pwc-archive/papers-with-abstracts huggingface dataset:
Named Entity Recognition with Bidirectional LSTM-CNNs
Authors: Jason P. C. Chiu, Eric Nichols
Date: 2015-11-26 00:00:00
Proceeding: TACL 2016 1
Tasks: Entity Linking, Feature Engineering, Named Entity Recognition, Named Entity Recognition (NER), Word Embeddings

Named entity recognition is a challenging task that has traditionally
required large amounts of knowledge in the form of feature engineering and
lexicons to achieve high performance. In this paper, we present a novel neural
network architecture that automatically detects word- and character-level
features using a hybrid bidirectional LSTM and CNN architecture, eliminating
the need for most feature engineering. We also propose a novel method of
encoding partial lexicon matches in neural networks and compare it to existing
approaches. Extensive evaluation shows that, given only tokenized text and
publicly available word embeddings, our system is competitive on the CoNLL-2003
dataset and surpasses the previously reported state of the art performance on
the OntoNotes 5.0 dataset by 2.13 F1 points. By using two lexicons constructed
from publicly-available sources, we establish new state of the art performance
with an F1 score of 91.62 on CoNLL-2003 and 86.28 on OntoNotes, surpassing
systems that employ heavy feature engineering, proprietary lexicons, and rich
entity linking information.
Binary Hashing with Semidefinite Relaxation and Augmented Lagrangian
Authors: Thanh-Toan Do, Anh-Dzung Doan, Duc-Thanh Nguyen, Ngai-Man Cheung
Date: 2016-07-19 00:00:00

This paper proposes two approaches for inferencing binary codes in two-step
(supervised, unsupervised) hashing. We first introduce an unified formulation
for both supervised and unsupervised hashing. Then, we cast the learning of one
bit as a Binary Quadratic Problem (BQP). We propose two approaches to solve
BQP. In the first approach, we relax BQP as a semidefinite programming problem
which its global optimum can be achieved. We theoretically prove that the
objective value of the binary solution achieved by this approach is well
bounded. In the second approach, we propose an augmented Lagrangian based
approach to solve BQP directly without relaxing the binary constraint.
Experimental results on three benchmark datasets show that our proposed methods
compare favorably with the state of the art.
Genetic Transfer or Population Diversification? Deciphering the Secret Ingredients of Evolutionary Multitask Optimization
Authors: Abhishek Gupta, Yew-Soon Ong
Date: 2016-07-19 00:00:00

Evolutionary multitasking has recently emerged as a novel paradigm that
enables the similarities and/or latent complementarities (if present) between
distinct optimization tasks to be exploited in an autonomous manner simply by
solving them together with a unified solution representation scheme. An
important matter underpinning future algorithmic advancements is to develop a
better understanding of the driving force behind successful multitask
problem-solving. In this regard, two (seemingly disparate) ideas have been put
forward, namely, (a) implicit genetic transfer as the key ingredient
facilitating the exchange of high-quality genetic material across tasks, and
(b) population diversification resulting in effective global search of the
unified search space encompassing all tasks. In this paper, we present some
empirical results that provide a clearer picture of the relationship between
the two aforementioned propositions. For the numerical experiments we make use
of Sudoku puzzles as case studies, mainly because of their feature that
outwardly unlike puzzle statements can often have nearly identical final
solutions. The experiments reveal that while on many occasions genetic transfer
and population diversity may be viewed as two sides of the same coin, the wider
implication of genetic transfer, as shall be shown herein, captures the true
essence of evolutionary multitasking to the fullest.
Context Matters: Refining Object Detection in Video with Recurrent Neural Networks
Authors: Subarna Tripathi, Zachary C. Lipton, Serge Belongie, Truong Nguyen
Date: 2016-07-15 00:00:00
Tasks: Object, object-detection, Object Detection

Given the vast amounts of video available online, and recent breakthroughs in
object detection with static images, object detection in video offers a
promising new frontier. However, motion blur and compression artifacts cause
substantial frame-level variability, even in videos that appear smooth to the
eye. Additionally, video datasets tend to have sparsely annotated frames. We
present a new framework for improving object detection in videos that captures
temporal context and encourages consistency of predictions. First, we train a
pseudo-labeler, that is, a domain-adapted convolutional neural network for
object detection. The pseudo-labeler is first trained individually on the
subset of labeled frames, and then subsequently applied to all frames. Then we
train a recurrent neural network that takes as input sequences of
pseudo-labeled frames and optimizes an objective that encourages both accuracy
on the target frame and consistency across consecutive frames. The approach
incorporates strong supervision of target frames, weak-supervision on context
frames, and regularization via a smoothness penalty. Our approach achieves mean
Average Precision (mAP) of 68.73, an improvement of 7.1 over the strongest
image-based baselines for the Youtube-Video Objects dataset. Our experiments
demonstrate that neighboring frames can provide valuable information, even
absent labels.
An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation
Authors: Jey Han Lau, Timothy Baldwin
Date: 2016-07-19 00:00:00
Proceeding: WS 2016 8
Tasks: Document Embedding, Word Embeddings

Recently, Le and Mikolov (2014) proposed doc2vec as an extension to word2vec
(Mikolov et al., 2013a) to learn document-level embeddings. Despite promising
results in the original paper, others have struggled to reproduce those
results. This paper presents a rigorous empirical evaluation of doc2vec over
two tasks. We compare doc2vec to two baselines and two state-of-the-art
document embedding methodologies. We found that doc2vec performs robustly when
using models trained on large external corpora, and can be further improved by
using pre-trained word embeddings. We also provide recommendations on
hyper-parameter settings for general purpose applications, and release source
code to induce document embeddings using our trained doc2vec models.
An equalised global graphical model-based approach for multi-camera object tracking
Authors: Weihua Chen, Lijun Cao, Xiaotang Chen, Kaiqi Huang
Date: 2015-02-12 00:00:00
Tasks: Object, Object Tracking, Visual Object Tracking

Non-overlapping multi-camera visual object tracking typically consists of two
steps: single camera object tracking and inter-camera object tracking. Most of
tracking methods focus on single camera object tracking, which happens in the
same scene, while for real surveillance scenes, inter-camera object tracking is
needed and single camera tracking methods can not work effectively. In this
paper, we try to improve the overall multi-camera object tracking performance
by a global graph model with an improved similarity metric. Our method treats
the similarities of single camera tracking and inter-camera tracking
differently and obtains the optimization in a global graph model. The results
show that our method can work better even in the condition of poor single
camera object tracking.
Geometry-Informed Material Recognition
Authors: Joseph DeGol, Mani Golparvar-Fard, Derek Hoiem
Date: 2016-07-18 00:00:00
Proceeding: CVPR 2016 6
Tasks: 3D geometry, General Classification, Management, Material Classification, Material Recognition

Our goal is to recognize material categories using images and geometry
information. In many applications, such as construction management, coarse
geometry information is available. We investigate how 3D geometry (surface
normals, camera intrinsic and extrinsic parameters) can be used with 2D
features (texture and color) to improve material classification. We introduce a
new dataset, GeoMat, which is the first to provide both image and geometry data
in the form of: (i) training and testing patches that were extracted at
different scales and perspectives from real world examples of each material
category, and (ii) a large scale construction site scene that includes 160
images and over 800,000 hand labeled 3D points. Our results show that using 2D
and 3D features both jointly and independently to model materials improves
classification accuracy across multiple scales and viewing directions for both
material patches and images of a large scale construction site scene.
Attribute Recognition from Adaptive Parts
Authors: Luwei Yang, Ligen Zhu, Yichen Wei, Shuang Liang, Ping Tan
Date: 2016-07-05 00:00:00
Tasks: Attribute

Previous part-based attribute recognition approaches perform part detection
and attribute recognition in separate steps. The parts are not optimized for
attribute recognition and therefore could be sub-optimal. We present an
end-to-end deep learning approach to overcome the limitation. It generates
object parts from key points and perform attribute recognition accordingly,
allowing adaptive spatial transform of the parts. Both key point estimation and
attribute recognition are learnt jointly in a multi-task setting. Extensive
experiments on two datasets verify the efficacy of proposed end-to-end
approach.
A topological insight into restricted Boltzmann machines
Authors: Decebal Constantin Mocanu, Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu, Antonio Liotta
Date: 2016-04-20 00:00:00

Restricted Boltzmann Machines (RBMs) and models derived from them have been
successfully used as basic building blocks in deep artificial neural networks
for automatic features extraction, unsupervised weights initialization, but
also as density estimators. Thus, their generative and discriminative
capabilities, but also their computational time are instrumental to a wide
range of applications. Our main contribution is to look at RBMs from a
topological perspective, bringing insights from network science. Firstly, here
we show that RBMs and Gaussian RBMs (GRBMs) are bipartite graphs which
naturally have a small-world topology. Secondly, we demonstrate both on
synthetic and real-world datasets that by constraining RBMs and GRBMs to a
scale-free topology (while still considering local neighborhoods and data
distribution), we reduce the number of weights that need to be computed by a
few orders of magnitude, at virtually no loss in generative performance.
Thirdly, we show that, for a fixed number of weights, our proposed sparse
models (which by design have a higher number of hidden neurons) achieve better
generative capabilities than standard fully connected RBMs and GRBMs (which by
design have a smaller number of hidden neurons), at no additional computational
costs.
On the use of Harrell's C for clinical risk prediction via random survival forests
Authors: Matthias Schmid, Marvin Wright, Andreas Ziegler
Date: 2015-07-11 00:00:00

Random survival forests (RSF) are a powerful method for risk prediction of
right-censored outcomes in biomedical research. RSF use the log-rank split
criterion to form an ensemble of survival trees. The most common approach to
evaluate the prediction accuracy of a RSF model is Harrell's concordance index
for survival data ('C index'). Conceptually, this strategy implies that the
split criterion in RSF is different from the evaluation criterion of interest.
This discrepancy can be overcome by using Harrell's C for both node splitting
and evaluation. We compare the difference between the two split criteria
analytically and in simulation studies with respect to the preference of more
unbalanced splits, termed end-cut preference (ECP). Specifically, we show that
the log-rank statistic has a stronger ECP compared to the C index. In
simulation studies and with the help of two medical data sets we demonstrate
that the accuracy of RSF predictions, as measured by Harrell's C, can be
improved if the log-rank statistic is replaced by the C index for node
splitting. This is especially true in situations where the censoring rate or
the fraction of informative continuous predictor variables is high. Conversely,
log-rank splitting is preferable in noisy scenarios. Both C-based and log-rank
splitting are implemented in the R~package ranger. We recommend Harrell's C as
split criterion for use in smaller scale clinical studies and the log-rank
split criterion for use in large-scale 'omics' studies.
Imitation Learning with Recurrent Neural Networks
Authors: Khanh Nguyen
Date: 2016-07-18 00:00:00
Tasks: Imitation Learning

We present a novel view that unifies two frameworks that aim to solve
sequential prediction problems: learning to search (L2S) and recurrent neural
networks (RNN). We point out equivalences between elements of the two
frameworks. By complementing what is missing from one framework comparing to
the other, we introduce a more advanced imitation learning framework that, on
one hand, augments L2S s notion of search space and, on the other hand,
enhances RNNs training procedure to be more robust to compounding errors
arising from training on highly correlated examples.
mpEAd: Multi-Population EA Diagrams
Authors: Sebastian Lenartowicz, Mark Wineberg
Date: 2016-07-18 00:00:00
Tasks: Evolutionary Algorithms

Multi-population evolutionary algorithms are, by nature, highly complex and
difficult to describe. Even two populations working in concert (or opposition)
present a myriad of potential configurations that are often difficult to relate
using text alone. Little effort has been made, however, to depict these kinds
of systems, relying solely on the simple structural connections (related using
ad hoc diagrams) between populations and often leaving out crucial details. In
this paper, we propose a notation and accompanying formalism for consistently
and powerfully depicting these structures and the relationships within them in
an intuitive and consistent way. Using our notation, we examine simple
co-evolutionary systems and discover new configurations by the simple process
of "drawing on a whiteboard". Finally, we demonstrate that even complex,
highly-interconnected systems with large numbers of populations can be
understood with ease using the advanced features of our formalism
Bag of Attributes for Video Event Retrieval
Authors: Leonardo A. Duarte, Otávio A. B. Penatti, Jurandy Almeida
Date: 2016-07-18 00:00:00
Tasks: Attribute, Retrieval

In this paper, we present the Bag-of-Attributes (BoA) model for video representation aiming at video event retrieval. The BoA model is based on a semantic feature space for representing videos, resulting in high-level video feature vectors. For creating a semantic space, i.e., the attribute space, we can train a classifier using a labeled image dataset, obtaining a classification model that can be understood as a high-level codebook. This model is used to map low-level frame vectors into high-level vectors (e.g., classifier probability scores). Then, we apply pooling operations to the frame vectors to create the final bag of attributes for the video. In the BoA representation, each dimension corresponds to one category (or attribute) of the semantic space. Other interesting properties are: compactness, flexibility regarding the classifier, and ability to encode multiple semantic concepts in a single video representation. Our experiments considered the semantic space created by state-of-the-art convolutional neural networks pre-trained on 1000 object categories of ImageNet. Such deep neural networks were used to classify each video frame and then different coding strategies were used to encode the probability distribution from the softmax layer into a frame vector. Next, different pooling strategies were used to combine frame vectors in the BoA representation for a video. Results using BoA were comparable or superior to the baselines in the task of video event retrieval using the EVVE dataset, with the advantage of providing a much more compact representation.
DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing
Authors: Michael Iliadis, Leonidas Spinoulas, Aggelos K. Katsaggelos
Date: 2016-07-12 00:00:00
Tasks: Compressive Sensing, Decoder, Video Compressive Sensing, Video Reconstruction

In this paper, we propose a novel encoder-decoder neural network model
referred to as DeepBinaryMask for video compressive sensing. In video
compressive sensing one frame is acquired using a set of coded masks (sensing
matrix) from which a number of video frames is reconstructed, equal to the
number of coded masks. The proposed framework is an end-to-end model where the
sensing matrix is trained along with the video reconstruction. The encoder
learns the binary elements of the sensing matrix and the decoder is trained to
recover the unknown video sequence. The reconstruction performance is found to
improve when using the trained sensing mask from the network as compared to
other mask designs such as random, across a wide variety of compressive sensing
reconstruction algorithms. Finally, our analysis and discussion offers insights
into understanding the characteristics of the trained mask designs that lead to
the improved reconstruction quality.
HeMIS: Hetero-Modal Image Segmentation
Authors: Mohammad Havaei, Nicolas Guizard, Nicolas Chapados, Yoshua Bengio
Date: 2016-07-18 00:00:00
Tasks: Image Segmentation, Imputation, Segmentation, Semantic Segmentation

We introduce a deep learning image segmentation framework that is extremely
robust to missing imaging modalities. Instead of attempting to impute or
synthesize missing data, the proposed approach learns, for each modality, an
embedding of the input image into a single latent vector space for which
arithmetic operations (such as taking the mean) are well defined. Points in
that space, which are averaged over modalities available at inference time, can
then be further processed to yield the desired segmentation. As such, any
combinatorial subset of available modalities can be provided as input, without
having to learn a combinatorial number of imputation models. Evaluated on two
neurological MRI datasets (brain tumors and MS lesions), the approach yields
state-of-the-art segmentation results when provided with all modalities;
moreover, its performance degrades remarkably gracefully when modalities are
removed, significantly more so than alternative mean-filling or other synthesis
approaches.
An Event Grouping Based Algorithm for University Course Timetabling Problem
Authors: Velin Kralev, Radoslava Kraleva, Borislav Yurukov
Date: 2016-07-18 00:00:00

This paper presents the study of an event grouping based algorithm for a
university course timetabling problem. Several publications which discuss the
problem and some approaches for its solution are analyzed. The grouping of
events in groups with an equal number of events in each group is not applicable
to all input data sets. For this reason, a universal approach to all possible
groupings of events in commensurate in size groups is proposed here. Also, an
implementation of an algorithm based on this approach is presented. The
methodology, conditions and the objectives of the experiment are described. The
experimental results are analyzed and the ensuing conclusions are stated. The
future guidelines for further research are formulated.
Query-Focused Extractive Video Summarization
Authors: Aidean Sharghi, Boqing Gong, Mubarak Shah
Date: 2016-07-18 00:00:00
Tasks: Query focused video summarization, Video Summarization

Video data is explosively growing. As a result of the "big video data",
intelligent algorithms for automatic video summarization have re-emerged as a
pressing need. We develop a probabilistic model, Sequential and Hierarchical
Determinantal Point Process (SH-DPP), for query-focused extractive video
summarization. Given a user query and a long video sequence, our algorithm
returns a summary by selecting key shots from the video. The decision to
include a shot in the summary depends on the shot's relevance to the user query
and importance in the context of the video, jointly. We verify our approach on
two densely annotated video datasets. The query-focused video summarization is
particularly useful for search engines, e.g., to display snippets of videos.
Is spoken language all-or-nothing? Implications for future speech-based human-machine interaction
Authors: Roger K. Moore
Date: 2016-07-18 00:00:00
Tasks: All

Recent years have seen significant market penetration for voice-based
personal assistants such as Apple's Siri. However, despite this success, user
take-up is frustratingly low. This position paper argues that there is a
habitability gap caused by the inevitable mismatch between the capabilities and
expectations of human users and the features and benefits provided by
contemporary technology. Suggestions are made as to how such problems might be
mitigated, but a more worrisome question emerges: "is spoken language
all-or-nothing"? The answer, based on contemporary views on the special nature
of (spoken) language, is that there may indeed be a fundamental limit to the
interaction that can take place between mismatched interlocutors (such as
humans and machines). However, it is concluded that interactions between native
and non-native speakers, or between adults and children, or even between humans
and dogs, might provide critical inspiration for the design of future
speech-based human-machine interaction.
On the Application of Support Vector Machines to the Prediction of Propagation Losses at 169 MHz for Smart Metering Applications
Authors: Martino Uccellari, Francesca Facchini, Matteo Sola, Emilio Sirignano, Giorgio M. Vitetta, Andrea Barbieri, Stefano Tondelli
Date: 2016-07-18 00:00:00

Recently, the need of deploying new wireless networks for smart gas metering
has raised the problem of radio planning in the169 MHz band. Unluckily,
software tools commonly adopted for radio planning in cellular communication
systems cannot be employed to solve this problem because of the substantially
lower transmission frequencies characterizing this application. In this
manuscript a novel data-centric solution, based on the use of support vector
machine techniques for classification and regression, is proposed. Our method
requires the availability of a limited set of received signal strength
measurements and the knowledge of a three-dimensional map of the propagation
environment of interest, and generates both an estimate of the coverage area
and a prediction of the field strength within it. Numerical results referring
to different Italian villages and cities evidence that our method is able to
achieve good accuracy at the price of an acceptable computational cost and of a
limited effort for the acquisition of measurements in the considered
environments.
A two-stage learning method for protein-protein interaction prediction
Authors: Amir Ahooye Atashin, Parsa Bagherzadeh, Kamaledin Ghiasi-Shirazi
Date: 2016-06-14 00:00:00
Tasks: Denoising, Prediction, Vocal Bursts Valence Prediction

In this paper, a new method for PPI (proteinprotein interaction) prediction
is proposed. In PPI prediction, a reliable and sufficient number of training
samples is not available, but a large number of unlabeled samples is in hand.
In the proposed method, the denoising auto encoders are employed for learning
robust features. The obtained robust features are used in order to train a
classifier with a better performance. The experimental results demonstrate the
capabilities of the proposed method.
  Protein-protein interaction; Denoising auto encoder;Robust features;
Unlabelled data;