pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical Object Detection with Deep Reinforcement Learning
Authors: Miriam Bellver, Xavier Giro-i-Nieto, Ferran Marques, Jordi Torres
Date: 2016-11-11 00:00:00
Tasks: Deep Reinforcement Learning, Object, object-detection, Object Detection, Region Proposal, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a method for performing hierarchical object detection in images
guided by a deep reinforcement learning agent. The key idea is to focus on
those parts of the image that contain richer information and zoom on them. We
train an intelligent agent that, given an image window, is capable of deciding
where to focus the attention among five different predefined region candidates
(smaller windows). This procedure is iterated providing a hierarchical image
analysis.We compare two different candidate proposal strategies to guide the
object search: with and without overlap. Moreover, our work compares two
different strategies to extract features from a convolutional neural network
for each region proposal: a first one that computes new feature maps for each
region proposal, and a second one that computes the feature maps for the whole
image to later generate crops for each region proposal. Experiments indicate
better results for the overlapping candidate proposal strategy and a loss of
performance for the cropped image features due to the loss of spatial
resolution. We argue that, while this loss seems unavoidable when working with
large amounts of object candidates, the much more reduced amount of region
proposals generated by our reinforcement learning agent allows considering to
extract features for each location without sharing convolutional computation
among regions.
Neural Machine Translation with Latent Semantic of Image and Text
Authors: Joji Toyama, Masanori Misono, Masahiro Suzuki, Kotaro Nakayama, Yutaka Matsuo
Date: 2016-11-25 00:00:00
Tasks: Machine Translation, Sentence, Translation

Although attention-based Neural Machine Translation have achieved great
success, attention-mechanism cannot capture the entire meaning of the source
sentence because the attention mechanism generates a target word depending
heavily on the relevant parts of the source sentence. The report of earlier
studies has introduced a latent variable to capture the entire meaning of
sentence and achieved improvement on attention-based Neural Machine
Translation. We follow this approach and we believe that the capturing meaning
of sentence benefits from image information because human beings understand the
meaning of language not only from textual information but also from perceptual
information such as that gained from vision. As described herein, we propose a
neural machine translation model that introduces a continuous latent variable
containing an underlying semantic extracted from texts and images. Our model,
which can be trained end-to-end, requires image information only when training.
Experiments conducted with an English--German translation task show that our
model outperforms over the baseline.
Asynchrony begets Momentum, with an Application to Deep Learning
Authors: Ioannis Mitliagkas, Ce Zhang, Stefan Hadjis, Christopher Ré
Date: 2016-05-31 00:00:00
Tasks: Deep Learning

Asynchronous methods are widely used in deep learning, but have limited
theoretical justification when applied to non-convex problems. We show that
running stochastic gradient descent (SGD) in an asynchronous manner can be
viewed as adding a momentum-like term to the SGD iteration. Our result does not
assume convexity of the objective function, so it is applicable to deep
learning systems. We observe that a standard queuing model of asynchrony
results in a form of momentum that is commonly used by deep learning
practitioners. This forges a link between queuing theory and asynchrony in deep
learning systems, which could be useful for systems builders. For convolutional
neural networks, we experimentally validate that the degree of asynchrony
directly correlates with the momentum, confirming our main result. An important
implication is that tuning the momentum parameter is important when considering
different levels of asynchrony. We assert that properly tuned momentum reduces
the number of steps required for convergence. Finally, our theory suggests new
ways of counteracting the adverse effects of asynchrony: a simple mechanism
like using negative algorithmic momentum can improve performance under high
asynchrony. Since asynchronous methods have better hardware efficiency, this
result may shed light on when asynchronous execution is more efficient for deep
learning systems.
Semantic Segmentation using Adversarial Networks
Authors: Pauline Luc, Camille Couprie, Soumith Chintala, Jakob Verbeek
Date: 2016-11-25 00:00:00
Tasks: Segmentation, Semantic Segmentation

Adversarial training has been shown to produce state of the art results for
generative image modeling. In this paper we propose an adversarial training
approach to train semantic segmentation models. We train a convolutional
semantic segmentation network along with an adversarial network that
discriminates segmentation maps coming either from the ground truth or from the
segmentation network. The motivation for our approach is that it can detect and
correct higher-order inconsistencies between ground truth segmentation maps and
the ones produced by the segmentation net. Our experiments show that our
adversarial training approach leads to improved accuracy on the Stanford
Background and PASCAL VOC 2012 datasets.
Color Constancy with Derivative Colors
Authors: Huan Lei, Guang Jiang, Long Quan
Date: 2016-11-25 00:00:00
Tasks: Color Constancy, Density Estimation

Information about the illuminant color is well contained in both achromatic
regions and the specular components of highlight regions. In this paper, we
propose a novel way to achieve color constancy by exploiting such clues. The
key to our approach lies in the use of suitably extracted derivative colors,
which are able to compute the illuminant color robustly with kernel density
estimation. While extracting derivative colors from achromatic regions to
approximate the illuminant color well is basically straightforward, the success
of our extraction in highlight regions is attributed to the different rates of
variation of the diffuse and specular magnitudes in the dichromatic reflection
model. The proposed approach requires no training phase and is simple to
implement. More significantly, it performs quite satisfactorily under
inter-database parameter settings. Our experiments on three standard databases
demonstrate its effectiveness and fine performance in comparison to
state-of-the-art methods.
Knowledge Sharing in Coalitions
Authors: Guifei Jiang, Dongmo Zhang, Laurent Perrussel
Date: 2015-12-07 00:00:00

The aim of this paper is to investigate the interplay between knowledge
shared by a group of agents and its coalition ability. We investigate this
relation in the standard context of imperfect information concurrent game. We
assume that whenever a set of agents form a coalition to achieve a goal, they
share their knowledge before acting. Based on this assumption, we propose a new
semantics for alternating-time temporal logic with imperfect information and
perfect recall. It turns out that this semantics is sufficient to preserve all
the desirable properties of coalition ability in traditional coalitional
logics. Meanwhile, we investigate how knowledge sharing within a group of
agents contributes to its coalitional ability through the interplay of
epistemic and coalition modalities. This work provides a partial answer to the
question: which kind of group knowledge is required for a group to achieve
their goals in the context of imperfect information.
Limitations and Alternatives for the Evaluation of Large-scale Link Prediction
Authors: Dario Garcia-Gasulla, Eduard Ayguadé, Jesús Labarta, Ulises Cortés
Date: 2016-11-02 00:00:00
Tasks: Link Prediction, Prediction

Link prediction, the problem of identifying missing links among a set of
inter-related data entities, is a popular field of research due to its
application to graph-like domains. Producing consistent evaluations of the
performance of the many link prediction algorithms being proposed can be
challenging due to variable graph properties, such as size and density. In this
paper we first discuss traditional data mining solutions which are applicable
to link prediction evaluation, arguing about their capacity for producing
faithful and useful evaluations. We also introduce an innovative modification
to a traditional evaluation methodology with the goal of adapting it to the
problem of evaluating link prediction algorithms when applied to large graphs,
by tackling the problem of class imbalance. We empirically evaluate the
proposed methodology and, building on these findings, make a case for its
importance on the evaluation of large-scale graph processing.
Deep Video Deblurring
Authors: Shuochen Su, Mauricio Delbracio, Jue Wang, Guillermo Sapiro, Wolfgang Heidrich, Oliver Wang
Date: 2016-11-25 00:00:00
Tasks: Deblurring, Image Deblurring, Scene Understanding, Single Image Deblurring, Video Deblurring

Motion blur from camera shake is a major problem in videos captured by
hand-held devices. Unlike single-image deblurring, video-based approaches can
take advantage of the abundant information that exists across neighboring
frames. As a result the best performing methods rely on aligning nearby frames.
However, aligning images is a computationally expensive and fragile procedure,
and methods that aggregate information must therefore be able to identify which
regions have been accurately aligned and which have not, a task which requires
high level scene understanding. In this work, we introduce a deep learning
solution to video deblurring, where a CNN is trained end-to-end to learn how to
accumulate information across frames. To train this network, we collected a
dataset of real videos recorded with a high framerate camera, which we use to
generate synthetic motion blur for supervision. We show that the features
learned from this dataset extend to deblurring motion blur that arises due to
camera shake in a wide range of videos, and compare the quality of results to a
number of other baselines.
Decision Support Systems in Fisheries and Aquaculture: A systematic review
Authors: Bjørn Magnus Mathisen, Peter Haro, Bård Hanssen, Sara Björk, Ståle Walderhaug
Date: 2016-11-25 00:00:00

Decision support systems help decision makers make better decisions in the
face of complex decision problems (e.g. investment or policy decisions).
Fisheries and Aquaculture is a domain where decision makers face such decisions
since they involve factors from many different scientific fields. No systematic
overview of literature describing decision support systems and their
application in fisheries and aquaculture has been conducted. This paper
summarizes scientific literature that describes decision support systems
applied to the domain of Fisheries and Aquaculture. We use an established
systematic mapping survey method to conduct our literature mapping. Our
research questions are: What decision support systems for fisheries and
aquaculture exists? What are the most investigated fishery and aquaculture
decision support systems topics and how have these changed over time? Do any
current DSS for fisheries provide real- time analytics? Do DSSes in Fisheries
and Aquaculture build their models using machine learning done on captured and
grounded data? The paper then detail how we employ the systematic mapping
method in answering these questions. This results in 27 papers being identified
as relevant and gives an exposition on the primary methods concluded in the
study for designing a decision support system. We provide an analysis of the
research done in the studies collected. We discovered that most literature does
not consider multiple aspects for multiple stakeholders in their work. In
addition we observed that little or no work has been done with real-time
analysis in these decision support systems.
Bidirectional LSTM-CRF for Clinical Concept Extraction
Authors: Raghavendra Chalapathy, Ehsan Zare Borzeshi, Massimo Piccardi
Date: 2016-11-25 00:00:00
Tasks: Clinical Concept Extraction, Word Embeddings

Automated extraction of concepts from patient clinical records is an
essential facilitator of clinical research. For this reason, the 2010 i2b2/VA
Natural Language Processing Challenges for Clinical Records introduced a
concept extraction task aimed at identifying and classifying concepts into
predefined categories (i.e., treatments, tests and problems). State-of-the-art
concept extraction approaches heavily rely on handcrafted features and
domain-specific resources which are hard to collect and define. For this
reason, this paper proposes an alternative, streamlined approach: a recurrent
neural network (the bidirectional LSTM with CRF decoding) initialized with
general-purpose, off-the-shelf word embeddings. The experimental results
achieved on the 2010 i2b2/VA reference corpora using the proposed framework
outperform all recent methods and ranks closely to the best submission from the
original 2010 i2b2/VA challenge.
A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models
Authors: Chelsea Finn, Paul Christiano, Pieter Abbeel, Sergey Levine
Date: 2016-11-11 00:00:00
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Generative adversarial networks (GANs) are a recently proposed class of
generative models in which a generator is trained to optimize a cost function
that is being simultaneously learned by a discriminator. While the idea of
learning cost functions is relatively new to the field of generative modeling,
learning costs has long been studied in control and reinforcement learning (RL)
domains, typically for imitation learning from demonstrations. In these fields,
learning cost function underlying observed behavior is known as inverse
reinforcement learning (IRL) or inverse optimal control. While at first the
connection between cost learning in RL and cost learning in generative modeling
may appear to be a superficial one, we show in this paper that certain IRL
methods are in fact mathematically equivalent to GANs. In particular, we
demonstrate an equivalence between a sample-based algorithm for maximum entropy
IRL and a GAN in which the generator's density can be evaluated and is provided
as an additional input to the discriminator. Interestingly, maximum entropy IRL
is a special case of an energy-based model. We discuss the interpretation of
GANs as an algorithm for training energy-based models, and relate this
interpretation to other recent work that seeks to connect GANs and EBMs. By
formally highlighting the connection between GANs, IRL, and EBMs, we hope that
researchers in all three communities can better identify and apply transferable
ideas from one domain to another, particularly for developing more stable and
scalable algorithms: a major challenge in all three domains.
A Unified Convex Surrogate for the Schatten-$p$ Norm
Authors: Chen Xu, Zhouchen Lin, Hongbin Zha
Date: 2016-11-25 00:00:00
Tasks: Matrix Completion

The Schatten-$p$ norm ($0<p<1$) has been widely used to replace the nuclear
norm for better approximating the rank function. However, existing methods are
either 1) not scalable for large scale problems due to relying on singular
value decomposition (SVD) in every iteration, or 2) specific to some $p$
values, e.g., $1/2$, and $2/3$. In this paper, we show that for any $p$, $p_1$,
and $p_2 >0$ satisfying $1/p=1/p_1+1/p_2$, there is an equivalence between the
Schatten-$p$ norm of one matrix and the Schatten-$p_1$ and the Schatten-$p_2$
norms of its two factor matrices. We further extend the equivalence to multiple
factor matrices and show that all the factor norms can be convex and smooth for
any $p>0$. In contrast, the original Schatten-$p$ norm for $0<p<1$ is
non-convex and non-smooth. As an example we conduct experiments on matrix
completion. To utilize the convexity of the factor matrix norms, we adopt the
accelerated proximal alternating linearized minimization algorithm and
establish its sequence convergence. Experiments on both synthetic and real
datasets exhibit its superior performance over the state-of-the-art methods.
Its speed is also highly competitive.
Local Discriminant Hyperalignment for multi-subject fMRI data alignment
Authors: Muhammad Yousefnezhad, Daoqiang Zhang
Date: 2016-11-25 00:00:00
Tasks: Multi-Subject Fmri Data Alignment

Multivariate Pattern (MVP) classification can map different cognitive states
to the brain tasks. One of the main challenges in MVP analysis is validating
the generated results across subjects. However, analyzing multi-subject fMRI
data requires accurate functional alignments between neuronal activities of
different subjects, which can rapidly increase the performance and robustness
of the final results. Hyperalignment (HA) is one of the most effective
functional alignment methods, which can be mathematically formulated by the
Canonical Correlation Analysis (CCA) methods. Since HA mostly uses the
unsupervised CCA techniques, its solution may not be optimized for MVP
analysis. By incorporating the idea of Local Discriminant Analysis (LDA) into
CCA, this paper proposes Local Discriminant Hyperalignment (LDHA) as a novel
supervised HA method, which can provide better functional alignment for MVP
analysis. Indeed, the locality is defined based on the stimuli categories in
the train-set, where the correlation between all stimuli in the same category
will be maximized and the correlation between distinct categories of stimuli
approaches to near zero. Experimental studies on multi-subject MVP analysis
confirm that the LDHA method achieves superior performance to other
state-of-the-art HA algorithms.
Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis
Authors: Yo Seob Han, Jaejun Yoo, Jong Chul Ye
Date: 2016-11-19 00:00:00
Tasks: compressed sensing, Computed Tomography (CT), CT Reconstruction, Image Reconstruction

Recently, compressed sensing (CS) computed tomography (CT) using sparse
projection views has been extensively investigated to reduce the potential risk
of radiation to patient. However, due to the insufficient number of projection
views, an analytic reconstruction approach results in severe streaking
artifacts and CS-based iterative approach is computationally very expensive. To
address this issue, here we propose a novel deep residual learning approach for
sparse view CT reconstruction. Specifically, based on a novel persistent
homology analysis showing that the manifold of streaking artifacts is
topologically simpler than original ones, a deep residual learning architecture
that estimates the streaking artifacts is developed. Once a streaking artifact
image is estimated, an artifact-free image can be obtained by subtracting the
streaking artifacts from the input image. Using extensive experiments with real
patient data set, we confirm that the proposed residual learning provides
significantly better image reconstruction performance with several orders of
magnitude faster computational speed.
A Multi-task Deep Network for Person Re-identification
Authors: Weihua Chen, Xiaotang Chen, Jian-Guo Zhang, Kaiqi Huang
Date: 2016-07-19 00:00:00
Tasks: Binary Classification, Person Re-Identification

Person re-identification (ReID) focuses on identifying people across
different scenes in video surveillance, which is usually formulated as a binary
classification task or a ranking task in current person ReID approaches. In
this paper, we take both tasks into account and propose a multi-task deep
network (MTDnet) that makes use of their own advantages and jointly optimize
the two tasks simultaneously for person ReID. To the best of our knowledge, we
are the first to integrate both tasks in one network to solve the person ReID.
We show that our proposed architecture significantly boosts the performance.
Furthermore, deep architecture in general requires a sufficient dataset for
training, which is usually not met in person ReID. To cope with this situation,
we further extend the MTDnet and propose a cross-domain architecture that is
capable of using an auxiliary set to assist training on small target sets. In
the experiments, our approach outperforms most of existing person ReID
algorithms on representative datasets including CUHK03, CUHK01, VIPeR, iLIDS
and PRID2011, which clearly demonstrates the effectiveness of the proposed
approach.
Kannada Spell Checker with Sandhi Splitter
Authors: A N Akshatha, Chandana G Upadhyaya, Rajashekara S Murthy
Date: 2016-11-25 00:00:00
Tasks: Machine Translation

Spelling errors are introduced in text either during typing, or when the user
does not know the correct phoneme or grapheme. If a language contains complex
words like sandhi where two or more morphemes join based on some rules, spell
checking becomes very tedious. In such situations, having a spell checker with
sandhi splitter which alerts the user by flagging the errors and providing
suggestions is very useful. A novel algorithm of sandhi splitting is proposed
in this paper. The sandhi splitter can split about 7000 most common sandhi
words in Kannada language used as test samples. The sandhi splitter was
integrated with a Kannada spell checker and a mechanism for generating
suggestions was added. A comprehensive, platform independent, standalone spell
checker with sandhi splitter application software was thus developed and tested
extensively for its efficiency and correctness. A comparative analysis of this
spell checker with sandhi splitter was made and results concluded that the
Kannada spell checker with sandhi splitter has an improved performance. It is
twice as fast, 200 times more space efficient, and it is 90% accurate in case
of complex nouns and 50% accurate for complex verbs. Such a spell checker with
sandhi splitter will be of foremost significance in machine translation
systems, voice processing, etc. This is the first sandhi splitter in Kannada
and the advantage of the novel algorithm is that, it can be extended to all
Indian languages.
Word2VisualVec: Image and Video to Sentence Matching by Visual Feature Prediction
Authors: Jianfeng Dong, Xirong Li, Cees G. M. Snoek
Date: 2016-04-23 00:00:00
Tasks: Sentence

This paper strives to find the sentence best describing the content of an
image or video. Different from existing works, which rely on a joint subspace
for image / video to sentence matching, we propose to do so in a visual space
only. We contribute Word2VisualVec, a deep neural network architecture that
learns to predict a deep visual encoding of textual input based on sentence
vectorization and a multi-layer perceptron. We thoroughly analyze its
architectural design, by varying the sentence vectorization strategy, network
depth and the deep feature to predict for image to sentence matching. We also
generalize Word2VisualVec for matching a video to a sentence, by extending the
predictive abilities to 3-D ConvNet features as well as a visual-audio
representation. Experiments on four challenging image and video benchmarks
detail Word2VisualVec's properties, capabilities for image and video to
sentence matching, and on all datasets its state-of-the-art results.