pwc-archive/papers-with-abstracts huggingface dataset:
Learning Syntactic Program Transformations from Examples
Authors: Reudismam Rolim, Gustavo Soares, Loris D'Antoni, Oleksandr Polozov, Sumit Gulwani, Rohit Gheyi, Ryo Suzuki, Bjoern Hartmann
Date: 2016-08-31 00:00:00

IDEs, such as Visual Studio, automate common transformations, such as Rename
and Extract Method refactorings. However, extending these catalogs of
transformations is complex and time-consuming. A similar phenomenon appears in
intelligent tutoring systems where instructors have to write cumbersome code
transformations that describe "common faults" to fix similar student
submissions to programming assignments. We present REFAZER, a technique for
automatically generating program transformations. REFAZER builds on the
observation that code edits performed by developers can be used as examples for
learning transformations. Example edits may share the same structure but
involve different variables and subexpressions, which must be generalized in a
transformation at the right level of abstraction. To learn transformations,
REFAZER leverages state-of-the-art programming-by-example methodology using the
following key components: (a) a novel domain-specific language (DSL) for
describing program transformations, (b) domain-specific deductive algorithms
for synthesizing transformations in the DSL, and (c) functions for ranking the
synthesized transformations. We instantiate and evaluate REFAZER in two
domains. First, given examples of edits used by students to fix incorrect
programming assignment submissions, we learn transformations that can fix other
students' submissions with similar faults. In our evaluation conducted on 4
programming tasks performed by 720 students, our technique helped to fix
incorrect submissions for 87% of the students. In the second domain, we use
repetitive edits applied by developers to the same project to synthesize a
program transformation that applies these edits to other locations in the code.
In our evaluation conducted on 59 scenarios of repetitive edits taken from 3 C#
open-source projects, REFAZER learns the intended program transformation in 83%
of the cases.
Towards Competitive Classifiers for Unbalanced Classification Problems: A Study on the Performance Scores
Authors: Jonathan Ortigosa-Hernández, Iñaki Inza, Jose A. Lozano
Date: 2016-08-31 00:00:00
Tasks: General Classification

Although a great methodological effort has been invested in proposing
competitive solutions to the class-imbalance problem, little effort has been
made in pursuing a theoretical understanding of this matter.
  In order to shed some light on this topic, we perform, through a novel
framework, an exhaustive analysis of the adequateness of the most commonly used
performance scores to assess this complex scenario. We conclude that using
unweighted H\"older means with exponent $p \leq 1$ to average the recalls of
all the classes produces adequate scores which are capable of determining
whether a classifier is competitive.
  Then, we review the major solutions presented in the class-imbalance
literature. Since any learning task can be defined as an optimisation problem
where a loss function, usually connected to a particular score, is minimised,
our goal, here, is to find whether the learning tasks found in the literature
are also oriented to maximise the previously detected adequate scores. We
conclude that they usually maximise the unweighted H\"older mean with $p = 1$
(a-mean).
  Finally, we provide bounds on the values of the studied performance scores
which guarantee a classifier with a higher recall than the random classifier in
each and every class.
Robustness of classifiers: from adversarial to random noise
Authors: Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
Date: 2016-08-31 00:00:00
Proceeding: NeurIPS 2016 12

Several recent works have shown that state-of-the-art classifiers are
vulnerable to worst-case (i.e., adversarial) perturbations of the datapoints.
On the other hand, it has been empirically observed that these same classifiers
are relatively robust to random noise. In this paper, we propose to study a
\textit{semi-random} noise regime that generalizes both the random and
worst-case noise regimes. We propose the first quantitative analysis of the
robustness of nonlinear classifiers in this general noise regime. We establish
precise theoretical bounds on the robustness of classifiers in this general
regime, which depend on the curvature of the classifier's decision boundary.
Our bounds confirm and quantify the empirical observations that classifiers
satisfying curvature constraints are robust to random noise. Moreover, we
quantify the robustness of classifiers in terms of the subspace dimension in
the semi-random noise regime, and show that our bounds remarkably interpolate
between the worst-case and random noise regimes. We perform experiments and
show that the derived bounds provide very accurate estimates when applied to
various state-of-the-art deep neural networks and datasets. This result
suggests bounds on the curvature of the classifiers' decision boundaries that
we support experimentally, and more generally offers important insights onto
the geometry of high dimensional classification problems.
Knowledge Representation Analysis of Graph Mining
Authors: Matthias van der Hallen, Sergey Paramonov, Michael Leuschel, Gerda Janssens
Date: 2016-08-31 00:00:00
Tasks: Graph Mining

Many problems, especially those with a composite structure, can naturally be
expressed in higher order logic. From a KR perspective modeling these problems
in an intuitive way is a challenging task. In this paper we study the graph
mining problem as an example of a higher order problem. In short, this problem
asks us to find a graph that frequently occurs as a subgraph among a set of
example graphs. We start from the problem's mathematical definition to solve it
in three state-of-the-art specification systems. For IDP and ASP, which have no
native support for higher order logic, we propose the use of encoding
techniques such as the disjoint union technique and the saturation technique.
ProB benefits from the higher order support for sets. We compare the
performance of the three approaches to get an idea of the overhead of the
higher order support.
  We propose higher-order language extensions for IDP-like specification
languages and discuss what kind of solver support is needed. Native higher
order shifts the burden of rewriting specifications using encoding techniques
from the user to the solver itself.
Hash2Vec, Feature Hashing for Word Embeddings
Authors: Luis Argerich, Joaquín Torré Zaffaroni, Matías J Cano
Date: 2016-08-31 00:00:00
Tasks: Document Classification, General Classification, Word Embeddings

In this paper we propose the application of feature hashing to create word
embeddings for natural language processing. Feature hashing has been used
successfully to create document vectors in related tasks like document
classification. In this work we show that feature hashing can be applied to
obtain word embeddings in linear time with the size of the data. The results
show that this algorithm, that does not need training, is able to capture the
semantic meaning of words. We compare the results against GloVe showing that
they are similar. As far as we know this is the first application of feature
hashing to the word embeddings problem and the results indicate this is a
scalable technique with practical results for NLP applications.
The Generalized Smallest Grammar Problem
Authors: Payam Siyari, Matthias Gallé
Date: 2016-08-31 00:00:00

The Smallest Grammar Problem -- the problem of finding the smallest
context-free grammar that generates exactly one given sequence -- has never
been successfully applied to grammatical inference. We investigate the reasons
and propose an extended formulation that seeks to minimize non-recursive
grammars, instead of straight-line programs. In addition, we provide very
efficient algorithms that approximate the minimization problem of this class of
grammars. Our empirical evaluation shows that we are able to find smaller
models than the current best approximations to the Smallest Grammar Problem on
standard benchmarks, and that the inferred rules capture much better the
syntactic structure of natural language.
Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level
Authors: Rie Johnson, Tong Zhang
Date: 2016-08-31 00:00:00
Tasks: Text Categorization

This paper reports the performances of shallow word-level convolutional
neural networks (CNN), our earlier work (2015), on the eight datasets with
relatively large training data that were used for testing the very deep
character-level CNN in Conneau et al. (2016). Our findings are as follows. The
shallow word-level CNNs achieve better error rates than the error rates
reported in Conneau et al., though the results should be interpreted with some
consideration due to the unique pre-processing of Conneau et al. The shallow
word-level CNN uses more parameters and therefore requires more storage than
the deep character-level CNN; however, the shallow word-level CNN computes much
faster.
Learning a Predictable and Generative Vector Representation for Objects
Authors: Rohit Girdhar, David F. Fouhey, Mikel Rodriguez, Abhinav Gupta
Date: 2016-03-29 00:00:00
Tasks: Retrieval

What is a good vector representation of an object? We believe that it should
be generative in 3D, in the sense that it can produce new 3D objects; as well
as be predictable from 2D, in the sense that it can be perceived from 2D
images. We propose a novel architecture, called the TL-embedding network, to
learn an embedding space with these properties. The network consists of two
components: (a) an autoencoder that ensures the representation is generative;
and (b) a convolutional network that ensures the representation is predictable.
This enables tackling a number of tasks including voxel prediction from 2D
images and 3D model retrieval. Extensive experimental analysis demonstrates the
usefulness and versatility of this embedding.
A Novel Online Real-time Classifier for Multi-label Data Streams
Authors: Rajasekar Venkatesan, Meng Joo Er, Shiqian Wu, Mahardhika Pratama
Date: 2016-08-31 00:00:00
Tasks: Classification, General Classification, Multi-class Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

In this paper, a novel extreme learning machine based online multi-label
classifier for real-time data streams is proposed. Multi-label classification
is one of the actively researched machine learning paradigm that has gained
much attention in the recent years due to its rapidly increasing real world
applications. In contrast to traditional binary and multi-class classification,
multi-label classification involves association of each of the input samples
with a set of target labels simultaneously. There are no real-time online
neural network based multi-label classifier available in the literature. In
this paper, we exploit the inherent nature of high speed exhibited by the
extreme learning machines to develop a novel online real-time classifier for
multi-label data streams. The developed classifier is experimented with
datasets from different application domains for consistency, performance and
speed. The experimental studies show that the proposed method outperforms the
existing state-of-the-art techniques in terms of speed and accuracy and can
classify multi-label data streams in real-time.
A High Speed Multi-label Classifier based on Extreme Learning Machines
Authors: Meng Joo Er, Rajasekar Venkatesan, Ning Wang
Date: 2016-08-31 00:00:00
Tasks: Classification, General Classification, Multi-class Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Vocal Bursts Intensity Prediction

In this paper a high speed neural network classifier based on extreme
learning machines for multi-label classification problem is proposed and
dis-cussed. Multi-label classification is a superset of traditional binary and
multi-class classification problems. The proposed work extends the extreme
learning machine technique to adapt to the multi-label problems. As opposed to
the single-label problem, both the number of labels the sample belongs to, and
each of those target labels are to be identified for multi-label classification
resulting in in-creased complexity. The proposed high speed multi-label
classifier is applied to six benchmark datasets comprising of different
application areas such as multi-media, text and biology. The training time and
testing time of the classifier are compared with those of the state-of-the-arts
methods. Experimental studies show that for all the six datasets, our proposed
technique have faster execution speed and better performance, thereby
outperforming all the existing multi-label clas-sification methods.
Facial Surface Analysis using Iso-Geodesic Curves in Three Dimensional Face Recognition System
Authors: Rachid Ahdid, El Mahdi Barrah, Said Safi, Bouzid Manaut
Date: 2016-08-31 00:00:00
Tasks: 3D Shape Classification, 3D Shape Retrieval, Face Recognition, Retrieval

In this paper, we present an automatic 3D face recognition system. This
system is based on the representation of human faces surfaces as collections of
Iso-Geodesic Curves (IGC) using 3D Fast Marching algorithm. To compare two
facial surfaces, we compute a geodesic distance between a pair of facial curves
using a Riemannian geometry. In the classifying step, we use: Neural Networks
(NN), K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). To test this
method and evaluate its performance, a simulation series of experiments were
performed on 3D Shape REtrieval Contest 2008 database (SHREC2008).
Demographic Dialectal Variation in Social Media: A Case Study of African-American English
Authors: Su Lin Blodgett, Lisa Green, Brendan O'Connor
Date: 2016-08-31 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Dependency Parsing, Language Identification

Though dialectal language is increasingly abundant on social media, few
resources exist for developing NLP tools to handle such language. We conduct a
case study of dialectal language in online conversational text by investigating
African-American English (AAE) on Twitter. We propose a distantly supervised
model to identify AAE-like language from demographics associated with
geo-located messages, and we verify that this language follows well-known AAE
linguistic phenomena. In addition, we analyze the quality of existing language
identification and dependency parsing tools on AAE-like text, demonstrating
that they perform poorly on such text compared to text associated with white
speakers. We also provide an ensemble classifier for language identification
which eliminates this disparity and release a new corpus of tweets containing
AAE-like language.
A Mathematical Framework for Feature Selection from Real-World Data with Non-Linear Observations
Authors: Martin Genzel, Gitta Kutyniok
Date: 2016-08-31 00:00:00
Tasks: feature selection, Variable Selection

In this paper, we study the challenge of feature selection based on a
relatively small collection of sample pairs $\{(x_i, y_i)\}_{1 \leq i \leq m}$.
The observations $y_i \in \mathbb{R}$ are thereby supposed to follow a noisy
single-index model, depending on a certain set of signal variables. A major
difficulty is that these variables usually cannot be observed directly, but
rather arise as hidden factors in the actual data vectors $x_i \in
\mathbb{R}^d$ (feature variables). We will prove that a successful variable
selection is still possible in this setup, even when the applied estimator does
not have any knowledge of the underlying model parameters and only takes the
'raw' samples $\{(x_i, y_i)\}_{1 \leq i \leq m}$ as input. The model
assumptions of our results will be fairly general, allowing for non-linear
observations, arbitrary convex signal structures as well as strictly convex
loss functions. This is particularly appealing for practical purposes, since in
many applications, already standard methods, e.g., the Lasso or logistic
regression, yield surprisingly good outcomes. Apart from a general discussion
of the practical scope of our theoretical findings, we will also derive a
rigorous guarantee for a specific real-world problem, namely sparse feature
extraction from (proteomics-based) mass spectrometry data.
European Union regulations on algorithmic decision-making and a "right to explanation"
Authors: Bryce Goodman, Seth Flaxman
Date: 2016-06-28 00:00:00
Tasks: Decision Making

We summarize the potential impact that the European Union's new General Data
Protection Regulation will have on the routine use of machine learning
algorithms. Slated to take effect as law across the EU in 2018, it will
restrict automated individual decision-making (that is, algorithms that make
decisions based on user-level predictors) which "significantly affect" users.
The law will also effectively create a "right to explanation," whereby a user
can ask for an explanation of an algorithmic decision that was made about them.
We argue that while this law will pose large challenges for industry, it
highlights opportunities for computer scientists to take the lead in designing
algorithms and evaluation frameworks which avoid discrimination and enable
explanation.
CliqueCNN: Deep Unsupervised Exemplar Learning
Authors: Miguel A. Bautista, Artsiom Sanakoyeu, Ekaterina Sutter, Björn Ommer
Date: 2016-08-31 00:00:00
Proceeding: NeurIPS 2016 12

Exemplar learning is a powerful paradigm for discovering visual similarities
in an unsupervised manner. In this context, however, the recent breakthrough in
deep learning could not yet unfold its full potential. With only a single
positive sample, a great imbalance between one positive and many negatives, and
unreliable relationships between most samples, training of Convolutional Neural
networks is impaired. Given weak estimates of local distance we propose a
single optimization problem to extract batches of samples with mutually
consistent relations. Conflicting relations are distributed over different
batches and similar samples are grouped into compact cliques. Learning exemplar
similarities is framed as a sequence of clique categorization tasks. The CNN
then consolidates transitivity relations within and between cliques and learns
a single representation for all samples without the need for labels. The
proposed unsupervised approach has shown competitive performance on detailed
posture analysis and object classification.
Multi-Person Pose Estimation with Local Joint-to-Person Associations
Authors: Umar Iqbal, Juergen Gall
Date: 2016-08-30 00:00:00
Tasks: Keypoint Detection, Multi-Person Pose Estimation, Outlier Detection, Pose Estimation

Despite of the recent success of neural networks for human pose estimation,
current approaches are limited to pose estimation of a single person and cannot
handle humans in groups or crowds. In this work, we propose a method that
estimates the poses of multiple persons in an image in which a person can be
occluded by another person or might be truncated. To this end, we consider
multi-person pose estimation as a joint-to-person association problem. We
construct a fully connected graph from a set of detected joint candidates in an
image and resolve the joint-to-person association and outlier detection using
integer linear programming. Since solving joint-to-person association jointly
for all persons in an image is an NP-hard problem and even approximations are
expensive, we solve the problem locally for each person. On the challenging
MPII Human Pose Dataset for multiple persons, our approach achieves the
accuracy of a state-of-the-art method, but it is 6,000 to 19,000 times faster.
Training Deep Spiking Neural Networks using Backpropagation
Authors: Jun Haeng Lee, Tobi Delbruck, Michael Pfeiffer
Date: 2016-08-31 00:00:00
Tasks: Event-based vision

Deep spiking neural networks (SNNs) hold great potential for improving the
latency and energy efficiency of deep neural networks through event-based
computation. However, training such networks is difficult due to the
non-differentiable nature of asynchronous spike events. In this paper, we
introduce a novel technique, which treats the membrane potentials of spiking
neurons as differentiable signals, where discontinuities at spike times are
only considered as noise. This enables an error backpropagation mechanism for
deep SNNs, which works directly on spike signals and membrane potentials. Thus,
compared with previous methods relying on indirect training and conversion, our
technique has the potential to capture the statics of spikes more precisely.
Our novel framework outperforms all previously reported results for SNNs on the
permutation invariant MNIST benchmark, as well as the N-MNIST benchmark
recorded with event-based vision sensors.
Multimodal Residual Learning for Visual QA
Authors: Jin-Hwa Kim, Sang-Woo Lee, Dong-Hyun Kwak, Min-Oh Heo, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang
Date: 2016-06-05 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Multiple-choice, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Deep neural networks continue to advance the state-of-the-art of image
recognition tasks with various methods. However, applications of these methods
to multimodality remain limited. We present Multimodal Residual Networks (MRN)
for the multimodal residual learning of visual question-answering, which
extends the idea of the deep residual learning. Unlike the deep residual
learning, MRN effectively learns the joint representation from vision and
language information. The main idea is to use element-wise multiplication for
the joint residual mappings exploiting the residual learning of the attentional
models in recent studies. Various alternative models introduced by
multimodality are explored based on our study. We achieve the state-of-the-art
results on the Visual QA dataset for both Open-Ended and Multiple-Choice tasks.
Moreover, we introduce a novel method to visualize the attention effect of the
joint representations for each learning block using back-propagation algorithm,
even though the visual features are collapsed without spatial information.
Binary Particle Swarm Optimization versus Hybrid Genetic Algorithm for Inferring Well Supported Phylogenetic Trees
Authors: Bassam AlKindy, Bashar Al-Nuaimi, Christophe Guyeux, Jean-François Couchot, Michel Salomon, Reem Alsrraj, Laurent Philippe
Date: 2016-08-31 00:00:00

The amount of completely sequenced chloroplast genomes increases rapidly
every day, leading to the possibility to build large-scale phylogenetic trees
of plant species. Considering a subset of close plant species defined according
to their chloroplasts, the phylogenetic tree that can be inferred by their core
genes is not necessarily well supported, due to the possible occurrence of
problematic genes (i.e., homoplasy, incomplete lineage sorting, horizontal gene
transfers, etc.) which may blur the phylogenetic signal. However, a trustworthy
phylogenetic tree can still be obtained provided such a number of blurring
genes is reduced. The problem is thus to determine the largest subset of core
genes that produces the best-supported tree. To discard problematic genes and
due to the overwhelming number of possible combinations, this article focuses
on how to extract the largest subset of sequences in order to obtain the most
supported species tree. Due to computational complexity, a distributed Binary
Particle Swarm Optimization (BPSO) is proposed in sequential and distributed
fashions. Obtained results from both versions of the BPSO are compared with
those computed using an hybrid approach embedding both genetic algorithms and
statistical tests. The proposal has been applied to different cases of plant
families, leading to encouraging results for these families.