pwc-archive/papers-with-abstracts huggingface dataset:
Ranking for Relevance and Display Preferences in Complex Presentation Layouts
Authors: Harrie Oosterhuis, Maarten de Rijke
Date: 2018-05-07 00:00:00
Tasks: Deep Reinforcement Learning, Learning-To-Rank, Reinforcement Learning

Learning to Rank has traditionally considered settings where given the
relevance information of objects, the desired order in which to rank the
objects is clear. However, with today's large variety of users and layouts this
is not always the case. In this paper, we consider so-called complex ranking
settings where it is not clear what should be displayed, that is, what the
relevant items are, and how they should be displayed, that is, where the most
relevant items should be placed. These ranking settings are complex as they
involve both traditional ranking and inferring the best display order. Existing
learning to rank methods cannot handle such complex ranking settings as they
assume that the display order is known beforehand. To address this gap we
introduce a novel Deep Reinforcement Learning method that is capable of
learning complex rankings, both the layout and the best ranking given the
layout, from weak reward signals. Our proposed method does so by selecting
documents and positions sequentially, hence it ranks both the documents and
positions, which is why we call it the Double-Rank Model (DRM). Our experiments
show that DRM outperforms all existing methods in complex ranking settings,
thus it leads to substantial ranking improvements in cases where the display
order is not known a priori.
Relating Eye-Tracking Measures With Changes In Knowledge on Search Tasks
Authors: Nilavra Bhattacharya, Jacek Gwizdka
Date: 2018-05-07 00:00:00

We conducted an eye-tracking study where 30 participants performed searches
on the web. We measured their topical knowledge before and after each task.
Their eye-fixations were labelled as "reading" or "scanning". The series of
reading fixations in a line, called "reading-sequences" were characterized by
their length in pixels, fixation duration, and the number of fixations making
up the sequence. We hypothesize that differences in knowledge-change of
participants are reflected in their eye-tracking measures related to reading.
Our results show that the participants with higher change in knowledge differ
significantly in terms of their total reading-sequence-length,
reading-sequence-duration, and number of reading fixations, when compared to
participants with lower knowledge-change.
A Review on Facial Micro-Expressions Analysis: Datasets, Features and Metrics
Authors: Walied Merghani, Adrian K. Davison, Moi Hoon Yap
Date: 2018-05-07 00:00:00

Facial micro-expressions are very brief, spontaneous facial expressions that
appear on the face of humans when they either deliberately or unconsciously
conceal an emotion. Micro-expression has shorter duration than
macro-expression, which makes it more challenging for human and machine. Over
the past ten years, automatic micro-expressions recognition has attracted
increasing attention from researchers in psychology, computer science,
security, neuroscience and other related disciplines. The aim of this paper is
to provide the insights of automatic micro-expressions and recommendations for
future research. There has been a lot of datasets released over the last decade
that facilitated the rapid growth in this field. However, comparison across
different datasets is difficult due to the inconsistency in experiment
protocol, features used and evaluation methods. To address these issues, we
review the datasets, features and the performance metrics deployed in the
literature. Relevant challenges such as the spatial temporal settings during
data collection, emotional classes versus objective classes in data labelling,
face regions in data analysis, standardisation of metrics and the requirements
for real-world implementation are discussed. We conclude by proposing some
promising future directions to advancing micro-expressions research.
Billion-scale Network Embedding with Iterative Random Projection
Authors: Ziwei Zhang, Peng Cui, Haoyang Li, Xiao Wang, Wenwu Zhu
Date: 2018-05-07 00:00:00
Tasks: Distributed Computing, Link Prediction, Network Embedding, Node Classification

Network embedding, which learns low-dimensional vector representation for
nodes in the network, has attracted considerable research attention recently.
However, the existing methods are incapable of handling billion-scale networks,
because they are computationally expensive and, at the same time, difficult to
be accelerated by distributed computing schemes. To address these problems, we
propose RandNE (Iterative Random Projection Network Embedding), a novel and
simple billion-scale network embedding method. Specifically, we propose a
Gaussian random projection approach to map the network into a low-dimensional
embedding space while preserving the high-order proximities between nodes. To
reduce the time complexity, we design an iterative projection procedure to
avoid the explicit calculation of the high-order proximities. Theoretical
analysis shows that our method is extremely efficient, and friendly to
distributed computing schemes without any communication cost in the
calculation. We also design a dynamic updating procedure which can efficiently
incorporate the dynamic changes of the networks without error aggregation.
Extensive experimental results demonstrate the efficiency and efficacy of
RandNE over state-of-the-art methods in several tasks including network
reconstruction, link prediction and node classification on multiple datasets
with different scales, ranging from thousands to billions of nodes and edges.
Weakly-supervised Contextualization of Knowledge Graph Facts
Authors: Nikos Voskarides, Edgar Meij, Ridho Reinanda, Abhinav Khaitan, Miles Osborne, Giorgio Stefanoni, Prabhanjan Kambadur, Maarten de Rijke
Date: 2018-05-07 00:00:00
Tasks: Knowledge Graphs, Learning-To-Rank

Knowledge graphs (KGs) model facts about the world, they consist of nodes
(entities such as companies and people) that are connected by edges (relations
such as founderOf). Facts encoded in KGs are frequently used by search
applications to augment result pages. When presenting a KG fact to the user,
providing other facts that are pertinent to that main fact can enrich the user
experience and support exploratory information needs. KG fact contextualization
is the task of augmenting a given KG fact with additional and useful KG facts.
The task is challenging because of the large size of KGs, discovering other
relevant facts even in a small neighborhood of the given fact results in an
enormous amount of candidates. We introduce a neural fact contextualization
method (NFCM) to address the KG fact contextualization task. NFCM first
generates a set of candidate facts in the neighborhood of a given fact and then
ranks the candidate facts using a supervised learning to rank model. The
ranking model combines features that we automatically learn from data and that
represent the query-candidate facts with a set of hand-crafted features we
devised or adjusted for this task. In order to obtain the annotations required
to train the learning to rank model at scale, we generate training data
automatically using distant supervision on a large entity-tagged text corpus.
We show that ranking functions learned on this data are effective at
contextualizing KG facts. Evaluation using human assessors shows that it
significantly outperforms several competitive baselines.
The Concept of the Deep Learning-Based System "Artificial Dispatcher" to Power System Control and Dispatch
Authors: Nikita Tomin, Victor Kurbatsky, Michael Negnevitsky
Date: 2018-05-07 00:00:00

Year by year control of normal and emergency conditions of up-to-date power
systems becomes an increasingly complicated problem. With the increasing
complexity the existing control system of power system conditions which
includes operative actions of the dispatcher and work of special automatic
devices proves to be insufficiently effective more and more frequently, which
raises risks of dangerous and emergency conditions in power systems. The paper
is aimed at compensating for the shortcomings of man (a cognitive barrier,
exposure to stresses and so on) and automatic devices by combining their strong
points, i.e. the dispatcher's intelligence and the speed of automatic devices
by virtue of development of the intelligent system "Artificial dispatcher" on
the basis of deep machine learning technology. For realization of the system
"Artificial dispatcher" in addition to deep learning it is planned to attract
the game theory approaches to formalize work of the up-to-date power system as
a game problem. The "gain" for "Artificial dispatcher" will consist in bringing
in a power system in the normal steady-state or post-emergency conditions by
means of the required control actions.
Planning and Learning with Stochastic Action Sets
Authors: Craig Boutilier, Alon Cohen, Amit Daniely, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans
Date: 2018-05-07 00:00:00
Tasks: Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

In many practical uses of reinforcement learning (RL) the set of actions available at a given state is a random variable, with realizations governed by an exogenous stochastic process. Somewhat surprisingly, the foundations for such sequential decision processes have been unaddressed. In this work, we formalize and investigate MDPs with stochastic action sets (SAS-MDPs) to provide these foundations. We show that optimal policies and value functions in this model have a structure that admits a compact representation. From an RL perspective, we show that Q-learning with sampled action sets is sound. In model-based settings, we consider two important special cases: when individual actions are available with independent probabilities; and a sampling-based model for unknown distributions. We develop poly-time value and policy iteration methods for both cases; and in the first, we offer a poly-time linear programming solution.
Lifted Filtering via Exchangeable Decomposition
Authors: Stefan Lüdtke, Max Schröder, Sebastian Bader, Kristian Kersting, Thomas Kirste
Date: 2018-01-31 00:00:00

We present a model for exact recursive Bayesian filtering based on lifted
multiset states. Combining multisets with lifting makes it possible to
simultaneously exploit multiple strategies for reducing inference complexity
when compared to list-based grounded state representations. The core idea is to
borrow the concept of Maximally Parallel Multiset Rewriting Systems and to
enhance it by concepts from Rao-Blackwellization and Lifted Inference, giving a
representation of state distributions that enables efficient inference. In
worlds where the random variables that define the system state are exchangeable
-- where the identity of entities does not matter -- it automatically uses a
representation that abstracts from ordering (achieving an exponential reduction
in complexity) -- and it automatically adapts when observations or system
dynamics destroy exchangeability by breaking symmetry.
Detecting Small, Densely Distributed Objects with Filter-Amplifier Networks and Loss Boosting
Authors: Zhenhua Chen, David Crandall, Robert Templeman
Date: 2018-02-21 00:00:00
Tasks: vehicle detection

Detecting small, densely distributed objects is a significant challenge:
small objects often contain less distinctive information compared to larger
ones, and finer-grained precision of bounding box boundaries are required. In
this paper, we propose two techniques for addressing this problem. First, we
estimate the likelihood that each pixel belongs to an object boundary rather
than predicting coordinates of bounding boxes (as YOLO, Faster-RCNN and SSD
do), by proposing a new architecture called Filter-Amplifier Networks (FANs).
Second, we introduce a technique called Loss Boosting (LB) which attempts to
soften the loss imbalance problem on each image. We test our algorithm on the
problem of detecting electrical components on a new, realistic, diverse dataset
of printed circuit boards (PCBs), as well as the problem of detecting vehicles
in the Vehicle Detection in Aerial Imagery (VEDAI) dataset.
  Experiments show that our method works significantly better than current
state-of-the-art algorithms with respect to accuracy, recall and average IoU.
Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring
Authors: Seungjun Nah, Tae Hyun Kim, Kyoung Mu Lee
Date: 2016-12-07 00:00:00
Proceeding: CVPR 2017 7
Tasks: Deblurring, Image Deblurring

Non-uniform blind deblurring for general dynamic scenes is a challenging
computer vision problem as blurs arise not only from multiple object motions
but also from camera shake, scene depth variation. To remove these complicated
motion blurs, conventional energy optimization based methods rely on simple
assumptions such that blur kernel is partially uniform or locally linear.
Moreover, recent machine learning based methods also depend on synthetic blur
datasets generated under these assumptions. This makes conventional deblurring
methods fail to remove blurs where blur kernel is difficult to approximate or
parameterize (e.g. object motion boundaries). In this work, we propose a
multi-scale convolutional neural network that restores sharp images in an
end-to-end manner where blur is caused by various sources. Together, we present
multi-scale loss function that mimics conventional coarse-to-fine approaches.
Furthermore, we propose a new large-scale dataset that provides pairs of
realistic blurry image and the corresponding ground truth sharp image that are
obtained by a high-speed camera. With the proposed model trained on this
dataset, we demonstrate empirically that our method achieves the
state-of-the-art performance in dynamic scene deblurring not only
qualitatively, but also quantitatively.
Multimodal Machine Translation with Reinforcement Learning
Authors: Xin Qian, Ziyi Zhong, Jieli Zhou
Date: 2018-05-07 00:00:00
Tasks: Image Description, Machine Translation, Multimodal Machine Translation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Translation

Multimodal machine translation is one of the applications that integrates
computer vision and language processing. It is a unique task given that in the
field of machine translation, many state-of-the-arts algorithms still only
employ textual information. In this work, we explore the effectiveness of
reinforcement learning in multimodal machine translation. We present a novel
algorithm based on the Advantage Actor-Critic (A2C) algorithm that specifically
cater to the multimodal machine translation task of the EMNLP 2018 Third
Conference on Machine Translation (WMT18). We experiment our proposed algorithm
on the Multi30K multilingual English-German image description dataset and the
Flickr30K image entity dataset. Our model takes two channels of inputs, image
and text, uses translation evaluation metrics as training rewards, and achieves
better results than supervised learning MLE baseline models. Furthermore, we
discuss the prospects and limitations of using reinforcement learning for
machine translation. Our experiment results suggest a promising reinforcement
learning solution to the general task of multimodal sequence to sequence
learning.
Fused Text Segmentation Networks for Multi-oriented Scene Text Detection
Authors: Yuchen Dai, Zheng Huang, Yuting Gao, Youxuan Xu, Kai Chen, Jie Guo, Weidong Qiu
Date: 2017-09-11 00:00:00
Tasks: Multi-Oriented Scene Text Detection, object-detection, Object Detection, Region Proposal, Scene Text Detection, Segmentation, Semantic Segmentation, Text Detection, Text Segmentation

In this paper, we introduce a novel end-end framework for multi-oriented
scene text detection from an instance-aware semantic segmentation perspective.
We present Fused Text Segmentation Networks, which combine multi-level features
during the feature extracting as text instance may rely on finer feature
expression compared to general objects. It detects and segments the text
instance jointly and simultaneously, leveraging merits from both semantic
segmentation task and region proposal based object detection task. Not
involving any extra pipelines, our approach surpasses the current state of the
art on multi-oriented scene text detection benchmarks: ICDAR2015 Incidental
Scene Text and MSRA-TD500 reaching Hmean 84.1% and 82.0% respectively. Morever,
we report a baseline on total-text containing curved text which suggests
effectiveness of the proposed approach.
(Nearly) Efficient Algorithms for the Graph Matching Problem on Correlated Random Graphs
Authors: Boaz Barak, Chi-Ning Chou, Zhixian Lei, Tselil Schramm, Yueqi Sheng
Date: 2018-05-07 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Graph Matching

We give a quasipolynomial time algorithm for the graph matching problem (also
known as noisy or robust graph isomorphism) on correlated random graphs.
Specifically, for every $\gamma>0$, we give a $n^{O(\log n)}$ time algorithm
that given a pair of $\gamma$-correlated $G(n,p)$ graphs $G_0,G_1$ with average
degree between $n^{\varepsilon}$ and $n^{1/153}$ for $\varepsilon = o(1)$,
recovers the "ground truth" permutation $\pi\in S_n$ that matches the vertices
of $G_0$ to the vertices of $G_n$ in the way that minimizes the number of
mismatched edges. We also give a recovery algorithm for a denser regime, and a
polynomial-time algorithm for distinguishing between correlated and
uncorrelated graphs.
  Prior work showed that recovery is information-theoretically possible in this
model as long the average degree was at least $\log n$, but sub-exponential
time algorithms were only known in the dense case (i.e., for $p > n^{-o(1)}$).
Moreover, "Percolation Graph Matching", which is the most common heuristic for
this problem, has been shown to require knowledge of $n^{\Omega(1)}$ "seeds"
(i.e., input/output pairs of the permutation $\pi$) to succeed in this regime.
In contrast our algorithms require no seed and succeed for $p$ which is as low
as $n^{o(1)-1}$.
An Additive Approximation to Multiplicative Noise
Authors: Ruanui Nicholson, Jari P. Kaipio
Date: 2018-05-07 00:00:00

Multiplicative noise models are often used instead of additive noise models
in cases in which the noise variance depends on the state. Furthermore, when
Poisson distributions with relatively small counts are approximated with normal
distributions, multiplicative noise approximations are straightforward to
implement. There are a number of limitations in existing approaches to
marginalize over multiplicative errors, such as positivity of the
multiplicative noise term. The focus in this paper is in large dimensional
(inverse) problems for which sampling type approaches have too high
computational complexity. In this paper, we propose an alternative approach to
carry out approximative marginalization over the multiplicative error by
embedding the statistics in an additive error term. The approach is essentially
a Bayesian one in that the statistics of the additive error is induced by the
statistics of the other unknowns. As an example, we consider a deconvolution
problem on random fields with different statistics of the multiplicative noise.
Furthermore, the approach allows for correlated multiplicative noise. We show
that the proposed approach provides feasible error estimates in the sense that
the posterior models support the actual image.
A Hierarchical Matcher using Local Classifier Chains
Authors: Lingfeng Zhang, Ioannis A. Kakadiaris
Date: 2018-05-07 00:00:00
Tasks: Face Recognition

This paper focuses on improving the performance of current convolutional
neural networks in visual recognition without changing the network
architecture. A hierarchical matcher is proposed that builds chains of local
binary neural networks after one global neural network over all the class
labels, named as Local Classifier Chains based Convolutional Neural Network
(LCC-CNN). The signature of each sample as two components: global component
based on the global network; local component based on local binary networks.
The local networks are built based on label pairs created by a similarity
matrix and confusion matrix. During matching, each sample travels through one
global network and a chain of local networks to obtain its final matching to
avoid error propagation. The proposed matcher has been evaluated with image
recognition, character recognition and face recognition datasets. The
experimental results indicate that the proposed matcher achieves better
performance when compared with methods using only a global deep network.
Compared with the UR2D system, the accuracy is improved significantly by 1% and
0.17% on the UHDB31 dataset and the IJB-A dataset, respectively.
Enhance word representation for out-of-vocabulary on Ubuntu dialogue corpus
Authors: Jianxiong Dong, Jim Huang
Date: 2018-02-07 00:00:00
Proceeding: ICLR 2018 1

Ubuntu dialogue corpus is the largest public available dialogue corpus to
make it feasible to build end-to-end deep neural network models directly from
the conversation data. One challenge of Ubuntu dialogue corpus is the large
number of out-of-vocabulary words. In this paper we proposed a method which
combines the general pre-trained word embedding vectors with those generated on
the task-specific training set to address this issue. We integrated character
embedding into Chen et al's Enhanced LSTM method (ESIM) and used it to evaluate
the effectiveness of our proposed method. For the task of next utterance
selection, the proposed method has demonstrated a significant performance
improvement against original ESIM and the new model has achieved
state-of-the-art results on both Ubuntu dialogue corpus and Douban conversation
corpus. In addition, we investigated the performance impact of end-of-utterance
and end-of-turn token tags.
Implementation of Stochastic Quasi-Newton's Method in PyTorch
Authors: Yingkai Li, Huidong Liu
Date: 2018-05-07 00:00:00

In this paper, we implement the Stochastic Damped LBFGS (SdLBFGS) for
stochastic non-convex optimization. We make two important modifications to the
original SdLBFGS algorithm. First, by initializing the Hessian at each step
using an identity matrix, the algorithm converges better than original
algorithm. Second, by performing direction normalization we could gain stable
optimization procedure without line search. Experiments on minimizing a 2D
non-convex function shows that our improved algorithm converges better than
original algorithm, and experiments on the CIFAR10 and MNIST datasets show that
our improved algorithm works stably and gives comparable or even better testing
accuracies than first order optimizers SGD, Adagrad, and second order
optimizers LBFGS in PyTorch.
Sharp Attention Network via Adaptive Sampling for Person Re-identification
Authors: Chen Shen, Guo-Jun Qi, Rongxin Jiang, Zhongming Jin, Hongwei Yong, Yaowu Chen, Xian-Sheng Hua
Date: 2018-05-07 00:00:00
Tasks: Person Re-Identification

In this paper, we present novel sharp attention networks by adaptively
sampling feature maps from convolutional neural networks (CNNs) for person
re-identification (re-ID) problem. Due to the introduction of sampling-based
attention models, the proposed approach can adaptively generate sharper
attention-aware feature masks. This greatly differs from the gating-based
attention mechanism that relies soft gating functions to select the relevant
features for person re-ID. In contrast, the proposed sampling-based attention
mechanism allows us to effectively trim irrelevant features by enforcing the
resultant feature masks to focus on the most discriminative features. It can
produce sharper attentions that are more assertive in localizing subtle
features relevant to re-identifying people across cameras. For this purpose, a
differentiable Gumbel-Softmax sampler is employed to approximate the Bernoulli
sampling to train the sharp attention networks. Extensive experimental
evaluations demonstrate the superiority of this new sharp attention model for
person re-ID over the other state-of-the-art methods on three challenging
benchmarks including CUHK03, Market-1501, and DukeMTMC-reID.