pwc-archive/papers-with-abstracts huggingface dataset:
Models of retrieval in sentence comprehension: A computational evaluation using Bayesian hierarchical modeling
Authors: Bruno Nicenboim, Shravan Vasishth
Date: 2016-12-13 00:00:00
Tasks: Retrieval, Sentence

Research on interference has provided evidence that the formation of
dependencies between non-adjacent words relies on a cue-based retrieval
mechanism. Two different models can account for one of the main predictions of
interference, i.e., a slowdown at a retrieval site, when several items share a
feature associated with a retrieval cue: Lewis and Vasishth's (2005)
activation-based model and McElree's (2000) direct access model. Even though
these two models have been used almost interchangeably, they are based on
different assumptions and predict differences in the relationship between
reading times and response accuracy. The activation-based model follows the
assumptions of ACT-R, and its retrieval process behaves as a lognormal race
between accumulators of evidence with a single variance. Under this model,
accuracy of the retrieval is determined by the winner of the race and retrieval
time by its rate of accumulation. In contrast, the direct access model assumes
a model of memory where only the probability of retrieval varies between items;
in this model, differences in latencies are a by-product of the possibility and
repairing incorrect retrievals. We implemented both models in a Bayesian
hierarchical framework in order to evaluate them and compare them. We show that
some aspects of the data are better fit under the direct access model than
under the activation-based model. We suggest that this finding does not rule
out the possibility that retrieval may be behaving as a race model with
assumptions that follow less closely the ones from the ACT-R framework. We show
that by introducing a modification of the activation model, i.e, by assuming
that the accumulation of evidence for retrieval of incorrect items is not only
slower but noisier (i.e., different variances for the correct and incorrect
items), the model can provide a fit as good as the one of the direct access
model.
An LSTM-Based Dynamic Customer Model for Fashion Recommendation
Authors: Sebastian Heinz, Christian Bracher, Roland Vollgraf
Date: 2017-08-24 00:00:00
Tasks: Articles, Collaborative Filtering

Online fashion sales present a challenging use case for personalized
recommendation: Stores offer a huge variety of items in multiple sizes. Small
stocks, high return rates, seasonality, and changing trends cause continuous
turnover of articles for sale on all time scales. Customers tend to shop
rarely, but often buy multiple items at once. We report on backtest experiments
with sales data of 100k frequent shoppers at Zalando, Europe's leading online
fashion platform. To model changing customer and store environments, our
recommendation method employs a pair of neural networks: To overcome the cold
start problem, a feedforward network generates article embeddings in "fashion
space," which serve as input to a recurrent neural network that predicts a
style vector in this space for each client, based on their past purchase
sequence. We compare our results with a static collaborative filtering
approach, and a popularity ranking baseline.
A Strongly Quasiconvex PAC-Bayesian Bound
Authors: Niklas Thiemann, Christian Igel, Olivier Wintenberger, Yevgeny Seldin
Date: 2016-08-19 00:00:00

We propose a new PAC-Bayesian bound and a way of constructing a hypothesis
space, so that the bound is convex in the posterior distribution and also
convex in a trade-off parameter between empirical performance of the posterior
distribution and its complexity. The complexity is measured by the
Kullback-Leibler divergence to a prior. We derive an alternating procedure for
minimizing the bound. We show that the bound can be rewritten as a
one-dimensional function of the trade-off parameter and provide sufficient
conditions under which the function has a single global minimum. When the
conditions are satisfied the alternating minimization is guaranteed to converge
to the global minimum of the bound. We provide experimental results
demonstrating that rigorous minimization of the bound is competitive with
cross-validation in tuning the trade-off between complexity and empirical
performance. In all our experiments the trade-off turned to be quasiconvex even
when the sufficient conditions were violated.
Active Sampling of Pairs and Points for Large-scale Linear Bipartite Ranking
Authors: Wei-Yuan Shen, Hsuan-Tien Lin
Date: 2017-08-24 00:00:00
Tasks: Active Learning

Bipartite ranking is a fundamental ranking problem that learns to order
relevant instances ahead of irrelevant ones. The pair-wise approach for
bi-partite ranking construct a quadratic number of pairs to solve the problem,
which is infeasible for large-scale data sets. The point-wise approach, albeit
more efficient, often results in inferior performance. That is, it is difficult
to conduct bipartite ranking accurately and efficiently at the same time. In
this paper, we develop a novel active sampling scheme within the pair-wise
approach to conduct bipartite ranking efficiently. The scheme is inspired from
active learning and can reach a competitive ranking performance while focusing
only on a small subset of the many pairs during training. Moreover, we propose
a general Combined Ranking and Classification (CRC) framework to accurately
conduct bipartite ranking. The framework unifies point-wise and pair-wise
approaches and is simply based on the idea of treating each instance point as a
pseudo-pair. Experiments on 14 real-word large-scale data sets demonstrate that
the proposed algorithm of Active Sampling within CRC, when coupled with a
linear Support Vector Machine, usually outperforms state-of-the-art point-wise
and pair-wise ranking approaches in terms of both accuracy and efficiency.
Relaxed Spatio-Temporal Deep Feature Aggregation for Real-Fake Expression Prediction
Authors: Savas Ozkan, Gozde Bozdagi Akar
Date: 2017-08-24 00:00:00

Frame-level visual features are generally aggregated in time with the
techniques such as LSTM, Fisher Vectors, NetVLAD etc. to produce a robust
video-level representation. We here introduce a learnable aggregation technique
whose primary objective is to retain short-time temporal structure between
frame-level features and their spatial interdependencies in the representation.
Also, it can be easily adapted to the cases where there have very scarce
training samples. We evaluate the method on a real-fake expression prediction
dataset to demonstrate its superiority. Our method obtains 65% score on the
test dataset in the official MAP evaluation and there is only one misclassified
decision with the best reported result in the Chalearn Challenge (i.e. 66:7%) .
Lastly, we believe that this method can be extended to different problems such
as action/event recognition in future.
Online Real-time Multiple Spatiotemporal Action Localisation and Prediction
Authors: Gurkirt Singh, Suman Saha, Michael Sapienza, Philip Torr, Fabio Cuzzolin
Date: 2016-11-25 00:00:00
Proceeding: ICCV 2017 10
Tasks: Early Action Prediction, Prediction

We present a deep-learning framework for real-time multiple spatio-temporal
(S/T) action localisation, classification and early prediction. Current
state-of-the-art approaches work offline and are too slow to be useful in real-
world settings. To overcome their limitations we introduce two major
developments. Firstly, we adopt real-time SSD (Single Shot MultiBox Detector)
convolutional neural networks to regress and classify detection boxes in each
video frame potentially containing an action of interest. Secondly, we design
an original and efficient online algorithm to incrementally construct and label
`action tubes' from the SSD frame level detections. As a result, our system is
not only capable of performing S/T detection in real time, but can also perform
early action prediction in an online fashion. We achieve new state-of-the-art
results in both S/T action localisation and early action prediction on the
challenging UCF101-24 and J-HMDB-21 benchmarks, even when compared to the top
offline competitors. To the best of our knowledge, ours is the first real-time
(up to 40fps) system able to perform online S/T action localisation and early
action prediction on the untrimmed videos of UCF101-24.
Generalized maximum entropy estimation
Authors: Tobias Sutter, David Sutter, Peyman Mohajerin Esfahani, John Lygeros
Date: 2017-08-24 00:00:00

We consider the problem of estimating a probability distribution that maximizes the entropy while satisfying a finite number of moment constraints, possibly corrupted by noise. Based on duality of convex programming, we present a novel approximation scheme using a smoothed fast gradient method that is equipped with explicit bounds on the approximation error. We further demonstrate how the presented scheme can be used for approximating the chemical master equation through the zero-information moment closure method, and for an approximate dynamic programming approach in the context of constrained Markov decision processes with uncountable state and action spaces.
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads
Authors: Tian Li, Jie Zhong, Ji Liu, Wentao Wu, Ce Zhang
Date: 2017-08-24 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning, Fairness, image-classification, Image Classification, Model Selection, Multi-Armed Bandits

We present ease.ml, a declarative machine learning service platform we built
to support more than ten research groups outside the computer science
departments at ETH Zurich for their machine learning needs. With ease.ml, a
user defines the high-level schema of a machine learning application and
submits the task via a Web interface. The system automatically deals with the
rest, such as model selection and data movement. In this paper, we describe the
ease.ml architecture and focus on a novel technical problem introduced by
ease.ml regarding resource allocation. We ask, as a "service provider" that
manages a shared cluster of machines among all our users running machine
learning workloads, what is the resource allocation strategy that maximizes the
global satisfaction of all our users?
  Resource allocation is a critical yet subtle issue in this multi-tenant
scenario, as we have to balance between efficiency and fairness. We first
formalize the problem that we call multi-tenant model selection, aiming for
minimizing the total regret of all users running automatic model selection
tasks. We then develop a novel algorithm that combines multi-armed bandits with
Bayesian optimization and prove a regret bound under the multi-tenant setting.
Finally, we report our evaluation of ease.ml on synthetic data and on one
service we are providing to our users, namely, image classification with deep
neural networks. Our experimental evaluation results show that our proposed
solution can be up to 9.8x faster in achieving the same global quality for all
users as the two popular heuristics used by our users before ease.ml.
Image-based Localization using Hourglass Networks
Authors: Iaroslav Melekhov, Juha Ylioinas, Juho Kannala, Esa Rahtu
Date: 2017-03-23 00:00:00
Tasks: Decoder, General Classification, Image-Based Localization, Transfer Learning

In this paper, we propose an encoder-decoder convolutional neural network
(CNN) architecture for estimating camera pose (orientation and location) from a
single RGB-image. The architecture has a hourglass shape consisting of a chain
of convolution and up-convolution layers followed by a regression part. The
up-convolution layers are introduced to preserve the fine-grained information
of the input image. Following the common practice, we train our model in
end-to-end manner utilizing transfer learning from large scale classification
data. The experiments demonstrate the performance of the approach on data
exhibiting different lighting conditions, reflections, and motion blur. The
results indicate a clear improvement over the previous state-of-the-art even
when compared to methods that utilize sequence of test frames instead of a
single frame.
Area Protection in Adversarial Path-Finding Scenarios with Multiple Mobile Agents on Graphs: a theoretical and experimental study of target-allocation strategies for defense coordination
Authors: Marika Ivanová, Pavel Surynek
Date: 2017-08-24 00:00:00

We address a problem of area protection in graph-based scenarios with
multiple agents. The problem consists of two adversarial teams of agents that
move in an undirected graph shared by both teams. Agents are placed in vertices
of the graph; at most one agent can occupy a vertex; and they can move into
adjacent vertices in a conflict free way. Teams have asymmetric goals: the aim
of one team - attackers - is to invade into given area while the aim of the
opponent team - defenders - is to protect the area from being entered by
attackers by occupying selected vertices. We study strategies for allocating
vertices to be occupied by the team of defenders to block attacking agents. We
show that the decision version of the problem of area protection is PSPACE-hard
under the assumption that agents can allocate their target vertices multiple
times. Further we develop various on-line vertex-allocation strategies for the
defender team in a simplified variant of the problem with single stage vertex
allocation and evaluated their performance in multiple benchmarks. The success
of a strategy is heavily dependent on the type of the instance, and so one of
the contributions of this work is that we identify suitable vertex-allocation
strategies for diverse instance types. In particular, we introduce a
simulation-based method that identifies and tries to capture bottlenecks in the
graph, that are frequently used by the attackers. Our experimental evaluation
suggests that this method often allows a successful defense even in instances
where the attackers significantly outnumber the defenders.
Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour Detection
Authors: Zizhao Zhang, Fuyong Xing, Hai Su, Xiaoshuang Shi, Lin Yang
Date: 2017-08-24 00:00:00
Tasks: Contour Detection, Medical Image Analysis

The fast growing deep learning technologies have become the main solution of
many machine learning problems for medical image analysis. Deep convolution
neural networks (CNNs), as one of the most important branch of the deep
learning family, have been widely investigated for various computer-aided
diagnosis tasks including long-term problems and continuously emerging new
problems. Image contour detection is a fundamental but challenging task that
has been studied for more than four decades. Recently, we have witnessed the
significantly improved performance of contour detection thanks to the
development of CNNs. Beyond purusing performance in existing natural image
benchmarks, contour detection plays a particularly important role in medical
image analysis. Segmenting various objects from radiology images or pathology
images requires accurate detection of contours. However, some problems, such as
discontinuity and shape constraints, are insufficiently studied in CNNs. It is
necessary to clarify the challenges to encourage further exploration. The
performance of CNN based contour detection relies on the state-of-the-art CNN
architectures. Careful investigation of their design principles and motivations
is critical and beneficial to contour detection. In this paper, we first review
recent development of medical image contour detection and point out the current
confronting challenges and problems. We discuss the development of general CNNs
and their applications in image contours (or edges) detection. We compare those
methods in detail, clarify their strengthens and weaknesses. Then we review
their recent applications in medical image analysis and point out limitations,
with the goal to light some potential directions in medical image analysis. We
expect the paper to cover comprehensive technical ingredients of advanced CNNs
to enrich the study in the medical image domain.
Combining Discrete and Neural Features for Sequence Labeling
Authors: Jie Yang, Zhiyang Teng, Meishan Zhang, Yue Zhang
Date: 2017-08-24 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), POS, POS Tagging

Neural network models have recently received heated research attention in the
natural language processing community. Compared with traditional models with
discrete features, neural models have two main advantages. First, they take
low-dimensional, real-valued embedding vectors as inputs, which can be trained
over large raw data, thereby addressing the issue of feature sparsity in
discrete models. Second, deep neural networks can be used to automatically
combine input features, and including non-local features that capture semantic
patterns that cannot be expressed using discrete indicator features. As a
result, neural network models have achieved competitive accuracies compared
with the best discrete models for a range of NLP tasks.
  On the other hand, manual feature templates have been carefully investigated
for most NLP tasks over decades and typically cover the most useful indicator
pattern for solving the problems. Such information can be complementary the
features automatically induced from neural networks, and therefore combining
discrete and neural features can potentially lead to better accuracy compared
with models that leverage discrete or neural features only.
  In this paper, we systematically investigate the effect of discrete and
neural feature combination for a range of fundamental NLP tasks based on
sequence labeling, including word segmentation, POS tagging and named entity
recognition for Chinese and English, respectively. Our results on standard
benchmarks show that state-of-the-art neural models can give accuracies
comparable to the best discrete models in the literature for most tasks and
combing discrete and neural features unanimously yield better results.
Contrast and visual saliency similarity-induced index for assessing image quality
Authors: Huizhen Jia, Lu Zhang, Tonghan Wang
Date: 2017-08-22 00:00:00
Tasks: Attribute, Full reference image quality assessment, Image Quality Assessment

Image quality that is consistent with human opinion is assessed by a
perceptual image quality assessment (IQA) that defines/utilizes a computational
model. A good model should take effectiveness and efficiency into
consideration, but most of the previously proposed IQA models do not
simultaneously consider these factors. Therefore, this study attempts to
develop an effective and efficient IQA metric. Contrast is an inherent visual
attribute that indicates image quality, and visual saliency (VS) is a quality
that attracts the attention of human beings. The proposed model utilized these
two features to characterize the image local quality. After obtaining the local
contrast quality map and the global VS quality map, we added the weighted
standard deviation of the previous two quality maps together to yield the final
quality score. The experimental results for three benchmark databases (LIVE,
TID2008, and CSIQ) demonstrated that our model performs the best in terms of a
correlation with the human judgment of visual quality. Furthermore, compared
with competing IQA models, this proposed model is more efficient.
An Image Analysis Approach to the Calligraphy of Books
Authors: Henrique F. de Arruda, Vanessa Q. Marinho, Thales S. Lima, Diego R. Amancio, Luciano da F. Costa
Date: 2017-08-24 00:00:00
Tasks: Authorship Attribution

Text network analysis has received increasing attention as a consequence of
its wide range of applications. In this work, we extend a previous work founded
on the study of topological features of mesoscopic networks. Here, the
geometrical properties of visualized networks are quantified in terms of
several image analysis techniques and used as subsidies for authorship
attribution. It was found that the visual features account for performance
similar to that achieved by using topological measurements. In addition, the
combination of these two types of features improved the performance.
Dynamic Tensor Clustering
Authors: Will Wei Sun, Lexin Li
Date: 2017-08-24 00:00:00
Tasks: Clustering, Computational Efficiency, Dimensionality Reduction, Functional Connectivity

Dynamic tensor data are becoming prevalent in numerous applications. Existing
tensor clustering methods either fail to account for the dynamic nature of the
data, or are inapplicable to a general-order tensor. Also there is often a gap
between statistical guarantee and computational efficiency for existing tensor
clustering solutions. In this article, we aim to bridge this gap by proposing a
new dynamic tensor clustering method, which takes into account both sparsity
and fusion structures, and enjoys strong statistical guarantees as well as high
computational efficiency. Our proposal is based upon a new structured tensor
factorization that encourages both sparsity and smoothness in parameters along
the specified tensor modes. Computationally, we develop a highly efficient
optimization algorithm that benefits from substantial dimension reduction. In
theory, we first establish a non-asymptotic error bound for the estimator from
the structured tensor factorization. Built upon this error bound, we then
derive the rate of convergence of the estimated cluster centers, and show that
the estimated clusters recover the true cluster structures with a high
probability. Moreover, our proposed method can be naturally extended to
co-clustering of multiple modes of the tensor data. The efficacy of our
approach is illustrated via simulations and a brain dynamic functional
connectivity analysis from an Autism spectrum disorder study.
A Study on Neural Network Language Modeling
Authors: Dengliang Shi
Date: 2017-08-24 00:00:00
Tasks: Language Modeling, Language Modelling

An exhaustive study on neural network language modeling (NNLM) is performed
in this paper. Different architectures of basic neural network language models
are described and examined. A number of different improvements over basic
neural network language models, including importance sampling, word classes,
caching and bidirectional recurrent neural network (BiRNN), are studied
separately, and the advantages and disadvantages of every technique are
evaluated. Then, the limits of neural network language modeling are explored
from the aspects of model architecture and knowledge representation. Part of
the statistical information from a word sequence will loss when it is processed
word by word in a certain order, and the mechanism of training neural network
by updating weight matrixes and vectors imposes severe restrictions on any
significant enhancement of NNLM. For knowledge representation, the knowledge
represented by neural network language models is the approximate probabilistic
distribution of word sequences from a certain training data set rather than the
knowledge of a language itself or the information conveyed by word sequences in
a natural language. Finally, some directions for improving neural network
language modeling further is discussed.