pwc-archive/papers-with-abstracts huggingface dataset:
A Visual Embedding for the Unsupervised Extraction of Abstract Semantics
Authors: D. Garcia-Gasulla, J. Béjar, U. Cortés, E. Ayguadé, J. Labarta, T. Suzumura, R. Chen
Date: 2015-07-31 00:00:00

Vector-space word representations obtained from neural network models have
been shown to enable semantic operations based on vector arithmetic. In this
paper, we explore the existence of similar information on vector
representations of images. For that purpose we define a methodology to obtain
large, sparse vector representations of image classes, and generate vectors
through the state-of-the-art deep learning architecture GoogLeNet for 20K
images obtained from ImageNet. We first evaluate the resultant vector-space
semantics through its correlation with WordNet distances, and find vector
distances to be strongly correlated with linguistic semantics. We then explore
the location of images within the vector space, finding elements close in
WordNet to be clustered together, regardless of significant visual variances
(e.g. 118 dog types). More surprisingly, we find that the space unsupervisedly
separates complex classes without prior knowledge (e.g. living things).
Afterwards, we consider vector arithmetics. Although we are unable to obtain
meaningful results on this regard, we discuss the various problem we
encountered, and how we consider to solve them. Finally, we discuss the impact
of our research for cognitive systems, focusing on the role of the architecture
being used.
Image Captioning and Visual Question Answering Based on Attributes and External Knowledge
Authors: Qi Wu, Chunhua Shen, Anton Van Den Hengel, Peng Wang, Anthony Dick
Date: 2016-03-09 00:00:00
Tasks: General Knowledge, Image Captioning, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Much recent progress in Vision-to-Language problems has been achieved through
a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural
Networks (RNNs). This approach does not explicitly represent high-level
semantic concepts, but rather seeks to progress directly from image features to
text. In this paper we first propose a method of incorporating high-level
concepts into the successful CNN-RNN approach, and show that it achieves a
significant improvement on the state-of-the-art in both image captioning and
visual question answering. We further show that the same mechanism can be used
to incorporate external knowledge, which is critically important for answering
high level visual questions. Specifically, we design a visual question
answering model that combines an internal representation of the content of an
image with information extracted from a general knowledge base to answer a
broad range of image-based questions. It particularly allows questions to be
asked about the contents of an image, even when the image itself does not
contain a complete answer. Our final model achieves the best reported results
on both image captioning and visual question answering on several benchmark
datasets.
Log-Linear RNNs: Towards Recurrent Neural Networks with Flexible Prior Knowledge
Authors: Marc Dymetman, Chunyang Xiao
Date: 2016-07-08 00:00:00
Tasks: Language Modelling, Representation Learning

We introduce LL-RNNs (Log-Linear RNNs), an extension of Recurrent Neural
Networks that replaces the softmax output layer by a log-linear output layer,
of which the softmax is a special case. This conceptually simple move has two
main advantages. First, it allows the learner to combat training data sparsity
by allowing it to model words (or more generally, output symbols) as complex
combinations of attributes without requiring that each combination is directly
observed in the training data (as the softmax does). Second, it permits the
inclusion of flexible prior knowledge in the form of a priori specified modular
features, where the neural network component learns to dynamically control the
weights of a log-linear distribution exploiting these features.
  We conduct experiments in the domain of language modelling of French, that
exploit morphological prior knowledge and show an important decrease in
perplexity relative to a baseline RNN.
  We provide other motivating iillustrations, and finally argue that the
log-linear and the neural-network components contribute complementary strengths
to the LL-RNN: the LL aspect allows the model to incorporate rich prior
knowledge, while the NN aspect, according to the "representation learning"
paradigm, allows the model to discover novel combination of characteristics.
A Two-Phase Approach Towards Identifying Argument Structure in Natural Language
Authors: Arkanath Pathak, Pawan Goyal, Plaban Bhowmick
Date: 2016-12-16 00:00:00
Proceeding: WS 2016 12
Tasks: Prediction, Vocal Bursts Valence Prediction

We propose a new approach for extracting argument structure from natural
language texts that contain an underlying argument. Our approach comprises of
two phases: Score Assignment and Structure Prediction. The Score Assignment
phase trains models to classify relations between argument units (Support,
Attack or Neutral). To that end, different training strategies have been
explored. We identify different linguistic and lexical features for training
the classifiers. Through ablation study, we observe that our novel use of
word-embedding features is most effective for this task. The Structure
Prediction phase makes use of the scores from the Score Assignment phase to
arrive at the optimal structure. We perform experiments on three argumentation
datasets, namely, AraucariaDB, Debatepedia and Wikipedia. We also propose two
baselines and observe that the proposed approach outperforms baseline systems
for the final task of Structure Prediction.
Performance Tuning of Hadoop MapReduce: A Noisy Gradient Approach
Authors: Sandeep Kumar, Sindhu Padakandla, Chandrashekar L, Priyank Parihar, K Gopinath, Shalabh Bhatnagar
Date: 2016-11-30 00:00:00

Hadoop MapReduce is a framework for distributed storage and processing of
large datasets that is quite popular in big data analytics. It has various
configuration parameters (knobs) which play an important role in deciding the
performance i.e., the execution time of a given big data processing job.
Default values of these parameters do not always result in good performance and
hence it is important to tune them. However, there is inherent difficulty in
tuning the parameters due to two important reasons - firstly, the parameter
search space is large and secondly, there are cross-parameter interactions.
Hence, there is a need for a dimensionality-free method which can automatically
tune the configuration parameters by taking into account the cross-parameter
dependencies. In this paper, we propose a novel Hadoop parameter tuning
methodology, based on a noisy gradient algorithm known as the simultaneous
perturbation stochastic approximation (SPSA). The SPSA algorithm tunes the
parameters by directly observing the performance of the Hadoop MapReduce
system. The approach followed is independent of parameter dimensions and
requires only $2$ observations per iteration while tuning. We demonstrate the
effectiveness of our methodology in achieving good performance on popular
Hadoop benchmarks namely \emph{Grep}, \emph{Bigram}, \emph{Inverted Index},
\emph{Word Co-occurrence} and \emph{Terasort}. Our method, when tested on a 25
node Hadoop cluster shows 66\% decrease in execution time of Hadoop jobs on an
average, when compared to the default configuration. Further, we also observe a
reduction of 45\% in execution times, when compared to prior methods.
Deep Residual Hashing
Authors: Sailesh Conjeti, Abhijit Guha Roy, Amin Katouzian, Nassir Navab
Date: 2016-12-16 00:00:00
Tasks: Binarization, Image Retrieval, Quantization, Representation Learning, Retrieval

Hashing aims at generating highly compact similarity preserving code words
which are well suited for large-scale image retrieval tasks.
  Most existing hashing methods first encode the images as a vector of
hand-crafted features followed by a separate binarization step to generate hash
codes. This two-stage process may produce sub-optimal encoding. In this paper,
for the first time, we propose a deep architecture for supervised hashing
through residual learning, termed Deep Residual Hashing (DRH), for an
end-to-end simultaneous representation learning and hash coding. The DRH model
constitutes four key elements: (1) a sub-network with multiple stacked residual
blocks; (2) hashing layer for binarization; (3) supervised retrieval loss
function based on neighbourhood component analysis for similarity preserving
embedding; and (4) hashing related losses and regularisation to control the
quantization error and improve the quality of hash coding. We present results
of extensive experiments on a large public chest x-ray image database with
co-morbidities and discuss the outcome showing substantial improvements over
the latest state-of-the art methods.
The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions
Authors: Peng Wang, Qi Wu, Chunhua Shen, Anton Van Den Hengel
Date: 2016-12-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: BIG-bench Machine Learning, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

One of the most intriguing features of the Visual Question Answering (VQA)
challenge is the unpredictability of the questions. Extracting the information
required to answer them demands a variety of image operations from detection
and counting, to segmentation and reconstruction. To train a method to perform
even one of these operations accurately from {image,question,answer} tuples
would be challenging, but to aim to achieve them all with a limited set of such
training data seems ambitious at best. We propose here instead a more general
and scalable approach which exploits the fact that very good methods to achieve
these operations already exist, and thus do not need to be trained. Our method
thus learns how to exploit a set of external off-the-shelf algorithms to
achieve its goal, an approach that has something in common with the Neural
Turing Machine. The core of our proposed method is a new co-attention model. In
addition, the proposed approach generates human-readable reasons for its
decision, and can still be trained end-to-end without ground truth reasons
being given. We demonstrate the effectiveness on two publicly available
datasets, Visual Genome and VQA, and show that it produces the state-of-the-art
results in both cases.
Generalization of metric classification algorithms for sequences classification and labelling
Authors: Roman Samarev, Andrey Vasnetsov, Elizaveta Smelkova
Date: 2016-10-15 00:00:00
Tasks: Chunking, Classification, General Classification

The article deals with the issue of modification of metric classification
algorithms. In particular, it studies the algorithm k-Nearest Neighbours for
its application to sequential data. A method of generalization of metric
classification algorithms is proposed. As a part of it, there has been
developed an algorithm for solving the problem of classification and labelling
of sequential data. The advantages of the developed algorithm of classification
in comparison with the existing one are also discussed in the article. There is
a comparison of the effectiveness of the proposed algorithm with the algorithm
of CRF in the task of chunking in the open data set CoNLL2000.
Deep Learning and Its Applications to Machine Health Monitoring: A Survey
Authors: Rui Zhao, Ruqiang Yan, Zhenghua Chen, Kezhi Mao, Peng Wang, Robert X. Gao
Date: 2016-12-16 00:00:00
Tasks: Deep Learning, Image Segmentation, Machine Translation, Object Recognition, Semantic Segmentation, speech-recognition, Speech Recognition, Translation

Since 2006, deep learning (DL) has become a rapidly growing research
direction, redefining state-of-the-art performances in a wide range of areas
such as object recognition, image segmentation, speech recognition and machine
translation. In modern manufacturing systems, data-driven machine health
monitoring is gaining in popularity due to the widespread deployment of
low-cost sensors and their connection to the Internet. Meanwhile, deep learning
provides useful tools for processing and analyzing these big machinery data.
The main purpose of this paper is to review and summarize the emerging research
work of deep learning on machine health monitoring. After the brief
introduction of deep learning techniques, the applications of deep learning in
machine health monitoring systems are reviewed mainly from the following
aspects: Auto-encoder (AE) and its variants, Restricted Boltzmann Machines and
its variants including Deep Belief Network (DBN) and Deep Boltzmann Machines
(DBM), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).
Finally, some new trends of DL-based machine health monitoring methods are
discussed.
Output Constraint Transfer for Kernelized Correlation Filter in Tracking
Authors: Baochang Zhang, Zhigang Li, Xian-Bin Cao, Qixiang Ye, Chen Chen, Linlin Shen, Alessandro Perina, Rongrong Ji
Date: 2016-12-16 00:00:00
Tasks: Bayesian Optimization

Kernelized Correlation Filter (KCF) is one of the state-of-the-art object
trackers. However, it does not reasonably model the distribution of correlation
response during tracking process, which might cause the drifting problem,
especially when targets undergo significant appearance changes due to
occlusion, camera shaking, and/or deformation. In this paper, we propose an
Output Constraint Transfer (OCT) method that by modeling the distribution of
correlation response in a Bayesian optimization framework is able to mitigate
the drifting problem. OCT builds upon the reasonable assumption that the
correlation response to the target image follows a Gaussian distribution, which
we exploit to select training samples and reduce model uncertainty. OCT is
rooted in a new theory which transfers data distribution to a constraint of the
optimized variable, leading to an efficient framework to calculate correlation
filters. Extensive experiments on a commonly used tracking benchmark show that
the proposed method significantly improves KCF, and achieves better performance
than other state-of-the-art trackers. To encourage further developments, the
source code is made available https://github.com/bczhangbczhang/OCT-KCF.
Medical Image Synthesis with Context-Aware Generative Adversarial Networks
Authors: Dong Nie, Roger Trullo, Caroline Petitjean, Su Ruan, Dinggang Shen
Date: 2016-12-16 00:00:00
Tasks: Computed Tomography (CT), Generative Adversarial Network, Image Generation

Computed tomography (CT) is critical for various clinical applications, e.g.,
radiotherapy treatment planning and also PET attenuation correction. However,
CT exposes radiation during acquisition, which may cause side effects to
patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and
does not involve any radiations. Therefore, recently, researchers are greatly
motivated to estimate CT image from its corresponding MR image of the same
subject for the case of radiotherapy planning. In this paper, we propose a
data-driven approach to address this challenging problem. Specifically, we
train a fully convolutional network to generate CT given an MR image. To better
model the nonlinear relationship from MRI to CT and to produce more realistic
images, we propose to use the adversarial training strategy and an image
gradient difference loss function. We further apply AutoContext Model to
implement a context-aware generative adversarial network. Experimental results
show that our method is accurate and robust for predicting CT images from MRI
images, and also outperforms three state-of-the-art methods under comparison.
Machine Reading with Background Knowledge
Authors: Ndapandula Nakashole, Tom M. Mitchell
Date: 2016-12-16 00:00:00
Tasks: Prepositional Phrase Attachment, Reading Comprehension

Intelligent systems capable of automatically understanding natural language
text are important for many artificial intelligence applications including
mobile phone voice assistants, computer vision, and robotics. Understanding
language often constitutes fitting new information into a previously acquired
view of the world. However, many machine reading systems rely on the text alone
to infer its meaning. In this paper, we pursue a different approach; machine
reading methods that make use of background knowledge to facilitate language
understanding. To this end, we have developed two methods: The first method
addresses prepositional phrase attachment ambiguity. It uses background
knowledge within a semi-supervised machine learning algorithm that learns from
both labeled and unlabeled data. This approach yields state-of-the-art results
on two datasets against strong baselines; The second method extracts
relationships from compound nouns. Our knowledge-aware method for compound noun
analysis accurately extracts relationships and significantly outperforms a
baseline that does not make use of background knowledge.
Mirrored Light Field Video Camera Adapter
Authors: Dorian Tsai, Donald G. Dansereau, Steve Martin, Peter Corke
Date: 2016-12-16 00:00:00

This paper proposes the design of a custom mirror-based light field camera
adapter that is cheap, simple in construction, and accessible. Mirrors of
different shape and orientation reflect the scene into an upwards-facing camera
to create an array of virtual cameras with overlapping field of view at
specified depths, and deliver video frame rate light fields. We describe the
design, construction, decoding and calibration processes of our mirror-based
light field camera adapter in preparation for an open-source release to benefit
the robotic vision community.
Fast, Dense Feature SDM on an iPhone
Authors: Ashton Fagg, Simon Lucey, Sridha Sridharan
Date: 2016-12-16 00:00:00
Tasks: regression

In this paper, we present our method for enabling dense SDM to run at over 90
FPS on a mobile device. Our contributions are two-fold. Drawing inspiration
from the FFT, we propose a Sparse Compositional Regression (SCR) framework,
which enables a significant speed up over classical dense regressors. Second,
we propose a binary approximation to SIFT features. Binary Approximated SIFT
(BASIFT) features, which are a computationally efficient approximation to SIFT,
a commonly used feature with SDM. We demonstrate the performance of our
algorithm on an iPhone 7, and show that we achieve similar accuracy to SDM.
A Stochastic Large Deformation Model for Computational Anatomy
Authors: Alexis Arnaudon, Darryl D. Holm, Akshay Pai, Stefan Sommer
Date: 2016-12-16 00:00:00
Tasks: Anatomy, model

In the study of shapes of human organs using computational anatomy,
variations are found to arise from inter-subject anatomical differences,
disease-specific effects, and measurement noise. This paper introduces a
stochastic model for incorporating random variations into the Large Deformation
Diffeomorphic Metric Mapping (LDDMM) framework. By accounting for randomness in
a particular setup which is crafted to fit the geometrical properties of LDDMM,
we formulate the template estimation problem for landmarks with noise and give
two methods for efficiently estimating the parameters of the noise fields from
a prescribed data set. One method directly approximates the time evolution of
the variance of each landmark by a finite set of differential equations, and
the other is based on an Expectation-Maximisation algorithm. In the second
method, the evaluation of the data likelihood is achieved without registering
the landmarks, by applying bridge sampling using a stochastically perturbed
version of the large deformation gradient flow algorithm. The method and the
estimation algorithms are experimentally validated on synthetic examples and
shape data of human corpora callosa.
Multi-Agent Path Finding with Delay Probabilities
Authors: Hang Ma, T. K. Satish Kumar, Sven Koenig
Date: 2016-12-15 00:00:00
Tasks: Multi-Agent Path Finding, valid

Several recently developed Multi-Agent Path Finding (MAPF) solvers scale to
large MAPF instances by searching for MAPF plans on 2 levels: The high-level
search resolves collisions between agents, and the low-level search plans paths
for single agents under the constraints imposed by the high-level search. We
make the following contributions to solve the MAPF problem with imperfect plan
execution with small average makespans: First, we formalize the MAPF Problem
with Delay Probabilities (MAPF-DP), define valid MAPF-DP plans and propose the
use of robust plan-execution policies for valid MAPF-DP plans to control how
each agent proceeds along its path. Second, we discuss 2 classes of
decentralized robust plan-execution policies (called Fully Synchronized
Policies and Minimal Communication Policies) that prevent collisions during
plan execution for valid MAPF-DP plans. Third, we present a 2-level MAPF-DP
solver (called Approximate Minimization in Expectation) that generates valid
MAPF-DP plans.
A Survey of Inductive Biases for Factorial Representation-Learning
Authors: Karl Ridgeway
Date: 2016-12-15 00:00:00
Tasks: Inductive Bias, Novelty Detection, Representation Learning, Survey, Transfer Learning

With the resurgence of interest in neural networks, representation learning
has re-emerged as a central focus in artificial intelligence. Representation
learning refers to the discovery of useful encodings of data that make
domain-relevant information explicit. Factorial representations identify
underlying independent causal factors of variation in data. A factorial
representation is compact and faithful, makes the causal factors explicit, and
facilitates human interpretation of data. Factorial representations support a
variety of applications, including the generation of novel examples, indexing
and search, novelty detection, and transfer learning.
  This article surveys various constraints that encourage a learning algorithm
to discover factorial representations. I dichotomize the constraints in terms
of unsupervised and supervised inductive bias. Unsupervised inductive biases
exploit assumptions about the environment, such as the statistical distribution
of factor coefficients, assumptions about the perturbations a factor should be
invariant to (e.g. a representation of an object can be invariant to rotation,
translation or scaling), and assumptions about how factors are combined to
synthesize an observation. Supervised inductive biases are constraints on the
representations based on additional information connected to observations.
Supervisory labels come in variety of types, which vary in how strongly they
constrain the representation, how many factors are labeled, how many
observations are labeled, and whether or not we know the associations between
the constraints and the factors they are related to.
  This survey brings together a wide variety of models that all touch on the
problem of learning factorial representations and lays out a framework for
comparing these models based on the strengths of the underlying supervised and
unsupervised inductive biases.
Automatic time-series phenotyping using massive feature extraction
Authors: Ben D. Fulcher, Nick S. Jones
Date: 2016-12-15 00:00:00
Tasks: Diversity, Time Series, Time Series Analysis

Across a far-reaching diversity of scientific and industrial applications, a
general key problem involves relating the structure of time-series data to a
meaningful outcome, such as detecting anomalous events from sensor recordings,
or diagnosing patients from physiological time-series measurements like heart
rate or brain activity. Currently, researchers must devote considerable effort
manually devising, or searching for, properties of their time series that are
suitable for the particular analysis problem at hand. Addressing this
non-systematic and time-consuming procedure, here we introduce a new tool,
hctsa, that selects interpretable and useful properties of time series
automatically, by comparing implementations over 7700 time-series features
drawn from diverse scientific literatures. Using two exemplar biological
applications, we show how hctsa allows researchers to leverage decades of
time-series research to quantify and understand informative structure in their
time-series data.