, the clustering
procedure is driven by an explicit objective function, which is feasible for
the whole l2-space. The classic egg-chicken loop in k-means has been simplified
to a pure stochastic optimization procedure. The procedure of k-means becomes
simpler and converges to a considerably better local optima. The effectiveness
of this new variant has been studied extensively in different contexts, such as
document clustering, nearest neighbor search and image clustering. Superior
performance is observed across different scenarios.
| Wan-Lei Zhao, Cheng-Hao Deng, Chong-Wah Ngo | null | 1610.02483 | null | null |
SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs | cs.DC cs.IR cs.LG stat.ML | Latent Dirichlet Allocation (LDA) is a popular tool for analyzing discrete
count data such as text and images. Applications require LDA to handle both
large datasets and a large number of topics. Though distributed CPU systems
have been used, GPU-based systems have emerged as a promising alternative
because of the high computational power and memory bandwidth of GPUs. However,
existing GPU-based LDA systems cannot support a large number of topics because
they use algorithms on dense data structures whose time and space complexity is
linear to the number of topics. In this paper, we propose SaberLDA, a GPU-based
LDA system that implements a sparsity-aware algorithm to achieve sublinear time
complexity and scales well to learn a large number of topics. To address the
challenges introduced by sparsity, we propose a novel data layout, a new
warp-based sampling kernel, and an efficient sparse count matrix updating
algorithm that improves locality, makes efficient utilization of GPU warps, and
reduces memory consumption. Experiments show that SaberLDA can learn from
billions-token-scale data with up to 10,000 topics, which is almost two orders
of magnitude larger than that of the previous GPU-based systems. With a single
GPU card, SaberLDA is able to learn 10,000 topics from a dataset of billions of
tokens in a few hours, which is only achievable with clusters with tens of
machines before.
| Kaiwei Li, Jianfei Chen, Wenguang Chen, Jun Zhu | null | 1610.02496 | null | null |
Revisiting Multiple Instance Neural Networks | stat.ML cs.LG | Recently neural networks and multiple instance learning are both attractive
topics in Artificial Intelligence related research fields. Deep neural networks
have achieved great success in supervised learning problems, and multiple
instance learning as a typical weakly-supervised learning method is effective
for many applications in computer vision, biometrics, nature language
processing, etc. In this paper, we revisit the problem of solving multiple
instance learning problems using neural networks. Neural networks are appealing
for solving multiple instance learning problem. The multiple instance neural
networks perform multiple instance learning in an end-to-end way, which take a
bag with various number of instances as input and directly output bag label.
All of the parameters in a multiple instance network are able to be optimized
via back-propagation. We propose a new multiple instance neural network to
learn bag representations, which is different from the existing multiple
instance neural networks that focus on estimating instance label. In addition,
recent tricks developed in deep learning have been studied in multiple instance
networks, we find deep supervision is effective for boosting bag classification
accuracy. In the experiments, the proposed multiple instance networks achieve
state-of-the-art or competitive performance on several MIL benchmarks.
Moreover, it is extremely fast for both testing and training, e.g., it takes
only 0.0003 second to predict a bag and a few seconds to train on a MIL
datasets on a moderate CPU.
| Xinggang Wang, Yongluan Yan, Peng Tang, Xiang Bai, Wenyu Liu | 10.1016/j.patcog.2017.08.026 | 1610.02501 | null | null |
Federated Optimization: Distributed Machine Learning for On-Device
Intelligence | cs.LG | We introduce a new and increasingly relevant setting for distributed
optimization in machine learning, where the data defining the optimization are
unevenly distributed over an extremely large number of nodes. The goal is to
train a high-quality centralized model. We refer to this setting as Federated
Optimization. In this setting, communication efficiency is of the utmost
importance and minimizing the number of rounds of communication is the
principal goal.
A motivating example arises when we keep the training data locally on users'
mobile devices instead of logging it to a data center for training. In
federated optimziation, the devices are used as compute nodes performing
computation on their local data in order to update a global model. We suppose
that we have extremely large number of devices in the network --- as many as
the number of users of a given service, each of which has only a tiny fraction
of the total data available. In particular, we expect the number of data points
available locally to be much smaller than the number of devices. Additionally,
since different users generate data with different patterns, it is reasonable
to assume that no device has a representative sample of the overall
distribution.
We show that existing algorithms are not suitable for this setting, and
propose a new algorithm which shows encouraging experimental results for sparse
convex problems. This work also sets a path for future research needed in the
context of \federated optimization.
| Jakub Kone\v{c}n\'y, H. Brendan McMahan, Daniel Ramage, Peter
Richt\'arik | null | 1610.02527 | null | null |
A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation | cs.LG | We describe recurrent neural networks (RNNs), which have attracted great
attention on sequential tasks, such as handwriting recognition, speech
recognition and image to text. However, compared to general feedforward neural
networks, RNNs have feedback loops, which makes it a little hard to understand
the backpropagation step. Thus, we focus on basics, especially the error
backpropagation to compute gradients with respect to model parameters. Further,
we go into detail on how error backpropagation algorithm is applied on long
short-term memory (LSTM) by unfolding the memory unit.
| Gang Chen | null | 1610.02583 | null | null |
A new selection strategy for selective cluster ensemble based on
Diversity and Independency | stat.ML cs.AI cs.LG | This research introduces a new strategy in cluster ensemble selection by
using Independency and Diversity metrics. In recent years, Diversity and
Quality, which are two metrics in evaluation procedure, have been used for
selecting basic clustering results in the cluster ensemble selection. Although
quality can improve the final results in cluster ensemble, it cannot control
the procedures of generating basic results, which causes a gap in prediction of
the generated basic results' accuracy. Instead of quality, this paper
introduces Independency as a supplementary method to be used in conjunction
with Diversity. Therefore, this paper uses a heuristic metric, which is based
on the procedure of converting code to graph in Software Testing, in order to
calculate the Independency of two basic clustering algorithms. Moreover, a new
modeling language, which we called as "Clustering Algorithms Independency
Language" (CAIL), is introduced in order to generate graphs which depict
Independency of algorithms. Also, Uniformity, which is a new similarity metric,
has been introduced for evaluating the diversity of basic results. As a
credential, our experimental results on varied different standard data sets
show that the proposed framework improves the accuracy of final results
dramatically in comparison with other cluster ensemble methods.
| Muhammad Yousefnezhad, Ali Reihanian, Daoqiang Zhang, Behrouz
Minaei-Bidgoli | 10.1016/j.engappai.2016.10.005 | 1610.02649 | null | null |
Dataiku's Solution to SPHERE's Activity Recognition Challenge | stat.ML cs.LG | Our team won the second prize of the Safe Aging with SPHERE Challenge
organized by SPHERE, in conjunction with ECML-PKDD and Driven Data. The goal of
the competition was to recognize activities performed by humans, using sensor
data. This paper presents our solution. It is based on a rich pre-processing
and state of the art machine learning methods. From the raw train data, we
generate a synthetic train set with the same statistical characteristics as the
test set. We then perform feature engineering. The machine learning modeling
part is based on stacking weak learners through a grid searched XGBoost
algorithm. Finally, we use post-processing to smooth our predictions over time.
| Maxime Voisin, Leo Dreyfus-Schmidt, Pierre Gutierrez, Samuel Ronsin
and Marc Beillevaire | null | 1610.02757 | null | null |
Robust Bayesian Compressed sensing | stat.ML cs.LG | We consider the problem of robust compressed sensing whose objective is to
recover a high-dimensional sparse signal from compressed measurements corrupted
by outliers. A new sparse Bayesian learning method is developed for robust
compressed sensing. The basic idea of the proposed method is to identify and
remove the outliers from sparse signal recovery. To automatically identify the
outliers, we employ a set of binary indicator hyperparameters to indicate which
observations are outliers. These indicator hyperparameters are treated as
random variables and assigned a beta process prior such that their values are
confined to be binary. In addition, a Gaussian-inverse Gamma prior is imposed
on the sparse signal to promote sparsity. Based on this hierarchical prior
model, we develop a variational Bayesian method to estimate the indicator
hyperparameters as well as the sparse signal. Simulation results show that the
proposed method achieves a substantial performance improvement over existing
robust compressed sensing techniques.
| Qian Wan, Huiping Duan, Jun Fang, Hongbin Li | null | 1610.02807 | null | null |
Ranking academic institutions on potential paper acceptance in upcoming
conferences | cs.AI cs.DL cs.LG | The crux of the problem in KDD Cup 2016 involves developing data mining
techniques to rank research institutions based on publications. Rank importance
of research institutions are derived from predictions on the number of full
research papers that would potentially get accepted in upcoming top-tier
conferences, utilizing public information on the web. This paper describes our
solution to KDD Cup 2016. We used a two step approach in which we first
identify full research papers corresponding to each conference of interest and
then train two variants of exponential smoothing models to make predictions.
Our solution achieves an overall score of 0.7508, while the winning submission
scored 0.7656 in the overall results.
| Jobin Wilson, Ram Mohan, Muhammad Arif, Santanu Chaudhury, Brejesh
Lall | null | 1610.02828 | null | null |
Heuristic Approaches for Generating Local Process Models through Log
Projections | cs.LG cs.AI cs.DB | Local Process Model (LPM) discovery is focused on the mining of a set of
process models where each model describes the behavior represented in the event
log only partially, i.e. subsets of possible events are taken into account to
create so-called local process models. Often such smaller models provide
valuable insights into the behavior of the process, especially when no adequate
and comprehensible single overall process model exists that is able to describe
the traces of the process from start to end. The practical application of LPM
discovery is however hindered by computational issues in the case of logs with
many activities (problems may already occur when there are more than 17 unique
activities). In this paper, we explore three heuristics to discover subsets of
activities that lead to useful log projections with the goal of speeding up LPM
discovery considerably while still finding high-quality LPMs. We found that a
Markov clustering approach to create projection sets results in the largest
improvement of execution time, with discovered LPMs still being better than
with the use of randomly generated activity sets of the same size. Another
heuristic, based on log entropy, yields a more moderate speedup, but enables
the discovery of higher quality LPMs. The third heuristic, based on the
relative information gain, shows unstable performance: for some data sets the
speedup and LPM quality are higher than with the log entropy based method,
while for other data sets there is no speedup at all.
| Niek Tax, Natalia Sidorova, Wil M. P. van der Aalst, Reinder Haakma | 10.1109/SSCI.2016.7849948 | 1610.02876 | null | null |
Personalizing a Dialogue System with Transfer Reinforcement Learning | cs.AI cs.CL cs.LG | It is difficult to train a personalized task-oriented dialogue system because
the data collected from each individual is often insufficient. Personalized
dialogue systems trained on a small dataset can overfit and make it difficult
to adapt to different user needs. One way to solve this problem is to consider
a collection of multiple users' data as a source domain and an individual
user's data as a target domain, and to perform a transfer learning from the
source to the target domain. By following this idea, we propose
"PETAL"(PErsonalized Task-oriented diALogue), a transfer-learning framework
based on POMDP to learn a personalized dialogue system. The system first learns
common dialogue knowledge from the source domain and then adapts this knowledge
to the target user. This framework can avoid the negative transfer problem by
considering differences between source and target users. The policy in the
personalized POMDP can learn to choose different actions appropriately for
different users. Experimental results on a real-world coffee-shopping data and
simulation data show that our personalized dialogue system can choose different
optimal actions for different users, and thus effectively improve the dialogue
quality under the personalized setting.
| Kaixiang Mo, Shuangyin Li, Yu Zhang, Jiajun Li, Qiang Yang | null | 1610.02891 | null | null |
A General Framework for Content-enhanced Network Representation Learning | cs.SI cs.CL cs.LG | This paper investigates the problem of network embedding, which aims at
learning low-dimensional vector representation of nodes in networks. Most
existing network embedding methods rely solely on the network structure, i.e.,
the linkage relationships between nodes, but ignore the rich content
information associated with it, which is common in real world networks and
beneficial to describing the characteristics of a node. In this paper, we
propose content-enhanced network embedding (CENE), which is capable of jointly
leveraging the network structure and the content information. Our approach
integrates text modeling and structure modeling in a general framework by
treating the content information as a special kind of node. Experiments on
several real world net- works with application to node classification show that
our models outperform all existing network embedding methods, demonstrating the
merits of content information and joint learning.
| Xiaofei Sun, Jiang Guo, Xiao Ding and Ting Liu | null | 1610.02906 | null | null |
Distributed Convex Optimization with Many Convex Constraints | math.OC cs.LG cs.NA stat.ML | We address the problem of solving convex optimization problems with many
convex constraints in a distributed setting. Our approach is based on an
extension of the alternating direction method of multipliers (ADMM) that
recently gained a lot of attention in the Big Data context. Although it has
been invented decades ago, ADMM so far can be applied only to unconstrained
problems and problems with linear equality or inequality constraints. Our
extension can handle arbitrary inequality constraints directly. It combines the
ability of ADMM to solve convex optimization problems in a distributed setting
with the ability of the Augmented Lagrangian method to solve constrained
optimization problems, and as we show, it inherits the convergence guarantees
of ADMM and the Augmented Lagrangian method.
| Joachim Giesen and S\"oren Laue | null | 1610.02967 | null | null |
Linear Hypothesis Testing in Dense High-Dimensional Linear Models | stat.ME cs.LG math.ST stat.ML stat.TH | We propose a methodology for testing linear hypothesis in high-dimensional
linear models. The proposed test does not impose any restriction on the size of
the model, i.e. model sparsity or the loading vector representing the
hypothesis. Providing asymptotically valid methods for testing general linear
functions of the regression parameters in high-dimensions is extremely
challenging -- especially without making restrictive or unverifiable
assumptions on the number of non-zero elements. We propose to test the moment
conditions related to the newly designed restructured regression, where the
inputs are transformed and augmented features. These new features incorporate
the structure of the null hypothesis directly. The test statistics are
constructed in such a way that lack of sparsity in the original model parameter
does not present a problem for the theoretical justification of our procedures.
We establish asymptotically exact control on Type I error without imposing any
sparsity assumptions on model parameter or the vector representing the linear
hypothesis. Our method is also shown to achieve certain optimality in detecting
deviations from the null hypothesis. We demonstrate the favorable finite-sample
performance of the proposed methods, via a number of numerical and a real data
example.
| Yinchu Zhu and Jelena Bradic | 10.1080/01621459.2017.1356319 | 1610.02987 | null | null |
Extrapolation and learning equations | cs.LG cs.AI | In classical machine learning, regression is treated as a black box process
of identifying a suitable function from a hypothesis set without attempting to
gain insight into the mechanism connecting inputs and outputs. In the natural
sciences, however, finding an interpretable function for a phenomenon is the
prime goal as it allows to understand and generalize results. This paper
proposes a novel type of function learning network, called equation learner
(EQL), that can learn analytical expressions and is able to extrapolate to
unseen domains. It is implemented as an end-to-end differentiable feed-forward
network and allows for efficient gradient based training. Due to sparsity
regularization concise interpretable expressions can be obtained. Often the
true underlying source expression is identified.
| Georg Martius and Christoph H. Lampert | null | 1610.02995 | null | null |
Fully Character-Level Neural Machine Translation without Explicit
Segmentation | cs.CL cs.LG | Most existing machine translation systems operate at the level of words,
relying on explicit segmentation to extract tokens. We introduce a neural
machine translation (NMT) model that maps a source character sequence to a
target character sequence without any segmentation. We employ a character-level
convolutional network with max-pooling at the encoder to reduce the length of
source representation, allowing the model to be trained at a speed comparable
to subword-level models while capturing local regularities. Our
character-to-character model outperforms a recently proposed baseline with a
subword-level encoder on WMT'15 DE-EN and CS-EN, and gives comparable
performance on FI-EN and RU-EN. We then demonstrate that it is possible to
share a single character-level encoder across multiple languages by training a
model on a many-to-one translation task. In this multilingual setting, the
character-level encoder significantly outperforms the subword-level encoder on
all the language pairs. We observe that on CS-EN, FI-EN and RU-EN, the quality
of the multilingual character-level translation even surpasses the models
specifically trained on that language pair alone, both in terms of BLEU score
and human judgment.
| Jason Lee, Kyunghyun Cho and Thomas Hofmann | null | 1610.03017 | null | null |
Latent Sequence Decompositions | stat.ML cs.CL cs.LG | We present the Latent Sequence Decompositions (LSD) framework. LSD decomposes
sequences with variable lengthed output units as a function of both the input
sequence and the output sequence. We present a training algorithm which samples
valid extensions and an approximate decoding algorithm. We experiment with the
Wall Street Journal speech recognition task. Our LSD model achieves 12.9% WER
compared to a character baseline of 14.8% WER. When combined with a
convolutional network on the encoder, we achieve 9.6% WER.
| William Chan, Yu Zhang, Quoc Le, Navdeep Jaitly | null | 1610.03035 | null | null |
Sketching Meets Random Projection in the Dual: A Provable Recovery
Algorithm for Big and High-dimensional Data | cs.LG math.OC stat.ML | Sketching techniques have become popular for scaling up machine learning
algorithms by reducing the sample size or dimensionality of massive data sets,
while still maintaining the statistical power of big data. In this paper, we
study sketching from an optimization point of view: we first show that the
iterative Hessian sketch is an optimization process with preconditioning, and
develop accelerated iterative Hessian sketch via the searching the conjugate
direction; we then establish primal-dual connections between the Hessian sketch
and dual random projection, and apply the preconditioned conjugate gradient
approach on the dual problem, which leads to the accelerated iterative dual
random projection methods. Finally to tackle the challenges from both large
sample size and high-dimensionality, we propose the primal-dual sketch, which
iteratively sketches the primal and dual formulations. We show that using a
logarithmic number of calls to solvers of small scale problem, primal-dual
sketch is able to recover the optimum of the original problem up to arbitrary
precision. The proposed algorithms are validated via extensive experiments on
synthetic and real data sets which complements our theoretical results.
| Jialei Wang, Jason D. Lee, Mehrdad Mahdavi, Mladen Kolar, Nathan
Srebro | null | 1610.03045 | null | null |
Dynamic Metric Learning from Pairwise Comparisons | cs.LG | Recent work in distance metric learning has focused on learning
transformations of data that best align with specified pairwise similarity and
dissimilarity constraints, often supplied by a human observer. The learned
transformations lead to improved retrieval, classification, and clustering
algorithms due to the better adapted distance or similarity measures. Here, we
address the problem of learning these transformations when the underlying
constraint generation process is nonstationary. This nonstationarity can be due
to changes in either the ground-truth clustering used to generate constraints
or changes in the feature subspaces in which the class structure is apparent.
We propose Online Convex Ensemble StrongLy Adaptive Dynamic Learning (OCELAD),
a general adaptive, online approach for learning and tracking optimal metrics
as they change over time that is highly robust to a variety of nonstationary
behaviors in the changing metric. We apply the OCELAD framework to an ensemble
of online learners. Specifically, we create a retro-initialized composite
objective mirror descent (COMID) ensemble (RICE) consisting of a set of
parallel COMID learners with different learning rates, demonstrate RICE-OCELAD
on both real and synthetic data sets and show significant performance
improvements relative to previously proposed batch and online distance metric
learning algorithms.
| Kristjan Greenewald and Stephen Kelley and Alfred Hero III | null | 1610.0309 | null | null |
Supervised Term Weighting Metrics for Sentiment Analysis in Short Text | cs.CL cs.IR cs.LG | Term weighting metrics assign weights to terms in order to discriminate the
important terms from the less crucial ones. Due to this characteristic, these
metrics have attracted growing attention in text classification and recently in
sentiment analysis. Using the weights given by such metrics could lead to more
accurate document representation which may improve the performance of the
classification. While previous studies have focused on proposing or comparing
different weighting metrics at two-classes document level sentiment analysis,
this study propose to analyse the results given by each metric in order to find
out the characteristics of good and bad weighting metrics. Therefore we present
an empirical study of fifteen global supervised weighting metrics with four
local weighting metrics adopted from information retrieval, we also give an
analysis to understand the behavior of each metric by observing and analysing
how each metric distributes the terms and deduce some characteristics which may
distinguish the good and bad metrics. The evaluation has been done using
Support Vector Machine on three different datasets: Twitter, restaurant and
laptop reviews.
| Hussam Hamdan, Patrice Bellot, Frederic Bechet | null | 1610.03106 | null | null |
Context-Aware Online Learning for Course Recommendation of MOOC Big Data | cs.LG cs.CY cs.IR | The Massive Open Online Course (MOOC) has expanded significantly in recent
years. With the widespread of MOOC, the opportunity to study the fascinating
courses for free has attracted numerous people of diverse educational
backgrounds all over the world. In the big data era, a key research topic for
MOOC is how to mine the needed courses in the massive course databases in cloud
for each individual student accurately and rapidly as the number of courses is
increasing fleetly. In this respect, the key challenge is how to realize
personalized course recommendation as well as to reduce the computing and
storage costs for the tremendous course data. In this paper, we propose a big
data-supported, context-aware online learning-based course recommender system
that could handle the dynamic and infinitely massive datasets, which recommends
courses by using personalized context information and historical statistics.
The context-awareness takes the personal preferences into consideration, making
the recommendation suitable for people with different backgrounds. Besides, the
algorithm achieves the sublinear regret performance, which means it can
gradually recommend the mostly preferred and matched courses to students. In
addition, our storage module is expanded to the distributed-connected storage
nodes, where the devised algorithm can handle massive course storage problems
from heterogeneous sources of course datasets. Comparing to existing
algorithms, our proposed algorithms achieve the linear time complexity and
space complexity. Experiment results verify the superiority of our algorithms
when comparing with existing ones in the MOOC big data setting.
| Yifan Hou, Pan Zhou, Ting Wang, Li Yu, Yuchong Hu, Dapeng Wu | null | 1610.03147 | null | null |
Navigational Instruction Generation as Inverse Reinforcement Learning
with Neural Machine Translation | cs.RO cs.AI cs.CL cs.LG | Modern robotics applications that involve human-robot interaction require
robots to be able to communicate with humans seamlessly and effectively.
Natural language provides a flexible and efficient medium through which robots
can exchange information with their human partners. Significant advancements
have been made in developing robots capable of interpreting free-form
instructions, but less attention has been devoted to endowing robots with the
ability to generate natural language. We propose a navigational guide model
that enables robots to generate natural language instructions that allow humans
to navigate a priori unknown environments. We first decide which information to
share with the user according to their preferences, using a policy trained from
human demonstrations via inverse reinforcement learning. We then "translate"
this information into a natural language instruction using a neural
sequence-to-sequence model that learns to generate free-form instructions from
natural language corpora. We evaluate our method on a benchmark route
instruction dataset and achieve a BLEU score of 72.18% when compared to
human-generated reference instructions. We additionally conduct navigation
experiments with human participants that demonstrate that our method generates
instructions that people follow as accurately and easily as those produced by
humans.
| Andrea F. Daniele and Mohit Bansal and Matthew R. Walter | null | 1610.03164 | null | null |
Error Asymmetry in Causal and Anticausal Regression | cs.AI cs.LG stat.ML | It is generally difficult to make any statements about the expected
prediction error in an univariate setting without further knowledge about how
the data were generated. Recent work showed that knowledge about the real
underlying causal structure of a data generation process has implications for
various machine learning settings. Assuming an additive noise and an
independence between data generating mechanism and its input, we draw a novel
connection between the intrinsic causal relationship of two variables and the
expected prediction error. We formulate the theorem that the expected error of
the true data generating function as prediction model is generally smaller when
the effect is predicted from its cause and, on the contrary, greater when the
cause is predicted from its effect. The theorem implies an asymmetry in the
error depending on the prediction direction. This is further corroborated with
empirical evaluations in artificial and real-world data sets.
| Patrick Bl\"obaum, Takashi Washio, Shohei Shimizu | 10.1007/s41237-017-0022-z | 1610.03263 | null | null |
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving | cs.AI cs.LG stat.ML | Autonomous driving is a multi-agent setting where the host vehicle must apply
sophisticated negotiation skills with other road users when overtaking, giving
way, merging, taking left and right turns and while pushing ahead in
unstructured urban roadways. Since there are many possible scenarios, manually
tackling all possible cases will likely yield a too simplistic policy.
Moreover, one must balance between unexpected behavior of other
drivers/pedestrians and at the same time not to be too defensive so that normal
traffic flow is maintained.
In this paper we apply deep reinforcement learning to the problem of forming
long term driving strategies. We note that there are two major challenges that
make autonomous driving different from other robotic tasks. First, is the
necessity for ensuring functional safety - something that machine learning has
difficulty with given that performance is optimized at the level of an
expectation over many instances. Second, the Markov Decision Process model
often used in robotics is problematic in our case because of unpredictable
behavior of other agents in this multi-agent scenario. We make three
contributions in our work. First, we show how policy gradient iterations can be
used without Markovian assumptions. Second, we decompose the problem into a
composition of a Policy for Desires (which is to be learned) and trajectory
planning with hard constraints (which is not learned). The goal of Desires is
to enable comfort of driving, while hard constraints guarantees the safety of
driving. Third, we introduce a hierarchical temporal abstraction we call an
"Option Graph" with a gating mechanism that significantly reduces the effective
horizon and thereby reducing the variance of the gradient estimation even
further.
| Shai Shalev-Shwartz and Shaked Shammah and Amnon Shashua | null | 1610.03295 | null | null |
A Greedy Approach for Budgeted Maximum Inner Product Search | cs.DS cs.LG | Maximum Inner Product Search (MIPS) is an important task in many machine
learning applications such as the prediction phase of a low-rank matrix
factorization model for a recommender system. There have been some works on how
to perform MIPS in sub-linear time recently. However, most of them do not have
the flexibility to control the trade-off between search efficient and search
quality. In this paper, we study the MIPS problem with a computational budget.
By carefully studying the problem structure of MIPS, we develop a novel
Greedy-MIPS algorithm, which can handle budgeted MIPS by design. While simple
and intuitive, Greedy-MIPS yields surprisingly superior performance compared to
state-of-the-art approaches. As a specific example, on a candidate set
containing half a million vectors of dimension 200, Greedy-MIPS runs 200x
faster than the naive approach while yielding search results with the top-5
precision greater than 75\%.
| Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei, and Inderjit S. Dhillon | null | 1610.03317 | null | null |
From phonemes to images: levels of representation in a recurrent neural
model of visually-grounded language learning | cs.CL cs.LG | We present a model of visually-grounded language learning based on stacked
gated recurrent neural networks which learns to predict visual features given
an image description in the form of a sequence of phonemes. The learning task
resembles that faced by human language learners who need to discover both
structure and meaning from noisy and ambiguous data across modalities. We show
that our model indeed learns to predict features of the visual context given
phonetically transcribed image descriptions, and show that it represents
linguistic information in a hierarchy of levels: lower layers in the stack are
comparatively more sensitive to form, whereas higher layers are more sensitive
to meaning.
| Lieke Gelderloos and Grzegorz Chrupa{\l}a | null | 1610.03342 | null | null |
Maximum entropy models capture melodic styles | stat.ML cs.LG | We introduce a Maximum Entropy model able to capture the statistics of
melodies in music. The model can be used to generate new melodies that emulate
the style of the musical corpus which was used to train it. Instead of using
the $n-$body interactions of $(n-1)-$order Markov models, traditionally used in
automatic music generation, we use a $k-$nearest neighbour model with pairwise
interactions only. In that way, we keep the number of parameters low and avoid
over-fitting problems typical of Markov models. We show that long-range musical
phrases don't need to be explicitly enforced using high-order Markov
interactions, but can instead emerge from multiple, competing, pairwise
interactions. We validate our Maximum Entropy model by contrasting how much the
generated sequences capture the style of the original corpus without
plagiarizing it. To this end we use a data-compression approach to discriminate
the levels of borrowing and innovation featured by the artificial sequences.
The results show that our modelling scheme outperforms both fixed-order and
variable-order Markov models. This shows that, despite being based only on
pairwise interactions, this Maximum Entropy scheme opens the possibility to
generate musically sensible alterations of the original phrases, providing a
way to generate innovation.
| Jason Sakellariou, Francesca Tria, Vittorio Loreto, Fran\c{c}ois
Pachet | null | 1610.03414 | null | null |
Deep Variational Canonical Correlation Analysis | cs.LG | We present deep variational canonical correlation analysis (VCCA), a deep
multi-view learning model that extends the latent variable model interpretation
of linear CCA to nonlinear observation models parameterized by deep neural
networks. We derive variational lower bounds of the data likelihood by
parameterizing the posterior probability of the latent variables from the view
that is available at test time. We also propose a variant of VCCA called
VCCA-private that can, in addition to the "common variables" underlying both
views, extract the "private variables" within each view, and disentangles the
shared and private information for multi-view data without hard supervision.
Experimental results on real-world datasets show that our methods are
competitive across domains.
| Weiran Wang, Xinchen Yan, Honglak Lee, Karen Livescu | null | 1610.03454 | null | null |
Learning in Implicit Generative Models | stat.ML cs.LG stat.CO | Generative adversarial networks (GANs) provide an algorithmic framework for
constructing generative models with several appealing properties: they do not
require a likelihood function to be specified, only a generating procedure;
they provide samples that are sharp and compelling; and they allow us to
harness our knowledge of building highly accurate neural network classifiers.
Here, we develop our understanding of GANs with the aim of forming a rich view
of this growing area of machine learning---to build connections to the diverse
set of statistical thinking on this topic, of which much can be gained by a
mutual exchange of ideas. We frame GANs within the wider landscape of
algorithms for learning in implicit generative models--models that only specify
a stochastic procedure with which to generate data--and relate these ideas to
modelling problems in related fields, such as econometrics and approximate
Bayesian computation. We develop likelihood-free inference methods and
highlight hypothesis testing as a principle for learning in implicit generative
models, using which we are able to derive the objective function used by GANs,
and many other related objectives. The testing viewpoint directs our focus to
the general problem of density ratio estimation. There are four approaches for
density ratio estimation, one of which is a solution using classifiers to
distinguish real from generated data. Other approaches such as divergence
minimisation and moment matching have also been explored in the GAN literature,
and we synthesise these views to form an understanding in terms of the
relationships between them and the wider literature, highlighting avenues for
future exploration and cross-pollination.
| Shakir Mohamed and Balaji Lakshminarayanan | null | 1610.03483 | null | null |
Transfer from Simulation to Real World through Learning Deep Inverse
Dynamics Model | cs.RO cs.AI cs.LG cs.SY | Developing control policies in simulation is often more practical and safer
than directly running experiments in the real world. This applies to policies
obtained from planning and optimization, and even more so to policies obtained
from reinforcement learning, which is often very data demanding. However, a
policy that succeeds in simulation often doesn't work when deployed on a real
robot. Nevertheless, often the overall gist of what the policy does in
simulation remains valid in the real world. In this paper we investigate such
settings, where the sequence of states traversed in simulation remains
reasonable for the real world, even if the details of the controls are not, as
could be the case when the key differences lie in detailed friction, contact,
mass and geometry properties. During execution, at each time step our approach
computes what the simulation-based control policy would do, but then, rather
than executing these controls on the real robot, our approach computes what the
simulation expects the resulting next state(s) will be, and then relies on a
learned deep inverse dynamics model to decide which real-world action is most
suitable to achieve those next states. Deep models are only as good as their
training data, and we also propose an approach for data collection to
(incrementally) learn the deep inverse dynamics model. Our experiments shows
our approach compares favorably with various baselines that have been developed
for dealing with simulation to real world model discrepancy, including output
error control and Gaussian dynamics adaptation.
| Paul Christiano, Zain Shah, Igor Mordatch, Jonas Schneider, Trevor
Blackwell, Joshua Tobin, Pieter Abbeel, and Wojciech Zaremba | null | 1610.03518 | null | null |
Minimax Filter: Learning to Preserve Privacy from Inference Attacks | cs.LG | Preserving privacy of continuous and/or high-dimensional data such as images,
videos and audios, can be challenging with syntactic anonymization methods
which are designed for discrete attributes. Differential privacy, which
provides a more formal definition of privacy, has shown more success in
sanitizing continuous data. However, both syntactic and differential privacy
are susceptible to inference attacks, i.e., an adversary can accurately infer
sensitive attributes from sanitized data. The paper proposes a novel
filter-based mechanism which preserves privacy of continuous and
high-dimensional attributes against inference attacks. Finding the optimal
utility-privacy tradeoff is formulated as a min-diff-max optimization problem.
The paper provides an ERM-like analysis of the generalization error and also a
practical algorithm to perform the optimization. In addition, the paper
proposes an extension that combines minimax filter and differentially-private
noisy mechanism. Advantages of the method over purely noisy mechanisms is
explained and demonstrated with examples. Experiments with several real-world
tasks including facial expression classification, speech emotion
classification, and activity classification from motion, show that the minimax
filter can simultaneously achieve similar or better target task accuracy and
lower inference accuracy, often significantly lower than previous methods.
| Jihun Hamm | null | 1610.03577 | null | null |
On statistical learning via the lens of compression | cs.LG cs.DM cs.LO math.CO math.LO | This work continues the study of the relationship between sample compression
schemes and statistical learning, which has been mostly investigated within the
framework of binary classification. The central theme of this work is
establishing equivalences between learnability and compressibility, and
utilizing these equivalences in the study of statistical learning theory.
We begin with the setting of multiclass categorization (zero/one loss). We
prove that in this case learnability is equivalent to compression of
logarithmic sample size, and that uniform convergence implies compression of
constant size.
We then consider Vapnik's general learning setting: we show that in order to
extend the compressibility-learnability equivalence to this case, it is
necessary to consider an approximate variant of compression.
Finally, we provide some applications of the compressibility-learnability
equivalences:
(i) Agnostic-case learnability and realizable-case learnability are
equivalent in multiclass categorization problems (in terms of sample
complexity).
(ii) This equivalence between agnostic-case learnability and realizable-case
learnability does not hold for general learning problems: There exists a
learning problem whose loss function takes just three values, under which
agnostic-case and realizable-case learnability are not equivalent.
(iii) Uniform convergence implies compression of constant size in multiclass
categorization problems. Part of the argument includes an analysis of the
uniform convergence rate in terms of the graph dimension, in which we improve
upon previous bounds.
(iv) A dichotomy for sample compression in multiclass categorization
problems: If a non-trivial compression exists then a compression of logarithmic
size exists.
(v) A compactness theorem for multiclass categorization problems.
| Ofir David and Shay Moran and Amir Yehudayoff | null | 1610.03592 | null | null |
Optimizing Memory Efficiency for Deep Convolutional Neural Networks on
GPUs | cs.DC cs.LG cs.NE cs.PF | Leveraging large data sets, deep Convolutional Neural Networks (CNNs) achieve
state-of-the-art recognition accuracy. Due to the substantial compute and
memory operations, however, they require significant execution time. The
massive parallel computing capability of GPUs make them as one of the ideal
platforms to accelerate CNNs and a number of GPU-based CNN libraries have been
developed. While existing works mainly focus on the computational efficiency of
CNNs, the memory efficiency of CNNs have been largely overlooked. Yet CNNs have
intricate data structures and their memory behavior can have significant impact
on the performance. In this work, we study the memory efficiency of various CNN
layers and reveal the performance implication from both data layouts and memory
access patterns. Experiments show the universal effect of our proposed
optimizations on both single layers and various networks, with up to 27.9x for
a single layer and up to 5.6x on the whole networks.
| Chao Li, Yi Yang, Min Feng, Srimat Chakradhar, Huiyang Zhou | null | 1610.03618 | null | null |
RetiNet: Automatic AMD identification in OCT volumetric data | cs.CV cs.LG cs.NE | Optical Coherence Tomography (OCT) provides a unique ability to image the eye
retina in 3D at micrometer resolution and gives ophthalmologist the ability to
visualize retinal diseases such as Age-Related Macular Degeneration (AMD).
While visual inspection of OCT volumes remains the main method for AMD
identification, doing so is time consuming as each cross-section within the
volume must be inspected individually by the clinician. In much the same way,
acquiring ground truth information for each cross-section is expensive and time
consuming. This fact heavily limits the ability to acquire large amounts of
ground truth, which subsequently impacts the performance of learning-based
methods geared at automatic pathology identification. To avoid this burden, we
propose a novel strategy for automatic analysis of OCT volumes where only
volume labels are needed. That is, we train a classifier in a semi-supervised
manner to conduct this task. Our approach uses a novel Convolutional Neural
Network (CNN) architecture, that only needs volume-level labels to be trained
to automatically asses whether an OCT volume is healthy or contains AMD. Our
architecture involves first learning a cross-section pathology classifier using
pseudo-labels that could be corrupted and then leverage these towards a more
accurate volume-level classification. We then show that our approach provides
excellent performances on a publicly available dataset and outperforms a number
of existing automatic techniques.
| Stefanos Apostolopoulos, Carlos Ciller, Sandro I. De Zanet, Sebastian
Wolf and Raphael Sznitman | null | 1610.03628 | null | null |
Deep Fruit Detection in Orchards | cs.RO cs.AI cs.CV cs.LG | An accurate and reliable image based fruit detection system is critical for
supporting higher level agriculture tasks such as yield mapping and robotic
harvesting. This paper presents the use of a state-of-the-art object detection
framework, Faster R-CNN, in the context of fruit detection in orchards,
including mangoes, almonds and apples. Ablation studies are presented to better
understand the practical deployment of the detection network, including how
much training data is required to capture variability in the dataset. Data
augmentation techniques are shown to yield significant performance gains,
resulting in a greater than two-fold reduction in the number of training images
required. In contrast, transferring knowledge between orchards contributed to
negligible performance gain over initialising the Deep Convolutional Neural
Network directly from ImageNet features. Finally, to operate over orchard data
containing between 100-1000 fruit per image, a tiling approach is introduced
for the Faster R-CNN framework. The study has resulted in the best yet
detection performance for these orchards relative to previous works, with an
F1-score of >0.9 achieved for apples and mangoes.
| Suchet Bargoti and James Underwood | null | 1610.03677 | null | null |
Optimistic Semi-supervised Least Squares Classification | stat.ML cs.LG | The goal of semi-supervised learning is to improve supervised classifiers by
using additional unlabeled training examples. In this work we study a simple
self-learning approach to semi-supervised learning applied to the least squares
classifier. We show that a soft-label and a hard-label variant of self-learning
can be derived by applying block coordinate descent to two related but slightly
different objective functions. The resulting soft-label approach is related to
an idea about dealing with missing data that dates back to the 1930s. We show
that the soft-label variant typically outperforms the hard-label variant on
benchmark datasets and partially explain this behaviour by studying the
relative difficulty of finding good local minima for the corresponding
objective functions.
| Jesse H. Krijthe and Marco Loog | null | 1610.03713 | null | null |
Exploring the Entire Regularization Path for the Asymmetric Cost Linear
Support Vector Machine | cs.LG stat.ML | We propose an algorithm for exploring the entire regularization path of
asymmetric-cost linear support vector machines. Empirical evidence suggests the
predictive power of support vector machines depends on the regularization
parameters of the training algorithms. The algorithms exploring the entire
regularization paths have been proposed for single-cost support vector machines
thereby providing the complete knowledge on the behavior of the trained model
over the hyperparameter space. Considering the problem in two-dimensional
hyperparameter space though enables our algorithm to maintain greater
flexibility in dealing with special cases and sheds light on problems
encountered by algorithms building the paths in one-dimensional spaces. We
demonstrate two-dimensional regularization paths for linear support vector
machines that we train on synthetic and real data.
| Daniel Wesierski | null | 1610.03738 | null | null |
Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble
of Autoencoders | cs.CV cs.AI cs.LG stat.ML | A fall is an abnormal activity that occurs rarely, so it is hard to collect
real data for falls. It is, therefore, difficult to use supervised learning
methods to automatically detect falls. Another challenge in using machine
learning methods to automatically detect falls is the choice of engineered
features. In this paper, we propose to use an ensemble of autoencoders to
extract features from different channels of wearable sensor data trained only
on normal activities. We show that the traditional approach of choosing a
threshold as the maximum of the reconstruction error on the training normal
data is not the right way to identify unseen falls. We propose two methods for
automatic tightening of reconstruction error from only the normal activities
for better identification of unseen falls. We present our results on two
activity recognition datasets and show the efficacy of our proposed method
against traditional autoencoder models and two standard one-class
classification methods.
| Shehroz S. Khan, Babak Taati | 10.1016/j.eswa.2017.06.011 | 1610.03761 | null | null |
Parallelizing Stochastic Gradient Descent for Least Squares Regression:
mini-batching, averaging, and model misspecification | stat.ML cs.DS cs.LG | This work characterizes the benefits of averaging schemes widely used in
conjunction with stochastic gradient descent (SGD). In particular, this work
provides a sharp analysis of: (1) mini-batching, a method of averaging many
samples of a stochastic gradient to both reduce the variance of the stochastic
gradient estimate and for parallelizing SGD and (2) tail-averaging, a method
involving averaging the final few iterates of SGD to decrease the variance in
SGD's final iterate. This work presents non-asymptotic excess risk bounds for
these schemes for the stochastic approximation problem of least squares
regression.
Furthermore, this work establishes a precise problem-dependent extent to
which mini-batch SGD yields provable near-linear parallelization speedups over
SGD with batch size one. This allows for understanding learning rate versus
batch size tradeoffs for the final iterate of an SGD method. These results are
then utilized in providing a highly parallelizable SGD method that obtains the
minimax risk with nearly the same number of serial updates as batch gradient
descent, improving significantly over existing SGD methods. A non-asymptotic
analysis of communication efficient parallelization schemes such as
model-averaging/parameter mixing methods is then provided.
Finally, this work sheds light on some fundamental differences in SGD's
behavior when dealing with agnostic noise in the (non-realizable) least squares
regression problem. In particular, the work shows that the stepsizes that
ensure minimax risk for the agnostic case must be a function of the noise
properties.
This paper builds on the operator view of analyzing SGD methods, introduced
by Defossez and Bach (2015), followed by developing a novel analysis in
bounding these operators to characterize the excess risk. These techniques are
of broader interest in analyzing computational aspects of stochastic
approximation.
| Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli,
Aaron Sidford | null | 1610.03774 | null | null |
Introduction to the "Industrial Benchmark" | cs.LG | A novel reinforcement learning benchmark, called Industrial Benchmark, is
introduced. The Industrial Benchmark aims at being be realistic in the sense,
that it includes a variety of aspects that we found to be vital in industrial
applications. It is not designed to be an approximation of any real system, but
to pose the same hardness and complexity.
| Daniel Hein, Alexander Hentschel, Volkmar Sterzing, Michel Tokic,
Steffen Udluft | null | 1610.03793 | null | null |
Generalization bound for kernel similarity learning | stat.ML cs.LG | Similarity learning has received a large amount of interest and is an
important tool for many scientific and industrial applications. In this
framework, we wish to infer the distance (similarity) between points with
respect to an arbitrary distance function $d$. Here, we formulate the problem
as a regression from a feature space $\mathcal{X}$ to an arbitrary vector space
$\mathcal{Y}$, where the Euclidean distance is proportional to $d$. We then
give Rademacher complexity bounds on the generalization error. We find that
with high probability, the complexity is bounded by the maximum of the radius
of $\mathcal{X}$ and the radius of $\mathcal{Y}$.
| Michael Rabadi | null | 1610.03899 | null | null |
Mapping Between fMRI Responses to Movies and their Natural Language
Annotations | q-bio.NC cs.CL cs.LG | Several research groups have shown how to correlate fMRI responses to the
meanings of presented stimuli. This paper presents new methods for doing so
when only a natural language annotation is available as the description of the
stimulus. We study fMRI data gathered from subjects watching an episode of BBCs
Sherlock [1], and learn bidirectional mappings between fMRI responses and
natural language representations. We show how to leverage data from multiple
subjects watching the same movie to improve the accuracy of the mappings,
allowing us to succeed at a scene classification task with 72% accuracy (random
guessing would give 4%) and at a scene ranking task with average rank in the
top 4% (random guessing would give 50%). The key ingredients are (a) the use of
the Shared Response Model (SRM) and its variant SRM-ICA [2, 3] to aggregate
fMRI data from multiple subjects, both of which are shown to be superior to
standard PCA in producing low-dimensional representations for the tasks in this
paper; (b) a sentence embedding technique adapted from the natural language
processing (NLP) literature [4] that produces semantic vector representation of
the annotations; (c) using previous timestep information in the featurization
of the predictor data.
| Kiran Vodrahalli, Po-Hsuan Chen, Yingyu Liang, Christopher Baldassano,
Janice Chen, Esther Yong, Christopher Honey, Uri Hasson, Peter Ramadge, Ken
Norman, Sanjeev Arora | null | 1610.03914 | null | null |
Compressing Neural Language Models by Sparse Word Representations | cs.CL cs.LG | Neural networks are among the state-of-the-art techniques for language
modeling. Existing neural language models typically map discrete words to
distributed, dense vector representations. After information processing of the
preceding context words by hidden layers, an output layer estimates the
probability of the next word. Such approaches are time- and memory-intensive
because of the large numbers of parameters for word embeddings and the output
layer. In this paper, we propose to compress neural language models by sparse
word representations. In the experiments, the number of parameters in our model
increases very slowly with the growth of the vocabulary size, which is almost
imperceptible. Moreover, our approach not only reduces the parameter space to a
large extent, but also improves the performance in terms of the perplexity
measure.
| Yunchuan Chen, Lili Mou, Yan Xu, Ge Li, Zhi Jin | null | 1610.0395 | null | null |
Dictionary Update for NMF-based Voice Conversion Using an
Encoder-Decoder Network | stat.ML cs.LG cs.SD | In this paper, we propose a dictionary update method for Nonnegative Matrix
Factorization (NMF) with high dimensional data in a spectral conversion (SC)
task. Voice conversion has been widely studied due to its potential
applications such as personalized speech synthesis and speech enhancement.
Exemplar-based NMF (ENMF) emerges as an effective and probably the simplest
choice among all techniques for SC, as long as a source-target parallel speech
corpus is given. ENMF-based SC systems usually need a large amount of bases
(exemplars) to ensure the quality of the converted speech. However, a small and
effective dictionary is desirable but hard to obtain via dictionary update, in
particular when high-dimensional features such as STRAIGHT spectra are used.
Therefore, we propose a dictionary update framework for NMF by means of an
encoder-decoder reformulation. Regarding NMF as an encoder-decoder network
makes it possible to exploit the whole parallel corpus more effectively and
efficiently when applied to SC. Our experiments demonstrate significant gains
of the proposed system with small dictionaries over conventional ENMF-based
systems with dictionaries of same or much larger size.
| Chin-Cheng Hsu, Hsin-Te Hwang, Yi-Chiao Wu, Yu Tsao, and Hsin-Min Wang | null | 1610.03988 | null | null |
Semi-Supervised Active Learning for Support Vector Machines: A Novel
Approach that Exploits Structure Information in Data | stat.ML cs.LG | In our today's information society more and more data emerges, e.g.~in social
networks, technical applications, or business applications. Companies try to
commercialize these data using data mining or machine learning methods. For
this purpose, the data are categorized or classified, but often at high
(monetary or temporal) costs. An effective approach to reduce these costs is to
apply any kind of active learning (AL) methods, as AL controls the training
process of a classifier by specific querying individual data points (samples),
which are then labeled (e.g., provided with class memberships) by a domain
expert. However, an analysis of current AL research shows that AL still has
some shortcomings. In particular, the structure information given by the
spatial pattern of the (un)labeled data in the input space of a classification
model (e.g.,~cluster information), is used in an insufficient way. In addition,
many existing AL techniques pay too little attention to their practical
applicability. To meet these challenges, this article presents several
techniques that together build a new approach for combining AL and
semi-supervised learning (SSL) for support vector machines (SVM) in
classification tasks. Structure information is captured by means of
probabilistic models that are iteratively improved at runtime when label
information becomes available. The probabilistic models are considered in a
selection strategy based on distance, density, diversity, and distribution (4DS
strategy) information for AL and in a kernel function (Responsibility Weighted
Mahalanobis kernel) for SVM. The approach fuses generative and discriminative
modeling techniques. With 20 benchmark data sets and with the MNIST data set it
is shown that our new solution yields significantly better results than
state-of-the-art methods.
| Tobias Reitmaier and Adrian Calma and Bernhard Sick | null | 1610.03995 | null | null |
Bank Card Usage Prediction Exploiting Geolocation Information | cs.LG cs.AI | We describe the solution of team ISMLL for the ECML-PKDD 2016 Discovery
Challenge on Bank Card Usage for both tasks. Our solution is based on three
pillars. Gradient boosted decision trees as a strong regression and
classification model, an intensive search for good hyperparameter
configurations and strong features that exploit geolocation information. This
approach achieved the best performance on the public leaderboard for the first
task and a decent fourth position for the second task.
| Martin Wistuba, Nghia Duong-Trung, Nicolas Schilling, Lars
Schmidt-Thieme | null | 1610.03996 | null | null |
Voice Conversion from Non-parallel Corpora Using Variational
Auto-encoder | stat.ML cs.LG cs.SD | We propose a flexible framework for spectral conversion (SC) that facilitates
training with unaligned corpora. Many SC frameworks require parallel corpora,
phonetic alignments, or explicit frame-wise correspondence for learning
conversion functions or for synthesizing a target spectrum with the aid of
alignments. However, these requirements gravely limit the scope of practical
applications of SC due to scarcity or even unavailability of parallel corpora.
We propose an SC framework based on variational auto-encoder which enables us
to exploit non-parallel corpora. The framework comprises an encoder that learns
speaker-independent phonetic representations and a decoder that learns to
reconstruct the designated speaker. It removes the requirement of parallel
corpora or phonetic alignments to train a spectral conversion system. We report
objective and subjective evaluations to validate our proposed method and
compare it to SC methods that have access to aligned corpora.
| Chin-Cheng Hsu, Hsin-Te Hwang, Yi-Chiao Wu, Yu Tsao and Hsin-Min Wang | null | 1610.04019 | null | null |
Predicting the dynamics of 2d objects with a deep residual network | cs.CV cs.LG | We investigate how a residual network can learn to predict the dynamics of
interacting shapes purely as an image-to-image regression task.
With a simple 2d physics simulator, we generate short sequences composed of
rectangles put in motion by applying a pulling force at a point picked at
random. The network is trained with a quadratic loss to predict the image of
the resulting configuration, given the image of the starting configuration and
an image indicating the point of grasping.
Experiments show that the network learns to predict accurately the resulting
image, which implies in particular that (1) it segments rectangles as distinct
components, (2) it infers which one contains the grasping point, (3) it models
properly the dynamic of a single rectangle, including the torque, (4) it
detects and handles collisions to some extent, and (5) it re-synthesizes
properly the entire scene with displaced rectangles.
| Fran\c{c}ois Fleuret | null | 1610.04032 | null | null |
Generalized Online Transfer Learning for Climate Control in Residential
Buildings | cs.SY cs.LG | This paper presents an online transfer learning framework for improving
temperature predictions in residential buildings. In transfer learning,
prediction models trained under a set of available data from a target domain
(e.g., house with limited data) can be improved through the use of data
generated from similar source domains (e.g., houses with rich data). Given also
the need for prediction models that can be trained online (e.g., as part of a
model-predictive-control implementation), this paper introduces the generalized
online transfer learning algorithm (GOTL). It employs a weighted combination of
the available predictors (i.e., the target and source predictors) and
guarantees convergence to the best weighted predictor. Furthermore, the use of
Transfer Component Analysis (TCA) allows for using more than a single source
domains, since it may facilitate the fit of a single model on more than one
source domains (houses). This allows GOTL to transfer knowledge from more than
one source domains. We further validate our results through experiments in
climate control for residential buildings and show that GOTL may lead to
non-negligible energy savings for given comfort levels.
| Thomas Grubinger, Georgios Chasparis, Thomas Natschlaeger | null | 1610.04042 | null | null |
Unorganized Malicious Attacks Detection | cs.IR cs.LG | Recommender system has attracted much attention during the past decade. Many
attack detection algorithms have been developed for better recommendations,
mostly focusing on shilling attacks, where an attack organizer produces a large
number of user profiles by the same strategy to promote or demote an item. This
work considers a different attack style: unorganized malicious attacks, where
attackers individually utilize a small number of user profiles to attack
different items without any organizer. This attack style occurs in many real
applications, yet relevant study remains open. We first formulate the
unorganized malicious attacks detection as a matrix completion problem, and
propose the Unorganized Malicious Attacks detection (UMA) approach, a proximal
alternating splitting augmented Lagrangian method. We verify, both
theoretically and empirically, the effectiveness of our proposed approach.
| Ming Pang and Wei Gao and Min Tao and Zhi-Hua Zhou | null | 1610.04086 | null | null |
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