pwc-archive/papers-with-abstracts huggingface dataset:
Native Language Identification using Stacked Generalization
Authors: Shervin Malmasi, Mark Dras
Date: 2017-03-19 00:00:00
Tasks: Language Identification, Native Language Identification

Ensemble methods using multiple classifiers have proven to be the most
successful approach for the task of Native Language Identification (NLI),
achieving the current state of the art. However, a systematic examination of
ensemble methods for NLI has yet to be conducted. Additionally, deeper ensemble
architectures such as classifier stacking have not been closely evaluated. We
present a set of experiments using three ensemble-based models, testing each
with multiple configurations and algorithms. This includes a rigorous
application of meta-classification models for NLI, achieving state-of-the-art
results on three datasets from different languages. We also present the first
use of statistical significance testing for comparing NLI systems, showing that
our results are significantly better than the previous state of the art. We
make available a collection of test set predictions to facilitate future
statistical tests.
A Controlled Set-Up Experiment to Establish Personalized Baselines for Real-Life Emotion Recognition
Authors: Varvara Kollia, Noureddine Tayebi
Date: 2017-03-19 00:00:00
Tasks: Emotion Recognition

We design, conduct and present the results of a highly personalized baseline
emotion recognition experiment, which aims to set reliable ground-truth
estimates for the subject's emotional state for real-life prediction under
similar conditions using a small number of physiological sensors. We also
propose an adaptive stimuli-selection mechanism that would use the user's
feedback as guide for future stimuli selection in the controlled-setup
experiment and generate optimal ground-truth personalized sessions
systematically. Initial results are very promising (85% accuracy) and variable
importance analysis shows that only a few features, which are easy-to-implement
in portable devices, would suffice to predict the subject's emotional state.
Universal Consistency and Robustness of Localized Support Vector Machines
Authors: Florian Dumpert
Date: 2017-03-19 00:00:00
Tasks: BIG-bench Machine Learning

The massive amount of available data potentially used to discover patters in
machine learning is a challenge for kernel based algorithms with respect to
runtime and storage capacities. Local approaches might help to relieve these
issues. From a statistical point of view local approaches allow additionally to
deal with different structures in the data in different ways. This paper
analyses properties of localized kernel based, non-parametric statistical
machine learning methods, in particular of support vector machines (SVMs) and
methods close to them. We will show there that locally learnt kernel methods
are universal consistent. Furthermore, we give an upper bound for the maxbias
in order to show statistical robustness of the proposed method.
Vision-based Real-Time Aerial Object Localization and Tracking for UAV Sensing System
Authors: Yuanwei Wu, Yao Sui, Guanghui Wang
Date: 2017-03-19 00:00:00
Tasks: Object, object-detection, Object Detection, Object Localization

The paper focuses on the problem of vision-based obstacle detection and
tracking for unmanned aerial vehicle navigation. A real-time object
localization and tracking strategy from monocular image sequences is developed
by effectively integrating the object detection and tracking into a dynamic
Kalman model. At the detection stage, the object of interest is automatically
detected and localized from a saliency map computed via the image background
connectivity cue at each frame; at the tracking stage, a Kalman filter is
employed to provide a coarse prediction of the object state, which is further
refined via a local detector incorporating the saliency map and the temporal
information between two consecutive frames. Compared to existing methods, the
proposed approach does not require any manual initialization for tracking, runs
much faster than the state-of-the-art trackers of its kind, and achieves
competitive tracking performance on a large number of image sequences.
Extensive experiments demonstrate the effectiveness and superior performance of
the proposed approach.
Recurrent Collective Classification
Authors: Shuangfei Fan, Bert Huang
Date: 2017-03-19 00:00:00
Tasks: Classification, General Classification

We propose a new method for training iterative collective classifiers for
labeling nodes in network data. The iterative classification algorithm (ICA) is
a canonical method for incorporating relational information into
classification. Yet, existing methods for training ICA models rely on the
assumption that relational features reflect the true labels of the nodes. This
unrealistic assumption introduces a bias that is inconsistent with the actual
prediction algorithm. In this paper, we introduce recurrent collective
classification (RCC), a variant of ICA analogous to recurrent neural network
prediction. RCC accommodates any differentiable local classifier and relational
feature functions. We provide gradient-based strategies for optimizing over
model parameters to more directly minimize the loss function. In our
experiments, this direct loss minimization translates to improved accuracy and
robustness on real network data. We demonstrate the robustness of RCC in
settings where local classification is very noisy, settings that are
particularly challenging for ICA.
Bernoulli Rank-$1$ Bandits for Click Feedback
Authors: Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Claire Vernade, Zheng Wen
Date: 2017-03-19 00:00:00
Tasks: Position

The probability that a user will click a search result depends both on its
relevance and its position on the results page. The position based model
explains this behavior by ascribing to every item an attraction probability,
and to every position an examination probability. To be clicked, a result must
be both attractive and examined. The probabilities of an item-position pair
being clicked thus form the entries of a rank-$1$ matrix. We propose the
learning problem of a Bernoulli rank-$1$ bandit where at each step, the
learning agent chooses a pair of row and column arms, and receives the product
of their Bernoulli-distributed values as a reward. This is a special case of
the stochastic rank-$1$ bandit problem considered in recent work that proposed
an elimination based algorithm Rank1Elim, and showed that Rank1Elim's regret
scales linearly with the number of rows and columns on "benign" instances.
These are the instances where the minimum of the average row and column rewards
$\mu$ is bounded away from zero. The issue with Rank1Elim is that it fails to
be competitive with straightforward bandit strategies as $\mu \rightarrow 0$.
In this paper we propose Rank1ElimKL which simply replaces the (crude)
confidence intervals of Rank1Elim with confidence intervals based on
Kullback-Leibler (KL) divergences, and with the help of a novel result
concerning the scaling of KL divergences we prove that with this change, our
algorithm will be competitive no matter the value of $\mu$. Experiments with
synthetic data confirm that on benign instances the performance of Rank1ElimKL
is significantly better than that of even Rank1Elim, while experiments with
models derived from real data confirm that the improvements are significant
across the board, regardless of whether the data is benign or not.
CLTune: A Generic Auto-Tuner for OpenCL Kernels
Authors: Cedric Nugteren, Valeriu Codreanu
Date: 2017-03-19 00:00:00
Tasks: GPU, Rolling Shutter Correction

This work presents CLTune, an auto-tuner for OpenCL kernels. It evaluates and
tunes kernel performance of a generic, user-defined search space of possible
parameter-value combinations. Example parameters include the OpenCL workgroup
size, vector data-types, tile sizes, and loop unrolling factors. CLTune can be
used in the following scenarios: 1) when there are too many tunable parameters
to explore manually, 2) when performance portability across OpenCL devices is
desired, or 3) when the optimal parameters change based on input argument
values (e.g. matrix dimensions). The auto-tuner is generic, easy to use,
open-source, and supports multiple search strategies including simulated
annealing and particle swarm optimisation. CLTune is evaluated on two GPU
case-studies inspired by the recent successes in deep learning: 2D convolution
and matrix-multiplication (GEMM). For 2D convolution, we demonstrate the need
for auto-tuning by optimizing for different filter sizes, achieving performance
on-par or better than the state-of-the-art. For matrix-multiplication, we use
CLTune to explore a parameter space of more than two-hundred thousand
configurations, we show the need for device-specific tuning, and outperform the
clBLAS library on NVIDIA, AMD and Intel GPUs.
Métodos de Otimização Combinatória Aplicados ao Problema de Compressão MultiFrases
Authors: Elvys Linhares Pontes, Thiago Gouveia da Silva, Andréa Carneiro Linhares, Juan-Manuel Torres-Moreno, Stéphane Huet
Date: 2017-03-19 00:00:00
Tasks: Combinatorial Optimization, Sentence

The Internet has led to a dramatic increase in the amount of available
information. In this context, reading and understanding this flow of
information have become costly tasks. In the last years, to assist people to
understand textual data, various Natural Language Processing (NLP) applications
based on Combinatorial Optimization have been devised. However, for
Multi-Sentences Compression (MSC), method which reduces the sentence length
without removing core information, the insertion of optimization methods
requires further study to improve the performance of MSC. This article
describes a method for MSC using Combinatorial Optimization and Graph Theory to
generate more informative sentences while maintaining their grammaticality. An
experiment led on a corpus of 40 clusters of sentences shows that our system
has achieved a very good quality and is better than the state-of-the-art.
Continuous multilinguality with language vectors
Authors: Robert Östling, Jörg Tiedemann
Date: 2016-12-22 00:00:00
Tasks: Language Modeling, Language Modelling

Most existing models for multilingual natural language processing (NLP) treat
language as a discrete category, and make predictions for either one language
or the other. In contrast, we propose using continuous vector representations
of language. We show that these can be learned efficiently with a
character-based neural language model, and used to improve inference about
language varieties not seen during training. In experiments with 1303 Bible
translations into 990 different languages, we empirically explore the capacity
of multilingual language models, and also show that the language vectors
capture genetic relationships between languages.
Near Optimal Hamiltonian-Control and Learning via Chattering
Authors: Peeyush Kumar, Wolf Kohn, Zelda B. Zabinsky
Date: 2017-03-19 00:00:00
Tasks: Scheduling

Many applications require solving non-linear control problems that are
classically not well behaved. This paper develops a simple and efficient
chattering algorithm that learns near optimal decision policies through an
open-loop feedback strategy. The optimal control problem reduces to a series of
linear optimization programs that can be easily solved to recover a relaxed
optimal trajectory. This algorithm is implemented on a real-time enterprise
scheduling and control process.
Multi-Timescale, Gradient Descent, Temporal Difference Learning with Linear Options
Authors: Peeyush Kumar, Doina Precup
Date: 2017-03-19 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Deliberating on large or continuous state spaces have been long standing
challenges in reinforcement learning. Temporal Abstraction have somewhat made
this possible, but efficiently planing using temporal abstraction still remains
an issue. Moreover using spatial abstractions to learn policies for various
situations at once while using temporal abstraction models is an open problem.
We propose here an efficient algorithm which is convergent under linear
function approximation while planning using temporally abstract actions. We
show how this algorithm can be used along with randomly generated option models
over multiple time scales to plan agents which need to act real time. Using
these randomly generated option models over multiple time scales are shown to
reduce number of decision epochs required to solve the given task, hence
effectively reducing the time needed for deliberation.
Multi-Objective Non-parametric Sequential Prediction
Authors: Guy Uziel, Ran El-Yaniv
Date: 2017-03-05 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Prediction

Online-learning research has mainly been focusing on minimizing one objective
function. In many real-world applications, however, several objective functions
have to be considered simultaneously. Recently, an algorithm for dealing with
several objective functions in the i.i.d. case has been presented. In this
paper, we extend the multi-objective framework to the case of stationary and
ergodic processes, thus allowing dependencies among observations. We first
identify an asymptomatic lower bound for any prediction strategy and then
present an algorithm whose predictions achieve the optimal solution while
fulfilling any continuous and convex constraining criterion.
Practical Black-Box Attacks against Machine Learning
Authors: Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami
Date: 2016-02-08 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning (ML) models, e.g., deep neural networks (DNNs), are
vulnerable to adversarial examples: malicious inputs modified to yield
erroneous model outputs, while appearing unmodified to human observers.
Potential attacks include having malicious content like malware identified as
legitimate or controlling vehicle behavior. Yet, all existing adversarial
example attacks require knowledge of either the model internals or its training
data. We introduce the first practical demonstration of an attacker controlling
a remotely hosted DNN with no such knowledge. Indeed, the only capability of
our black-box adversary is to observe labels given by the DNN to chosen inputs.
Our attack strategy consists in training a local model to substitute for the
target DNN, using inputs synthetically generated by an adversary and labeled by
the target DNN. We use the local substitute to craft adversarial examples, and
find that they are misclassified by the targeted DNN. To perform a real-world
and properly-blinded evaluation, we attack a DNN hosted by MetaMind, an online
deep learning API. We find that their DNN misclassifies 84.24% of the
adversarial examples crafted with our substitute. We demonstrate the general
applicability of our strategy to many ML techniques by conducting the same
attack against models hosted by Amazon and Google, using logistic regression
substitutes. They yield adversarial examples misclassified by Amazon and Google
at rates of 96.19% and 88.94%. We also find that this black-box attack strategy
is capable of evading defense strategies previously found to make adversarial
example crafting harder.
A Fully-Automated Pipeline for Detection and Segmentation of Liver Lesions and Pathological Lymph Nodes
Authors: Assaf Hoogi, John W. Lambert, Yefeng Zheng, Dorin Comaniciu, Daniel L. Rubin
Date: 2017-03-19 00:00:00
Tasks: Computed Tomography (CT), Diversity, Lesion Detection, Lesion Segmentation, Organ Detection, Segmentation

We propose a fully-automated method for accurate and robust detection and
segmentation of potentially cancerous lesions found in the liver and in lymph
nodes. The process is performed in three steps, including organ detection,
lesion detection and lesion segmentation. Our method applies machine learning
techniques such as marginal space learning and convolutional neural networks,
as well as active contour models. The method proves to be robust in its
handling of extremely high lesion diversity. We tested our method on volumetric
computed tomography (CT) images, including 42 volumes containing liver lesions
and 86 volumes containing 595 pathological lymph nodes. Preliminary results
under 10-fold cross validation show that for both the liver lesions and the
lymph nodes, a total detection sensitivity of 0.53 and average Dice score of
$0.71 \pm 0.15$ for segmentation were obtained.
Multilevel Context Representation for Improving Object Recognition
Authors: Andreas Kölsch, Muhammad Zeshan Afzal, Marcus Liwicki
Date: 2017-03-19 00:00:00
Tasks: Data Augmentation, Object, Object Recognition, Translation

In this work, we propose the combined usage of low- and high-level blocks of
convolutional neural networks (CNNs) for improving object recognition. While
recent research focused on either propagating the context from all layers, e.g.
ResNet, (including the very low-level layers) or having multiple loss layers
(e.g. GoogLeNet), the importance of the features close to the higher layers is
ignored. This paper postulates that the use of context closer to the high-level
layers provides the scale and translation invariance and works better than
using the top layer only. In particular, we extend AlexNet and GoogLeNet by
additional connections in the top $n$ layers. In order to demonstrate the
effectiveness of the proposed approach, we evaluated it on the standard
ImageNet task. The relative reduction of the classification error is around
1-2% without affecting the computational cost. Furthermore, we show that this
approach is orthogonal to typical test data augmentation techniques, as
recently introduced by Szegedy et al. (leading to a runtime reduction of 144
during test time).
Learning long-term dependencies for action recognition with a biologically-inspired deep network
Authors: Yemin Shi, Yonghong Tian, Yao-Wei Wang, Tiejun Huang
Date: 2016-11-16 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Recognition, Temporal Action Localization

Despite a lot of research efforts devoted in recent years, how to efficiently
learn long-term dependencies from sequences still remains a pretty challenging
task. As one of the key models for sequence learning, recurrent neural network
(RNN) and its variants such as long short term memory (LSTM) and gated
recurrent unit (GRU) are still not powerful enough in practice. One possible
reason is that they have only feedforward connections, which is different from
the biological neural system that is typically composed of both feedforward and
feedback connections. To address this problem, this paper proposes a
biologically-inspired deep network, called shuttleNet\footnote{Our code is
available at \url{https://github.com/shiyemin/shuttlenet}}. Technologically,
the shuttleNet consists of several processors, each of which is a GRU while
associated with multiple groups of cells and states. Unlike traditional RNNs,
all processors inside shuttleNet are loop connected to mimic the brain's
feedforward and feedback connections, in which they are shared across multiple
pathways in the loop connection. Attention mechanism is then employed to select
the best information flow pathway. Extensive experiments conducted on two
benchmark datasets (i.e UCF101 and HMDB51) show that we can beat
state-of-the-art methods by simply embedding shuttleNet into a CNN-RNN
framework.
Zero-Shot Learning by Generating Pseudo Feature Representations
Authors: Jiang Lu, Jin Li, Ziang Yan, Chang-Shui Zhang
Date: 2017-03-19 00:00:00
Tasks: Attribute, Novel Concepts, Retrieval, Zero-Shot Learning

Zero-shot learning (ZSL) is a challenging task aiming at recognizing novel
classes without any training instances. In this paper we present a simple but
high-performance ZSL approach by generating pseudo feature representations
(GPFR). Given the dataset of seen classes and side information of unseen
classes (e.g. attributes), we synthesize feature-level pseudo representations
for novel concepts, which allows us access to the formulation of unseen class
predictor. Firstly we design a Joint Attribute Feature Extractor (JAFE) to
acquire understandings about attributes, then construct a cognitive repository
of attributes filtered by confidence margins, and finally generate pseudo
feature representations using a probability based sampling strategy to
facilitate subsequent training process of class predictor. We demonstrate the
effectiveness in ZSL settings and the extensibility in supervised recognition
scenario of our method on a synthetic colored MNIST dataset (C-MNIST). For
several popular ZSL benchmark datasets, our approach also shows compelling
results on zero-shot recognition task, especially leading to tremendous
improvement to state-of-the-art mAP on zero-shot retrieval task.
Direct Monocular Odometry Using Points and Lines
Authors: Shichao Yang, Sebastian Scherer
Date: 2017-03-19 00:00:00
Tasks: State Estimation, Stereo Matching, Stereo Matching Hand, Visual Odometry

Most visual odometry algorithm for a monocular camera focuses on points,
either by feature matching, or direct alignment of pixel intensity, while
ignoring a common but important geometry entity: edges. In this paper, we
propose an odometry algorithm that combines points and edges to benefit from
the advantages of both direct and feature based methods. It works better in
texture-less environments and is also more robust to lighting changes and fast
motion by increasing the convergence basin. We maintain a depth map for the
keyframe then in the tracking part, the camera pose is recovered by minimizing
both the photometric error and geometric error to the matched edge in a
probabilistic framework. In the mapping part, edge is used to speed up and
increase stereo matching accuracy. On various public datasets, our algorithm
achieves better or comparable performance than state-of-the-art monocular
odometry methods. In some challenging texture-less environments, our algorithm
reduces the state estimation error over 50%.
Recent Advances in Features Extraction and Description Algorithms: A Comprehensive Survey
Authors: Ehab Salahat, Murad Qasaimeh
Date: 2017-03-19 00:00:00

Computer vision is one of the most active research fields in information
technology today. Giving machines and robots the ability to see and comprehend
the surrounding world at the speed of sight creates endless potential
applications and opportunities. Feature detection and description algorithms
can be indeed considered as the retina of the eyes of such machines and robots.
However, these algorithms are typically computationally intensive, which
prevents them from achieving the speed of sight real-time performance. In
addition, they differ in their capabilities and some may favor and work better
given a specific type of input compared to others. As such, it is essential to
compactly report their pros and cons as well as their performances and recent
advances. This paper is dedicated to provide a comprehensive overview on the
state-of-the-art and recent advances in feature detection and description
algorithms. Specifically, it starts by overviewing fundamental concepts. It
then compares, reports and discusses their performance and capabilities. The
Maximally Stable Extremal Regions algorithm and the Scale Invariant Feature
Transform algorithms, being two of the best of their type, are selected to
report their recent algorithmic derivatives.