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Analysing Soccer Games with Clustering and Conceptors | cs.LG | We present a new approach for identifying situations and behaviours, which we
call "moves", from soccer games in the 2D simulation league. Being able to
identify key situations and behaviours are useful capabilities for analysing
soccer matches, anticipating opponent behaviours to aid selection of
appropriate tactics, and also as a prerequisite for automatic learning of
behaviours and policies. To support a wide set of strategies, our goal is to
identify situations from data, in an unsupervised way without making use of
pre-defined soccer specific concepts such as "pass" or "dribble". The recurrent
neural networks we use in our approach act as a high-dimensional projection of
the recent history of a situation on the field. Similar situations, i.e., with
similar histories, are found by clustering of network states. The same networks
are also used to learn so-called conceptors, that are lower-dimensional
manifolds that describe trajectories through a high-dimensional state space
that enable situation-specific predictions from the same neural network. With
the proposed approach, we can segment games into sequences of situations that
are learnt in an unsupervised way, and learn conceptors that are useful for the
prediction of the near future of the respective situation.
| Olivia Michael and Oliver Obst and Falk Schmidsberger and Frieder
Stolzenburg | 10.1007/978-3-030-00308-1_10 | 1708.05821 | null | null |
A Data and Model-Parallel, Distributed and Scalable Framework for
Training of Deep Networks in Apache Spark | stat.ML cs.AI cs.CV cs.DC cs.LG | Training deep networks is expensive and time-consuming with the training
period increasing with data size and growth in model parameters. In this paper,
we provide a framework for distributed training of deep networks over a cluster
of CPUs in Apache Spark. The framework implements both Data Parallelism and
Model Parallelism making it suitable to use for deep networks which require
huge training data and model parameters which are too big to fit into the
memory of a single machine. It can be scaled easily over a cluster of cheap
commodity hardware to attain significant speedup and obtain better results
making it quite economical as compared to farm of GPUs and supercomputers. We
have proposed a new algorithm for training of deep networks for the case when
the network is partitioned across the machines (Model Parallelism) along with
detailed cost analysis and proof of convergence of the same. We have developed
implementations for Fully-Connected Feedforward Networks, Convolutional Neural
Networks, Recurrent Neural Networks and Long Short-Term Memory architectures.
We present the results of extensive simulations demonstrating the speedup and
accuracy obtained by our framework for different sizes of the data and model
parameters with variation in the number of worker cores/partitions; thereby
showing that our proposed framework can achieve significant speedup (upto 11X
for CNN) and is also quite scalable.
| Disha Shrivastava, Santanu Chaudhury and Dr. Jayadeva | null | 1708.0584 | null | null |
A Brief Survey of Deep Reinforcement Learning | cs.LG cs.AI cs.CV stat.ML | Deep reinforcement learning is poised to revolutionise the field of AI and
represents a step towards building autonomous systems with a higher level
understanding of the visual world. Currently, deep learning is enabling
reinforcement learning to scale to problems that were previously intractable,
such as learning to play video games directly from pixels. Deep reinforcement
learning algorithms are also applied to robotics, allowing control policies for
robots to be learned directly from camera inputs in the real world. In this
survey, we begin with an introduction to the general field of reinforcement
learning, then progress to the main streams of value-based and policy-based
methods. Our survey will cover central algorithms in deep reinforcement
learning, including the deep $Q$-network, trust region policy optimisation, and
asynchronous advantage actor-critic. In parallel, we highlight the unique
advantages of deep neural networks, focusing on visual understanding via
reinforcement learning. To conclude, we describe several current areas of
research within the field.
| Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony
Bharath | 10.1109/MSP.2017.2743240 | 1708.05866 | null | null |
Electricity Theft Detection using Machine Learning | cs.CR cs.CY cs.LG | Non-technical losses (NTL) in electric power grids arise through electricity
theft, broken electric meters or billing errors. They can harm the power
supplier as well as the whole economy of a country through losses of up to 40%
of the total power distribution. For NTL detection, researchers use artificial
intelligence to analyse data. This work is about improving the extraction of
more meaningful features from a data set. With these features, the prediction
quality will increase.
| Niklas Dahringer | null | 1708.05907 | null | null |
Accelerating Kernel Classifiers Through Borders Mapping | stat.ML cs.LG | Support vector machines (SVM) and other kernel techniques represent a family
of powerful statistical classification methods with high accuracy and broad
applicability. Because they use all or a significant portion of the training
data, however, they can be slow, especially for large problems. Piecewise
linear classifiers are similarly versatile, yet have the additional advantages
of simplicity, ease of interpretation and, if the number of component linear
classifiers is not too large, speed. Here we show how a simple, piecewise
linear classifier can be trained from a kernel-based classifier in order to
improve the classification speed. The method works by finding the root of the
difference in conditional probabilities between pairs of opposite classes to
build up a representation of the decision boundary. When tested on 17 different
datasets, it succeeded in improving the classification speed of a SVM for 12 of
them by up to two orders-of-magnitude. Of these, two were less accurate than a
simple, linear classifier. The method is best suited to problems with continuum
features data and smooth probability functions. Because the component linear
classifiers are built up individually from an existing classifier, rather than
through a simultaneous optimization procedure, the classifier is also fast to
train.
| Peter Mills | 10.1007/s11554-018-0769-9 | 1708.05917 | null | null |
A Deep Q-Network for the Beer Game: A Deep Reinforcement Learning
algorithm to Solve Inventory Optimization Problems | cs.LG cs.MA | The beer game is a widely used in-class game that is played in supply chain
management classes to demonstrate the bullwhip effect. The game is a
decentralized, multi-agent, cooperative problem that can be modeled as a serial
supply chain network in which agents cooperatively attempt to minimize the
total cost of the network even though each agent can only observe its own local
information. Each agent chooses order quantities to replenish its stock. Under
some conditions, a base-stock replenishment policy is known to be optimal.
However, in a decentralized supply chain in which some agents (stages) may act
irrationally (as they do in the beer game), there is no known optimal policy
for an agent wishing to act optimally.
We propose a machine learning algorithm, based on deep Q-networks, to
optimize the replenishment decisions at a given stage. When playing alongside
agents who follow a base-stock policy, our algorithm obtains near-optimal order
quantities. It performs much better than a base-stock policy when the other
agents use a more realistic model of human ordering behavior. Unlike most other
algorithms in the literature, our algorithm does not have any limits on the
beer game parameter values. Like any deep learning algorithm, training the
algorithm can be computationally intensive, but this can be performed ahead of
time; the algorithm executes in real time when the game is played. Moreover, we
propose a transfer learning approach so that the training performed for one
agent and one set of cost coefficients can be adapted quickly for other agents
and costs. Our algorithm can be extended to other decentralized multi-agent
cooperative games with partially observed information, which is a common type
of situation in real-world supply chain problems.
| Afshin Oroojlooyjadid, MohammadReza Nazari, Lawrence Snyder, Martin
Tak\'a\v{c} | null | 1708.05924 | null | null |
Explaining Anomalies in Groups with Characterizing Subspace Rules | cs.LG stat.ML | Anomaly detection has numerous applications and has been studied vastly. We
consider a complementary problem that has a much sparser literature: anomaly
description. Interpretation of anomalies is crucial for practitioners for
sense-making, troubleshooting, and planning actions. To this end, we present a
new approach called x-PACS (for eXplaining Patterns of Anomalies with
Characterizing Subspaces), which "reverse-engineers" the known anomalies by
identifying (1) the groups (or patterns) that they form, and (2) the
characterizing subspace and feature rules that separate each anomalous pattern
from normal instances. Explaining anomalies in groups not only saves analyst
time and gives insight into various types of anomalies, but also draws
attention to potentially critical, repeating anomalies.
In developing x-PACS, we first construct a desiderata for the anomaly
description problem. From a descriptive data mining perspective, our method
exhibits five desired properties in our desiderata. Namely, it can unearth
anomalous patterns (i) of multiple different types, (ii) hidden in arbitrary
subspaces of a high dimensional space, (iii) interpretable by the analysts,
(iv) different from normal patterns of the data, and finally (v) succinct,
providing the shortest data description. Furthermore, x-PACS is highly
parallelizable and scales linearly in terms of data size.
No existing work on anomaly description satisfies all of these properties
simultaneously. While not our primary goal, the anomalous patterns we find
serve as interpretable "signatures" and can be used for detection. We show the
effectiveness of x-PACS in explanation as well as detection on real-world
datasets as compared to state-of-the-art.
| Meghanath Macha and Leman Akoglu | null | 1708.05929 | null | null |
Neural Networks Compression for Language Modeling | stat.ML cs.CL cs.LG cs.NE | In this paper, we consider several compression techniques for the language
modeling problem based on recurrent neural networks (RNNs). It is known that
conventional RNNs, e.g, LSTM-based networks in language modeling, are
characterized with either high space complexity or substantial inference time.
This problem is especially crucial for mobile applications, in which the
constant interaction with the remote server is inappropriate. By using the Penn
Treebank (PTB) dataset we compare pruning, quantization, low-rank
factorization, tensor train decomposition for LSTM networks in terms of model
size and suitability for fast inference.
| Artem M. Grachev, Dmitry I. Ignatov, Andrey V. Savchenko | 10.1007/978-3-319-69900-4_44 | 1708.05963 | null | null |
Stochastic Primal-Dual Proximal ExtraGradient Descent for Compositely
Regularized Optimization | cs.LG math.OC stat.ML | We consider a wide range of regularized stochastic minimization problems with
two regularization terms, one of which is composed with a linear function. This
optimization model abstracts a number of important applications in artificial
intelligence and machine learning, such as fused Lasso, fused logistic
regression, and a class of graph-guided regularized minimization. The
computational challenges of this model are in two folds. On one hand, the
closed-form solution of the proximal mapping associated with the composed
regularization term or the expected objective function is not available. On the
other hand, the calculation of the full gradient of the expectation in the
objective is very expensive when the number of input data samples is
considerably large. To address these issues, we propose a stochastic variant of
extra-gradient type methods, namely \textsf{Stochastic Primal-Dual Proximal
ExtraGradient descent (SPDPEG)}, and analyze its convergence property for both
convex and strongly convex objectives. For general convex objectives, the
uniformly average iterates generated by \textsf{SPDPEG} converge in expectation
with $O(1/\sqrt{t})$ rate. While for strongly convex objectives, the uniformly
and non-uniformly average iterates generated by \textsf{SPDPEG} converge with
$O(\log(t)/t)$ and $O(1/t)$ rates, respectively. The order of the rate of the
proposed algorithm is known to match the best convergence rate for first-order
stochastic algorithms. Experiments on fused logistic regression and
graph-guided regularized logistic regression problems show that the proposed
algorithm performs very efficiently and consistently outperforms other
competing algorithms.
| Tianyi Lin and Linbo Qiao and Teng Zhang and Jiashi Feng and Bofeng
Zhang | 10.1016/j.neucom.2017.07.066 | 1708.05978 | null | null |
Perceptual audio loss function for deep learning | cs.SD cs.LG | PESQ and POLQA , are standards are standards for automated assessment of
voice quality of speech as experienced by human beings. The predictions of
those objective measures should come as close as possible to subjective quality
scores as obtained in subjective listening tests. Wavenet is a deep neural
network originally developed as a deep generative model of raw audio
wave-forms. Wavenet architecture is based on dilated causal convolutions, which
exhibit very large receptive fields. In this short paper we suggest using the
Wavenet architecture, in particular its large receptive filed in order to learn
PESQ algorithm. By doing so we can use it as a differentiable loss function for
speech enhancement.
| Dan Elbaz, Michael Zibulevsky | null | 1708.05987 | null | null |
A Capacity Scaling Law for Artificial Neural Networks | cs.NE cs.LG | We derive the calculation of two critical numbers predicting the behavior of
perceptron networks. First, we derive the calculation of what we call the
lossless memory (LM) dimension. The LM dimension is a generalization of the
Vapnik--Chervonenkis (VC) dimension that avoids structured data and therefore
provides an upper bound for perfectly fitting almost any training data. Second,
we derive what we call the MacKay (MK) dimension. This limit indicates a 50%
chance of not being able to train a given function. Our derivations are
performed by embedding a neural network into Shannon's communication model
which allows to interpret the two points as capacities measured in bits. We
present a proof and practical experiments that validate our upper bounds with
repeatable experiments using different network configurations, diverse
implementations, varying activation functions, and several learning algorithms.
The bottom line is that the two capacity points scale strictly linear with the
number of weights. Among other practical applications, our result allows to
compare and benchmark different neural network implementations independent of a
concrete learning task. Our results provide insight into the capabilities and
limits of neural networks and generate valuable know how for experimental
design decisions.
| Gerald Friedland and Mario Krell | null | 1708.06019 | null | null |
Improving Deep Learning using Generic Data Augmentation | cs.LG stat.ML | Deep artificial neural networks require a large corpus of training data in
order to effectively learn, where collection of such training data is often
expensive and laborious. Data augmentation overcomes this issue by artificially
inflating the training set with label preserving transformations. Recently
there has been extensive use of generic data augmentation to improve
Convolutional Neural Network (CNN) task performance. This study benchmarks
various popular data augmentation schemes to allow researchers to make informed
decisions as to which training methods are most appropriate for their data
sets. Various geometric and photometric schemes are evaluated on a
coarse-grained data set using a relatively simple CNN. Experimental results,
run using 4-fold cross-validation and reported in terms of Top-1 and Top-5
accuracy, indicate that cropping in geometric augmentation significantly
increases CNN task performance.
| Luke Taylor, Geoff Nitschke | null | 1708.0602 | null | null |
Meta-Learning MCMC Proposals | cs.AI cs.LG stat.ML | Effective implementations of sampling-based probabilistic inference often
require manually constructed, model-specific proposals. Inspired by recent
progresses in meta-learning for training learning agents that can generalize to
unseen environments, we propose a meta-learning approach to building effective
and generalizable MCMC proposals. We parametrize the proposal as a neural
network to provide fast approximations to block Gibbs conditionals. The learned
neural proposals generalize to occurrences of common structural motifs across
different models, allowing for the construction of a library of learned
inference primitives that can accelerate inference on unseen models with no
model-specific training required. We explore several applications including
open-universe Gaussian mixture models, in which our learned proposals
outperform a hand-tuned sampler, and a real-world named entity recognition
task, in which our sampler yields higher final F1 scores than classical
single-site Gibbs sampling.
| Tongzhou Wang, Yi Wu, David A. Moore, Stuart J. Russell | null | 1708.0604 | null | null |
nuts-flow/ml: data pre-processing for deep learning | cs.LG cs.SE | Data preprocessing is a fundamental part of any machine learning application
and frequently the most time-consuming aspect when developing a machine
learning solution. Preprocessing for deep learning is characterized by
pipelines that lazily load data and perform data transformation, augmentation,
batching and logging. Many of these functions are common across applications
but require different arrangements for training, testing or inference. Here we
introduce a novel software framework named nuts-flow/ml that encapsulates
common preprocessing operations as components, which can be flexibly arranged
to rapidly construct efficient preprocessing pipelines for deep learning.
| S. Maetschke, R. Tennakoon, C. Vecchiola and R. Garnavi | null | 1708.06046 | null | null |
Evasion Attacks against Machine Learning at Test Time | cs.CR cs.LG | In security-sensitive applications, the success of machine learning depends
on a thorough vetting of their resistance to adversarial data. In one
pertinent, well-motivated attack scenario, an adversary may attempt to evade a
deployed system at test time by carefully manipulating attack samples. In this
work, we present a simple but effective gradient-based approach that can be
exploited to systematically assess the security of several, widely-used
classification algorithms against evasion attacks. Following a recently
proposed framework for security evaluation, we simulate attack scenarios that
exhibit different risk levels for the classifier by increasing the attacker's
knowledge of the system and her ability to manipulate attack samples. This
gives the classifier designer a better picture of the classifier performance
under evasion attacks, and allows him to perform a more informed model
selection (or parameter setting). We evaluate our approach on the relevant
security task of malware detection in PDF files, and show that such systems can
be easily evaded. We also sketch some countermeasures suggested by our
analysis.
| Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim
Srndic, Pavel Laskov, Giorgio Giacinto, Fabio Roli | 10.1007/978-3-642-40994-3_25 | 1708.06131 | null | null |
General Backpropagation Algorithm for Training Second-order Neural
Networks | cs.LG stat.ML | The artificial neural network is a popular framework in machine learning. To
empower individual neurons, we recently suggested that the current type of
neurons could be upgraded to 2nd order counterparts, in which the linear
operation between inputs to a neuron and the associated weights is replaced
with a nonlinear quadratic operation. A single 2nd order neurons already has a
strong nonlinear modeling ability, such as implementing basic fuzzy logic
operations. In this paper, we develop a general backpropagation (BP) algorithm
to train the network consisting of 2nd-order neurons. The numerical studies are
performed to verify of the generalized BP algorithm.
| Fenglei Fan, Wenxiang Cong, Ge Wang | null | 1708.06243 | null | null |
A Flow Model of Neural Networks | cs.LG cs.AI cs.NE | Based on a natural connection between ResNet and transport equation or its
characteristic equation, we propose a continuous flow model for both ResNet and
plain net. Through this continuous model, a ResNet can be explicitly
constructed as a refinement of a plain net. The flow model provides an
alternative perspective to understand phenomena in deep neural networks, such
as why it is necessary and sufficient to use 2-layer blocks in ResNets, why
deeper is better, and why ResNets are even deeper, and so on. It also opens a
gate to bring in more tools from the huge area of differential equations.
| Zhen Li, Zuoqiang Shi | null | 1708.06257 | null | null |
Deep vs. Diverse Architectures for Classification Problems | stat.ML cs.LG | This study compares various superlearner and deep learning architectures
(machine-learning-based and neural-network-based) for classification problems
across several simulated and industrial datasets to assess performance and
computational efficiency, as both methods have nice theoretical convergence
properties. Superlearner formulations outperform other methods at small to
moderate sample sizes (500-2500) on nonlinear and mixed linear/nonlinear
predictor relationship datasets, while deep neural networks perform well on
linear predictor relationship datasets of all sizes. This suggests faster
convergence of the superlearner compared to deep neural network architectures
on many messy classification problems for real-world data.
Superlearners also yield interpretable models, allowing users to examine
important signals in the data; in addition, they offer flexible formulation,
where users can retain good performance with low-computational-cost base
algorithms.
K-nearest-neighbor (KNN) regression demonstrates improvements using the
superlearner framework, as well; KNN superlearners consistently outperform deep
architectures and KNN regression, suggesting that superlearners may be better
able to capture local and global geometric features through utilizing a variety
of algorithms to probe the data space.
| Colleen M. Farrelly | null | 1708.06347 | null | null |
Automated Website Fingerprinting through Deep Learning | cs.CR cs.LG | Several studies have shown that the network traffic that is generated by a
visit to a website over Tor reveals information specific to the website through
the timing and sizes of network packets. By capturing traffic traces between
users and their Tor entry guard, a network eavesdropper can leverage this
meta-data to reveal which website Tor users are visiting. The success of such
attacks heavily depends on the particular set of traffic features that are used
to construct the fingerprint. Typically, these features are manually engineered
and, as such, any change introduced to the Tor network can render these
carefully constructed features ineffective. In this paper, we show that an
adversary can automate the feature engineering process, and thus automatically
deanonymize Tor traffic by applying our novel method based on deep learning. We
collect a dataset comprised of more than three million network traces, which is
the largest dataset of web traffic ever used for website fingerprinting, and
find that the performance achieved by our deep learning approaches is
comparable to known methods which include various research efforts spanning
over multiple years. The obtained success rate exceeds 96% for a closed world
of 100 websites and 94% for our biggest closed world of 900 classes. In our
open world evaluation, the most performant deep learning model is 2% more
accurate than the state-of-the-art attack. Furthermore, we show that the
implicit features automatically learned by our approach are far more resilient
to dynamic changes of web content over time. We conclude that the ability to
automatically construct the most relevant traffic features and perform accurate
traffic recognition makes our deep learning based approach an efficient,
flexible and robust technique for website fingerprinting.
| Vera Rimmer, Davy Preuveneers, Marc Juarez, Tom Van Goethem, Wouter
Joosen | 10.14722/ndss.2018.23105 | 1708.06376 | null | null |
SafePredict: A Meta-Algorithm for Machine Learning That Uses Refusals to
Guarantee Correctness | cs.LG cs.AI math.ST stat.TH | SafePredict is a novel meta-algorithm that works with any base prediction
algorithm for online data to guarantee an arbitrarily chosen correctness rate,
$1-\epsilon$, by allowing refusals. Allowing refusals means that the
meta-algorithm may refuse to emit a prediction produced by the base algorithm
on occasion so that the error rate on non-refused predictions does not exceed
$\epsilon$. The SafePredict error bound does not rely on any assumptions on the
data distribution or the base predictor. When the base predictor happens not to
exceed the target error rate $\epsilon$, SafePredict refuses only a finite
number of times. When the error rate of the base predictor changes through time
SafePredict makes use of a weight-shifting heuristic that adapts to these
changes without knowing when the changes occur yet still maintains the
correctness guarantee. Empirical results show that (i) SafePredict compares
favorably with state-of-the art confidence based refusal mechanisms which fail
to offer robust error guarantees; and (ii) combining SafePredict with such
refusal mechanisms can in many cases further reduce the number of refusals. Our
software (currently in Python) is included in the supplementary material.
| Mustafa A. Kocak, David Ramirez, Elza Erkip, and Dennis E. Shasha | null | 1708.06425 | null | null |
Sum-Product Graphical Models | stat.ML cs.LG | This paper introduces a new probabilistic architecture called Sum-Product
Graphical Model (SPGM). SPGMs combine traits from Sum-Product Networks (SPNs)
and Graphical Models (GMs): Like SPNs, SPGMs always enable tractable inference
using a class of models that incorporate context specific independence. Like
GMs, SPGMs provide a high-level model interpretation in terms of conditional
independence assumptions and corresponding factorizations. Thus, the new
architecture represents a class of probability distributions that combines, for
the first time, the semantics of graphical models with the evaluation
efficiency of SPNs. We also propose a novel algorithm for learning both the
structure and the parameters of SPGMs. A comparative empirical evaluation
demonstrates competitive performances of our approach in density estimation.
| Mattia Desana and Christoph Schn\"orr | null | 1708.06438 | null | null |
Learning Efficient Convolutional Networks through Network Slimming | cs.CV cs.AI cs.LG | The deployment of deep convolutional neural networks (CNNs) in many real
world applications is largely hindered by their high computational cost. In
this paper, we propose a novel learning scheme for CNNs to simultaneously 1)
reduce the model size; 2) decrease the run-time memory footprint; and 3) lower
the number of computing operations, without compromising accuracy. This is
achieved by enforcing channel-level sparsity in the network in a simple but
effective way. Different from many existing approaches, the proposed method
directly applies to modern CNN architectures, introduces minimum overhead to
the training process, and requires no special software/hardware accelerators
for the resulting models. We call our approach network slimming, which takes
wide and large networks as input models, but during training insignificant
channels are automatically identified and pruned afterwards, yielding thin and
compact models with comparable accuracy. We empirically demonstrate the
effectiveness of our approach with several state-of-the-art CNN models,
including VGGNet, ResNet and DenseNet, on various image classification
datasets. For VGGNet, a multi-pass version of network slimming gives a 20x
reduction in model size and a 5x reduction in computing operations.
| Zhuang Liu and Jianguo Li and Zhiqiang Shen and Gao Huang and Shoumeng
Yan and Changshui Zhang | null | 1708.06519 | null | null |
Stacked transfer learning for tropical cyclone intensity prediction | cs.LG stat.ML | Tropical cyclone wind-intensity prediction is a challenging task considering
drastic changes climate patterns over the last few decades. In order to develop
robust prediction models, one needs to consider different characteristics of
cyclones in terms of spatial and temporal characteristics. Transfer learning
incorporates knowledge from a related source dataset to compliment a target
datasets especially in cases where there is lack or data. Stacking is a form of
ensemble learning focused for improving generalization that has been recently
used for transfer learning problems which is referred to as transfer stacking.
In this paper, we employ transfer stacking as a means of studying the effects
of cyclones whereby we evaluate if cyclones in different geographic locations
can be helpful for improving generalization performs. Moreover, we use
conventional neural networks for evaluating the effects of duration on cyclones
in prediction performance. Therefore, we develop an effective strategy that
evaluates the relationships between different types of cyclones through
transfer learning and conventional learning methods via neural networks.
| Ratneel Vikash Deo, Rohitash Chandra and Anuraganand Sharma | null | 1708.06539 | null | null |
Reinforcement Learning in POMDPs with Memoryless Options and
Option-Observation Initiation Sets | cs.AI cs.LG | Many real-world reinforcement learning problems have a hierarchical nature,
and often exhibit some degree of partial observability. While hierarchy and
partial observability are usually tackled separately (for instance by combining
recurrent neural networks and options), we show that addressing both problems
simultaneously is simpler and more efficient in many cases. More specifically,
we make the initiation set of options conditional on the previously-executed
option, and show that options with such Option-Observation Initiation Sets
(OOIs) are at least as expressive as Finite State Controllers (FSCs), a
state-of-the-art approach for learning in POMDPs. OOIs are easy to design based
on an intuitive description of the task, lead to explainable policies and keep
the top-level and option policies memoryless. Our experiments show that OOIs
allow agents to learn optimal policies in challenging POMDPs, while being much
more sample-efficient than a recurrent neural network over options.
| Denis Steckelmacher, Diederik M. Roijers, Anna Harutyunyan, Peter
Vrancx, H\'el\`ene Plisnier, Ann Now\'e | null | 1708.06551 | null | null |
Long-Short Range Context Neural Networks for Language Modeling | cs.CL cs.LG | The goal of language modeling techniques is to capture the statistical and
structural properties of natural languages from training corpora. This task
typically involves the learning of short range dependencies, which generally
model the syntactic properties of a language and/or long range dependencies,
which are semantic in nature. We propose in this paper a new multi-span
architecture, which separately models the short and long context information
while it dynamically merges them to perform the language modeling task. This is
done through a novel recurrent Long-Short Range Context (LSRC) network, which
explicitly models the local (short) and global (long) context using two
separate hidden states that evolve in time. This new architecture is an
adaptation of the Long-Short Term Memory network (LSTM) to take into account
the linguistic properties. Extensive experiments conducted on the Penn Treebank
(PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a
significant reduction of the perplexity when compared to state-of-the-art
language modeling techniques.
| Youssef Oualil, Mittul Singh, Clayton Greenberg, Dietrich Klakow | null | 1708.06555 | null | null |
Nonparametric regression using deep neural networks with ReLU activation
function | math.ST cs.LG stat.ML stat.TH | Consider the multivariate nonparametric regression model. It is shown that
estimators based on sparsely connected deep neural networks with ReLU
activation function and properly chosen network architecture achieve the
minimax rates of convergence (up to $\log n$-factors) under a general
composition assumption on the regression function. The framework includes many
well-studied structural constraints such as (generalized) additive models.
While there is a lot of flexibility in the network architecture, the tuning
parameter is the sparsity of the network. Specifically, we consider large
networks with number of potential network parameters exceeding the sample size.
The analysis gives some insights into why multilayer feedforward neural
networks perform well in practice. Interestingly, for ReLU activation function
the depth (number of layers) of the neural network architectures plays an
important role and our theory suggests that for nonparametric regression,
scaling the network depth with the sample size is natural. It is also shown
that under the composition assumption wavelet estimators can only achieve
suboptimal rates.
| Johannes Schmidt-Hieber | 10.1214/19-AOS1875 | 1708.06633 | null | null |
Learning Combinations of Sigmoids Through Gradient Estimation | stat.ML cs.LG | We develop a new approach to learn the parameters of regression models with
hidden variables. In a nutshell, we estimate the gradient of the regression
function at a set of random points, and cluster the estimated gradients. The
centers of the clusters are used as estimates for the parameters of hidden
units. We justify this approach by studying a toy model, whereby the regression
function is a linear combination of sigmoids. We prove that indeed the
estimated gradients concentrate around the parameter vectors of the hidden
units, and provide non-asymptotic bounds on the number of required samples. To
the best of our knowledge, no comparable guarantees have been proven for linear
combinations of sigmoids.
| Stratis Ioannidis and Andrea Montanari | null | 1708.06678 | null | null |
BadNets: Identifying Vulnerabilities in the Machine Learning Model
Supply Chain | cs.CR cs.LG | Deep learning-based techniques have achieved state-of-the-art performance on
a wide variety of recognition and classification tasks. However, these networks
are typically computationally expensive to train, requiring weeks of
computation on many GPUs; as a result, many users outsource the training
procedure to the cloud or rely on pre-trained models that are then fine-tuned
for a specific task. In this paper we show that outsourced training introduces
new security risks: an adversary can create a maliciously trained network (a
backdoored neural network, or a \emph{BadNet}) that has state-of-the-art
performance on the user's training and validation samples, but behaves badly on
specific attacker-chosen inputs. We first explore the properties of BadNets in
a toy example, by creating a backdoored handwritten digit classifier. Next, we
demonstrate backdoors in a more realistic scenario by creating a U.S. street
sign classifier that identifies stop signs as speed limits when a special
sticker is added to the stop sign; we then show in addition that the backdoor
in our US street sign detector can persist even if the network is later
retrained for another task and cause a drop in accuracy of {25}\% on average
when the backdoor trigger is present. These results demonstrate that backdoors
in neural networks are both powerful and---because the behavior of neural
networks is difficult to explicate---stealthy. This work provides motivation
for further research into techniques for verifying and inspecting neural
networks, just as we have developed tools for verifying and debugging software.
| Tianyu Gu, Brendan Dolan-Gavitt, Siddharth Garg | null | 1708.06733 | null | null |
Twin Networks: Matching the Future for Sequence Generation | cs.LG stat.ML | We propose a simple technique for encouraging generative RNNs to plan ahead.
We train a "backward" recurrent network to generate a given sequence in reverse
order, and we encourage states of the forward model to predict cotemporal
states of the backward model. The backward network is used only during
training, and plays no role during sampling or inference. We hypothesize that
our approach eases modeling of long-term dependencies by implicitly forcing the
forward states to hold information about the longer-term future (as contained
in the backward states). We show empirically that our approach achieves 9%
relative improvement for a speech recognition task, and achieves significant
improvement on a COCO caption generation task.
| Dmitriy Serdyuk, Nan Rosemary Ke, Alessandro Sordoni, Adam Trischler,
Chris Pal, Yoshua Bengio | null | 1708.06742 | null | null |
Dynamic Input Structure and Network Assembly for Few-Shot Learning | cs.LG stat.ML | The ability to learn from a small number of examples has been a difficult
problem in machine learning since its inception. While methods have succeeded
with large amounts of training data, research has been underway in how to
accomplish similar performance with fewer examples, known as one-shot or more
generally few-shot learning. This technique has been shown to have promising
performance, but in practice requires fixed-size inputs making it impractical
for production systems where class sizes can vary. This impedes training and
the final utility of few-shot learning systems. This paper describes an
approach to constructing and training a network that can handle arbitrary
example sizes dynamically as the system is used.
| Nathan Hilliard, Nathan O. Hodas, Courtney D. Corley | null | 1708.06819 | null | null |
Learning Anytime Predictions in Neural Networks via Adaptive Loss
Balancing | cs.LG cs.AI | This work considers the trade-off between accuracy and test-time
computational cost of deep neural networks (DNNs) via \emph{anytime}
predictions from auxiliary predictions. Specifically, we optimize auxiliary
losses jointly in an \emph{adaptive} weighted sum, where the weights are
inversely proportional to average of each loss. Intuitively, this balances the
losses to have the same scale. We demonstrate theoretical considerations that
motivate this approach from multiple viewpoints, including connecting it to
optimizing the geometric mean of the expectation of each loss, an objective
that ignores the scale of losses. Experimentally, the adaptive weights induce
more competitive anytime predictions on multiple recognition data-sets and
models than non-adaptive approaches including weighing all losses equally. In
particular, anytime neural networks (ANNs) can achieve the same accuracy faster
using adaptive weights on a small network than using static constant weights on
a large one. For problems with high performance saturation, we also show a
sequence of exponentially deepening ANNscan achieve near-optimal anytime
results at any budget, at the cost of a const fraction of extra computation.
| Hanzhang Hu, Debadeepta Dey, Martial Hebert, J. Andrew Bagnell | null | 1708.06832 | null | null |
On Relaxing Determinism in Arithmetic Circuits | cs.AI cs.LG | The past decade has seen a significant interest in learning tractable
probabilistic representations. Arithmetic circuits (ACs) were among the first
proposed tractable representations, with some subsequent representations being
instances of ACs with weaker or stronger properties. In this paper, we provide
a formal basis under which variants on ACs can be compared, and where the
precise roles and semantics of their various properties can be made more
transparent. This allows us to place some recent developments on ACs in a
clearer perspective and to also derive new results for ACs. This includes an
exponential separation between ACs with and without determinism; completeness
and incompleteness results; and tractability results (or lack thereof) when
computing most probable explanations (MPEs).
| Arthur Choi and Adnan Darwiche | null | 1708.06846 | null | null |
Learning Deep Neural Network Representations for Koopman Operators of
Nonlinear Dynamical Systems | cs.LG cs.AI math.DS | The Koopman operator has recently garnered much attention for its value in
dynamical systems analysis and data-driven model discovery. However, its
application has been hindered by the computational complexity of extended
dynamic mode decomposition; this requires a combinatorially large basis set to
adequately describe many nonlinear systems of interest, e.g. cyber-physical
infrastructure systems, biological networks, social systems, and fluid
dynamics. Often the dictionaries generated for these problems are manually
curated, requiring domain-specific knowledge and painstaking tuning. In this
paper we introduce a deep learning framework for learning Koopman operators of
nonlinear dynamical systems. We show that this novel method automatically
selects efficient deep dictionaries, outperforming state-of-the-art methods. We
benchmark this method on partially observed nonlinear systems, including the
glycolytic oscillator and show it is able to predict quantitatively 100 steps
into the future, using only a single timepoint, and qualitative oscillatory
behavior 400 steps into the future.
| Enoch Yeung, Soumya Kundu, Nathan Hodas | null | 1708.0685 | null | null |
Human experts vs. machines in taxa recognition | stat.ML cs.LG q-bio.QM | The step of expert taxa recognition currently slows down the response time of
many bioassessments. Shifting to quicker and cheaper state-of-the-art machine
learning approaches is still met with expert scepticism towards the ability and
logic of machines. In our study, we investigate both the differences in
accuracy and in the identification logic of taxonomic experts and machines. We
propose a systematic approach utilizing deep Convolutional Neural Nets with the
transfer learning paradigm and extensively evaluate it over a multi-pose
taxonomic dataset with hierarchical labels specifically created for this
comparison. We also study the prediction accuracy on different ranks of
taxonomic hierarchy in detail. We used support vector machine classifier as a
benchmark. Our results revealed that human experts using actual specimens yield
the lowest classification error ($\overline{CE}=6.1\%$). However, a much
faster, automated approach using deep Convolutional Neural Nets comes close to
human accuracy ($\overline{CE}=11.4\%$) when a typical flat classification
approach is used. Contrary to previous findings in the literature, we find that
for machines following a typical flat classification approach commonly used in
machine learning performs better than forcing machines to adopt a hierarchical,
local per parent node approach used by human taxonomic experts
($\overline{CE}=13.8\%$). Finally, we publicly share our unique dataset to
serve as a public benchmark dataset in this field.
| Johanna \"Arje, Jenni Raitoharju, Alexandros Iosifidis, Ville
Tirronen, Kristian Meissner, Moncef Gabbouj, Serkan Kiranyaz, Salme
K\"arkk\"ainen | 10.1016/j.image.2020.115917 | 1708.06899 | null | null |
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the
iCub Humanoid | cs.LG cs.RO stat.ML | Deep neural networks have been widely adopted in recent years, exhibiting
impressive performances in several application domains. It has however been
shown that they can be fooled by adversarial examples, i.e., images altered by
a barely-perceivable adversarial noise, carefully crafted to mislead
classification. In this work, we aim to evaluate the extent to which
robot-vision systems embodying deep-learning algorithms are vulnerable to
adversarial examples, and propose a computationally efficient countermeasure to
mitigate this threat, based on rejecting classification of anomalous inputs. We
then provide a clearer understanding of the safety properties of deep networks
through an intuitive empirical analysis, showing that the mapping learned by
such networks essentially violates the smoothness assumption of learning
algorithms. We finally discuss the main limitations of this work, including the
creation of real-world adversarial examples, and sketch promising research
directions.
| Marco Melis, Ambra Demontis, Battista Biggio, Gavin Brown, Giorgio
Fumera and Fabio Roli | null | 1708.06939 | null | null |
Generating Visual Representations for Zero-Shot Classification | cs.CV cs.AI cs.LG | This paper addresses the task of learning an image clas-sifier when some
categories are defined by semantic descriptions only (e.g. visual attributes)
while the others are defined by exemplar images as well. This task is often
referred to as the Zero-Shot classification task (ZSC). Most of the previous
methods rely on learning a common embedding space allowing to compare visual
features of unknown categories with semantic descriptions. This paper argues
that these approaches are limited as i) efficient discrimi-native classifiers
can't be used ii) classification tasks with seen and unseen categories
(Generalized Zero-Shot Classification or GZSC) can't be addressed efficiently.
In contrast , this paper suggests to address ZSC and GZSC by i) learning a
conditional generator using seen classes ii) generate artificial training
examples for the categories without exemplars. ZSC is then turned into a
standard supervised learning problem. Experiments with 4 generative models and
5 datasets experimentally validate the approach, giving state-of-the-art
results on both ZSC and GZSC.
| Maxime Bucher (1), St\'ephane Herbin (1), Fr\'ed\'eric Jurie ((1)
Palaiseau) | null | 1708.06975 | null | null |
Variational autoencoders for tissue heterogeneity exploration from
(almost) no preprocessed mass spectrometry imaging data | q-bio.QM cs.LG stat.ML | The paper presents the application of Variational Autoencoders (VAE) for data
dimensionality reduction and explorative analysis of mass spectrometry imaging
data (MSI). The results confirm that VAEs are capable of detecting the patterns
associated with the different tissue sub-types with performance than standard
approaches.
| Paolo Inglese, James L. Alexander, Anna Mroz, Zoltan Takats, Robert
Glen | null | 1708.07012 | null | null |
Bayesian Learning of Clique Tree Structure | cs.LG math.PR stat.ML | The problem of categorical data analysis in high dimensions is considered. A
discussion of the fundamental difficulties of probability modeling is provided,
and a solution to the derivation of high dimensional probability distributions
based on Bayesian learning of clique tree decomposition is presented. The main
contributions of this paper are an automated determination of the optimal
clique tree structure for probability modeling, the resulting derived
probability distribution, and a corresponding unified approach to clustering
and anomaly detection based on the probability distribution.
| Cetin Savkli, J. Ryan Carr, Philip Graff, Lauren Kennell | null | 1708.07025 | null | null |
Scale-invariant unconstrained online learning | cs.LG stat.ML | We consider a variant of online convex optimization in which both the
instances (input vectors) and the comparator (weight vector) are unconstrained.
We exploit a natural scale invariance symmetry in our unconstrained setting:
the predictions of the optimal comparator are invariant under any linear
transformation of the instances. Our goal is to design online algorithms which
also enjoy this property, i.e. are scale-invariant. We start with the case of
coordinate-wise invariance, in which the individual coordinates (features) can
be arbitrarily rescaled. We give an algorithm, which achieves essentially
optimal regret bound in this setup, expressed by means of a coordinate-wise
scale-invariant norm of the comparator. We then study general invariance with
respect to arbitrary linear transformations. We first give a negative result,
showing that no algorithm can achieve a meaningful bound in terms of
scale-invariant norm of the comparator in the worst case. Next, we compliment
this result with a positive one, providing an algorithm which "almost" achieves
the desired bound, incurring only a logarithmic overhead in terms of the norm
of the instances.
| Wojciech Kot{\l}owski | null | 1708.07042 | null | null |
Identifying Growth-Patterns in Children by Applying Cluster analysis to
Electronic Medical Records | stat.AP cs.LG | Obesity is one of the leading health concerns in the United States.
Researchers and health care providers are interested in understanding factors
affecting obesity and detecting the likelihood of obesity as early as possible.
In this paper, we set out to recognize children who have higher risk of obesity
by identifying distinct growth patterns in them. This is done by using
clustering methods, which group together children who share similar body
measurements over a period of time. The measurements characterizing children
within the same cluster are plotted as a function of age. We refer to these
plots as growthpattern curves. We show that distinct growth-pattern curves are
associated with different clusters and thus can be used to separate children
into the topmost (heaviest), middle, or bottom-most cluster based on early
growth measurements.
| Moumita Bhattacharya, Deborah Ehrenthal, Hagit Shatkay | 10.1109/BIBM.2014.6999183 | 1708.07058 | null | null |
Forecasting day-ahead electricity prices in Europe: the importance of
considering market integration | q-fin.ST cs.CE cs.LG cs.NE stat.AP | Motivated by the increasing integration among electricity markets, in this
paper we propose two different methods to incorporate market integration in
electricity price forecasting and to improve the predictive performance. First,
we propose a deep neural network that considers features from connected markets
to improve the predictive accuracy in a local market. To measure the importance
of these features, we propose a novel feature selection algorithm that, by
using Bayesian optimization and functional analysis of variance, evaluates the
effect of the features on the algorithm performance. In addition, using market
integration, we propose a second model that, by simultaneously predicting
prices from two markets, improves the forecasting accuracy even further. As a
case study, we consider the electricity market in Belgium and the improvements
in forecasting accuracy when using various French electricity features. We show
that the two proposed models lead to improvements that are statistically
significant. Particularly, due to market integration, the predictive accuracy
is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage
error). In addition, we show that the proposed feature selection algorithm is
able to perform a correct assessment, i.e. to discard the irrelevant features.
| Jesus Lago, Fjo De Ridder, Peter Vrancx, Bart De Schutter | 10.1016/j.apenergy.2017.11.098 | 1708.07061 | null | null |
Neighborhood-Based Label Propagation in Large Protein Graphs | cs.DC cs.LG | Understanding protein function is one of the keys to understanding life at
the molecular level. It is also important in several scenarios including human
disease and drug discovery. In this age of rapid and affordable biological
sequencing, the number of sequences accumulating in databases is rising with an
increasing rate. This presents many challenges for biologists and computer
scientists alike. In order to make sense of this huge quantity of data, these
sequences should be annotated with functional properties. UniProtKB consists of
two components: i) the UniProtKB/Swiss-Prot database containing protein
sequences with reliable information manually reviewed by expert bio-curators
and ii) the UniProtKB/TrEMBL database that is used for storing and processing
the unknown sequences. Hence, for all proteins we have available the sequence
along with few more information such as the taxon and some structural domains.
Pairwise similarity can be defined and computed on proteins based on such
attributes. Other important attributes, while present for proteins in
Swiss-Prot, are often missing for proteins in TrEMBL, such as their function
and cellular localization. The enormous number of protein sequences now in
TrEMBL calls for rapid procedures to annotate them automatically. In this work,
we present DistNBLP, a novel Distributed Neighborhood-Based Label Propagation
approach for large-scale annotation of proteins. To do this, the functional
annotations of reviewed proteins are used to predict those of non-reviewed
proteins using label propagation on a graph representation of the protein
database. DistNBLP is built on top of the "akka" toolkit for building resilient
distributed message-driven applications.
| Sabeur Aridhi (1), Seyed Ziaeddin Alborzi (1), Malika Sma\"il-Tabbone
(2), Marie-Dominique Devignes (1), David Ritchie (1) ((1) CAPSID, (2)
ORPAILLEUR) | null | 1708.07074 | null | null |
Super-Convergence: Very Fast Training of Neural Networks Using Large
Learning Rates | cs.LG cs.CV cs.NE stat.ML | In this paper, we describe a phenomenon, which we named "super-convergence",
where neural networks can be trained an order of magnitude faster than with
standard training methods. The existence of super-convergence is relevant to
understanding why deep networks generalize well. One of the key elements of
super-convergence is training with one learning rate cycle and a large maximum
learning rate. A primary insight that allows super-convergence training is that
large learning rates regularize the training, hence requiring a reduction of
all other forms of regularization in order to preserve an optimal
regularization balance. We also derive a simplification of the Hessian Free
optimization method to compute an estimate of the optimal learning rate.
Experiments demonstrate super-convergence for Cifar-10/100, MNIST and Imagenet
datasets, and resnet, wide-resnet, densenet, and inception architectures. In
addition, we show that super-convergence provides a greater boost in
performance relative to standard training when the amount of labeled training
data is limited. The architectures and code to replicate the figures in this
paper are available at github.com/lnsmith54/super-convergence. See
http://www.fast.ai/2018/04/30/dawnbench-fastai/ for an application of
super-convergence to win the DAWNBench challenge (see
https://dawn.cs.stanford.edu/benchmark/).
| Leslie N. Smith and Nicholay Topin | null | 1708.0712 | null | null |
Classification via Tensor Decompositions of Echo State Networks | cs.LG cs.NE stat.ML | This work introduces a tensor-based method to perform supervised
classification on spatiotemporal data processed in an echo state network.
Typically when performing supervised classification tasks on data processed in
an echo state network, the entire collection of hidden layer node states from
the training dataset is shaped into a matrix, allowing one to use standard
linear algebra techniques to train the output layer. However, the collection of
hidden layer states is multidimensional in nature, and representing it as a
matrix may lead to undesirable numerical conditions or loss of spatial and
temporal correlations in the data.
This work proposes a tensor-based supervised classification method on echo
state network data that preserves and exploits the multidimensional nature of
the hidden layer states. The method, which is based on orthogonal Tucker
decompositions of tensors, is compared with the standard linear output weight
approach in several numerical experiments on both synthetic and natural data.
The results show that the tensor-based approach tends to outperform the
standard approach in terms of classification accuracy.
| Ashley Prater | null | 1708.07147 | null | null |
Towards an Automatic Turing Test: Learning to Evaluate Dialogue
Responses | cs.CL cs.AI cs.LG | Automatically evaluating the quality of dialogue responses for unstructured
domains is a challenging problem. Unfortunately, existing automatic evaluation
metrics are biased and correlate very poorly with human judgements of response
quality. Yet having an accurate automatic evaluation procedure is crucial for
dialogue research, as it allows rapid prototyping and testing of new models
with fewer expensive human evaluations. In response to this challenge, we
formulate automatic dialogue evaluation as a learning problem. We present an
evaluation model (ADEM) that learns to predict human-like scores to input
responses, using a new dataset of human response scores. We show that the ADEM
model's predictions correlate significantly, and at a level much higher than
word-overlap metrics such as BLEU, with human judgements at both the utterance
and system-level. We also show that ADEM can generalize to evaluating dialogue
models unseen during training, an important step for automatic dialogue
evaluation.
| Ryan Lowe, Michael Noseworthy, Iulian V. Serban, Nicolas
Angelard-Gontier, Yoshua Bengio, Joelle Pineau | null | 1708.07149 | null | null |
Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian
Information | math.OC cs.CC cs.LG stat.ML | We consider variants of trust-region and cubic regularization methods for
non-convex optimization, in which the Hessian matrix is approximated. Under
mild conditions on the inexact Hessian, and using approximate solution of the
corresponding sub-problems, we provide iteration complexity to achieve $
\epsilon $-approximate second-order optimality which have shown to be tight.
Our Hessian approximation conditions constitute a major relaxation over the
existing ones in the literature. Consequently, we are able to show that such
mild conditions allow for the construction of the approximate Hessian through
various random sampling methods. In this light, we consider the canonical
problem of finite-sum minimization, provide appropriate uniform and non-uniform
sub-sampling strategies to construct such Hessian approximations, and obtain
optimal iteration complexity for the corresponding sub-sampled trust-region and
cubic regularization methods.
| Peng Xu, Fred Roosta, Michael W. Mahoney | null | 1708.07164 | null | null |
Massively-Parallel Feature Selection for Big Data | cs.LG stat.ML | We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for
feature selection (FS) in Big Data settings (high dimensionality and/or sample
size). To tackle the challenges of Big Data FS PFBP partitions the data matrix
both in terms of rows (samples, training examples) as well as columns
(features). By employing the concepts of $p$-values of conditional independence
tests and meta-analysis techniques PFBP manages to rely only on computations
local to a partition while minimizing communication costs. Then, it employs
powerful and safe (asymptotically sound) heuristics to make early, approximate
decisions, such as Early Dropping of features from consideration in subsequent
iterations, Early Stopping of consideration of features within the same
iteration, or Early Return of the winner in each iteration. PFBP provides
asymptotic guarantees of optimality for data distributions faithfully
representable by a causal network (Bayesian network or maximal ancestral
graph). Our empirical analysis confirms a super-linear speedup of the algorithm
with increasing sample size, linear scalability with respect to the number of
features and processing cores, while dominating other competitive algorithms in
its class.
| Ioannis Tsamardinos and Giorgos Borboudakis and Pavlos Katsogridakis
and Polyvios Pratikakis and Vassilis Christophides | null | 1708.07178 | null | null |
Bootstrapping the Out-of-sample Predictions for Efficient and Accurate
Cross-Validation | cs.LG | Cross-Validation (CV), and out-of-sample performance-estimation protocols in
general, are often employed both for (a) selecting the optimal combination of
algorithms and values of hyper-parameters (called a configuration) for
producing the final predictive model, and (b) estimating the predictive
performance of the final model. However, the cross-validated performance of the
best configuration is optimistically biased. We present an efficient bootstrap
method that corrects for the bias, called Bootstrap Bias Corrected CV (BBC-CV).
BBC-CV's main idea is to bootstrap the whole process of selecting the
best-performing configuration on the out-of-sample predictions of each
configuration, without additional training of models. In comparison to the
alternatives, namely the nested cross-validation and a method by Tibshirani and
Tibshirani, BBC-CV is computationally more efficient, has smaller variance and
bias, and is applicable to any metric of performance (accuracy, AUC,
concordance index, mean squared error). Subsequently, we employ again the idea
of bootstrapping the out-of-sample predictions to speed up the CV process.
Specifically, using a bootstrap-based hypothesis test we stop training of
models on new folds of statistically-significantly inferior configurations. We
name the method Bootstrap Corrected with Early Dropping CV (BCED-CV) that is
both efficient and provides accurate performance estimates.
| Ioannis Tsamardinos, Elissavet Greasidou, Michalis Tsagris and Giorgos
Borboudakis | null | 1708.0718 | null | null |
3D Morphable Models as Spatial Transformer Networks | cs.CV cs.LG | In this paper, we show how a 3D Morphable Model (i.e. a statistical model of
the 3D shape of a class of objects such as faces) can be used to spatially
transform input data as a module (a 3DMM-STN) within a convolutional neural
network. This is an extension of the original spatial transformer network in
that we are able to interpret and normalise 3D pose changes and
self-occlusions. The trained localisation part of the network is independently
useful since it learns to fit a 3D morphable model to a single image. We show
that the localiser can be trained using only simple geometric loss functions on
a relatively small dataset yet is able to perform robust normalisation on
highly uncontrolled images including occlusion, self-occlusion and large pose
changes.
| Anil Bas, Patrik Huber, William A. P. Smith, Muhammad Awais, Josef
Kittler | 10.1109/ICCVW.2017.110 | 1708.07199 | null | null |
Proportionate gradient updates with PercentDelta | cs.LG | Deep Neural Networks are generally trained using iterative gradient updates.
Magnitudes of gradients are affected by many factors, including choice of
activation functions and initialization. More importantly, gradient magnitudes
can greatly differ across layers, with some layers receiving much smaller
gradients than others. causing some layers to train slower than others and
therefore slowing down the overall convergence. We analytically explain this
disproportionality. Then we propose to explicitly train all layers at the same
speed, by scaling the gradient w.r.t. every trainable tensor to be proportional
to its current value. In particular, at every batch, we want to update all
trainable tensors, such that the relative change of the L1-norm of the tensors
is the same, across all layers of the network, throughout training time.
Experiments on MNIST show that our method appropriately scales gradients, such
that the relative change in trainable tensors is approximately equal across
layers. In addition, measuring the test accuracy with training time, shows that
our method trains faster than other methods, giving higher test accuracy given
same budget of training steps.
| Sami Abu-El-Haija | null | 1708.07227 | null | null |
GALILEO: A Generalized Low-Entropy Mixture Model | stat.ML cs.DS cs.LG math.PR | We present a new method of generating mixture models for data with
categorical attributes. The keys to this approach are an entropy-based density
metric in categorical space and annealing of high-entropy/low-density
components from an initial state with many components. Pruning of low-density
components using the entropy-based density allows GALILEO to consistently find
high-quality clusters and the same optimal number of clusters. GALILEO has
shown promising results on a range of test datasets commonly used for
categorical clustering benchmarks. We demonstrate that the scaling of GALILEO
is linear in the number of records in the dataset, making this method suitable
for very large categorical datasets.
| Cetin Savkli, Jeffrey Lin, Philip Graff, Matthew Kinsey | null | 1708.07242 | null | null |
On the Compressive Power of Deep Rectifier Networks for High Resolution
Representation of Class Boundaries | cs.LG cs.AI stat.ML | This paper provides a theoretical justification of the superior
classification performance of deep rectifier networks over shallow rectifier
networks from the geometrical perspective of piecewise linear (PWL) classifier
boundaries. We show that, for a given threshold on the approximation error, the
required number of boundary facets to approximate a general smooth boundary
grows exponentially with the dimension of the data, and thus the number of
boundary facets, referred to as boundary resolution, of a PWL classifier is an
important quality measure that can be used to estimate a lower bound on the
classification errors. However, learning naively an exponentially large number
of boundary facets requires the determination of an exponentially large number
of parameters and also requires an exponentially large number of training
patterns. To overcome this issue of "curse of dimensionality", compressive
representations of high resolution classifier boundaries are required. To show
the superior compressive power of deep rectifier networks over shallow
rectifier networks, we prove that the maximum boundary resolution of a single
hidden layer rectifier network classifier grows exponentially with the number
of units when this number is smaller than the dimension of the patterns. When
the number of units is larger than the dimension of the patterns, the growth
rate is reduced to a polynomial order. Consequently, the capacity of generating
a high resolution boundary will increase if the same large number of units are
arranged in multiple layers instead of a single hidden layer. Taking high
dimensional spherical boundaries as examples, we show how deep rectifier
networks can utilize geometric symmetries to approximate a boundary with the
same accuracy but with a significantly fewer number of parameters than single
hidden layer nets.
| Senjian An, Mohammed Bennamoun and Farid Boussaid | null | 1708.07244 | null | null |
Learning 6-DOF Grasping Interaction via Deep Geometry-aware 3D
Representations | cs.RO cs.AI cs.CV cs.LG | This paper focuses on the problem of learning 6-DOF grasping with a parallel
jaw gripper in simulation. We propose the notion of a geometry-aware
representation in grasping based on the assumption that knowledge of 3D
geometry is at the heart of interaction. Our key idea is constraining and
regularizing grasping interaction learning through 3D geometry prediction.
Specifically, we formulate the learning of deep geometry-aware grasping model
in two steps: First, we learn to build mental geometry-aware representation by
reconstructing the scene (i.e., 3D occupancy grid) from RGBD input via
generative 3D shape modeling. Second, we learn to predict grasping outcome with
its internal geometry-aware representation. The learned outcome prediction
model is used to sequentially propose grasping solutions via
analysis-by-synthesis optimization. Our contributions are fourfold: (1) To best
of our knowledge, we are presenting for the first time a method to learn a
6-DOF grasping net from RGBD input; (2) We build a grasping dataset from
demonstrations in virtual reality with rich sensory and interaction
annotations. This dataset includes 101 everyday objects spread across 7
categories, additionally, we propose a data augmentation strategy for effective
learning; (3) We demonstrate that the learned geometry-aware representation
leads to about 10 percent relative performance improvement over the baseline
CNN on grasping objects from our dataset. (4) We further demonstrate that the
model generalizes to novel viewpoints and object instances.
| Xinchen Yan, Jasmine Hsu, Mohi Khansari, Yunfei Bai, Arkanath Pathak,
Abhinav Gupta, James Davidson, Honglak Lee | null | 1708.07303 | null | null |
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning
Workloads | cs.DB cs.LG stat.ML | We present ease.ml, a declarative machine learning service platform we built
to support more than ten research groups outside the computer science
departments at ETH Zurich for their machine learning needs. With ease.ml, a
user defines the high-level schema of a machine learning application and
submits the task via a Web interface. The system automatically deals with the
rest, such as model selection and data movement. In this paper, we describe the
ease.ml architecture and focus on a novel technical problem introduced by
ease.ml regarding resource allocation. We ask, as a "service provider" that
manages a shared cluster of machines among all our users running machine
learning workloads, what is the resource allocation strategy that maximizes the
global satisfaction of all our users?
Resource allocation is a critical yet subtle issue in this multi-tenant
scenario, as we have to balance between efficiency and fairness. We first
formalize the problem that we call multi-tenant model selection, aiming for
minimizing the total regret of all users running automatic model selection
tasks. We then develop a novel algorithm that combines multi-armed bandits with
Bayesian optimization and prove a regret bound under the multi-tenant setting.
Finally, we report our evaluation of ease.ml on synthetic data and on one
service we are providing to our users, namely, image classification with deep
neural networks. Our experimental evaluation results show that our proposed
solution can be up to 9.8x faster in achieving the same global quality for all
users as the two popular heuristics used by our users before ease.ml.
| Tian Li, Jie Zhong, Ji Liu, Wentao Wu, Ce Zhang | null | 1708.07308 | null | null |
Generalized maximum entropy estimation | math.OC cs.IT cs.LG math.IT | We consider the problem of estimating a probability distribution that
maximizes the entropy while satisfying a finite number of moment constraints,
possibly corrupted by noise. Based on duality of convex programming, we present
a novel approximation scheme using a smoothed fast gradient method that is
equipped with explicit bounds on the approximation error. We further
demonstrate how the presented scheme can be used for approximating the chemical
master equation through the zero-information moment closure method, and for an
approximate dynamic programming approach in the context of constrained Markov
decision processes with uncountable state and action spaces.
| Tobias Sutter, David Sutter, Peyman Mohajerin Esfahani, John Lygeros | null | 1708.07311 | null | null |
Active Sampling of Pairs and Points for Large-scale Linear Bipartite
Ranking | cs.LG cs.IR | Bipartite ranking is a fundamental ranking problem that learns to order
relevant instances ahead of irrelevant ones. The pair-wise approach for
bi-partite ranking construct a quadratic number of pairs to solve the problem,
which is infeasible for large-scale data sets. The point-wise approach, albeit
more efficient, often results in inferior performance. That is, it is difficult
to conduct bipartite ranking accurately and efficiently at the same time. In
this paper, we develop a novel active sampling scheme within the pair-wise
approach to conduct bipartite ranking efficiently. The scheme is inspired from
active learning and can reach a competitive ranking performance while focusing
only on a small subset of the many pairs during training. Moreover, we propose
a general Combined Ranking and Classification (CRC) framework to accurately
conduct bipartite ranking. The framework unifies point-wise and pair-wise
approaches and is simply based on the idea of treating each instance point as a
pseudo-pair. Experiments on 14 real-word large-scale data sets demonstrate that
the proposed algorithm of Active Sampling within CRC, when coupled with a
linear Support Vector Machine, usually outperforms state-of-the-art point-wise
and pair-wise ranking approaches in terms of both accuracy and efficiency.
| Wei-Yuan Shen and Hsuan-Tien Lin | null | 1708.07336 | null | null |
An LSTM-Based Dynamic Customer Model for Fashion Recommendation | cs.IR cs.LG | Online fashion sales present a challenging use case for personalized
recommendation: Stores offer a huge variety of items in multiple sizes. Small
stocks, high return rates, seasonality, and changing trends cause continuous
turnover of articles for sale on all time scales. Customers tend to shop
rarely, but often buy multiple items at once. We report on backtest experiments
with sales data of 100k frequent shoppers at Zalando, Europe's leading online
fashion platform. To model changing customer and store environments, our
recommendation method employs a pair of neural networks: To overcome the cold
start problem, a feedforward network generates article embeddings in "fashion
space," which serve as input to a recurrent neural network that predicts a
style vector in this space for each client, based on their past purchase
sequence. We compare our results with a static collaborative filtering
approach, and a popularity ranking baseline.
| Sebastian Heinz, Christian Bracher, Roland Vollgraf | null | 1708.07347 | null | null |
Mixing time estimation in reversible Markov chains from a single sample
path | math.ST cs.LG math.PR stat.ML stat.TH | The spectral gap $\gamma$ of a finite, ergodic, and reversible Markov chain
is an important parameter measuring the asymptotic rate of convergence. In
applications, the transition matrix $P$ may be unknown, yet one sample of the
chain up to a fixed time $n$ may be observed. We consider here the problem of
estimating $\gamma$ from this data. Let $\pi$ be the stationary distribution of
$P$, and $\pi_\star = \min_x \pi(x)$. We show that if $n =
\tilde{O}\bigl(\frac{1}{\gamma \pi_\star}\bigr)$, then $\gamma$ can be
estimated to within multiplicative constants with high probability. When $\pi$
is uniform on $d$ states, this matches (up to logarithmic correction) a lower
bound of $\tilde{\Omega}\bigl(\frac{d}{\gamma}\bigr)$ steps required for
precise estimation of $\gamma$. Moreover, we provide the first procedure for
computing a fully data-dependent interval, from a single finite-length
trajectory of the chain, that traps the mixing time $t_{\text{mix}}$ of the
chain at a prescribed confidence level. The interval does not require the
knowledge of any parameters of the chain. This stands in contrast to previous
approaches, which either only provide point estimates, or require a reset
mechanism, or additional prior knowledge. The interval is constructed around
the relaxation time $t_{\text{relax}} = 1/\gamma$, which is strongly related to
the mixing time, and the width of the interval converges to zero roughly at a
$1/\sqrt{n}$ rate, where $n$ is the length of the sample path.
| Daniel Hsu, Aryeh Kontorovich, David A. Levin, Yuval Peres, Csaba
Szepesv\'ari | null | 1708.07367 | null | null |
Differentially Private Regression for Discrete-Time Survival Analysis | cs.LG cs.CR cs.DB | In survival analysis, regression models are used to understand the effects of
explanatory variables (e.g., age, sex, weight, etc.) to the survival
probability. However, for sensitive survival data such as medical data, there
are serious concerns about the privacy of individuals in the data set when
medical data is used to fit the regression models. The closest work addressing
such privacy concerns is the work on Cox regression which linearly projects the
original data to a lower dimensional space. However, the weakness of this
approach is that there is no formal privacy guarantee for such projection. In
this work, we aim to propose solutions for the regression problem in survival
analysis with the protection of differential privacy which is a golden standard
of privacy protection in data privacy research. To this end, we extend the
Output Perturbation and Objective Perturbation approaches which are originally
proposed to protect differential privacy for the Empirical Risk Minimization
(ERM) problems. In addition, we also propose a novel sampling approach based on
the Markov Chain Monte Carlo (MCMC) method to practically guarantee
differential privacy with better accuracy. We show that our proposed approaches
achieve good accuracy as compared to the non-private results while guaranteeing
differential privacy for individuals in the private data set.
| Th\^ong T. Nguy\^en and Siu Cheung Hui | null | 1708.07436 | null | null |
Bayesian Compressive Sensing Using Normal Product Priors | stat.ML cs.IT cs.LG math.IT | In this paper, we introduce a new sparsity-promoting prior, namely, the
"normal product" prior, and develop an efficient algorithm for sparse signal
recovery under the Bayesian framework. The normal product distribution is the
distribution of a product of two normally distributed variables with zero means
and possibly different variances. Like other sparsity-encouraging distributions
such as the Student's $t$-distribution, the normal product distribution has a
sharp peak at origin, which makes it a suitable prior to encourage sparse
solutions. A two-stage normal product-based hierarchical model is proposed. We
resort to the variational Bayesian (VB) method to perform the inference.
Simulations are conducted to illustrate the effectiveness of our proposed
algorithm as compared with other state-of-the-art compressed sensing
algorithms.
| Zhou Zhou, Kaihui Liu, and Jun Fang | null | 1708.0745 | null | null |
Supervised Speech Separation Based on Deep Learning: An Overview | cs.CL cs.LG cs.NE cs.SD | Speech separation is the task of separating target speech from background
interference. Traditionally, speech separation is studied as a signal
processing problem. A more recent approach formulates speech separation as a
supervised learning problem, where the discriminative patterns of speech,
speakers, and background noise are learned from training data. Over the past
decade, many supervised separation algorithms have been put forward. In
particular, the recent introduction of deep learning to supervised speech
separation has dramatically accelerated progress and boosted separation
performance. This article provides a comprehensive overview of the research on
deep learning based supervised speech separation in the last several years. We
first introduce the background of speech separation and the formulation of
supervised separation. Then we discuss three main components of supervised
separation: learning machines, training targets, and acoustic features. Much of
the overview is on separation algorithms where we review monaural methods,
including speech enhancement (speech-nonspeech separation), speaker separation
(multi-talker separation), and speech dereverberation, as well as
multi-microphone techniques. The important issue of generalization, unique to
supervised learning, is discussed. This overview provides a historical
perspective on how advances are made. In addition, we discuss a number of
conceptual issues, including what constitutes the target source.
| DeLiang Wang and Jitong Chen | null | 1708.07524 | null | null |
Accurate parameter estimation for Bayesian Network Classifiers using
Hierarchical Dirichlet Processes | cs.LG stat.ML | This paper introduces a novel parameter estimation method for the probability
tables of Bayesian network classifiers (BNCs), using hierarchical Dirichlet
processes (HDPs). The main result of this paper is to show that improved
parameter estimation allows BNCs to outperform leading learning methods such as
Random Forest for both 0-1 loss and RMSE, albeit just on categorical datasets.
As data assets become larger, entering the hyped world of "big", efficient
accurate classification requires three main elements: (1) classifiers with
low-bias that can capture the fine-detail of large datasets (2) out-of-core
learners that can learn from data without having to hold it all in main memory
and (3) models that can classify new data very efficiently.
The latest Bayesian network classifiers (BNCs) satisfy these requirements.
Their bias can be controlled easily by increasing the number of parents of the
nodes in the graph. Their structure can be learned out of core with a limited
number of passes over the data. However, as the bias is made lower to
accurately model classification tasks, so is the accuracy of their parameters'
estimates, as each parameter is estimated from ever decreasing quantities of
data. In this paper, we introduce the use of Hierarchical Dirichlet Processes
for accurate BNC parameter estimation.
We conduct an extensive set of experiments on 68 standard datasets and
demonstrate that our resulting classifiers perform very competitively with
Random Forest in terms of prediction, while keeping the out-of-core capability
and superior classification time.
| Francois Petitjean, Wray Buntine, Geoffrey I. Webb and Nayyar Zaidi | null | 1708.07581 | null | null |
Joint Structured Learning and Predictions under Logical Constraints in
Conditional Random Fields | stat.ML cs.LG | This paper is concerned with structured machine learning, in a supervised
machine learning context. It discusses how to make joint structured learning on
interdependent objects of different nature, as well as how to enforce logical
con-straints when predicting labels. We explain how this need arose in a
Document Understanding task. We then discuss a general extension to Conditional
Random Field (CRF) for this purpose and present the contributed open source
implementation on top of the open source PyStruct library. We evaluate its
performance on a publicly available dataset.
| Jean-Luc Meunier | null | 1708.07644 | null | null |
Understanding and Comparing Deep Neural Networks for Age and Gender
Classification | stat.ML cs.AI cs.CV cs.IR cs.LG | Recently, deep neural networks have demonstrated excellent performances in
recognizing the age and gender on human face images. However, these models were
applied in a black-box manner with no information provided about which facial
features are actually used for prediction and how these features depend on
image preprocessing, model initialization and architecture choice. We present a
study investigating these different effects.
In detail, our work compares four popular neural network architectures,
studies the effect of pretraining, evaluates the robustness of the considered
alignment preprocessings via cross-method test set swapping and intuitively
visualizes the model's prediction strategies in given preprocessing conditions
using the recent Layer-wise Relevance Propagation (LRP) algorithm. Our
evaluations on the challenging Adience benchmark show that suitable parameter
initialization leads to a holistic perception of the input, compensating
artefactual data representations. With a combination of simple preprocessing
steps, we reach state of the art performance in gender recognition.
| Sebastian Lapuschkin, Alexander Binder, Klaus-Robert M\"uller,
Wojciech Samek | null | 1708.07689 | null | null |
A Function Approximation Method for Model-based High-Dimensional Inverse
Reinforcement Learning | cs.LG cs.RO | This works handles the inverse reinforcement learning problem in
high-dimensional state spaces, which relies on an efficient solution of
model-based high-dimensional reinforcement learning problems. To solve the
computationally expensive reinforcement learning problems, we propose a
function approximation method to ensure that the Bellman Optimality Equation
always holds, and then estimate a function based on the observed human actions
for inverse reinforcement learning problems. The time complexity of the
proposed method is linearly proportional to the cardinality of the action set,
thus it can handle high-dimensional even continuous state spaces efficiently.
We test the proposed method in a simulated environment to show its accuracy,
and three clinical tasks to show how it can be used to evaluate a doctor's
proficiency.
| Kun Li, Joel W. Burdick | null | 1708.07738 | null | null |
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
Algorithms | cs.LG cs.CV stat.ML | We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images
of 70,000 fashion products from 10 categories, with 7,000 images per category.
The training set has 60,000 images and the test set has 10,000 images.
Fashion-MNIST is intended to serve as a direct drop-in replacement for the
original MNIST dataset for benchmarking machine learning algorithms, as it
shares the same image size, data format and the structure of training and
testing splits. The dataset is freely available at
https://github.com/zalandoresearch/fashion-mnist
| Han Xiao, Kashif Rasul, Roland Vollgraf | null | 1708.07747 | null | null |
Modular Learning Component Attacks: Today's Reality, Tomorrow's
Challenge | cs.CR cs.LG stat.ML | Many of today's machine learning (ML) systems are not built from scratch, but
are compositions of an array of {\em modular learning components} (MLCs). The
increasing use of MLCs significantly simplifies the ML system development
cycles. However, as most MLCs are contributed and maintained by third parties,
their lack of standardization and regulation entails profound security
implications.
In this paper, for the first time, we demonstrate that potentially harmful
MLCs pose immense threats to the security of ML systems. We present a broad
class of {\em logic-bomb} attacks in which maliciously crafted MLCs trigger
host systems to malfunction in a predictable manner. By empirically studying
two state-of-the-art ML systems in the healthcare domain, we explore the
feasibility of such attacks. For example, we show that, without prior knowledge
about the host ML system, by modifying only 3.3{\textperthousand} of the MLC's
parameters, each with distortion below $10^{-3}$, the adversary is able to
force the misdiagnosis of target victims' skin cancers with 100\% success rate.
We provide analytical justification for the success of such attacks, which
points to the fundamental characteristics of today's ML models: high
dimensionality, non-linearity, and non-convexity. The issue thus seems
fundamental to many ML systems. We further discuss potential countermeasures to
mitigate MLC-based attacks and their potential technical challenges.
| Xinyang Zhang, Yujie Ji and Ting Wang | null | 1708.07807 | null | null |
Second-Order Optimization for Non-Convex Machine Learning: An Empirical
Study | math.OC cs.LG math.NA stat.ML | While first-order optimization methods such as stochastic gradient descent
(SGD) are popular in machine learning (ML), they come with well-known
deficiencies, including relatively-slow convergence, sensitivity to the
settings of hyper-parameters such as learning rate, stagnation at high training
errors, and difficulty in escaping flat regions and saddle points. These issues
are particularly acute in highly non-convex settings such as those arising in
neural networks. Motivated by this, there has been recent interest in
second-order methods that aim to alleviate these shortcomings by capturing
curvature information. In this paper, we report detailed empirical evaluations
of a class of Newton-type methods, namely sub-sampled variants of trust region
(TR) and adaptive regularization with cubics (ARC) algorithms, for non-convex
ML problems. In doing so, we demonstrate that these methods not only can be
computationally competitive with hand-tuned SGD with momentum, obtaining
comparable or better generalization performance, but also they are highly
robust to hyper-parameter settings. Further, in contrast to SGD with momentum,
we show that the manner in which these Newton-type methods employ curvature
information allows them to seamlessly escape flat regions and saddle points.
| Peng Xu, Farbod Roosta-Khorasani, Michael W. Mahoney | null | 1708.07827 | null | null |
Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms,
and Applications | cs.LG cs.CV cs.NA | Recently, convex formulations of low-rank matrix factorization problems have
received considerable attention in machine learning. However, such formulations
often require solving for a matrix of the size of the data matrix, making it
challenging to apply them to large scale datasets. Moreover, in many
applications the data can display structures beyond simply being low-rank,
e.g., images and videos present complex spatio-temporal structures that are
largely ignored by standard low-rank methods. In this paper we study a matrix
factorization technique that is suitable for large datasets and captures
additional structure in the factors by using a particular form of
regularization that includes well-known regularizers such as total variation
and the nuclear norm as particular cases. Although the resulting optimization
problem is non-convex, we show that if the size of the factors is large enough,
under certain conditions, any local minimizer for the factors yields a global
minimizer. A few practical algorithms are also provided to solve the matrix
factorization problem, and bounds on the distance from a given approximate
solution of the optimization problem to the global optimum are derived.
Examples in neural calcium imaging video segmentation and hyperspectral
compressed recovery show the advantages of our approach on high-dimensional
datasets.
| Benjamin D. Haeffele and Rene Vidal | null | 1708.0785 | null | null |
Active Expansion Sampling for Learning Feasible Domains in an Unbounded
Input Space | cs.LG stat.ML | Many engineering problems require identifying feasible domains under implicit
constraints. One example is finding acceptable car body styling designs based
on constraints like aesthetics and functionality. Current active-learning based
methods learn feasible domains for bounded input spaces. However, we usually
lack prior knowledge about how to set those input variable bounds. Bounds that
are too small will fail to cover all feasible domains; while bounds that are
too large will waste query budget. To avoid this problem, we introduce Active
Expansion Sampling (AES), a method that identifies (possibly disconnected)
feasible domains over an unbounded input space. AES progressively expands our
knowledge of the input space, and uses successive exploitation and exploration
stages to switch between learning the decision boundary and searching for new
feasible domains. We show that AES has a misclassification loss guarantee
within the explored region, independent of the number of iterations or labeled
samples. Thus it can be used for real-time prediction of samples' feasibility
within the explored region. We evaluate AES on three test examples and compare
AES with two adaptive sampling methods -- the Neighborhood-Voronoi algorithm
and the straddle heuristic -- that operate over fixed input variable bounds.
| Wei Chen, Mark Fuge | null | 1708.07888 | null | null |
Robust Task Clustering for Deep Many-Task Learning | cs.LG cs.AI cs.CL stat.ML | We investigate task clustering for deep-learning based multi-task and
few-shot learning in a many-task setting. We propose a new method to measure
task similarities with cross-task transfer performance matrix for the deep
learning scenario. Although this matrix provides us critical information
regarding similarity between tasks, its asymmetric property and unreliable
performance scores can affect conventional clustering methods adversely.
Additionally, the uncertain task-pairs, i.e., the ones with extremely
asymmetric transfer scores, may collectively mislead clustering algorithms to
output an inaccurate task-partition. To overcome these limitations, we propose
a novel task-clustering algorithm by using the matrix completion technique. The
proposed algorithm constructs a partially-observed similarity matrix based on
the certainty of cluster membership of the task-pairs. We then use a matrix
completion algorithm to complete the similarity matrix. Our theoretical
analysis shows that under mild constraints, the proposed algorithm will
perfectly recover the underlying "true" similarity matrix with a high
probability. Our results show that the new task clustering method can discover
task clusters for training flexible and superior neural network models in a
multi-task learning setup for sentiment classification and dialog intent
classification tasks. Our task clustering approach also extends metric-based
few-shot learning methods to adapt multiple metrics, which demonstrates
empirical advantages when the tasks are diverse.
| Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Gerald
Tesauro, Haoyu Wang, Bowen Zhou | null | 1708.07918 | null | null |
m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time
Series | cs.LG stat.ME | Multivariate time series (MTS) have become increasingly common in healthcare
domains where human vital signs and laboratory results are collected for
predictive diagnosis. Recently, there have been increasing efforts to visualize
healthcare MTS data based on star charts or parallel coordinates. However, such
techniques might not be ideal for visualizing a large MTS dataset, since it is
difficult to obtain insights or interpretations due to the inherent high
dimensionality of MTS. In this paper, we propose 'm-TSNE': a simple and novel
framework to visualize high-dimensional MTS data by projecting them into a
low-dimensional (2-D or 3-D) space while capturing the underlying data
properties. Our framework is easy to use and provides interpretable insights
for healthcare professionals to understand MTS data. We evaluate our
visualization framework on two real-world datasets and demonstrate that the
results of our m-TSNE show patterns that are easy to understand while the other
methods' visualization may have limitations in interpretability.
| Minh Nguyen, Sanjay Purushotham, Hien To, Cyrus Shahabi | null | 1708.07942 | null | null |
Sales Forecast in E-commerce using Convolutional Neural Network | cs.LG stat.ML | Sales forecast is an essential task in E-commerce and has a crucial impact on
making informed business decisions. It can help us to manage the workforce,
cash flow and resources such as optimizing the supply chain of manufacturers
etc. Sales forecast is a challenging problem in that sales is affected by many
factors including promotion activities, price changes, and user preferences
etc. Traditional sales forecast techniques mainly rely on historical sales data
to predict future sales and their accuracies are limited. Some more recent
learning-based methods capture more information in the model to improve the
forecast accuracy. However, these methods require case-by-case manual feature
engineering for specific commercial scenarios, which is usually a difficult,
time-consuming task and requires expert knowledge. To overcome the limitations
of existing methods, we propose a novel approach in this paper to learn
effective features automatically from the structured data using the
Convolutional Neural Network (CNN). When fed with raw log data, our approach
can automatically extract effective features from that and then forecast sales
using those extracted features. We test our method on a large real-world
dataset from CaiNiao.com and the experimental results validate the
effectiveness of our method.
| Kui Zhao, Can Wang | null | 1708.07946 | null | null |
Faster Clustering via Non-Backtracking Random Walks | stat.ML cs.LG cs.SI | This paper presents VEC-NBT, a variation on the unsupervised graph clustering
technique VEC, which improves upon the performance of the original algorithm
significantly for sparse graphs. VEC employs a novel application of the
state-of-the-art word2vec model to embed a graph in Euclidean space via random
walks on the nodes of the graph. In VEC-NBT, we modify the original algorithm
to use a non-backtracking random walk instead of the normal backtracking random
walk used in VEC. We introduce a modification to a non-backtracking random
walk, which we call a begrudgingly-backtracking random walk, and show
empirically that using this model of random walks for VEC-NBT requires shorter
walks on the graph to obtain results with comparable or greater accuracy than
VEC, especially for sparser graphs.
| Brian Rappaport, Anuththari Gamage, Shuchin Aeron | null | 1708.07967 | null | null |
3D Object Reconstruction from a Single Depth View with Adversarial
Learning | cs.CV cs.AI cs.LG cs.RO | In this paper, we propose a novel 3D-RecGAN approach, which reconstructs the
complete 3D structure of a given object from a single arbitrary depth view
using generative adversarial networks. Unlike the existing work which typically
requires multiple views of the same object or class labels to recover the full
3D geometry, the proposed 3D-RecGAN only takes the voxel grid representation of
a depth view of the object as input, and is able to generate the complete 3D
occupancy grid by filling in the occluded/missing regions. The key idea is to
combine the generative capabilities of autoencoders and the conditional
Generative Adversarial Networks (GAN) framework, to infer accurate and
fine-grained 3D structures of objects in high-dimensional voxel space.
Extensive experiments on large synthetic datasets show that the proposed
3D-RecGAN significantly outperforms the state of the art in single view 3D
object reconstruction, and is able to reconstruct unseen types of objects. Our
code and data are available at: https://github.com/Yang7879/3D-RecGAN.
| Bo Yang, Hongkai Wen, Sen Wang, Ronald Clark, Andrew Markham, Niki
Trigoni | null | 1708.07969 | null | null |
Plausible Deniability for Privacy-Preserving Data Synthesis | cs.CR cs.DB cs.LG stat.ML | Releasing full data records is one of the most challenging problems in data
privacy. On the one hand, many of the popular techniques such as data
de-identification are problematic because of their dependence on the background
knowledge of adversaries. On the other hand, rigorous methods such as the
exponential mechanism for differential privacy are often computationally
impractical to use for releasing high dimensional data or cannot preserve high
utility of original data due to their extensive data perturbation.
This paper presents a criterion called plausible deniability that provides a
formal privacy guarantee, notably for releasing sensitive datasets: an output
record can be released only if a certain amount of input records are
indistinguishable, up to a privacy parameter. This notion does not depend on
the background knowledge of an adversary. Also, it can efficiently be checked
by privacy tests. We present mechanisms to generate synthetic datasets with
similar statistical properties to the input data and the same format. We study
this technique both theoretically and experimentally. A key theoretical result
shows that, with proper randomization, the plausible deniability mechanism
generates differentially private synthetic data. We demonstrate the efficiency
of this generative technique on a large dataset; it is shown to preserve the
utility of original data with respect to various statistical analysis and
machine learning measures.
| Vincent Bindschaedler, Reza Shokri, Carl A. Gunter | null | 1708.07975 | null | null |
Deep learning with convolutional neural networks for decoding and
visualization of EEG pathology | cs.LG cs.NE stat.ML | We apply convolutional neural networks (ConvNets) to the task of
distinguishing pathological from normal EEG recordings in the Temple University
Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet
architectures recently shown to decode task-related information from EEG at
least as well as established algorithms designed for this purpose. In decoding
EEG pathology, both ConvNets reached substantially better accuracies (about 6%
better, ~85% vs. ~79%) than the only published result for this dataset, and
were still better when using only 1 minute of each recording for training and
only six seconds of each recording for testing. We used automated methods to
optimize architectural hyperparameters and found intriguingly different ConvNet
architectures, e.g., with max pooling as the only nonlinearity. Visualizations
of the ConvNet decoding behavior showed that they used spectral power changes
in the delta (0-4 Hz) and theta (4-8 Hz) frequency range, possibly alongside
other features, consistent with expectations derived from spectral analysis of
the EEG data and from the textual medical reports. Analysis of the textual
medical reports also highlighted the potential for accuracy increases by
integrating contextual information, such as the age of subjects. In summary,
the ConvNets and visualization techniques used in this study constitute a next
step towards clinically useful automated EEG diagnosis and establish a new
baseline for future work on this topic.
| Robin Tibor Schirrmeister, Lukas Gemein, Katharina Eggensperger, Frank
Hutter, Tonio Ball | null | 1708.08012 | null | null |
On the Protection of Private Information in Machine Learning Systems:
Two Recent Approaches | stat.ML cs.CR cs.LG | The recent, remarkable growth of machine learning has led to intense interest
in the privacy of the data on which machine learning relies, and to new
techniques for preserving privacy. However, older ideas about privacy may well
remain valid and useful. This note reviews two recent works on privacy in the
light of the wisdom of some of the early literature, in particular the
principles distilled by Saltzer and Schroeder in the 1970s.
| Mart\'in Abadi, \'Ulfar Erlingsson, Ian Goodfellow, H. Brendan
McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang | 10.1109/CSF.2017.10 | 1708.08022 | null | null |
Imbalanced Malware Images Classification: a CNN based Approach | cs.CV cs.LG stat.ML | Deep convolutional neural networks (CNNs) can be applied to malware binary
detection via image classification. The performance, however, is degraded due
to the imbalance of malware families (classes). To mitigate this issue, we
propose a simple yet effective weighted softmax loss which can be employed as
the final layer of deep CNNs. The original softmax loss is weighted, and the
weight value can be determined according to class size. A scaling parameter is
also included in computing the weight. Proper selection of this parameter is
studied and an empirical option is suggested. The weighted loss aims at
alleviating the impact of data imbalance in an end-to-end learning fashion. To
validate the efficacy, we deploy the proposed weighted loss in a pre-trained
deep CNN model and fine-tune it to achieve promising results on malware images
classification. Extensive experiments also demonstrate that the new loss
function can well fit other typical CNNs, yielding an improved classification
performance.
| Songqing Yue and Tianyang Wang | null | 1708.08042 | null | null |
Anomaly Detection in Wireless Sensor Networks | cs.LG stat.ML | Wireless sensor networks usually comprise a large number of sensors
monitoring changes in variables. These changes in variables represent changes
in physical quantities. The changes can occur for various reasons; these
reasons are highlighted in this work. Outliers are unusual measurements.
Outliers are important; they are information-bearing occurrences. This work
seeks to identify them based on an approach presented in [1]. A critical review
of most previous works in this area has been presented in [2], and few more are
considered here just to set the stage. The main work can be described as this;
given a set of measurements from sensors that represent a normal situation, [1]
proceeds by first estimating the probability density function (pdf) of the set
using a data-split approach, then estimate the entropy of the set using the
arithmetic mean as an approximation for the expectation. The increase in
entropy that occurs when strange data is recorded is used to detect unusual
measurements in the test set depending on the desired confidence interval or
false alarm rate. The results presented in [1] have been confirmed for
different test signals such as the Gaussian, Beta, in one dimension and beta in
two dimensions, and a beta and uniform mixture distribution in two dimensions.
Finally, the method was confirmed on real data and the results are presented.
The major drawbacks of [1] were identified, and a method that seeks to mitigate
this using the Bhattacharyya distance is presented. This method detects more
subtle anomalies, especially the type that would pass as normal in [1].
Finally, recommendations for future research are presented: the subject of
interpretability, especially for subtle measurements, being the most elusive as
of today.
| Pelumi Oluwasanya | null | 1708.08053 | null | null |
Local Gaussian Processes for Efficient Fine-Grained Traffic Speed
Prediction | cs.AI cs.LG | Traffic speed is a key indicator for the efficiency of an urban
transportation system. Accurate modeling of the spatiotemporally varying
traffic speed thus plays a crucial role in urban planning and development. This
paper addresses the problem of efficient fine-grained traffic speed prediction
using big traffic data obtained from static sensors. Gaussian processes (GPs)
have been previously used to model various traffic phenomena, including flow
and speed. However, GPs do not scale with big traffic data due to their cubic
time complexity. In this work, we address their efficiency issues by proposing
local GPs to learn from and make predictions for correlated subsets of data.
The main idea is to quickly group speed variables in both spatial and temporal
dimensions into a finite number of clusters, so that future and unobserved
traffic speed queries can be heuristically mapped to one of such clusters. A
local GP corresponding to that cluster can then be trained on the fly to make
predictions in real-time. We call this method localization. We use non-negative
matrix factorization for localization and propose simple heuristics for cluster
mapping. We additionally leverage on the expressiveness of GP kernel functions
to model road network topology and incorporate side information. Extensive
experiments using real-world traffic data collected in the two U.S. cities of
Pittsburgh and Washington, D.C., show that our proposed local GPs significantly
improve both runtime performances and prediction accuracies compared to the
baseline global and local GPs.
| Truc Viet Le, Richard J. Oentaryo, Siyuan Liu, Hoong Chuin Lau | 10.1109/TBDATA.2016.2620488 | 1708.08079 | null | null |
Learning MSO-definable hypotheses on string | cs.LG cs.LO | We study the classification problems over string data for hypotheses
specified by formulas of monadic second-order logic MSO. The goal is to design
learning algorithms that run in time polynomial in the size of the training
set, independently of or at least sublinear in the size of the whole data set.
We prove negative as well as positive results. If the data set is an
unprocessed string to which our algorithms have local access, then learning in
sublinear time is impossible even for hypotheses definable in a small fragment
of first-order logic. If we allow for a linear time pre-processing of the
string data to build an index data structure, then learning of MSO-definable
hypotheses is possible in time polynomial in the size of the training set,
independently of the size of the whole data set.
| Martin Grohe and Christof L\"oding and Martin Ritzert | null | 1708.08081 | null | null |
An Ensemble Framework for Detecting Community Changes in Dynamic
Networks | cs.SI cs.LG | Dynamic networks, especially those representing social networks, undergo
constant evolution of their community structure over time. Nodes can migrate
between different communities, communities can split into multiple new
communities, communities can merge together, etc. In order to represent dynamic
networks with evolving communities it is essential to use a dynamic model
rather than a static one. Here we use a dynamic stochastic block model where
the underlying block model is different at different times. In order to
represent the structural changes expressed by this dynamic model the network
will be split into discrete time segments and a clustering algorithm will
assign block memberships for each segment. In this paper we show that using an
ensemble of clustering assignments accommodates for the variance in scalable
clustering algorithms and produces superior results in terms of
pairwise-precision and pairwise-recall. We also demonstrate that the dynamic
clustering produced by the ensemble can be visualized as a flowchart which
encapsulates the community evolution succinctly.
| Timothy La Fond, Geoffrey Sanders, Christine Klymko, Van Emden Henson | null | 1708.08136 | null | null |
Study of Set-Membership Kernel Adaptive Algorithms and Applications | cs.LG | Adaptive algorithms based on kernel structures have been a topic of
significant research over the past few years. The main advantage is that they
form a family of universal approximators, offering an elegant solution to
problems with nonlinearities. Nevertheless these methods deal with kernel
expansions, creating a growing structure also known as dictionary, whose size
depends on the number of new inputs. In this paper we derive the set-membership
kernel-based normalized least-mean square (SM-NKLMS) algorithm, which is
capable of limiting the size of the dictionary created in stationary
environments. We also derive as an extension the set-membership kernelized
affine projection (SM-KAP) algorithm. Finally several experiments are presented
to compare the proposed SM-NKLMS and SM-KAP algorithms to the existing methods.
| R. C. de Lamare and Andr\'e Flores | null | 1708.08142 | null | null |
ByRDiE: Byzantine-resilient distributed coordinate descent for
decentralized learning | cs.LG cs.DC math.OC stat.ML | Distributed machine learning algorithms enable learning of models from
datasets that are distributed over a network without gathering the data at a
centralized location. While efficient distributed algorithms have been
developed under the assumption of faultless networks, failures that can render
these algorithms nonfunctional occur frequently in the real world. This paper
focuses on the problem of Byzantine failures, which are the hardest to
safeguard against in distributed algorithms. While Byzantine fault tolerance
has a rich history, existing work does not translate into efficient and
practical algorithms for high-dimensional learning in fully distributed (also
known as decentralized) settings. In this paper, an algorithm termed
Byzantine-resilient distributed coordinate descent (ByRDiE) is developed and
analyzed that enables distributed learning in the presence of Byzantine
failures. Theoretical analysis (convex settings) and numerical experiments
(convex and nonconvex settings) highlight its usefulness for high-dimensional
distributed learning in the presence of Byzantine failures.
| Zhixiong Yang and Waheed U. Bajwa | 10.1109/TSIPN.2019.2928176 | 1708.08155 | null | null |
Hyperprior on symmetric Dirichlet distribution | cs.LG | In this article we introduce how to put vague hyperprior on Dirichlet
distribution, and we update the parameter of it by adaptive rejection sampling
(ARS). Finally we analyze this hyperprior in an over-fitted mixture model by
some synthetic experiments.
| Jun Lu | null | 1708.08177 | null | null |
ChemGAN challenge for drug discovery: can AI reproduce natural chemical
diversity? | stat.ML cs.AI cs.LG | Generating molecules with desired chemical properties is important for drug
discovery. The use of generative neural networks is promising for this task.
However, from visual inspection, it often appears that generated samples lack
diversity. In this paper, we quantify this internal chemical diversity, and we
raise the following challenge: can a nontrivial AI model reproduce natural
chemical diversity for desired molecules? To illustrate this question, we
consider two generative models: a Reinforcement Learning model and the recently
introduced ORGAN. Both fail at this challenge. We hope this challenge will
stimulate research in this direction.
| Mostapha Benhenda (LAGA) | null | 1708.08227 | null | null |
Efficient Decision Trees for Multi-class Support Vector Machines Using
Entropy and Generalization Error Estimation | cs.LG stat.ML | We propose new methods for Support Vector Machines (SVMs) using tree
architecture for multi-class classi- fication. In each node of the tree, we
select an appropriate binary classifier using entropy and generalization error
estimation, then group the examples into positive and negative classes based on
the selected classi- fier and train a new classifier for use in the
classification phase. The proposed methods can work in time complexity between
O(log2N) to O(N) where N is the number of classes. We compared the performance
of our proposed methods to the traditional techniques on the UCI machine
learning repository using 10-fold cross-validation. The experimental results
show that our proposed methods are very useful for the problems that need fast
classification time or problems with a large number of classes as the proposed
methods run much faster than the traditional techniques but still provide
comparable accuracy.
| Pittipol Kantavat, Boonserm Kijsirikul, Patoomsiri Songsiri, Ken-ichi
Fukui, Masayuki Numao | null | 1708.08231 | null | null |
A New Learning Paradigm for Random Vector Functional-Link Network: RVFL+ | stat.ML cs.LG cs.NE | In school, a teacher plays an important role in various classroom teaching
patterns. Likewise to this human learning activity, the learning using
privileged information (LUPI) paradigm provides additional information
generated by the teacher to 'teach' learning models during the training stage.
Therefore, this novel learning paradigm is a typical Teacher-Student
Interaction mechanism. This paper is the first to present a random vector
functional link network based on the LUPI paradigm, called RVFL+. Rather than
simply combining two existing approaches, the newly-derived RVFL+ fills the gap
between classical randomized neural networks and the newfashioned LUPI
paradigm, which offers an alternative way to train RVFL networks. Moreover, the
proposed RVFL+ can perform in conjunction with the kernel trick for highly
complicated nonlinear feature learning, which is termed KRVFL+. Furthermore,
the statistical property of the proposed RVFL+ is investigated, and we present
a sharp and high-quality generalization error bound based on the Rademacher
complexity. Competitive experimental results on 14 real-world datasets
illustrate the great effectiveness and efficiency of the novel RVFL+ and
KRVFL+, which can achieve better generalization performance than
state-of-the-art methods.
| Peng-Bo Zhang and Zhi-Xin Yang | 10.1016/j.neunet.2019.09.039 | 1708.08282 | null | null |
Open-World Visual Recognition Using Knowledge Graphs | cs.LG cs.CV stat.ML | In a real-world setting, visual recognition systems can be brought to make
predictions for images belonging to previously unknown class labels. In order
to make semantically meaningful predictions for such inputs, we propose a
two-step approach that utilizes information from knowledge graphs. First, a
knowledge-graph representation is learned to embed a large set of entities into
a semantic space. Second, an image representation is learned to embed images
into the same space. Under this setup, we are able to predict structured
properties in the form of relationship triples for any open-world image. This
is true even when a set of labels has been omitted from the training protocols
of both the knowledge graph and image embeddings. Furthermore, we append this
learning framework with appropriate smoothness constraints and show how prior
knowledge can be incorporated into the model. Both these improvements combined
increase performance for visual recognition by a factor of six compared to our
baseline. Finally, we propose a new, extended dataset which we use for
experiments.
| Vincent P.A. Lonij, Ambrish Rawat, Maria-Irina Nicolae | null | 1708.0831 | null | null |
Deep Learning Sparse Ternary Projections for Compressed Sensing of
Images | cs.CV cs.LG stat.ML | Compressed sensing (CS) is a sampling theory that allows reconstruction of
sparse (or compressible) signals from an incomplete number of measurements,
using of a sensing mechanism implemented by an appropriate projection matrix.
The CS theory is based on random Gaussian projection matrices, which satisfy
recovery guarantees with high probability; however, sparse ternary {0, -1, +1}
projections are more suitable for hardware implementation. In this paper, we
present a deep learning approach to obtain very sparse ternary projections for
compressed sensing. Our deep learning architecture jointly learns a pair of a
projection matrix and a reconstruction operator in an end-to-end fashion. The
experimental results on real images demonstrate the effectiveness of the
proposed approach compared to state-of-the-art methods, with significant
advantage in terms of complexity.
| Duc Minh Nguyen and Evaggelia Tsiligianni and Nikos Deligiannis | null | 1708.08311 | null | null |
Improving Robustness of ML Classifiers against Realizable Evasion
Attacks Using Conserved Features | cs.CR cs.LG | Machine learning (ML) techniques are increasingly common in security
applications, such as malware and intrusion detection. However, ML models are
often susceptible to evasion attacks, in which an adversary makes changes to
the input (such as malware) in order to avoid being detected. A conventional
approach to evaluate ML robustness to such attacks, as well as to design robust
ML, is by considering simplified feature-space models of attacks, where the
attacker changes ML features directly to effect evasion, while minimizing or
constraining the magnitude of this change. We investigate the effectiveness of
this approach to designing robust ML in the face of attacks that can be
realized in actual malware (realizable attacks). We demonstrate that in the
context of structure-based PDF malware detection, such techniques appear to
have limited effectiveness, but they are effective with content-based
detectors. In either case, we show that augmenting the feature space models
with conserved features (those that cannot be unilaterally modified without
compromising malicious functionality) significantly improves performance.
Finally, we show that feature space models enable generalized robustness when
faced with a variety of realizable attacks, as compared to classifiers which
are tuned to be robust to a specific realizable attack.
| Liang Tong, Bo Li, Chen Hajaj, Chaowei Xiao, Ning Zhang, Yevgeniy
Vorobeychik | null | 1708.08327 | null | null |
Framing U-Net via Deep Convolutional Framelets: Application to
Sparse-view CT | cs.CV cs.LG stat.ML | X-ray computed tomography (CT) using sparse projection views is a recent
approach to reduce the radiation dose. However, due to the insufficient
projection views, an analytic reconstruction approach using the filtered back
projection (FBP) produces severe streaking artifacts. Recently, deep learning
approaches using large receptive field neural networks such as U-Net have
demonstrated impressive performance for sparse- view CT reconstruction.
However, theoretical justification is still lacking. Inspired by the recent
theory of deep convolutional framelets, the main goal of this paper is,
therefore, to reveal the limitation of U-Net and propose new multi-resolution
deep learning schemes. In particular, we show that the alternative U- Net
variants such as dual frame and the tight frame U-Nets satisfy the so-called
frame condition which make them better for effective recovery of high frequency
edges in sparse view- CT. Using extensive experiments with real patient data
set, we demonstrate that the new network architectures provide better
reconstruction performance.
| Yoseob Han and Jong Chul Ye | null | 1708.08333 | null | null |
Folding membrane proteins by deep transfer learning | q-bio.BM cs.LG | Computational elucidation of membrane protein (MP) structures is challenging
partially due to lack of sufficient solved structures for homology modeling.
Here we describe a high-throughput deep transfer learning method that first
predicts MP contacts by learning from non-membrane proteins (non-MPs) and then
predicting three-dimensional structure models using the predicted contacts as
distance restraints. Tested on 510 non-redundant MPs, our method has contact
prediction accuracy at least 0.18 better than existing methods, predicts
correct folds for 218 MPs (TMscore at least 0.6), and generates
three-dimensional models with RMSD less than 4 Angstrom and 5 Angstrom for 57
and 108 MPs, respectively. A rigorous blind test in the continuous automated
model evaluation (CAMEO) project shows that our method predicted
high-resolution three-dimensional models for two recent test MPs of 210
residues with RMSD close to 2 Angstrom. We estimated that our method could
predict correct folds for between 1,345 and 1,871 reviewed human multi-pass MPs
including a few hundred new folds, which shall facilitate the discovery of
drugs targeting at membrane proteins.
| Sheng Wang, Zhen Li, Yizhou Yu and Jinbo Xu | null | 1708.08407 | null | null |
On denoising autoencoders trained to minimise binary cross-entropy | cs.CV cs.LG stat.ML | Denoising autoencoders (DAEs) are powerful deep learning models used for
feature extraction, data generation and network pre-training. DAEs consist of
an encoder and decoder which may be trained simultaneously to minimise a loss
(function) between an input and the reconstruction of a corrupted version of
the input. There are two common loss functions used for training autoencoders,
these include the mean-squared error (MSE) and the binary cross-entropy (BCE).
When training autoencoders on image data a natural choice of loss function is
BCE, since pixel values may be normalised to take values in [0,1] and the
decoder model may be designed to generate samples that take values in (0,1). We
show theoretically that DAEs trained to minimise BCE may be used to take
gradient steps in the data space towards regions of high probability under the
data-generating distribution. Previously this had only been shown for DAEs
trained using MSE. As a consequence of the theory, iterative application of a
trained DAE moves a data sample from regions of low probability to regions of
higher probability under the data-generating distribution. Firstly, we validate
the theory by showing that novel data samples, consistent with the training
data, may be synthesised when the initial data samples are random noise.
Secondly, we motivate the theory by showing that initial data samples
synthesised via other methods may be improved via iterative application of a
trained DAE to those initial samples.
| Antonia Creswell, Kai Arulkumaran, Anil A. Bharath | null | 1708.08487 | null | null |
Subspace Selection to Suppress Confounding Source Domain Information in
AAM Transfer Learning | cs.CV cs.AI cs.LG | Active appearance models (AAMs) are a class of generative models that have
seen tremendous success in face analysis. However, model learning depends on
the availability of detailed annotation of canonical landmark points. As a
result, when accurate AAM fitting is required on a different set of variations
(expression, pose, identity), a new dataset is collected and annotated. To
overcome the need for time consuming data collection and annotation, transfer
learning approaches have received recent attention. The goal is to transfer
knowledge from previously available datasets (source) to a new dataset
(target). We propose a subspace transfer learning method, in which we select a
subspace from the source that best describes the target space. We propose a
metric to compute the directional similarity between the source eigenvectors
and the target subspace. We show an equivalence between this metric and the
variance of target data when projected onto source eigenvectors. Using this
equivalence, we select a subset of source principal directions that capture the
variance in target data. To define our model, we augment the selected source
subspace with the target subspace learned from a handful of target examples. In
experiments done on six publicly available datasets, we show that our approach
outperforms the state of the art in terms of the RMS fitting error as well as
the percentage of test examples for which AAM fitting converges to the ground
truth.
| Azin Asgarian, Ahmed Bilal Ashraf, David Fleet, and Babak Taati | null | 1708.08508 | null | null |
An inexact subsampled proximal Newton-type method for large-scale
machine learning | cs.LG cs.NA stat.ML | We propose a fast proximal Newton-type algorithm for minimizing regularized
finite sums that returns an $\epsilon$-suboptimal point in
$\tilde{\mathcal{O}}(d(n + \sqrt{\kappa d})\log(\frac{1}{\epsilon}))$ FLOPS,
where $n$ is number of samples, $d$ is feature dimension, and $\kappa$ is the
condition number. As long as $n > d$, the proposed method is more efficient
than state-of-the-art accelerated stochastic first-order methods for non-smooth
regularizers which requires $\tilde{\mathcal{O}}(d(n + \sqrt{\kappa
n})\log(\frac{1}{\epsilon}))$ FLOPS. The key idea is to form the subsampled
Newton subproblem in a way that preserves the finite sum structure of the
objective, thereby allowing us to leverage recent developments in stochastic
first-order methods to solve the subproblem. Experimental results verify that
the proposed algorithm outperforms previous algorithms for $\ell_1$-regularized
logistic regression on real datasets.
| Xuanqing Liu, Cho-Jui Hsieh, Jason D. Lee and Yuekai Sun | null | 1708.08552 | null | null |
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous
Cars | cs.SE cs.AI cs.LG | Recent advances in Deep Neural Networks (DNNs) have led to the development of
DNN-driven autonomous cars that, using sensors like camera, LiDAR, etc., can
drive without any human intervention. Most major manufacturers including Tesla,
GM, Ford, BMW, and Waymo/Google are working on building and testing different
types of autonomous vehicles. The lawmakers of several US states including
California, Texas, and New York have passed new legislation to fast-track the
process of testing and deployment of autonomous vehicles on their roads.
However, despite their spectacular progress, DNNs, just like traditional
software, often demonstrate incorrect or unexpected corner case behaviors that
can lead to potentially fatal collisions. Several such real-world accidents
involving autonomous cars have already happened including one which resulted in
a fatality. Most existing testing techniques for DNN-driven vehicles are
heavily dependent on the manual collection of test data under different driving
conditions which become prohibitively expensive as the number of test
conditions increases.
In this paper, we design, implement and evaluate DeepTest, a systematic
testing tool for automatically detecting erroneous behaviors of DNN-driven
vehicles that can potentially lead to fatal crashes. First, our tool is
designed to automatically generated test cases leveraging real-world changes in
driving conditions like rain, fog, lighting conditions, etc. DeepTest
systematically explores different parts of the DNN logic by generating test
inputs that maximize the numbers of activated neurons. DeepTest found thousands
of erroneous behaviors under different realistic driving conditions (e.g.,
blurring, rain, fog, etc.) many of which lead to potentially fatal crashes in
three top performing DNNs in the Udacity self-driving car challenge.
| Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray | null | 1708.08559 | null | null |
On the Reconstruction Risk of Convolutional Sparse Dictionary Learning | math.ST cs.IT cs.LG math.IT stat.ML stat.TH | Sparse dictionary learning (SDL) has become a popular method for adaptively
identifying parsimonious representations of a dataset, a fundamental problem in
machine learning and signal processing. While most work on SDL assumes a
training dataset of independent and identically distributed samples, a variant
known as convolutional sparse dictionary learning (CSDL) relaxes this
assumption, allowing more general sequential data sources, such as time series
or other dependent data. Although recent work has explored the statistical
properties of classical SDL, the statistical properties of CSDL remain
unstudied. This paper begins to study this by identifying the minimax
convergence rate of CSDL in terms of reconstruction risk, by both upper
bounding the risk of an established CSDL estimator and proving a matching
information-theoretic lower bound. Our results indicate that consistency in
reconstruction risk is possible precisely in the `ultra-sparse' setting, in
which the sparsity (i.e., the number of feature occurrences) is in $o(N)$ in
terms of the length N of the training sequence. Notably, our results make very
weak assumptions, allowing arbitrary dictionaries and dependent measurement
noise. Finally, we verify our theoretical results with numerical experiments on
synthetic data.
| Shashank Singh, Barnab\'as P\'oczos, and Jian Ma | null | 1708.08587 | null | null |
EC3: Combining Clustering and Classification for Ensemble Learning | cs.LG stat.ML | Classification and clustering algorithms have been proved to be successful
individually in different contexts. Both of them have their own advantages and
limitations. For instance, although classification algorithms are more powerful
than clustering methods in predicting class labels of objects, they do not
perform well when there is a lack of sufficient manually labeled reliable data.
On the other hand, although clustering algorithms do not produce label
information for objects, they provide supplementary constraints (e.g., if two
objects are clustered together, it is more likely that the same label is
assigned to both of them) that one can leverage for label prediction of a set
of unknown objects. Therefore, systematic utilization of both these types of
algorithms together can lead to better prediction performance. In this paper,
We propose a novel algorithm, called EC3 that merges classification and
clustering together in order to support both binary and multi-class
classification. EC3 is based on a principled combination of multiple
classification and multiple clustering methods using an optimization function.
We theoretically show the convexity and optimality of the problem and solve it
by block coordinate descent method. We additionally propose iEC3, a variant of
EC3 that handles imbalanced training data. We perform an extensive experimental
analysis by comparing EC3 and iEC3 with 14 baseline methods (7 well-known
standalone classifiers, 5 ensemble classifiers, and 2 existing methods that
merge classification and clustering) on 13 standard benchmark datasets. We show
that our methods outperform other baselines for every single dataset, achieving
at most 10% higher AUC. Moreover our methods are faster (1.21 times faster than
the best baseline), more resilient to noise and class imbalance than the best
baseline method.
| Tanmoy Chakraborty | null | 1708.08591 | null | null |
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