pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing Sentence Relation Modeling with Auxiliary Character-level Embedding
Authors: Peng Li, Heng Huang
Date: 2016-03-30 00:00:00
Tasks: Relation, Sentence

Neural network based approaches for sentence relation modeling automatically
generate hidden matching features from raw sentence pairs. However, the quality
of matching feature representation may not be satisfied due to complex semantic
relations such as entailment or contradiction. To address this challenge, we
propose a new deep neural network architecture that jointly leverage
pre-trained word embedding and auxiliary character embedding to learn sentence
meanings. The two kinds of word sequence representations as inputs into
multi-layer bidirectional LSTM to learn enhanced sentence representation. After
that, we construct matching features followed by another temporal CNN to learn
high-level hidden matching feature representations. Experimental results
demonstrate that our approach consistently outperforms the existing methods on
standard evaluation datasets.
Clinical Information Extraction via Convolutional Neural Network
Authors: Peng Li, Heng Huang
Date: 2016-03-30 00:00:00

We report an implementation of a clinical information extraction tool that
leverages deep neural network to annotate event spans and their attributes from
raw clinical notes and pathology reports. Our approach uses context words and
their part-of-speech tags and shape information as features. Then we hire
temporal (1D) convolutional neural network to learn hidden feature
representations. Finally, we use Multilayer Perceptron (MLP) to predict event
spans. The empirical evaluation demonstrates that our approach significantly
outperforms baselines.
Partial Face Detection for Continuous Authentication
Authors: Upal Mahbub, Vishal M. Patel, Deepak Chandra, Brandon Barbello, Rama Chellappa
Date: 2016-03-30 00:00:00
Tasks: Face Detection

In this paper, a part-based technique for real time detection of users' faces
on mobile devices is proposed. This method is specifically designed for
detecting partially cropped and occluded faces captured using a smartphone's
front-facing camera for continuous authentication. The key idea is to detect
facial segments in the frame and cluster the results to obtain the region which
is most likely to contain a face. Extensive experimentation on a mobile dataset
of 50 users shows that our method performs better than many state-of-the-art
face detection methods in terms of accuracy and processing speed.
A latent-observed dissimilarity measure
Authors: Yasushi Terazono
Date: 2016-03-30 00:00:00
Tasks: Representation Learning

Quantitatively assessing relationships between latent variables and observed
variables is important for understanding and developing generative models and
representation learning. In this paper, we propose latent-observed
dissimilarity (LOD) to evaluate the dissimilarity between the probabilistic
characteristics of latent and observed variables. We also define four essential
types of generative models with different independence/conditional independence
configurations. Experiments using tractable real-world data show that LOD can
effectively capture the differences between models and reflect the capability
for higher layer learning. They also show that the conditional independence of
latent variables given observed variables contributes to improving the
transmission of information and characteristics from lower layers to higher
layers.
Binary Quadratic Programing for Online Tracking of Hundreds of People in Extremely Crowded Scenes
Authors: Afshin Dehghan, Mubarak Shah
Date: 2016-03-30 00:00:00
Tasks: Multi-Object Tracking, Object Tracking

Multi-object tracking has been studied for decades. However, when it comes to
tracking pedestrians in extremely crowded scenes, we are limited to only few
works. This is an important problem which gives rise to several challenges.
Pre-trained object detectors fail to localize targets in crowded sequences.
This consequently limits the use of data-association based multi-target
tracking methods which rely on the outcome of an object detector. Additionally,
the small apparent target size makes it challenging to extract features to
discriminate targets from their surroundings. Finally, the large number of
targets greatly increases computational complexity which in turn makes it hard
to extend existing multi-target tracking approaches to high-density crowd
scenarios. In this paper, we propose a tracker that addresses the
aforementioned problems and is capable of tracking hundreds of people
efficiently. We formulate online crowd tracking as Binary Quadratic Programing.
Our formulation employs target's individual information in the form of
appearance and motion as well as contextual cues in the form of neighborhood
motion, spatial proximity and grouping constraints, and solves detection and
data association simultaneously. In order to solve the proposed quadratic
optimization efficiently, where state-of art commercial quadratic programing
solvers fail to find the answer in a reasonable amount of time, we propose to
use the most recent version of the Modified Frank Wolfe algorithm, which takes
advantage of SWAP-steps to speed up the optimization. We show that the proposed
formulation can track hundreds of targets efficiently and improves state-of-art
results by significant margins on eleven challenging high density crowd
sequences.
Optimal Recommendation to Users that React: Online Learning for a Class of POMDPs
Authors: Rahul Meshram, Aditya Gopalan, D. Manjunath
Date: 2016-03-30 00:00:00
Tasks: Recommendation Systems, Reinforcement Learning, Thompson Sampling

We describe and study a model for an Automated Online Recommendation System
(AORS) in which a user's preferences can be time-dependent and can also depend
on the history of past recommendations and play-outs. The three key features of
the model that makes it more realistic compared to existing models for
recommendation systems are (1) user preference is inherently latent, (2)
current recommendations can affect future preferences, and (3) it allows for
the development of learning algorithms with provable performance guarantees.
The problem is cast as an average-cost restless multi-armed bandit for a given
user, with an independent partially observable Markov decision process (POMDP)
for each item of content. We analyze the POMDP for a single arm, describe its
structural properties, and characterize its optimal policy. We then develop a
Thompson sampling-based online reinforcement learning algorithm to learn the
parameters of the model and optimize utility from the binary responses of the
users to continuous recommendations. We then analyze the performance of the
learning algorithm and characterize the regret. Illustrative numerical results
and directions for extension to the restless hidden Markov multi-armed bandit
problem are also presented.
Real-Time Depth Refinement for Specular Objects
Authors: Roy Or - El, Rom Hershkovitz, Aaron Wetzler, Guy Rosman, Alfred M. Bruckstein, Ron Kimmel
Date: 2015-11-28 00:00:00
Proceeding: CVPR 2016 6

The introduction of consumer RGB-D scanners set off a major boost in 3D
computer vision research. Yet, the precision of existing depth scanners is not
accurate enough to recover fine details of a scanned object. While modern
shading based depth refinement methods have been proven to work well with
Lambertian objects, they break down in the presence of specularities. We
present a novel shape from shading framework that addresses this issue and
enhances both diffuse and specular objects' depth profiles. We take advantage
of the built-in monochromatic IR projector and IR images of the RGB-D scanners
and present a lighting model that accounts for the specular regions in the
input image. Using this model, we reconstruct the depth map in real-time. Both
quantitative tests and visual evaluations prove that the proposed method
produces state of the art depth reconstruction results.
Iterated Ontology Revision by Reinterpretation
Authors: Özgür Lütfü Özçep
Date: 2016-03-30 00:00:00

Iterated applications of belief change operators are essential for different
scenarios such as that of ontology evolution where new information is not
presented at once but only in piecemeal fashion within a sequence. I discuss
iterated applications of so called reinterpretation operators that trace
conflicts between ontologies back to the ambiguous of symbols and that provide
conflict resolution strategies with bridging axioms. The discussion centers on
adaptations of the classical iteration postulates according to Darwiche and
Pearl. The main result of the paper is that reinterpretation operators fulfill
the postulates for sequences containing only atomic triggers. For complex
triggers, a fulfillment is not guaranteed and indeed there are different
reasons for the different postulates why they should not be fulfilled in the
particular scenario of ontology revision with well developed ontologies.
Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings
Authors: Spandana Gella, Mirella Lapata, Frank Keller
Date: 2016-03-30 00:00:00
Proceeding: NAACL 2016 6
Tasks: Image Description, Image Retrieval, Retrieval, Word Sense Disambiguation

We introduce a new task, visual sense disambiguation for verbs: given an
image and a verb, assign the correct sense of the verb, i.e., the one that
describes the action depicted in the image. Just as textual word sense
disambiguation is useful for a wide range of NLP tasks, visual sense
disambiguation can be useful for multimodal tasks such as image retrieval,
image description, and text illustration. We introduce VerSe, a new dataset
that augments existing multimodal datasets (COCO and TUHOI) with sense labels.
We propose an unsupervised algorithm based on Lesk which performs visual sense
disambiguation using textual, visual, or multimodal embeddings. We find that
textual embeddings perform well when gold-standard textual annotations (object
labels and image descriptions) are available, while multimodal embeddings
perform well on unannotated images. We also verify our findings by using the
textual and multimodal embeddings as features in a supervised setting and
analyse the performance of visual sense disambiguation task. VerSe is made
publicly available and can be downloaded at:
https://github.com/spandanagella/verse.
Nonparametric modal regression
Authors: Yen-Chi Chen, Christopher R. Genovese, Ryan J. Tibshirani, Larry Wasserman
Date: 2014-12-04 00:00:00
Tasks: regression

Modal regression estimates the local modes of the distribution of $Y$ given
$X=x$, instead of the mean, as in the usual regression sense, and can hence
reveal important structure missed by usual regression methods. We study a
simple nonparametric method for modal regression, based on a kernel density
estimate (KDE) of the joint distribution of $Y$ and $X$. We derive asymptotic
error bounds for this method, and propose techniques for constructing
confidence sets and prediction sets. The latter is used to select the smoothing
bandwidth of the underlying KDE. The idea behind modal regression is connected
to many others, such as mixture regression and density ridge estimation, and we
discuss these ties as well.
Exploiting Facial Landmarks for Emotion Recognition in the Wild
Authors: Matthew Day
Date: 2016-03-30 00:00:00
Tasks: Emotion Recognition

In this paper, we describe an entry to the third Emotion Recognition in the
Wild Challenge, EmotiW2015. We detail the associated experiments and show that,
through more accurately locating the facial landmarks, and considering only the
distances between them, we can achieve a surprising level of performance. The
resulting system is not only more accurate than the challenge baseline, but
also much simpler.
Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders
Authors: Simon Šuster, Ivan Titov, Gertjan van Noord
Date: 2016-03-30 00:00:00
Proceeding: NAACL 2016 6
Tasks: Decoder, Sentence, Word Embeddings

We present an approach to learning multi-sense word embeddings relying both
on monolingual and bilingual information. Our model consists of an encoder,
which uses monolingual and bilingual context (i.e. a parallel sentence) to
choose a sense for a given word, and a decoder which predicts context words
based on the chosen sense. The two components are estimated jointly. We observe
that the word representations induced from bilingual data outperform the
monolingual counterparts across a range of evaluation tasks, even though
crosslingual information is not available at test time.
Structured Feature Learning for Pose Estimation
Authors: Xiao Chu, Wanli Ouyang, Hongsheng Li, Xiaogang Wang
Date: 2016-03-30 00:00:00
Proceeding: CVPR 2016 6
Tasks: Pose Estimation

In this paper, we propose a structured feature learning framework to reason
the correlations among body joints at the feature level in human pose
estimation. Different from existing approaches of modelling structures on score
maps or predicted labels, feature maps preserve substantially richer
descriptions of body joints. The relationships between feature maps of joints
are captured with the introduced geometrical transform kernels, which can be
easily implemented with a convolution layer. Features and their relationships
are jointly learned in an end-to-end learning system. A bi-directional tree
structured model is proposed, so that the feature channels at a body joint can
well receive information from other joints. The proposed framework improves
feature learning substantially. With very simple post processing, it reaches
the best mean PCP on the LSP and FLIC datasets. Compared with the baseline of
learning features at each joint separately with ConvNet, the mean PCP has been
improved by 18% on FLIC. The code is released to the public.
Unsupervised Measure of Word Similarity: How to Outperform Co-occurrence and Vector Cosine in VSMs
Authors: Enrico Santus, Tin-Shing Chiu, Qin Lu, Alessandro Lenci, Chu-Ren Huang
Date: 2016-03-30 00:00:00
Tasks: Word Similarity

In this paper, we claim that vector cosine, which is generally considered
among the most efficient unsupervised measures for identifying word similarity
in Vector Space Models, can be outperformed by an unsupervised measure that
calculates the extent of the intersection among the most mutually dependent
contexts of the target words. To prove it, we describe and evaluate APSyn, a
variant of the Average Precision that, without any optimization, outperforms
the vector cosine and the co-occurrence on the standard ESL test set, with an
improvement ranging between +9.00% and +17.98%, depending on the number of
chosen top contexts.
Robustness of Bayesian Pool-based Active Learning Against Prior Misspecification
Authors: Nguyen Viet Cuong, Nan Ye, Wee Sun Lee
Date: 2016-03-30 00:00:00
Tasks: Active Learning

We study the robustness of active learning (AL) algorithms against prior
misspecification: whether an algorithm achieves similar performance using a
perturbed prior as compared to using the true prior. In both the average and
worst cases of the maximum coverage setting, we prove that all
$\alpha$-approximate algorithms are robust (i.e., near $\alpha$-approximate) if
the utility is Lipschitz continuous in the prior. We further show that
robustness may not be achieved if the utility is non-Lipschitz. This suggests
we should use a Lipschitz utility for AL if robustness is required. For the
minimum cost setting, we can also obtain a robustness result for approximate AL
algorithms. Our results imply that many commonly used AL algorithms are robust
against perturbed priors. We then propose the use of a mixture prior to
alleviate the problem of prior misspecification. We analyze the robustness of
the uniform mixture prior and show experimentally that it performs reasonably
well in practice.
Dense Image Representation with Spatial Pyramid VLAD Coding of CNN for Locally Robust Captioning
Authors: Andrew Shin, Masataka Yamaguchi, Katsunori Ohnishi, Tatsuya Harada
Date: 2016-03-30 00:00:00
Tasks: General Classification, Image Captioning

The workflow of extracting features from images using convolutional neural
networks (CNN) and generating captions with recurrent neural networks (RNN) has
become a de-facto standard for image captioning task. However, since CNN
features are originally designed for classification task, it is mostly
concerned with the main conspicuous element of the image, and often fails to
correctly convey information on local, secondary elements. We propose to
incorporate coding with vector of locally aggregated descriptors (VLAD) on
spatial pyramid for CNN features of sub-regions in order to generate image
representations that better reflect the local information of the images. Our
results show that our method of compact VLAD coding can match CNN features with
as little as 3% of dimensionality and, when combined with spatial pyramid, it
results in image captions that more accurately take local elements into
account.
Performance of a community detection algorithm based on semidefinite programming
Authors: Adel Javanmard, Andrea Montanari, Federico Ricci-Tersenghi
Date: 2016-03-30 00:00:00
Tasks: Community Detection, Stochastic Block Model

The problem of detecting communities in a graph is maybe one the most studied
inference problems, given its simplicity and widespread diffusion among several
disciplines. A very common benchmark for this problem is the stochastic block
model or planted partition problem, where a phase transition takes place in the
detection of the planted partition by changing the signal-to-noise ratio.
Optimal algorithms for the detection exist which are based on spectral methods,
but we show these are extremely sensible to slight modification in the
generative model. Recently Javanmard, Montanari and Ricci-Tersenghi
(arXiv:1511.08769) have used statistical physics arguments, and numerical
simulations to show that finding communities in the stochastic block model via
semidefinite programming is quasi optimal. Further, the resulting semidefinite
relaxation can be solved efficiently, and is very robust with respect to
changes in the generative model. In this paper we study in detail several
practical aspects of this new algorithm based on semidefinite programming for
the detection of the planted partition. The algorithm turns out to be very
fast, allowing the solution of problems with $O(10^5)$ variables in few second
on a laptop computer.
Vector Quantization for Machine Vision
Authors: Vincenzo Liguori
Date: 2016-03-30 00:00:00
Tasks: Quantization

This paper shows how to reduce the computational cost for a variety of common
machine vision tasks by operating directly in the compressed domain,
particularly in the context of hardware acceleration. Pyramid Vector
Quantization (PVQ) is the compression technique of choice and its properties
are exploited to simplify Support Vector Machines (SVM), Convolutional Neural
Networks(CNNs), Histogram of Oriented Gradients (HOG) features, interest points
matching and other algorithms.
Towards Geo-Distributed Machine Learning
Authors: Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino, Giovanni Matteo Fumarola
Date: 2016-03-30 00:00:00
Tasks: BIG-bench Machine Learning

Latency to end-users and regulatory requirements push large companies to
build data centers all around the world. The resulting data is "born"
geographically distributed. On the other hand, many machine learning
applications require a global view of such data in order to achieve the best
results. These types of applications form a new class of learning problems,
which we call Geo-Distributed Machine Learning (GDML). Such applications need
to cope with: 1) scarce and expensive cross-data center bandwidth, and 2)
growing privacy concerns that are pushing for stricter data sovereignty
regulations. Current solutions to learning from geo-distributed data sources
revolve around the idea of first centralizing the data in one data center, and
then training locally. As machine learning algorithms are
communication-intensive, the cost of centralizing the data is thought to be
offset by the lower cost of intra-data center communication during training. In
this work, we show that the current centralized practice can be far from
optimal, and propose a system for doing geo-distributed training. Furthermore,
we argue that the geo-distributed approach is structurally more amenable to
dealing with regulatory constraints, as raw data never leaves the source data
center. Our empirical evaluation on three real datasets confirms the general
validity of our approach, and shows that GDML is not only possible but also
advisable in many scenarios.
Palmprint Recognition Using Deep Scattering Convolutional Network
Authors: Shervin Minaee, Yao Wang
Date: 2016-03-30 00:00:00
Tasks: Translation

Palmprint recognition has drawn a lot of attention during the recent years.
Many algorithms have been proposed for palmprint recognition in the past,
majority of them being based on features extracted from the transform domain.
Many of these transform domain features are not translation or rotation
invariant, and therefore a great deal of preprocessing is needed to align the
images. In this paper, a powerful image representation, called scattering
network/transform, is used for palmprint recognition. Scattering network is a
convolutional network where its architecture and filters are predefined wavelet
transforms. The first layer of scattering network captures similar features to
SIFT descriptors and the higher-layer features capture higher-frequency content
of the signal which are lost in SIFT and other similar descriptors. After
extraction of the scattering features, their dimensionality is reduced by
applying principal component analysis (PCA) which reduces the computational
complexity of the recognition task. Two different classifiers are used for
recognition: multi-class SVM and minimum-distance classifier. The proposed
scheme has been tested on a well-known palmprint database and achieved accuracy
rate of 99.95% and 100% using minimum distance classifier and SVM respectively.
Comparative Study of Deep Learning Software Frameworks
Authors: Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, Mohak Shah
Date: 2015-11-19 00:00:00
Tasks: CPU, Deep Learning, GPU

Deep learning methods have resulted in significant performance improvements
in several application domains and as such several software frameworks have
been developed to facilitate their implementation. This paper presents a
comparative study of five deep learning frameworks, namely Caffe, Neon,
TensorFlow, Theano, and Torch, on three aspects: extensibility, hardware
utilization, and speed. The study is performed on several types of deep
learning architectures and we evaluate the performance of the above frameworks
when employed on a single machine for both (multi-threaded) CPU and GPU (Nvidia
Titan X) settings. The speed performance metrics used here include the gradient
computation time, which is important during the training phase of deep
networks, and the forward time, which is important from the deployment
perspective of trained networks. For convolutional networks, we also report how
each of these frameworks support various convolutional algorithms and their
corresponding performance. From our experiments, we observe that Theano and
Torch are the most easily extensible frameworks. We observe that Torch is best
suited for any deep architecture on CPU, followed by Theano. It also achieves
the best performance on the GPU for large convolutional and fully connected
networks, followed closely by Neon. Theano achieves the best performance on GPU
for training and deployment of LSTM networks. Caffe is the easiest for
evaluating the performance of standard deep architectures. Finally, TensorFlow
is a very flexible framework, similar to Theano, but its performance is
currently not competitive compared to the other studied frameworks.