pwc-archive/papers-with-abstracts huggingface dataset:
Integrating sentiment and social structure to determine preference alignments: The Irish Marriage Referendum
Authors: David J. P. O'Sullivan, Guillermo Garduño-Hernández, James P. Gleeson, Mariano Beguerisse-Díaz
Date: 2017-01-01 00:00:00

We examine the relationship between social structure and sentiment through
the analysis of a large collection of tweets about the Irish Marriage
Referendum of 2015. We obtain the sentiment of every tweet with the hashtags
#marref and #marriageref that was posted in the days leading to the referendum,
and construct networks to aggregate sentiment and use it to study the
interactions among users. Our results show that the sentiment of mention tweets
posted by users is correlated with the sentiment of received mentions, and
there are significantly more connections between users with similar sentiment
scores than among users with opposite scores in the mention and follower
networks. We combine the community structure of the two networks with the
activity level of the users and sentiment scores to find groups of users who
support voting `yes' or `no' in the referendum. There were numerous
conversations between users on opposing sides of the debate in the absence of
follower connections, which suggests that there were efforts by some users to
establish dialogue and debate across ideological divisions. Our analysis shows
that social structure can be integrated successfully with sentiment to analyse
and understand the disposition of social media users. These results have
potential applications in the integration of data and meta-data to study
opinion dynamics, public opinion modelling, and polling.
Reproducing and learning new algebraic operations on word embeddings using genetic programming
Authors: Roberto Santana
Date: 2017-02-18 00:00:00
Tasks: Word Embeddings

Word-vector representations associate a high dimensional real-vector to every
word from a corpus. Recently, neural-network based methods have been proposed
for learning this representation from large corpora. This type of
word-to-vector embedding is able to keep, in the learned vector space, some of
the syntactic and semantic relationships present in the original word corpus.
This, in turn, serves to address different types of language classification
tasks by doing algebraic operations defined on the vectors. The general
practice is to assume that the semantic relationships between the words can be
inferred by the application of a-priori specified algebraic operations. Our
general goal in this paper is to show that it is possible to learn methods for
word composition in semantic spaces. Instead of expressing the compositional
method as an algebraic operation, we will encode it as a program, which can be
linear, nonlinear, or involve more intricate expressions. More remarkably, this
program will be evolved from a set of initial random programs by means of
genetic programming (GP). We show that our method is able to reproduce the same
behavior as human-designed algebraic operators. Using a word analogy task as
benchmark, we also show that GP-generated programs are able to obtain accuracy
values above those produced by the commonly used human-designed rule for
algebraic manipulation of word vectors. Finally, we show the robustness of our
approach by executing the evolved programs on the word2vec GoogleNews vectors,
learned over 3 billion running words, and assessing their accuracy in the same
word analogy task.
Utilizing High-level Visual Feature for Indoor Shopping Mall Navigation
Authors: Ziwei Xu, Haitian Zheng, Minjian Pang, Yangchun Zhu, Xiongfei Su, Guyue Zhou, Lu Fang
Date: 2016-10-06 00:00:00
Tasks: Visual Navigation, Vocal Bursts Intensity Prediction

Towards robust and convenient indoor shopping mall navigation, we propose a
novel learning-based scheme to utilize the high-level visual information from
the storefront images captured by personal devices of users. Specifically, we
decompose the visual navigation problem into localization and map generation
respectively. Given a storefront input image, a novel feature fusion scheme
(denoted as FusionNet) is proposed by fusing the distinguishing DNN-based
appearance feature and text feature for robust recognition of store brands,
which serves for accurate localization. Regarding the map generation, we
convert the user-captured indicator map of the shopping mall into a topological
map by parsing the stores and their connectivity. Experimental results
conducted on the real shopping malls demonstrate that the proposed system
achieves robust localization and precise map generation, enabling accurate
navigation.
Brain Inspired Cognitive Model with Attention for Self-Driving Cars
Authors: Shitao Chen, Songyi Zhang, Jinghao Shang, Badong Chen, Nanning Zheng
Date: 2017-02-18 00:00:00
Tasks: Autonomous Driving, Decision Making, Self-Driving Cars

Perception-driven approach and end-to-end system are two major vision-based
frameworks for self-driving cars. However, it is difficult to introduce
attention and historical information of autonomous driving process, which are
the essential factors for achieving human-like driving into these two methods.
In this paper, we propose a novel model for self-driving cars named
brain-inspired cognitive model with attention (CMA). This model consists of
three parts: a convolutional neural network for simulating human visual cortex,
a cognitive map built to describe relationships between objects in complex
traffic scene and a recurrent neural network that combines with the real-time
updated cognitive map to implement attention mechanism and long-short term
memory. The benefit of our model is that can accurately solve three tasks
simultaneously:1) detection of the free space and boundaries of the current and
adjacent lanes. 2)estimation of obstacle distance and vehicle attitude, and 3)
learning of driving behavior and decision making from human driver. More
significantly, the proposed model could accept external navigating instructions
during an end-to-end driving process. For evaluation, we build a large-scale
road-vehicle dataset which contains more than forty thousand labeled road
images captured by three cameras on our self-driving car. Moreover, human
driving activities and vehicle states are recorded in the meanwhile.
Do logarithmic proximity measures outperform plain ones in graph clustering?
Authors: Vladimir Ivashkin, Pavel Chebotarev
Date: 2016-05-03 00:00:00
Tasks: Clustering, Graph Clustering

We consider a number of graph kernels and proximity measures including
commute time kernel, regularized Laplacian kernel, heat kernel, exponential
diffusion kernel (also called "communicability"), etc., and the corresponding
distances as applied to clustering nodes in random graphs and several
well-known datasets. The model of generating random graphs involves edge
probabilities for the pairs of nodes that belong to the same class or different
predefined classes of nodes. It turns out that in most cases, logarithmic
measures (i.e., measures resulting after taking logarithm of the proximities)
perform better while distinguishing underlying classes than the "plain"
measures. A comparison in terms of reject curves of inter-class and intra-class
distances confirms this conclusion. A similar conclusion can be made for
several well-known datasets. A possible origin of this effect is that most
kernels have a multiplicative nature, while the nature of distances used in
cluster algorithms is an additive one (cf. the triangle inequality). The
logarithmic transformation is a tool to transform the first nature to the
second one. Moreover, some distances corresponding to the logarithmic measures
possess a meaningful cutpoint additivity property. In our experiments, the
leader is usually the logarithmic Communicability measure. However, we indicate
some more complicated cases in which other measures, typically, Communicability
and plain Walk, can be the winners.
Audio Event and Scene Recognition: A Unified Approach using Strongly and Weakly Labeled Data
Authors: Anurag Kumar, Bhiksha Raj
Date: 2016-11-12 00:00:00
Tasks: Scene Recognition, Weakly-supervised Learning

In this paper we propose a novel learning framework called Supervised and
Weakly Supervised Learning where the goal is to learn simultaneously from
weakly and strongly labeled data. Strongly labeled data can be simply
understood as fully supervised data where all labeled instances are available.
In weakly supervised learning only data is weakly labeled which prevents one
from directly applying supervised learning methods. Our proposed framework is
motivated by the fact that a small amount of strongly labeled data can give
considerable improvement over only weakly supervised learning. The primary
problem domain focus of this paper is acoustic event and scene detection in
audio recordings. We first propose a naive formulation for leveraging labeled
data in both forms. We then propose a more general framework for Supervised and
Weakly Supervised Learning (SWSL). Based on this general framework, we propose
a graph based approach for SWSL. Our main method is based on manifold
regularization on graphs in which we show that the unified learning can be
formulated as a constraint optimization problem which can be solved by
iterative concave-convex procedure (CCCP). Our experiments show that our
proposed framework can address several concerns of audio content analysis using
weakly labeled data.
Collaborative Deep Reinforcement Learning for Joint Object Search
Authors: Xiangyu Kong, Bo Xin, Yizhou Wang, Gang Hua
Date: 2017-02-18 00:00:00
Proceeding: CVPR 2017 7
Tasks: Active Object Localization, Deep Reinforcement Learning, Object, object-detection, Object Detection, Object Localization, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We examine the problem of joint top-down active search of multiple objects
under interaction, e.g., person riding a bicycle, cups held by the table, etc..
Such objects under interaction often can provide contextual cues to each other
to facilitate more efficient search. By treating each detector as an agent, we
present the first collaborative multi-agent deep reinforcement learning
algorithm to learn the optimal policy for joint active object localization,
which effectively exploits such beneficial contextual information. We learn
inter-agent communication through cross connections with gates between the
Q-networks, which is facilitated by a novel multi-agent deep Q-learning
algorithm with joint exploitation sampling. We verify our proposed method on
multiple object detection benchmarks. Not only does our model help to improve
the performance of state-of-the-art active localization models, it also reveals
interesting co-detection patterns that are intuitively interpretable.
Thresholding based Efficient Outlier Robust PCA
Authors: Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli
Date: 2017-02-18 00:00:00

We consider the problem of outlier robust PCA (OR-PCA) where the goal is to
recover principal directions despite the presence of outlier data points. That
is, given a data matrix $M^*$, where $(1-\alpha)$ fraction of the points are
noisy samples from a low-dimensional subspace while $\alpha$ fraction of the
points can be arbitrary outliers, the goal is to recover the subspace
accurately. Existing results for \OR-PCA have serious drawbacks: while some
results are quite weak in the presence of noise, other results have runtime
quadratic in dimension, rendering them impractical for large scale
applications.
  In this work, we provide a novel thresholding based iterative algorithm with
per-iteration complexity at most linear in the data size. Moreover, the
fraction of outliers, $\alpha$, that our method can handle is tight up to
constants while providing nearly optimal computational complexity for a general
noise setting. For the special case where the inliers are obtained from a
low-dimensional subspace with additive Gaussian noise, we show that a
modification of our thresholding based method leads to significant improvement
in recovery error (of the subspace) even in the presence of a large fraction of
outliers.
The Ciona17 Dataset for Semantic Segmentation of Invasive Species in a Marine Aquaculture Environment
Authors: Angus Galloway, Graham W. Taylor, Aaron Ramsay, Medhat Moussa
Date: 2017-02-18 00:00:00
Tasks: Semantic Segmentation

An original dataset for semantic segmentation, Ciona17, is introduced, which
to the best of the authors' knowledge, is the first dataset of its kind with
pixel-level annotations pertaining to invasive species in a marine environment.
Diverse outdoor illumination, a range of object shapes, colour, and severe
occlusion provide a significant real world challenge for the computer vision
community. An accompanying ground-truthing tool for superpixel labeling, Truth
and Crop, is also introduced. Finally, we provide a baseline using a variant of
Fully Convolutional Networks, and report results in terms of the standard mean
intersection over union (mIoU) metric.
Supervised Syntax-based Alignment between English Sentences and Abstract Meaning Representation Graphs
Authors: Chenhui Chu, Sadao Kurohashi
Date: 2016-06-07 00:00:00
Tasks: Abstract Meaning Representation, AMR Parsing

As alignment links are not given between English sentences and Abstract
Meaning Representation (AMR) graphs in the AMR annotation, automatic alignment
becomes indispensable for training an AMR parser. Previous studies formalize it
as a string-to-string problem and solve it in an unsupervised way, which
suffers from data sparseness due to the small size of training data for
English-AMR alignment. In this paper, we formalize it as a syntax-based
alignment problem and solve it in a supervised manner based on syntax trees,
which can address the data sparseness problem by generalizing English-AMR
tokens to syntax tags. Experiments verify the effectiveness of the proposed
method not only for English-AMR alignment, but also for AMR parsing.
Defect detection for patterned fabric images based on GHOG and low-rank decomposition
Authors: Chunlei Li, Guangshuai Gao, Zhoufeng Liu, Di Huang, Sheng Liu, Miao Yu
Date: 2017-02-18 00:00:00
Tasks: Computational Efficiency, Defect Detection

In order to accurately detect defects in patterned fabric images, a novel
detection algorithm based on Gabor-HOG (GHOG) and low-rank decomposition is
proposed in this paper. Defect-free pattern fabric images have the specified
direction, while defects damage their regularity of direction. Therefore, a
direction-aware descriptor is designed, denoted as GHOG, a combination of Gabor
and HOG, which is extremely valuable for localizing the defect region. Upon
devising a powerful directional descriptor, an efficient low-rank decomposition
model is constructed to divide the matrix generated by the directional feature
extracted from image blocks into a low-rank matrix (background information) and
a sparse matrix (defect information). A nonconvex log det(.) as a smooth
surrogate function for the rank instead of the nuclear norm is also exploited
to improve the efficiency of the low-rank model. Moreover, the computational
efficiency is further improved by utilizing the alternative direction method of
multipliers (ADMM). Thereafter, the saliency map generated by the sparse matrix
is segmented via the optimal threshold algorithm to locate the defect regions.
Experimental results show that the proposed method can effectively detect
patterned fabric defects and outperform the state-of-the-art methods.
Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection
Authors: Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes
Date: 2017-02-18 00:00:00
Tasks: Caption Generation, Event Detection, Machine Translation, Trajectory Prediction

As humans we possess an intuitive ability for navigation which we master
through years of practice; however existing approaches to model this trait for
diverse tasks including monitoring pedestrian flow and detecting abnormal
events have been limited by using a variety of hand-crafted features. Recent
research in the area of deep-learning has demonstrated the power of learning
features directly from the data; and related research in recurrent neural
networks has shown exemplary results in sequence-to-sequence problems such as
neural machine translation and neural image caption generation. Motivated by
these approaches, we propose a novel method to predict the future motion of a
pedestrian given a short history of their, and their neighbours, past
behaviour. The novelty of the proposed method is the combined attention model
which utilises both "soft attention" as well as "hard-wired" attention in order
to map the trajectory information from the local neighbourhood to the future
positions of the pedestrian of interest. We illustrate how a simple
approximation of attention weights (i.e hard-wired) can be merged together with
soft attention weights in order to make our model applicable for challenging
real world scenarios with hundreds of neighbours. The navigational capability
of the proposed method is tested on two challenging publicly available
surveillance databases where our model outperforms the current-state-of-the-art
methods. Additionally, we illustrate how the proposed architecture can be
directly applied for the task of abnormal event detection without handcrafting
the features.
Bi-Level Online Control without Regret
Authors: Andrey Bernstein
Date: 2017-02-18 00:00:00

This paper considers a bi-level discrete-time control framework with
real-time constraints, consisting of several local controllers and a central
controller. The objective is to bridge the gap between the online convex
optimization and real-time control literature by proposing an online control
algorithm with small dynamic regret, which is a natural performance criterion
in nonstationary environments related to real-time control problems. We
illustrate how the proposed algorithm can be applied to real-time control of
power setpoints in an electrical grid.
Learning from networked examples in a k-partite graph
Authors: Yuyi Wang, Jan Ramon, Zheng-Chu Guo
Date: 2013-06-03 00:00:00
Tasks: BIG-bench Machine Learning

Many machine learning algorithms are based on the assumption that training
examples are drawn independently. However, this assumption does not hold
anymore when learning from a networked sample where two or more training
examples may share common features. We propose an efficient weighting method
for learning from networked examples and show the sample error bound which is
better than previous work.
Dataset Augmentation in Feature Space
Authors: Terrance DeVries, Graham W. Taylor
Date: 2017-02-17 00:00:00
Tasks: Representation Learning

Dataset augmentation, the practice of applying a wide array of
domain-specific transformations to synthetically expand a training set, is a
standard tool in supervised learning. While effective in tasks such as visual
recognition, the set of transformations must be carefully designed,
implemented, and tested for every new domain, limiting its re-use and
generality. In this paper, we adopt a simpler, domain-agnostic approach to
dataset augmentation. We start with existing data points and apply simple
transformations such as adding noise, interpolating, or extrapolating between
them. Our main insight is to perform the transformation not in input space, but
in a learned feature space. A re-kindling of interest in unsupervised
representation learning makes this technique timely and more effective. It is a
simple proposal, but to-date one that has not been tested empirically. Working
in the space of context vectors generated by sequence-to-sequence models, we
demonstrate a technique that is effective for both static and sequential data.
Analysis and Optimization of fastText Linear Text Classifier
Authors: Vladimir Zolotov, David Kung
Date: 2017-02-17 00:00:00
Tasks: Classification, Deep Learning, General Classification, text-classification, Text Classification

The paper [1] shows that simple linear classifier can compete with complex
deep learning algorithms in text classification applications. Combining bag of
words (BoW) and linear classification techniques, fastText [1] attains same or
only slightly lower accuracy than deep learning algorithms [2-9] that are
orders of magnitude slower. We proved formally that fastText can be transformed
into a simpler equivalent classifier, which unlike fastText does not have any
hidden layer. We also proved that the necessary and sufficient dimensionality
of the word vector embedding space is exactly the number of document classes.
These results help constructing more optimal linear text classifiers with
guaranteed maximum classification capabilities. The results are proven exactly
by pure formal algebraic methods without attracting any empirical data.
Learning to Use Learners' Advice
Authors: Adish Singla, Hamed Hassani, Andreas Krause
Date: 2017-02-16 00:00:00
Tasks: Blocking, Multi-Armed Bandits

In this paper, we study a variant of the framework of online learning using
expert advice with limited/bandit feedback. We consider each expert as a
learning entity, seeking to more accurately reflecting certain real-world
applications. In our setting, the feedback at any time $t$ is limited in a
sense that it is only available to the expert $i^t$ that has been selected by
the central algorithm (forecaster), \emph{i.e.}, only the expert $i^t$ receives
feedback from the environment and gets to learn at time $t$. We consider a
generic black-box approach whereby the forecaster does not control or know the
learning dynamics of the experts apart from knowing the following no-regret
learning property: the average regret of any expert $j$ vanishes at a rate of
at least $O(t_j^{\regretRate-1})$ with $t_j$ learning steps where $\regretRate
\in [0, 1]$ is a parameter.
  In the spirit of competing against the best action in hindsight in
multi-armed bandits problem, our goal here is to be competitive w.r.t. the
cumulative losses the algorithm could receive by following the policy of always
selecting one expert. We prove the following hardness result: without any
coordination between the forecaster and the experts, it is impossible to design
a forecaster achieving no-regret guarantees. In order to circumvent this
hardness result, we consider a practical assumption allowing the forecaster to
"guide" the learning process of the experts by filtering/blocking some of the
feedbacks observed by them from the environment, \emph{i.e.}, not allowing the
selected expert $i^t$ to learn at time $t$ for some time steps. Then, we design
a novel no-regret learning algorithm \algo for this problem setting by
carefully guiding the feedbacks observed by experts. We prove that \algo
achieves the worst-case expected cumulative regret of $O(\Time^\frac{1}{2 -
\regretRate})$ after $\Time$ time steps.