pwc-archive/papers-with-abstracts huggingface dataset:
Probabilistic Combination of Noisy Points and Planes for RGB-D Odometry
Authors: Pedro F. Proença, Yang Gao
Date: 2017-05-18 00:00:00
Tasks: Motion Estimation, Visual Odometry

This work proposes a visual odometry method that combines points and plane
primitives, extracted from a noisy depth camera. Depth measurement uncertainty
is modelled and propagated through the extraction of geometric primitives to
the frame-to-frame motion estimation, where pose is optimized by weighting the
residuals of 3D point and planes matches, according to their uncertainties.
Results on an RGB-D dataset show that the combination of points and planes,
through the proposed method, is able to perform well in poorly textured
environments, where point-based odometry is bound to fail.
Multi-Language Identification Using Convolutional Recurrent Neural Network
Authors: Vrishabh Ajay Lakhani, Rohan Mahadev
Date: 2016-11-12 00:00:00
Tasks: Language Identification, Speaker Recognition

Language Identification, being an important aspect of Automatic Speaker
Recognition has had many changes and new approaches to ameliorate performance
over the last decade. We compare the performance of using audio spectrum in the
log scale and using Polyphonic sound sequences from raw audio samples to train
the neural network and to classify speech as either English or Spanish. To
achieve this, we use the novel approach of using a Convolutional Recurrent
Neural Network using Long Short Term Memory (LSTM) or a Gated Recurrent Unit
(GRU) for forward propagation of the neural network. Our hypothesis is that the
performance of using polyphonic sound sequence as features and both LSTM and
GRU as the gating mechanisms for the neural network outperform the traditional
MFCC features using a unidirectional Deep Neural Network.
Universal Dependencies Parsing for Colloquial Singaporean English
Authors: Hongmin Wang, Yue Zhang, GuangYong Leonard Chan, Jie Yang, Hai Leong Chieu
Date: 2017-05-18 00:00:00
Proceeding: ACL 2017 7
Tasks: Dependency Parsing, Sentiment Analysis

Singlish can be interesting to the ACL community both linguistically as a
major creole based on English, and computationally for information extraction
and sentiment analysis of regional social media. We investigate dependency
parsing of Singlish by constructing a dependency treebank under the Universal
Dependencies scheme, and then training a neural network model by integrating
English syntactic knowledge into a state-of-the-art parser trained on the
Singlish treebank. Results show that English knowledge can lead to 25% relative
error reduction, resulting in a parser of 84.47% accuracies. To the best of our
knowledge, we are the first to use neural stacking to improve cross-lingual
dependency parsing on low-resource languages. We make both our annotation and
parser available for further research.
Evolving Ensemble Fuzzy Classifier
Authors: Mahardhika Pratama, Witold Pedrycz, Edwin Lughofer
Date: 2017-05-18 00:00:00
Tasks: Drift Detection, Ensemble Learning, Ensemble Pruning, feature selection

The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it addresses the bias and variance dilemma better than its single model counterpart and features a reconfigurable structure, which is well suited to the given context. While various extensions of ensemble learning for mining non-stationary data streams can be found in the literature, most of them are crafted under a static base classifier and revisits preceding samples in the sliding window for a retraining step. This feature causes computationally prohibitive complexity and is not flexible enough to cope with rapidly changing environments. Their complexities are often demanding because it involves a large collection of offline classifiers due to the absence of structural complexities reduction mechanisms and lack of an online feature selection mechanism. A novel evolving ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in this paper. pENsemble differs from existing architectures in the fact that it is built upon an evolving classifier from data streams, termed Parsimonious Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism, which estimates a localized generalization error of a base classifier. A dynamic online feature selection scenario is integrated into the pENsemble. This method allows for dynamic selection and deselection of input features on the fly. pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble structure. The efficacy of the pENsemble has been numerically demonstrated through rigorous numerical studies with dynamic and evolving data streams where it delivers the most encouraging performance in attaining a tradeoff between accuracy and complexity.
Information Density as a Factor for Variation in the Embedding of Relative Clauses
Authors: Augustin Speyer, Robin Lemke
Date: 2017-05-18 00:00:00
Tasks: Language Modeling, Language Modelling

In German, relative clauses can be positioned in-situ or extraposed. A
potential factor for the variation might be information density. In this study,
this hypothesis is tested with a corpus of 17th century German funeral sermons.
For each referent in the relative clauses and their matrix clauses, the
attention state was determined (first calculation). In a second calculation,
for each word the surprisal values were determined, using a bi-gram language
model. In a third calculation, the surprisal values were accommodated as to
whether it is the first occurrence of the word in question or not. All three
calculations pointed in the same direction: With in-situ relative clauses, the
rate of new referents was lower and the average surprisal values were lower,
especially the accommodated surprisal values, than with extraposed relative
clauses. This indicated that in-formation density is a factor governing the
choice between in-situ and extraposed relative clauses. The study also sheds
light on the intrinsic relation-ship between the information theoretic concept
of information density and in-formation structural concepts such as givenness
which are used under a more linguistic perspective.
Entity Linking for Queries by Searching Wikipedia Sentences
Authors: Chuanqi Tan, Furu Wei, Pengjie Ren, Weifeng Lv, Ming Zhou
Date: 2017-04-10 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Articles, Entity Linking, Word Embeddings

We present a simple yet effective approach for linking entities in queries.
The key idea is to search sentences similar to a query from Wikipedia articles
and directly use the human-annotated entities in the similar sentences as
candidate entities for the query. Then, we employ a rich set of features, such
as link-probability, context-matching, word embeddings, and relatedness among
candidate entities as well as their related entities, to rank the candidates
under a regression based framework. The advantages of our approach lie in two
aspects, which contribute to the ranking process and final linking result.
First, it can greatly reduce the number of candidate entities by filtering out
irrelevant entities with the words in the query. Second, we can obtain the
query sensitive prior probability in addition to the static link-probability
derived from all Wikipedia articles. We conduct experiments on two benchmark
datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ
dataset. Experimental results show that our method outperforms state-of-the-art
systems and yields 75.0% in F1 on the ERD14 dataset and 56.9% on the GERDAQ
dataset.
Delving into adversarial attacks on deep policies
Authors: Jernej Kos, Dawn Song
Date: 2017-05-18 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Adversarial examples have been shown to exist for a variety of deep learning
architectures. Deep reinforcement learning has shown promising results on
training agent policies directly on raw inputs such as image pixels. In this
paper we present a novel study into adversarial attacks on deep reinforcement
learning polices. We compare the effectiveness of the attacks using adversarial
examples vs. random noise. We present a novel method for reducing the number of
times adversarial examples need to be injected for a successful attack, based
on the value function. We further explore how re-training on random noise and
FGSM perturbations affects the resilience against adversarial examples.
Diversified Visual Attention Networks for Fine-Grained Object Classification
Authors: Bo Zhao, Xiao Wu, Jiashi Feng, Qiang Peng, Shuicheng Yan
Date: 2016-06-28 00:00:00
Tasks: Classification, Diversity, General Classification, Object

Fine-grained object classification is a challenging task due to the subtle
inter-class difference and large intra-class variation. Recently, visual
attention models have been applied to automatically localize the discriminative
regions of an image for better capturing critical difference and demonstrated
promising performance. However, without consideration of the diversity in
attention process, most of existing attention models perform poorly in
classifying fine-grained objects. In this paper, we propose a diversified
visual attention network (DVAN) to address the problems of fine-grained object
classification, which substan- tially relieves the dependency on
strongly-supervised information for learning to localize discriminative regions
compared with attentionless models. More importantly, DVAN explicitly pursues
the diversity of attention and is able to gather discriminative information to
the maximal extent. Multiple attention canvases are generated to extract
convolutional features for attention. An LSTM recurrent unit is employed to
learn the attentiveness and discrimination of attention canvases. The proposed
DVAN has the ability to attend the object from coarse to fine granularity, and
a dynamic internal representation for classification is built up by
incrementally combining the information from different locations and scales of
the image. Extensive experiments con- ducted on CUB-2011, Stanford Dogs and
Stanford Cars datasets have demonstrated that the proposed diversified visual
attention networks achieve competitive performance compared to the state-
of-the-art approaches, without using any prior knowledge, user interaction or
external resource in training or testing.
Vehicle Routing with Drones
Authors: Rami Daknama, Elisabeth Kraus
Date: 2017-05-18 00:00:00

We introduce a package service model where trucks as well as drones can
deliver packages. Drones can travel on trucks or fly; but while flying, drones
can only carry one package at a time and have to return to a truck to charge
after each delivery. We present a heuristic algorithm to solve the problem of
finding a good schedule for all drones and trucks. The algorithm is based on
two nested local searches, thus the definition of suitable neighbourhoods of
solutions is crucial for the algorithm. Empirical tests show that our algorithm
performs significantly better than a natural Greedy algorithm. Moreover, the
savings compared to solutions without drones turn out to be substantial,
suggesting that delivery systems might considerably benefit from using drones
in addition to trucks.
Learning To Score Olympic Events
Authors: Paritosh Parmar, Brendan Tran Morris
Date: 2016-11-16 00:00:00
Tasks: Action Quality Assessment, Action Recognition, Temporal Action Localization

Estimating action quality, the process of assigning a "score" to the
execution of an action, is crucial in areas such as sports and health care.
Unlike action recognition, which has millions of examples to learn from, the
action quality datasets that are currently available are small -- typically
comprised of only a few hundred samples. This work presents three frameworks
for evaluating Olympic sports which utilize spatiotemporal features learned
using 3D convolutional neural networks (C3D) and perform score regression with
i) SVR, ii) LSTM, and iii) LSTM followed by SVR. An efficient training
mechanism for the limited data scenarios is presented for clip-based training
with LSTM. The proposed systems show significant improvement over existing
quality assessment approaches on the task of predicting scores of Olympic
events {diving, vault, figure skating}. While the SVR-based frameworks yield
better results, LSTM-based frameworks are more natural for describing an action
and can be used for improvement feedback.
Linear Dimensionality Reduction in Linear Time: Johnson-Lindenstrauss-type Guarantees for Random Subspace
Authors: Nick Lim, Robert J. Durrant
Date: 2017-05-18 00:00:00
Tasks: Dimensionality Reduction

We consider the problem of efficient randomized dimensionality reduction with
norm-preservation guarantees. Specifically we prove data-dependent
Johnson-Lindenstrauss-type geometry preservation guarantees for Ho's random
subspace method: When data satisfy a mild regularity condition -- the extent of
which can be estimated by sampling from the data -- then random subspace
approximately preserves the Euclidean geometry of the data with high
probability. Our guarantees are of the same order as those for random
projection, namely the required dimension for projection is logarithmic in the
number of data points, but have a larger constant term in the bound which
depends upon this regularity. A challenging situation is when the original data
have a sparse representation, since this implies a very large projection
dimension is required: We show how this situation can be improved for sparse
binary data by applying an efficient `densifying' preprocessing, which neither
changes the Euclidean geometry of the data nor requires an explicit
matrix-matrix multiplication. We corroborate our theoretical findings with
experiments on both dense and sparse high-dimensional datasets from several
application domains.
Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks
Authors: Matthias Plappert, Christian Mandery, Tamim Asfour
Date: 2017-05-18 00:00:00
Tasks: Feature Engineering, Machine Translation, Sentence

Linking human whole-body motion and natural language is of great interest for
the generation of semantic representations of observed human behaviors as well
as for the generation of robot behaviors based on natural language input. While
there has been a large body of research in this area, most approaches that
exist today require a symbolic representation of motions (e.g. in the form of
motion primitives), which have to be defined a-priori or require complex
segmentation algorithms. In contrast, recent advances in the field of neural
networks and especially deep learning have demonstrated that sub-symbolic
representations that can be learned end-to-end usually outperform more
traditional approaches, for applications such as machine translation. In this
paper we propose a generative model that learns a bidirectional mapping between
human whole-body motion and natural language using deep recurrent neural
networks (RNNs) and sequence-to-sequence learning. Our approach does not
require any segmentation or manual feature engineering and learns a distributed
representation, which is shared for all motions and descriptions. We evaluate
our approach on 2,846 human whole-body motions and 6,187 natural language
descriptions thereof from the KIT Motion-Language Dataset. Our results clearly
demonstrate the effectiveness of the proposed model: We show that our model
generates a wide variety of realistic motions only from descriptions thereof in
form of a single sentence. Conversely, our model is also capable of generating
correct and detailed natural language descriptions from human motions.
Median-Truncated Nonconvex Approach for Phase Retrieval with Outliers
Authors: Huishuai Zhang, Yuejie Chi, Yingbin Liang
Date: 2016-03-11 00:00:00
Tasks: Retrieval

This paper investigates the phase retrieval problem, which aims to recover a
signal from the magnitudes of its linear measurements. We develop statistically
and computationally efficient algorithms for the situation when the
measurements are corrupted by sparse outliers that can take arbitrary values.
We propose a novel approach to robustify the gradient descent algorithm by
using the sample median as a guide for pruning spurious samples in
initialization and local search. Adopting the Poisson loss and the reshaped
quadratic loss respectively, we obtain two algorithms termed median-TWF and
median-RWF, both of which provably recover the signal from a near-optimal
number of measurements when the measurement vectors are composed of i.i.d.
Gaussian entries, up to a logarithmic factor, even when a constant fraction of
the measurements are adversarially corrupted. We further show that both
algorithms are stable in the presence of additional dense bounded noise. Our
analysis is accomplished by developing non-trivial concentration results of
median-related quantities, which may be of independent interest. We provide
numerical experiments to demonstrate the effectiveness of our approach.
Asynchronous parallel primal-dual block coordinate update methods for affinely constrained convex programs
Authors: Yangyang Xu
Date: 2017-05-18 00:00:00

Recent several years have witnessed the surge of asynchronous (async-) parallel computing methods due to the extremely big data involved in many modern applications and also the advancement of multi-core machines and computer clusters. In optimization, most works about async-parallel methods are on unconstrained problems or those with block separable constraints. In this paper, we propose an async-parallel method based on block coordinate update (BCU) for solving convex problems with nonseparable linear constraint. Running on a single node, the method becomes a novel randomized primal-dual BCU with adaptive stepsize for multi-block affinely constrained problems. For these problems, Gauss-Seidel cyclic primal-dual BCU needs strong convexity to have convergence. On the contrary, merely assuming convexity, we show that the objective value sequence generated by the proposed algorithm converges in probability to the optimal value and also the constraint residual to zero. In addition, we establish an ergodic $O(1/k)$ convergence result, where $k$ is the number of iterations. Numerical experiments are performed to demonstrate the efficiency of the proposed method and significantly better speed-up performance than its sync-parallel counterpart.
Maximum Margin Principal Components
Authors: Xianghui Luo, Robert J. Durrant
Date: 2017-05-17 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification

Principal Component Analysis (PCA) is a very successful dimensionality
reduction technique, widely used in predictive modeling. A key factor in its
widespread use in this domain is the fact that the projection of a dataset onto
its first $K$ principal components minimizes the sum of squared errors between
the original data and the projected data over all possible rank $K$
projections. Thus, PCA provides optimal low-rank representations of data for
least-squares linear regression under standard modeling assumptions. On the
other hand, when the loss function for a prediction problem is not the
least-squares error, PCA is typically a heuristic choice of dimensionality
reduction -- in particular for classification problems under the zero-one loss.
In this paper we target classification problems by proposing a straightforward
alternative to PCA that aims to minimize the difference in margin distribution
between the original and the projected data. Extensive experiments show that
our simple approach typically outperforms PCA on any particular dataset, in
terms of classification error, though this difference is not always
statistically significant, and despite being a filter method is frequently
competitive with Partial Least Squares (PLS) and Lasso on a wide range of
datasets.
Learning Gaussian Graphical Models Using Discriminated Hub Graphical Lasso
Authors: Zhen Li, Jingtian Bai, Weilian Zhou
Date: 2017-05-17 00:00:00

We develop a new method called Discriminated Hub Graphical Lasso (DHGL) based
on Hub Graphical Lasso (HGL) by providing prior information of hubs. We apply
this new method in two situations: with known hubs and without known hubs. Then
we compare DHGL with HGL using several measures of performance. When some hubs
are known, we can always estimate the precision matrix better via DHGL than
HGL. When no hubs are known, we use Graphical Lasso (GL) to provide information
of hubs and find that the performance of DHGL will always be better than HGL if
correct prior information is given and will seldom degenerate when the prior
information is wrong.
Optimizing and Visualizing Deep Learning for Benign/Malignant Classification in Breast Tumors
Authors: Darvin Yi, Rebecca Lynn Sawyer, David Cohn III, Jared Dunnmon, Carson Lam, Xuerong Xiao, Daniel Rubin
Date: 2017-05-17 00:00:00
Tasks: Deep Learning, General Classification

Breast cancer has the highest incidence and second highest mortality rate for
women in the US. Our study aims to utilize deep learning for benign/malignant
classification of mammogram tumors using a subset of cases from the Digital
Database of Screening Mammography (DDSM). Though it was a small dataset from
the view of Deep Learning (about 1000 patients), we show that currently state
of the art architectures of deep learning can find a robust signal, even when
trained from scratch. Using convolutional neural networks (CNNs), we are able
to achieve an accuracy of 85% and an ROC AUC of 0.91, while leading
hand-crafted feature based methods are only able to achieve an accuracy of 71%.
We investigate an amalgamation of architectures to show that our best result is
reached with an ensemble of the lightweight GoogLe Nets tasked with
interpreting both the coronal caudal view and the mediolateral oblique view,
simply averaging the probability scores of both views to make the final
prediction. In addition, we have created a novel method to visualize what
features the neural network detects for the benign/malignant classification,
and have correlated those features with well known radiological features, such
as spiculation. Our algorithm significantly improves existing classification
methods for mammography lesions and identifies features that correlate with
established clinical markers.
Misdirected Registration Uncertainty
Authors: Jie Luo, Karteek Popuri, Dana Cobzas, Hongyi Ding, William M. Wells III, Masashi Sugiyama
Date: 2017-04-26 00:00:00
Tasks: Image Registration, Medical Image Registration

Being a task of establishing spatial correspondences, medical image
registration is often formalized as finding the optimal transformation that
best aligns two images. Since the transformation is such an essential component
of registration, most existing researches conventionally quantify the
registration uncertainty, which is the confidence in the estimated spatial
correspondences, by the transformation uncertainty. In this paper, we give
concrete examples and reveal that using the transformation uncertainty to
quantify the registration uncertainty is inappropriate and sometimes
misleading. Based on this finding, we also raise attention to an important yet
subtle aspect of probabilistic image registration, that is whether it is
reasonable to determine the correspondence of a registered voxel solely by the
mode of its transformation distribution.