pwc-archive/papers-with-abstracts huggingface dataset:
Neural Ranking Models with Weak Supervision
Authors: Mostafa Dehghani, Hamed Zamani, Aliaksei Severyn, Jaap Kamps, W. Bruce Croft
Date: 2017-04-28 00:00:00
Tasks: Ad-Hoc Information Retrieval, Information Retrieval, Retrieval

Despite the impressive improvements achieved by unsupervised deep neural
networks in computer vision and NLP tasks, such improvements have not yet been
observed in ranking for information retrieval. The reason may be the complexity
of the ranking problem, as it is not obvious how to learn from queries and
documents when no supervised signal is available. Hence, in this paper, we
propose to train a neural ranking model using weak supervision, where labels
are obtained automatically without human annotators or any external resources
(e.g., click data). To this aim, we use the output of an unsupervised ranking
model, such as BM25, as a weak supervision signal. We further train a set of
simple yet effective ranking models based on feed-forward neural networks. We
study their effectiveness under various learning scenarios (point-wise and
pair-wise models) and using different input representations (i.e., from
encoding query-document pairs into dense/sparse vectors to using word embedding
representation). We train our networks using tens of millions of training
instances and evaluate it on two standard collections: a homogeneous news
collection(Robust) and a heterogeneous large-scale web collection (ClueWeb).
Our experiments indicate that employing proper objective functions and letting
the networks to learn the input representation based on weakly supervised data
leads to impressive performance, with over 13% and 35% MAP improvements over
the BM25 model on the Robust and the ClueWeb collections. Our findings also
suggest that supervised neural ranking models can greatly benefit from
pre-training on large amounts of weakly labeled data that can be easily
obtained from unsupervised IR models.
An Automatic Contextual Analysis and Clustering Classifiers Ensemble approach to Sentiment Analysis
Authors: Murtadha Talib AL-Sharuee, Fei Liu, Mahardhika Pratama
Date: 2017-05-29 00:00:00
Tasks: Clustering, Ensemble Learning, Negation, Sentiment Analysis, Spelling Correction

Products reviews are one of the major resources to determine the public
sentiment. The existing literature on reviews sentiment analysis mainly
utilizes supervised paradigm, which needs labeled data to be trained on and
suffers from domain-dependency. This article addresses these issues by
describes a completely automatic approach for sentiment analysis based on
unsupervised ensemble learning. The method consists of two phases. The first
phase is contextual analysis, which has five processes, namely (1) data
preparation; (2) spelling correction; (3) intensifier handling; (4) negation
handling and (5) contrast handling. The second phase comprises the unsupervised
learning approach, which is an ensemble of clustering classifiers using a
majority voting mechanism with different weight schemes. The base classifier of
the ensemble method is a modified k-means algorithm. The base classifier is
modified by extracting initial centroids from the feature set via using
SentWordNet (SWN). We also introduce new sentiment analysis problems of
Australian airlines and home builders which offer potential benchmark problems
in the sentiment analysis field. Our experiments on datasets from different
domains show that contextual analysis and the ensemble phases improve the
clustering performance in term of accuracy, stability and generalization
ability.
Pose-Aware Person Recognition
Authors: Vijay Kumar, Anoop Namboodiri, Manohar Paluri, C. V. Jawahar
Date: 2017-05-29 00:00:00
Proceeding: CVPR 2017 7
Tasks: Person Recognition

Person recognition methods that use multiple body regions have shown
significant improvements over traditional face-based recognition. One of the
primary challenges in full-body person recognition is the extreme variation in
pose and view point. In this work, (i) we present an approach that tackles pose
variations utilizing multiple models that are trained on specific poses, and
combined using pose-aware weights during testing. (ii) For learning a person
representation, we propose a network that jointly optimizes a single loss over
multiple body regions. (iii) Finally, we introduce new benchmarks to evaluate
person recognition in diverse scenarios and show significant improvements over
previously proposed approaches on all the benchmarks including the photo album
setting of PIPA.
Dynamics of core of language vocabulary
Authors: Valery D. Solovyev, Vladimir V. Bochkarev, Anna V. Shevlyakova
Date: 2017-05-29 00:00:00

Studies of the overall structure of vocabulary and its dynamics became
possible due to creation of diachronic text corpora, especially Google Books
Ngram. This article discusses the question of core change rate and the degree
to which the core words cover the texts. Different periods of the last three
centuries and six main European languages presented in Google Books Ngram are
compared. The main result is high stability of core change rate, which is
analogous to stability of the Swadesh list.
Structural Conditions for Projection-Cost Preservation via Randomized Matrix Multiplication
Authors: Agniva Chowdhury, Jiasen Yang, Petros Drineas
Date: 2017-05-29 00:00:00

Projection-cost preservation is a low-rank approximation guarantee which
ensures that the cost of any rank-$k$ projection can be preserved using a
smaller sketch of the original data matrix. We present a general structural
result outlining four sufficient conditions to achieve projection-cost
preservation. These conditions can be satisfied using tools from the Randomized
Linear Algebra literature.
Role Playing Learning for Socially Concomitant Mobile Robot Navigation
Authors: Mingming Li, Rui Jiang, Shuzhi Sam Ge, Tong Heng Lee
Date: 2017-05-29 00:00:00
Tasks: Navigate, Reinforcement Learning, Reinforcement Learning (RL), Robot Navigation

In this paper, we present the Role Playing Learning (RPL) scheme for a mobile
robot to navigate socially with its human companion in populated environments.
Neural networks (NN) are constructed to parameterize a stochastic policy that
directly maps sensory data collected by the robot to its velocity outputs,
while respecting a set of social norms. An efficient simulative learning
environment is built with maps and pedestrians trajectories collected from a
number of real-world crowd data sets. In each learning iteration, a robot
equipped with the NN policy is created virtually in the learning environment to
play itself as a companied pedestrian and navigate towards a goal in a socially
concomitant manner. Thus, we call this process Role Playing Learning, which is
formulated under a reinforcement learning (RL) framework. The NN policy is
optimized end-to-end using Trust Region Policy Optimization (TRPO), with
consideration of the imperfectness of robot's sensor measurements. Simulative
and experimental results are provided to demonstrate the efficacy and
superiority of our method.
Understanding Trainable Sparse Coding via Matrix Factorization
Authors: Thomas Moreau, Joan Bruna
Date: 2016-09-01 00:00:00

Sparse coding is a core building block in many data analysis and machine
learning pipelines. Typically it is solved by relying on generic optimization
techniques, that are optimal in the class of first-order methods for
non-smooth, convex functions, such as the Iterative Soft Thresholding Algorithm
and its accelerated version (ISTA, FISTA). However, these methods don't exploit
the particular structure of the problem at hand nor the input data
distribution. An acceleration using neural networks was proposed in
\cite{Gregor10}, coined LISTA, which showed empirically that one could achieve
high quality estimates with few iterations by modifying the parameters of the
proximal splitting appropriately.
  In this paper we study the reasons for such acceleration. Our mathematical
analysis reveals that it is related to a specific matrix factorization of the
Gram kernel of the dictionary, which attempts to nearly diagonalise the kernel
with a basis that produces a small perturbation of the $\ell_1$ ball. When this
factorization succeeds, we prove that the resulting splitting algorithm enjoys
an improved convergence bound with respect to the non-adaptive version.
Moreover, our analysis also shows that conditions for acceleration occur mostly
at the beginning of the iterative process, consistent with numerical
experiments. We further validate our analysis by showing that on dictionaries
where this factorization does not exist, adaptive acceleration fails.
Temporal anomaly detection: calibrating the surprise
Authors: Eyal Gutflaish, Aryeh Kontorovich, Sivan Sabato, Ofer Biller, Oded Sofer
Date: 2017-05-29 00:00:00
Tasks: Anomaly Detection

We propose a hybrid approach to temporal anomaly detection in access data of
users to databases --- or more generally, any kind of subject-object
co-occurrence data. We consider a high-dimensional setting that also requires
fast computation at test time. Our methodology identifies anomalies based on a
single stationary model, instead of requiring a full temporal one, which would
be prohibitive in this setting. We learn a low-rank stationary model from the
training data, and then fit a regression model for predicting the expected
likelihood score of normal access patterns in the future. The disparity between
the predicted likelihood score and the observed one is used to assess the
`surprise' at test time. This approach enables calibration of the anomaly
score, so that time-varying normal behavior patterns are not considered
anomalous. We provide a detailed description of the algorithm, including a
convergence analysis, and report encouraging empirical results. One of the data
sets that we tested, TDA, is new for the public domain. It consists of two
months' worth of database access records from a live system. Our code is
publicly available at https://github.com/eyalgut/TLR_anomaly_detection.git. The
TDA data set is available at
https://www.kaggle.com/eyalgut/binary-traffic-matrices.
On the Power Spectral Density Applied to the Analysis of Old Canvases
Authors: Francisco J. Simois, Juan J. Murillo-Fuentes
Date: 2017-05-29 00:00:00

A routine task for art historians is painting diagnostics, such as dating or
attribution. Signal processing of the X-ray image of a canvas provides useful
information about its fabric. However, previous methods may fail when very old
and deteriorated artworks or simply canvases of small size are studied. We
present a new framework to analyze and further characterize the paintings from
their radiographs. First, we start from a general analysis of lattices and
provide new unifying results about the theoretical spectra of weaves. Then, we
use these results to infer the main structure of the fabric, like the type of
weave and the thread densities. We propose a practical estimation of these
theoretical results from paintings with the averaged power spectral density
(PSD), which provides a more robust tool. Furthermore, we found that the PSD
provides a fingerprint that characterizes the whole canvas. We search and
discuss some distinctive features we may find in that fingerprint. We apply
these results to several masterpieces of the 17th and 18th centuries from the
Museo Nacional del Prado to show that this approach yields accurate results in
thread counting and is very useful for paintings comparison, even in situations
where previous methods fail.
Recurrent Soft Attention Model for Common Object Recognition
Authors: Liliang Ren
Date: 2017-05-04 00:00:00
Tasks: model, Object, object-detection, Object Detection, Object Recognition

We propose the Recurrent Soft Attention Model, which integrates the visual
attention from the original image to a LSTM memory cell through a down-sample
network. The model recurrently transmits visual attention to the memory cells
for glimpse mask generation, which is a more natural way for attention
integration and exploitation in general object detection and recognition
problem. We test our model under the metric of the top-1 accuracy on the
CIFAR-10 dataset. The experiment shows that our down-sample network and
feedback mechanism plays an effective role among the whole network structure.
Learning Network Structures from Contagion
Authors: Adisak Supeesun, Jittat Fakcharoenphol
Date: 2017-05-29 00:00:00

In 2014, Amin, Heidari, and Kearns proved that tree networks can be learned
by observing only the infected set of vertices of the contagion process under
the independent cascade model, in both the active and passive query models.
They also showed empirically that simple extensions of their algorithms work on
sparse networks. In this work, we focus on the active model. We prove that a
simple modification of Amin et al.'s algorithm works on more general classes of
networks, namely (i) networks with large girth and low path growth rate, and
(ii) networks with bounded degree. This also provides partial theoretical
explanation for Amin et al.'s experiments on sparse networks.
RandomOut: Using a convolutional gradient norm to rescue convolutional filters
Authors: Joseph Paul Cohen, Henry Z. Lo, Wei Ding
Date: 2016-02-18 00:00:00

Filters in convolutional neural networks are sensitive to their
initialization. The random numbers used to initialize filters are a bias and
determine if you will "win" and converge to a satisfactory local minimum so we
call this The Filter Lottery. We observe that the 28x28 Inception-V3 model
without Batch Normalization fails to train 26% of the time when varying the
random seed alone. This is a problem that affects the trial and error process
of designing a network. Because random seeds have a large impact it makes it
hard to evaluate a network design without trying many different random starting
weights. This work aims to reduce the bias imposed by the initial weights so a
network converges more consistently. We propose to evaluate and replace
specific convolutional filters that have little impact on the prediction. We
use the gradient norm to evaluate the impact of a filter on error, and
re-initialize filters when the gradient norm of its weights falls below a
specific threshold. This consistently improves accuracy on the 28x28
Inception-V3 with a median increase of +3.3%. In effect our method RandomOut
increases the number of filters explored without increasing the size of the
network. We observe that the RandomOut method has more consistent
generalization performance, having a standard deviation of 1.3% instead of 2%
when varying random seeds, and does so faster and with fewer parameters.
Ensemble of Part Detectors for Simultaneous Classification and Localization
Authors: Xiaopeng Zhang, Hongkai Xiong, Weiyao Lin, Qi Tian
Date: 2017-05-29 00:00:00
Tasks: Classification, Clustering, Diversity, General Classification, image-classification, Image Classification, Multiple Instance Learning, Object Localization

Part-based representation has been proven to be effective for a variety of
visual applications. However, automatic discovery of discriminative parts
without object/part-level annotations is challenging. This paper proposes a
discriminative mid-level representation paradigm based on the responses of a
collection of part detectors, which only requires the image-level labels.
Towards this goal, we first develop a detector-based spectral clustering method
to mine the representative and discriminative mid-level patterns for detector
initialization. The advantage of the proposed pattern mining technology is that
the distance metric based on detectors only focuses on discriminative details,
and a set of such grouped detectors offer an effective way for consistent
pattern mining. Relying on the discovered patterns, we further formulate the
detector learning process as a confidence-loss sparse Multiple Instance
Learning (cls-MIL) task, which considers the diversity of the positive samples,
while avoid drifting away the well localized ones by assigning a confidence
value to each positive sample. The responses of the learned detectors can form
an effective mid-level image representation for both image classification and
object localization. Experiments conducted on benchmark datasets demonstrate
the superiority of our method over existing approaches.
Improving the Expected Improvement Algorithm
Authors: Chao Qin, Diego Klabjan, Daniel Russo
Date: 2017-05-29 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Bayesian Optimization

The expected improvement (EI) algorithm is a popular strategy for information
collection in optimization under uncertainty. The algorithm is widely known to
be too greedy, but nevertheless enjoys wide use due to its simplicity and
ability to handle uncertainty and noise in a coherent decision theoretic
framework. To provide rigorous insight into EI, we study its properties in a
simple setting of Bayesian optimization where the domain consists of a finite
grid of points. This is the so-called best-arm identification problem, where
the goal is to allocate measurement effort wisely to confidently identify the
best arm using a small number of measurements. In this framework, one can show
formally that EI is far from optimal. To overcome this shortcoming, we
introduce a simple modification of the expected improvement algorithm.
Surprisingly, this simple change results in an algorithm that is asymptotically
optimal for Gaussian best-arm identification problems, and provably outperforms
standard EI by an order of magnitude.
Supervised Complementary Entity Recognition with Augmented Key-value Pairs of Knowledge
Authors: Hu Xu, Lei Shu, Philip S. Yu
Date: 2017-05-29 00:00:00
Tasks: Sentiment Analysis

Extracting opinion targets is an important task in sentiment analysis on
product reviews and complementary entities (products) are one important type of
opinion targets that may work together with the reviewed product. In this
paper, we address the problem of Complementary Entity Recognition (CER) as a
supervised sequence labeling with the capability of expanding domain knowledge
as key-value pairs from unlabeled reviews, by automatically learning and
enhancing knowledge-based features. We use Conditional Random Field (CRF) as
the base learner and augment CRF with knowledge-based features (called the
Knowledge-based CRF or KCRF for short). We conduct experiments to show that
KCRF effectively improves the performance of supervised CER task.
An Attention-Driven Approach of No-Reference Image Quality Assessment
Authors: Diqi Chen, Yizhou Wang, Tianfu Wu, Wen Gao
Date: 2016-12-12 00:00:00
Tasks: Image Quality Assessment, Multi-Task Learning, No-Reference Image Quality Assessment, NR-IQA, Reinforcement Learning, Representation Learning

In this paper, we present a novel method of no-reference image quality
assessment (NR-IQA), which is to predict the perceptual quality score of a
given image without using any reference image. The proposed method harnesses
three functions (i) the visual attention mechanism, which affects many aspects
of visual perception including image quality assessment, however, is overlooked
in the NR-IQA literature. The method assumes that the fixation areas on an
image contain key information to the process of IQA. (ii) the robust averaging
strategy, which is a means \--- supported by psychology studies \--- to
integrating multiple/step-wise evidence to make a final perceptual judgment.
(iii) the multi-task learning, which is believed to be an effectual means to
shape representation learning and could result in a more generalized model.
  To exploit the synergy of the three, we consider the NR-IQA as a dynamic
perception process, in which the model samples a sequence of "informative"
areas and aggregates the information to learn a representation for the tasks of
jointly predicting the image quality score and the distortion type.
  The model learning is implemented by a reinforcement strategy, in which the
rewards of both tasks guide the learning of the optimal sampling policy to
acquire the "task-informative" image regions so that the predictions can be
made accurately and efficiently (in terms of the sampling steps). The
reinforcement learning is realized by a deep network with the policy gradient
method and trained through back-propagation.
  In experiments, the model is tested on the TID2008 dataset and it outperforms
several state-of-the-art methods. Furthermore, the model is very efficient in
the sense that a small number of fixations are used in NR-IQA.
Learning the Sparse and Low Rank PARAFAC Decomposition via the Elastic Net
Authors: Songting Shi, Xiang Li, Arkadiusz Sitek, Quanzheng Li
Date: 2017-05-29 00:00:00
Tasks: Missing Values

In this article, we derive a Bayesian model to learning the sparse and low
rank PARAFAC decomposition for the observed tensor with missing values via the
elastic net, with property to find the true rank and sparse factor matrix which
is robust to the noise. We formulate efficient block coordinate descent
algorithm and admax stochastic block coordinate descent algorithm to solve it,
which can be used to solve the large scale problem. To choose the appropriate
rank and sparsity in PARAFAC decomposition, we will give a solution path by
gradually increasing the regularization to increase the sparsity and decrease
the rank. When we find the sparse structure of the factor matrix, we can fixed
the sparse structure, using a small to regularization to decreasing the
recovery error, and one can choose the proper decomposition from the solution
path with sufficient sparse factor matrix with low recovery error. We test the
power of our algorithm on the simulation data and real data, which show it is
powerful.
Robust Online Matrix Factorization for Dynamic Background Subtraction
Authors: Hongwei Yong, Deyu Meng, WangMeng Zuo, Lei Zhang
Date: 2017-05-28 00:00:00

We propose an effective online background subtraction method, which can be
robustly applied to practical videos that have variations in both foreground
and background. Different from previous methods which often model the
foreground as Gaussian or Laplacian distributions, we model the foreground for
each frame with a specific mixture of Gaussians (MoG) distribution, which is
updated online frame by frame. Particularly, our MoG model in each frame is
regularized by the learned foreground/background knowledge in previous frames.
This makes our online MoG model highly robust, stable and adaptive to practical
foreground and background variations. The proposed model can be formulated as a
concise probabilistic MAP model, which can be readily solved by EM algorithm.
We further embed an affine transformation operator into the proposed model,
which can be automatically adjusted to fit a wide range of video background
transformations and make the method more robust to camera movements. With using
the sub-sampling technique, the proposed method can be accelerated to execute
more than 250 frames per second on average, meeting the requirement of
real-time background subtraction for practical video processing tasks. The
superiority of the proposed method is substantiated by extensive experiments
implemented on synthetic and real videos, as compared with state-of-the-art
online and offline background subtraction methods.
Subject Specific Stream Classification Preprocessing Algorithm for Twitter Data Stream
Authors: Nisansa de Silva, Danaja Maldeniya, Chamilka Wijeratne
Date: 2017-05-28 00:00:00
Tasks: General Classification

Micro-blogging service Twitter is a lucrative source for data mining
applications on global sentiment. But due to the omnifariousness of the
subjects mentioned in each data item; it is inefficient to run a data mining
algorithm on the raw data. This paper discusses an algorithm to accurately
classify the entire stream in to a given number of mutually exclusive
collectively exhaustive streams upon each of which the data mining algorithm
can be run separately yielding more relevant results with a high efficiency.