pwc-archive/papers-with-abstracts huggingface dataset:
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe
Authors: Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi
Date: 2017-02-21 00:00:00

Two of the most fundamental prototypes of greedy optimization are the
matching pursuit and Frank-Wolfe algorithms. In this paper, we take a unified
view on both classes of methods, leading to the first explicit convergence
rates of matching pursuit methods in an optimization sense, for general sets of
atoms. We derive sublinear ($1/t$) convergence for both classes on general
smooth objectives, and linear convergence on strongly convex objectives, as
well as a clear correspondence of algorithm variants. Our presented algorithms
and rates are affine invariant, and do not need any incoherence or sparsity
assumptions.
Deep Probabilistic Programming
Authors: Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei
Date: 2017-01-13 00:00:00
Tasks: Probabilistic Programming, Variational Inference

We propose Edward, a Turing-complete probabilistic programming language.
Edward defines two compositional representations---random variables and
inference. By treating inference as a first class citizen, on a par with
modeling, we show that probabilistic programming can be as flexible and
computationally efficient as traditional deep learning. For flexibility, Edward
makes it easy to fit the same model using a variety of composable inference
methods, ranging from point estimation to variational inference to MCMC. In
addition, Edward can reuse the modeling representation as part of inference,
facilitating the design of rich variational models and generative adversarial
networks. For efficiency, Edward is integrated into TensorFlow, providing
significant speedups over existing probabilistic systems. For example, we show
on a benchmark logistic regression task that Edward is at least 35x faster than
Stan and 6x faster than PyMC3. Further, Edward incurs no runtime overhead: it
is as fast as handwritten TensorFlow.
Faster Coordinate Descent via Adaptive Importance Sampling
Authors: Dmytro Perekrestenko, Volkan Cevher, Martin Jaggi
Date: 2017-03-07 00:00:00

Coordinate descent methods employ random partial updates of decision
variables in order to solve huge-scale convex optimization problems. In this
work, we introduce new adaptive rules for the random selection of their
updates. By adaptive, we mean that our selection rules are based on the dual
residual or the primal-dual gap estimates and can change at each iteration. We
theoretically characterize the performance of our selection rules and
demonstrate improvements over the state-of-the-art, and extend our theory and
algorithms to general convex objectives. Numerical evidence with hinge-loss
support vector machines and Lasso confirm that the practice follows the theory.
Learning opacity in Stratal Maximum Entropy Grammar
Authors: Aleksei Nazarov, Joe Pater
Date: 2017-03-07 00:00:00
Tasks: Learning Theory

Opaque phonological patterns are sometimes claimed to be difficult to learn;
specific hypotheses have been advanced about the relative difficulty of
particular kinds of opaque processes (Kiparsky 1971, 1973), and the kind of
data that will be helpful in learning an opaque pattern (Kiparsky 2000). In
this paper, we present a computationally implemented learning theory for one
grammatical theory of opacity: a Maximum Entropy version of Stratal OT
(Berm\'udez-Otero 1999, Kiparsky 2000), and test it on simplified versions of
opaque French tense-lax vowel alternations and the opaque interaction of
diphthong raising and flapping in Canadian English. We find that the difficulty
of opacity can be influenced by evidence for stratal affiliation: the Canadian
English case is easier if the learner encounters application of raising outside
the flapping context, or non-application of raising between words (i.e., <life>
with a raised vowel; <lie for> with a non-raised vowel).
Deep Learning for Automated Quality Assessment of Color Fundus Images in Diabetic Retinopathy Screening
Authors: Sajib Kumar Saha, Basura Fernando, Jorge Cuadros, Di Xiao, Yogesan Kanagasingam
Date: 2017-03-07 00:00:00
Tasks: Deep Learning, Image Quality Assessment

Purpose To develop a computer based method for the automated assessment of
image quality in the context of diabetic retinopathy (DR) to guide the
photographer.
  Methods A deep learning framework was trained to grade the images
automatically. A large representative set of 7000 color fundus images were used
for the experiment which were obtained from the EyePACS that were made
available by the California Healthcare Foundation. Three retinal image analysis
experts were employed to categorize these images into Accept and Reject classes
based on the precise definition of image quality in the context of DR. A deep
learning framework was trained using 3428 images.
  Results A total of 3572 images were used for the evaluation of the proposed
method. The method shows an accuracy of 100% to successfully categorise Accept
and Reject images.
  Conclusion Image quality is an essential prerequisite for the grading of DR.
In this paper we have proposed a deep learning based automated image quality
assessment method in the context of DR. The method can be easily incorporated
with the fundus image capturing system and thus can guide the photographer
whether a recapture is necessary or not.
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification
Authors: Jan Deriu, Aurelien Lucchi, Valeria De Luca, Aliaksei Severyn, Simon Müller, Mark Cieliebak, Thomas Hofmann, Martin Jaggi
Date: 2017-03-07 00:00:00
Tasks: General Classification, Language Modeling, Language Modelling, Sentiment Analysis, Sentiment Classification

This paper presents a novel approach for multi-lingual sentiment
classification in short texts. This is a challenging task as the amount of
training data in languages other than English is very limited. Previously
proposed multi-lingual approaches typically require to establish a
correspondence to English for which powerful classifiers are already available.
In contrast, our method does not require such supervision. We leverage large
amounts of weakly-supervised data in various languages to train a multi-layer
convolutional network and demonstrate the importance of using pre-training of
such networks. We thoroughly evaluate our approach on various multi-lingual
datasets, including the recent SemEval-2016 sentiment prediction benchmark
(Task 4), where we achieved state-of-the-art performance. We also compare the
performance of our model trained individually for each language to a variant
trained for all languages at once. We show that the latter model reaches
slightly worse - but still acceptable - performance when compared to the single
language model, while benefiting from better generalization properties across
languages.
End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks
Authors: Min-Je Choi, Sehun Jeong, Hakjoo Oh, Jaegul Choo
Date: 2017-03-07 00:00:00
Tasks: Question Answering

Detecting buffer overruns from a source code is one of the most common and
yet challenging tasks in program analysis. Current approaches have mainly
relied on rigid rules and handcrafted features devised by a few experts,
limiting themselves in terms of flexible applicability and robustness due to
diverse bug patterns and characteristics existing in sophisticated real-world
software programs. In this paper, we propose a novel, data-driven approach that
is completely end-to-end without requiring any hand-crafted features, thus free
from any program language-specific structural limitations. In particular, our
approach leverages a recently proposed neural network model called memory
networks that have shown the state-of-the-art performances mainly in
question-answering tasks. Our experimental results using source codes
demonstrate that our proposed model is capable of accurately detecting simple
buffer overruns. We also present in-depth analyses on how a memory network can
learn to understand the semantics in programming languages solely from raw
source codes, such as tracing variables of interest, identifying numerical
values, and performing their quantitative comparisons.
Object classification in images of Neoclassical furniture using Deep Learning
Authors: Bernhard Bermeitinger, André Freitas, Simon Donig, Siegfried Handschuh
Date: 2017-03-07 00:00:00
Tasks: Classification, Deep Learning, General Classification, Object, Object Recognition

This short paper outlines research results on object classification in images
of Neoclassical furniture. The motivation was to provide an object recognition
framework which is able to support the alignment of furniture images with a
symbolic level model. A data-driven bottom-up research routine in the
Neoclassica research framework is the main use-case. It strives to deliver
tools for analyzing the spread of aesthetic forms which are considered as a
cultural transfer process.
An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service
Authors: Ismaïl Saadi, Melvin Wong, Bilal Farooq, Jacques Teller, Mario Cools
Date: 2017-03-07 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we present machine learning approaches for characterizing and
forecasting the short-term demand for on-demand ride-hailing services. We
propose the spatio-temporal estimation of the demand that is a function of
variable effects related to traffic, pricing and weather conditions. With
respect to the methodology, a single decision tree, bootstrap-aggregated
(bagged) decision trees, random forest, boosted decision trees, and artificial
neural network for regression have been adapted and systematically compared
using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and
slope. To better assess the quality of the models, they have been tested on a
real case study using the data of DiDi Chuxing, the main on-demand ride hailing
service provider in China. In the current study, 199,584 time-slots describing
the spatio-temporal ride-hailing demand has been extracted with an
aggregated-time interval of 10 mins. All the methods are trained and validated
on the basis of two independent samples from this dataset. The results revealed
that boosted decision trees provide the best prediction accuracy (RMSE=16.41),
while avoiding the risk of over-fitting, followed by artificial neural network
(20.09), random forest (23.50), bagged decision trees (24.29) and single
decision tree (33.55).
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Authors: Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter
Date: 2016-05-23 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning, Hyperparameter Optimization

Bayesian optimization has become a successful tool for hyperparameter
optimization of machine learning algorithms, such as support vector machines or
deep neural networks. Despite its success, for large datasets, training and
validating a single configuration often takes hours, days, or even weeks, which
limits the achievable performance. To accelerate hyperparameter optimization,
we propose a generative model for the validation error as a function of
training set size, which is learned during the optimization process and allows
exploration of preliminary configurations on small subsets, by extrapolating to
the full dataset. We construct a Bayesian optimization procedure, dubbed
Fabolas, which models loss and training time as a function of dataset size and
automatically trades off high information gain about the global optimum against
computational cost. Experiments optimizing support vector machines and deep
neural networks show that Fabolas often finds high-quality solutions 10 to 100
times faster than other state-of-the-art Bayesian optimization methods or the
recently proposed bandit strategy Hyperband.
Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy
Authors: Quan Zou, Shixiang Wan, Ying Ju, Jijun Tang, Xiangxiang Zeng
Date: 2017-03-07 00:00:00
Tasks: Dimensionality Reduction, Prediction

Background: It is necessary and essential to discovery protein function from
the novel primary sequences. Wet lab experimental procedures are not only
time-consuming, but also costly, so predicting protein structure and function
reliably based only on amino acid sequence has significant value. TATA-binding
protein (TBP) is a kind of DNA binding protein, which plays a key role in the
transcription regulation. Our study proposed an automatic approach for
identifying TATA-binding proteins efficiently, accurately, and conveniently.
This method would guide for the special protein identification with
computational intelligence strategies. Results: Firstly, we proposed novel
fingerprint features for TBP based on pseudo amino acid composition,
physicochemical properties, and secondary structure. Secondly, hierarchical
features dimensionality reduction strategies were employed to improve the
performance furthermore. Currently, Pretata achieves 92.92% TATA- binding
protein prediction accuracy, which is better than all other existing methods.
Conclusions: The experiments demonstrate that our method could greatly improve
the prediction accuracy and speed, thus allowing large-scale NGS data
prediction to be practical. A web server is developed to facilitate the other
researchers, which can be accessed at http://server.malab.cn/preTata/.
A ranking approach to global optimization
Authors: Cédric Malherbe, Nicolas Vayatis
Date: 2016-03-14 00:00:00
Tasks: global-optimization

We consider the problem of maximizing an unknown function over a compact and
convex set using as few observations as possible. We observe that the
optimization of the function essentially relies on learning the induced
bipartite ranking rule of f. Based on this idea, we relate global optimization
to bipartite ranking which allows to address problems with high dimensional
input space, as well as cases of functions with weak regularity properties. The
paper introduces novel meta-algorithms for global optimization which rely on
the choice of any bipartite ranking method. Theoretical properties are provided
as well as convergence guarantees and equivalences between various optimization
methods are obtained as a by-product. Eventually, numerical evidence is given
to show that the main algorithm of the paper which adapts empirically to the
underlying ranking structure essentially outperforms existing state-of-the-art
global optimization algorithms in typical benchmarks.
Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce
Authors: Devashish Shankar, Sujay Narumanchi, H A Ananya, Pramod Kompalli, Krishnendu Chaudhury
Date: 2017-03-07 00:00:00
Tasks: 2k, Image Retrieval, Retrieval

In this paper, we present a unified end-to-end approach to build a large
scale Visual Search and Recommendation system for e-commerce. Previous works
have targeted these problems in isolation. We believe a more effective and
elegant solution could be obtained by tackling them together. We propose a
unified Deep Convolutional Neural Network architecture, called VisNet, to learn
embeddings to capture the notion of visual similarity, across several semantic
granularities. We demonstrate the superiority of our approach for the task of
image retrieval, by comparing against the state-of-the-art on the Exact
Street2Shop dataset. We then share the design decisions and trade-offs made
while deploying the model to power Visual Recommendations across a catalog of
50M products, supporting 2K queries a second at Flipkart, India's largest
e-commerce company. The deployment of our solution has yielded a significant
business impact, as measured by the conversion-rate.
Convolutional Recurrent Neural Networks for Bird Audio Detection
Authors: EmreÇakır, Sharath Adavanne, Giambattista Parascandolo, Konstantinos Drossos, Tuomas Virtanen
Date: 2017-03-07 00:00:00
Tasks: Bird Audio Detection

Bird sounds possess distinctive spectral structure which may exhibit small
shifts in spectrum depending on the bird species and environmental conditions.
In this paper, we propose using convolutional recurrent neural networks on the
task of automated bird audio detection in real-life environments. In the
proposed method, convolutional layers extract high dimensional, local frequency
shift invariant features, while recurrent layers capture longer term
dependencies between the features extracted from short time frames. This method
achieves 88.5% Area Under ROC Curve (AUC) score on the unseen evaluation data
and obtains the second place in the Bird Audio Detection challenge.
Deep Robust Kalman Filter
Authors: Shirli Di-Castro Shashua, Shie Mannor
Date: 2017-03-07 00:00:00
Tasks: Decision Making, Sequential Decision Making

A Robust Markov Decision Process (RMDP) is a sequential decision making model
that accounts for uncertainty in the parameters of dynamic systems. This
uncertainty introduces difficulties in learning an optimal policy, especially
for environments with large state spaces. We propose two algorithms, RTD-DQN
and Deep-RoK, for solving large-scale RMDPs using nonlinear approximation
schemes such as deep neural networks. The RTD-DQN algorithm incorporates the
robust Bellman temporal difference error into a robust loss function, yielding
robust policies for the agent. The Deep-RoK algorithm is a robust Bayesian
method, based on the Extended Kalman Filter (EKF), that accounts for both the
uncertainty in the weights of the approximated value function and the
uncertainty in the transition probabilities, improving the robustness of the
agent. We provide theoretical results for our approach and test the proposed
algorithms on a continuous state domain.
Large-scale image analysis using docker sandboxing
Authors: B Sengupta, E Vazquez, M Sasdelli, Y Qian, M Peniak, L Netherton, G Delfino
Date: 2017-03-07 00:00:00
Tasks: General Classification, Retrieval

With the advent of specialized hardware such as Graphics Processing Units
(GPUs), large scale image localization, classification and retrieval have seen
increased prevalence. Designing scalable software architecture that co-evolves
with such specialized hardware is a challenge in the commercial setting. In
this paper, we describe one such architecture (\textit{Cortexica}) that
leverages scalability of GPUs and sandboxing offered by docker containers. This
allows for the flexibility of mixing different computer architectures as well
as computational algorithms with the security of a trusted environment. We
illustrate the utility of this framework in a commercial setting i.e.,
searching for multiple products in an image by combining image localisation and
retrieval.
An expressive dissimilarity measure for relational clustering using neighbourhood trees
Authors: Sebastijan Dumancic, Hendrik Blockeel
Date: 2016-04-29 00:00:00
Tasks: Clustering

Clustering is an underspecified task: there are no universal criteria for
what makes a good clustering. This is especially true for relational data,
where similarity can be based on the features of individuals, the relationships
between them, or a mix of both. Existing methods for relational clustering have
strong and often implicit biases in this respect. In this paper, we introduce a
novel similarity measure for relational data. It is the first measure to
incorporate a wide variety of types of similarity, including similarity of
attributes, similarity of relational context, and proximity in a hypergraph. We
experimentally evaluate how using this similarity affects the quality of
clustering on very different types of datasets. The experiments demonstrate
that (a) using this similarity in standard clustering methods consistently
gives good results, whereas other measures work well only on datasets that
match their bias; and (b) on most datasets, the novel similarity outperforms
even the best among the existing ones.
X-ray Astronomical Point Sources Recognition Using Granular Binary-tree SVM
Authors: Zhixian Ma, Weitian Li, Lei Wang, Haiguang Xu, Jie Zhu
Date: 2017-03-07 00:00:00

The study on point sources in astronomical images is of special importance,
since most energetic celestial objects in the Universe exhibit a point-like
appearance. An approach to recognize the point sources (PS) in the X-ray
astronomical images using our newly designed granular binary-tree support
vector machine (GBT-SVM) classifier is proposed. First, all potential point
sources are located by peak detection on the image. The image and spectral
features of these potential point sources are then extracted. Finally, a
classifier to recognize the true point sources is build through the extracted
features. Experiments and applications of our approach on real X-ray
astronomical images are demonstrated. comparisons between our approach and
other SVM-based classifiers are also carried out by evaluating the precision
and recall rates, which prove that our approach is better and achieves a higher
accuracy of around 89%.
Discriminative models for multi-instance problems with tree-structure
Authors: Tomas Pevny, Petr Somol
Date: 2017-03-07 00:00:00

Modeling network traffic is gaining importance in order to counter modern
threats of ever increasing sophistication. It is though surprisingly difficult
and costly to construct reliable classifiers on top of telemetry data due to
the variety and complexity of signals that no human can manage to interpret in
full. Obtaining training data with sufficiently large and variable body of
labels can thus be seen as prohibitive problem. The goal of this work is to
detect infected computers by observing their HTTP(S) traffic collected from
network sensors, which are typically proxy servers or network firewalls, while
relying on only minimal human input in model training phase. We propose a
discriminative model that makes decisions based on all computer's traffic
observed during predefined time window (5 minutes in our case). The model is
trained on collected traffic samples over equally sized time window per large
number of computers, where the only labels needed are human verdicts about the
computer as a whole (presumed infected vs. presumed clean). As part of training
the model itself recognizes discriminative patterns in traffic targeted to
individual servers and constructs the final high-level classifier on top of
them. We show the classifier to perform with very high precision, while the
learned traffic patterns can be interpreted as Indicators of Compromise. In the
following we implement the discriminative model as a neural network with
special structure reflecting two stacked multi-instance problems. The main
advantages of the proposed configuration include not only improved accuracy and
ability to learn from gross labels, but also automatic learning of server types
(together with their detectors) which are typically visited by infected
computers.