pwc-archive/papers-with-abstracts huggingface dataset:
Simple And Efficient Architecture Search for Convolutional Neural Networks
Authors: Thomas Elsken, Jan-Hendrik Metzen, Frank Hutter
Date: 2017-11-13 00:00:00
Proceeding: ICLR 2018 1
Tasks: GPU, Neural Architecture Search

Neural networks have recently had a lot of success for many tasks. However,
neural network architectures that perform well are still typically designed
manually by experts in a cumbersome trial-and-error process. We propose a new
method to automatically search for well-performing CNN architectures based on a
simple hill climbing procedure whose operators apply network morphisms,
followed by short optimization runs by cosine annealing. Surprisingly, this
simple method yields competitive results, despite only requiring resources in
the same order of magnitude as training a single network. E.g., on CIFAR-10,
our method designs and trains networks with an error rate below 6% in only 12
hours on a single GPU; training for one day reduces this error further, to
almost 5%.
Grafting for Combinatorial Boolean Model using Frequent Itemset Mining
Authors: Taito Lee, Shin Matsushima, Kenji Yamanishi
Date: 2017-11-07 00:00:00
Tasks: Computational Efficiency

This paper introduces the combinatorial Boolean model (CBM), which is defined
as the class of linear combinations of conjunctions of Boolean attributes. This
paper addresses the issue of learning CBM from labeled data. CBM is of high
knowledge interpretability but na\"{i}ve learning of it requires exponentially
large computation time with respect to data dimension and sample size. To
overcome this computational difficulty, we propose an algorithm GRAB (GRAfting
for Boolean datasets), which efficiently learns CBM within the
$L_1$-regularized loss minimization framework. The key idea of GRAB is to
reduce the loss minimization problem to the weighted frequent itemset mining,
in which frequent patterns are efficiently computable. We employ benchmark
datasets to empirically demonstrate that GRAB is effective in terms of
computational efficiency, prediction accuracy and knowledge discovery.
A Supervised Learning Concept for Reducing User Interaction in Passenger Cars
Authors: Marius Stärk, Damian Backes, Christian Kehl
Date: 2017-11-13 00:00:00

In this article an automation system for human-machine-interfaces (HMI) for
setpoint adjustment using supervised learning is presented. We use HMIs of
multi-modal thermal conditioning systems in passenger cars as example for a
complex setpoint selection system. The goal is the reduction of interaction
complexity up to full automation. The approach is not limited to climate
control applications but can be extended to other setpoint-based HMIs.
Targeted Advertising Based on Browsing History
Authors: Yong Zhang, Hongming Zhou, Nganmeng Tan, Saeed Bagheri, Meng Joo Er
Date: 2017-11-13 00:00:00

Audience interest, demography, purchase behavior and other possible
classifications are ex- tremely important factors to be carefully studied in a
targeting campaign. This information can help advertisers and publishers
deliver advertisements to the right audience group. How- ever, it is not easy
to collect such information, especially for the online audience with whom we
have limited interaction and minimum deterministic knowledge. In this paper, we
pro- pose a predictive framework that can estimate online audience demographic
attributes based on their browsing histories. Under the proposed framework,
first, we retrieve the content of the websites visited by audience, and
represent the content as website feature vectors; second, we aggregate the
vectors of websites that audience have visited and arrive at feature vectors
representing the users; finally, the support vector machine is exploited to
predict the audience demographic attributes. The key to achieving good
prediction performance is preparing representative features of the audience.
Word Embedding, a widely used tech- nique in natural language processing tasks,
together with term frequency-inverse document frequency weighting scheme is
used in the proposed method. This new representation ap- proach is unsupervised
and very easy to implement. The experimental results demonstrate that the new
audience feature representation method is more powerful than existing baseline
methods, leading to a great improvement in prediction accuracy.
Vertical Landing for Micro Air Vehicles using Event-Based Optical Flow
Authors: Bas J. Pijnacker Hordijk, Kirk Y. W. Scheper, Guido C. H. E. de Croon
Date: 2017-01-31 00:00:00
Tasks: Event-based Optical Flow, Optical Flow Estimation, valid

Small flying robots can perform landing maneuvers using bio-inspired optical
flow by maintaining a constant divergence. However, optical flow is typically
estimated from frame sequences recorded by standard miniature cameras. This
requires processing full images on-board, limiting the update rate of
divergence measurements, and thus the speed of the control loop and the robot.
Event-based cameras overcome these limitations by only measuring pixel-level
brightness changes at microsecond temporal accuracy, hence providing an
efficient mechanism for optical flow estimation. This paper presents, to the
best of our knowledge, the first work integrating event-based optical flow
estimation into the control loop of a flying robot. We extend an existing
'local plane fitting' algorithm to obtain an improved and more computationally
efficient optical flow estimation method, valid for a wide range of optical
flow velocities. This method is validated for real event sequences. In
addition, a method for estimating the divergence from event-based optical flow
is introduced, which accounts for the aperture problem. The developed
algorithms are implemented in a constant divergence landing controller on-board
of a quadrotor. Experiments show that, using event-based optical flow, accurate
divergence estimates can be obtained over a wide range of speeds. This enables
the quadrotor to perform very fast landing maneuvers.
A Parallel Best-Response Algorithm with Exact Line Search for Nonconvex Sparsity-Regularized Rank Minimization
Authors: Yang Yang, Marius Pesavento
Date: 2017-11-13 00:00:00

In this paper, we propose a convergent parallel best-response algorithm with
the exact line search for the nondifferentiable nonconvex sparsity-regularized
rank minimization problem. On the one hand, it exhibits a faster convergence
than subgradient algorithms and block coordinate descent algorithms. On the
other hand, its convergence to a stationary point is guaranteed, while ADMM
algorithms only converge for convex problems. Furthermore, the exact line
search procedure in the proposed algorithm is performed efficiently in
closed-form to avoid the meticulous choice of stepsizes, which is however a
common bottleneck in subgradient algorithms and successive convex approximation
algorithms. Finally, the proposed algorithm is numerically tested.
An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network
Authors: Xiaolei Shen, Jiachi Zhang, Chenjun Yan, Hong Zhou
Date: 2017-11-13 00:00:00
Tasks: Classification, General Classification

In this paper, we present a new automatic diagnosis method of facial acne
vulgaris based on convolutional neural network. This method is proposed to
overcome the shortcoming of classification types in previous methods. The core
of our method is to extract features of images based on convolutional neural
network and achieve classification by classifier. We design a binary classifier
of skin-and-non-skin to detect skin area and a seven-classifier to achieve the
classification of facial acne vulgaris and healthy skin. In the experiment, we
compared the effectiveness of our convolutional neural network and the
pre-trained VGG16 neural network on the ImageNet dataset. And we use the ROC
curve and normal confusion matrix to evaluate the performance of the binary
classifier and the seven-classifier. The results of our experiment show that
the pre-trained VGG16 neural network is more effective in extracting image
features. The classifiers based on the pre-trained VGG16 neural network achieve
the skin detection and acne classification and have good robustness.
Word, Subword or Character? An Empirical Study of Granularity in Chinese-English NMT
Authors: Yining Wang, Long Zhou, Jiajun Zhang, Cheng-qing Zong
Date: 2017-11-13 00:00:00
Tasks: Machine Translation, NMT, Translation

Neural machine translation (NMT), a new approach to machine translation, has
been proved to outperform conventional statistical machine translation (SMT)
across a variety of language pairs. Translation is an open-vocabulary problem,
but most existing NMT systems operate with a fixed vocabulary, which causes the
incapability of translating rare words. This problem can be alleviated by using
different translation granularities, such as character, subword and hybrid
word-character. Translation involving Chinese is one of the most difficult
tasks in machine translation, however, to the best of our knowledge, there has
not been any other work exploring which translation granularity is most
suitable for Chinese in NMT. In this paper, we conduct an extensive comparison
using Chinese-English NMT as a case study. Furthermore, we discuss the
advantages and disadvantages of various translation granularities in detail.
Our experiments show that subword model performs best for Chinese-to-English
translation with the vocabulary which is not so big while hybrid word-character
model is most suitable for English-to-Chinese translation. Moreover,
experiments of different granularities show that Hybrid_BPE method can achieve
best result on Chinese-to-English translation task.
Thresholding Bandit for Dose-ranging: The Impact of Monotonicity
Authors: Aurélien Garivier, Pierre Ménard, Laurent Rossi, Pierre Menard
Date: 2017-11-13 00:00:00
Tasks: valid

We analyze the sample complexity of the thresholding bandit problem, with and
without the assumption that the mean values of the arms are increasing. In each
case, we provide a lower bound valid for any risk $\delta$ and any
$\delta$-correct algorithm; in addition, we propose an algorithm whose sample
complexity is of the same order of magnitude for small risks. This work is
motivated by phase 1 clinical trials, a practically important setting where the
arm means are increasing by nature, and where no satisfactory solution is
available so far.
Sparsification of the Alignment Path Search Space in Dynamic Time Warping
Authors: Saeid Soheily-Khah, Pierre-François Marteau
Date: 2017-11-13 00:00:00
Tasks: Dynamic Time Warping, General Classification, Time Series, Time Series Analysis

Temporal data are naturally everywhere, especially in the digital era that
sees the advent of big data and internet of things. One major challenge that
arises during temporal data analysis and mining is the comparison of time
series or sequences, which requires to determine a proper distance or
(dis)similarity measure. In this context, the Dynamic Time Warping (DTW) has
enjoyed success in many domains, due to its 'temporal elasticity', a property
particularly useful when matching temporal data. Unfortunately this
dissimilarity measure suffers from a quadratic computational cost, which
prohibits its use for large scale applications. This work addresses the
sparsification of the alignment path search space for DTW-like measures,
essentially to lower their computational cost without loosing on the quality of
the measure. As a result of our sparsification approach, two new
(dis)similarity measures, namely SP-DTW (Sparsified-Paths search space DTW) and
its kernelization SP-K rdtw (Sparsified-Paths search space K rdtw kernel) are
proposed for time series comparison. A wide range of public datasets is used to
evaluate the efficiency (estimated in term of speed-up ratio and classification
accuracy) of the proposed (dis)similarity measures on the 1-Nearest Neighbor
(1-NN) and the Support Vector Machine (SVM) classification algorithms. Our
experiment shows that our proposed measures provide a significant speed-up
without loosing on accuracy. Furthermore, at the cost of a slight reduction of
the speedup they significantly outperform on the accuracy criteria the old but
well known Sakoe-Chiba approach that reduces the DTW path search space using a
symmetric corridor.
Digitising Cultural Complexity: Representing Rich Cultural Data in a Big Data environment
Authors: Jennifer Edmond, Georgina Nugent Folan
Date: 2017-11-13 00:00:00

One of the major terminological forces driving ICT integration in research
today is that of "big data." While the phrase sounds inclusive and integrative,
"big data" approaches are highly selective, excluding input that cannot be
effectively structured, represented, or digitised. Data of this complex sort is
precisely the kind that human activity produces, but the technological
imperative to enhance signal through the reduction of noise does not
accommodate this richness. Data and the computational approaches that
facilitate "big data" have acquired a perceived objectivity that belies their
curated, malleable, reactive, and performative nature. In an input environment
where anything can "be data" once it is entered into the system as "data," data
cleaning and processing, together with the metadata and information
architectures that structure and facilitate our cultural archives acquire a
capacity to delimit what data are. This engenders a process of simplification
that has major implications for the potential for future innovation within
research environments that depend on rich material yet are increasingly
mediated by digital technologies. This paper presents the preliminary findings
of the European-funded KPLEX (Knowledge Complexity) project which investigates
the delimiting effect digital mediation and datafication has on rich, complex
cultural data. The paper presents a systematic review of existing implicit
definitions of data, elaborating on the implications of these definitions and
highlighting the ways in which metadata and computational technologies can
restrict the interpretative potential of data. It sheds light on the gap
between analogue or augmented digital practices and fully computational ones,
and the strategies researchers have developed to deal with this gap. The paper
proposes a reconceptualisation of data as it is functionally employed within
digitally-mediated research so as to incorporate and acknowledge the richness
and complexity of our source materials.
Visual Concepts and Compositional Voting
Authors: Jianyu Wang, Zhishuai Zhang, Cihang Xie, Yuyin Zhou, Vittal Premachandran, Jun Zhu, Lingxi Xie, Alan Yuille
Date: 2017-11-13 00:00:00
Tasks: Clustering, Semantic Part Detection

It is very attractive to formulate vision in terms of pattern theory
\cite{Mumford2010pattern}, where patterns are defined hierarchically by
compositions of elementary building blocks. But applying pattern theory to real
world images is currently less successful than discriminative methods such as
deep networks. Deep networks, however, are black-boxes which are hard to
interpret and can easily be fooled by adding occluding objects. It is natural
to wonder whether by better understanding deep networks we can extract building
blocks which can be used to develop pattern theoretic models. This motivates us
to study the internal representations of a deep network using vehicle images
from the PASCAL3D+ dataset. We use clustering algorithms to study the
population activities of the features and extract a set of visual concepts
which we show are visually tight and correspond to semantic parts of vehicles.
To analyze this we annotate these vehicles by their semantic parts to create a
new dataset, VehicleSemanticParts, and evaluate visual concepts as unsupervised
part detectors. We show that visual concepts perform fairly well but are
outperformed by supervised discriminative methods such as Support Vector
Machines (SVM). We next give a more detailed analysis of visual concepts and
how they relate to semantic parts. Following this, we use the visual concepts
as building blocks for a simple pattern theoretical model, which we call
compositional voting. In this model several visual concepts combine to detect
semantic parts. We show that this approach is significantly better than
discriminative methods like SVM and deep networks trained specifically for
semantic part detection. Finally, we return to studying occlusion by creating
an annotated dataset with occlusion, called VehicleOcclusion, and show that
compositional voting outperforms even deep networks when the amount of
occlusion becomes large.
All-Transfer Learning for Deep Neural Networks and its Application to Sepsis Classification
Authors: Yoshihide Sawada, Yoshikuni Sato, Toru Nakada, Kei Ujimoto, Nobuhiro Hayashi
Date: 2017-11-13 00:00:00
Tasks: All, Deep Learning, General Classification, image-classification, Image Classification, Transfer Learning

In this article, we propose a transfer learning method for deep neural
networks (DNNs). Deep learning has been widely used in many applications.
However, applying deep learning is problematic when a large amount of training
data are not available. One of the conventional methods for solving this
problem is transfer learning for DNNs. In the field of image recognition,
state-of-the-art transfer learning methods for DNNs re-use parameters trained
on source domain data except for the output layer. However, this method may
result in poor classification performance when the amount of target domain data
is significantly small. To address this problem, we propose a method called
All-Transfer Deep Learning, which enables the transfer of all parameters of a
DNN. With this method, we can compute the relationship between the source and
target labels by the source domain knowledge. We applied our method to actual
two-dimensional electrophoresis image~(2-DE image) classification for
determining if an individual suffers from sepsis; the first attempt to apply a
classification approach to 2-DE images for proteomics, which has attracted
considerable attention as an extension beyond genomics. The results suggest
that our proposed method outperforms conventional transfer learning methods for
DNNs.
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
Authors: Xiaojun Xu, Chang Liu, Dawn Song
Date: 2017-11-13 00:00:00
Proceeding: ICLR 2018 1
Tasks: Decoder, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Text-To-SQL

Synthesizing SQL queries from natural language is a long-standing open
problem and has been attracting considerable interest recently. Toward solving
the problem, the de facto approach is to employ a sequence-to-sequence-style
model. Such an approach will necessarily require the SQL queries to be
serialized. Since the same SQL query may have multiple equivalent
serializations, training a sequence-to-sequence-style model is sensitive to the
choice from one of them. This phenomenon is documented as the "order-matters"
problem. Existing state-of-the-art approaches rely on reinforcement learning to
reward the decoder when it generates any of the equivalent serializations.
However, we observe that the improvement from reinforcement learning is
limited.
  In this paper, we propose a novel approach, i.e., SQLNet, to fundamentally
solve this problem by avoiding the sequence-to-sequence structure when the
order does not matter. In particular, we employ a sketch-based approach where
the sketch contains a dependency graph so that one prediction can be done by
taking into consideration only the previous predictions that it depends on. In
addition, we propose a sequence-to-set model as well as the column attention
mechanism to synthesize the query based on the sketch. By combining all these
novel techniques, we show that SQLNet can outperform the prior art by 9% to 13%
on the WikiSQL task.
Faithful to the Original: Fact Aware Neural Abstractive Summarization
Authors: Ziqiang Cao, Furu Wei, Wenjie Li, Sujian Li
Date: 2017-11-13 00:00:00
Tasks: Abstractive Text Summarization, Extractive Summarization, Informativeness, Open Information Extraction, Summarization

Unlike extractive summarization, abstractive summarization has to fuse
different parts of the source text, which inclines to create fake facts. Our
preliminary study reveals nearly 30% of the outputs from a state-of-the-art
neural summarization system suffer from this problem. While previous
abstractive summarization approaches usually focus on the improvement of
informativeness, we argue that faithfulness is also a vital prerequisite for a
practical abstractive summarization system. To avoid generating fake facts in a
summary, we leverage open information extraction and dependency parse
technologies to extract actual fact descriptions from the source text. The
dual-attention sequence-to-sequence framework is then proposed to force the
generation conditioned on both the source text and the extracted fact
descriptions. Experiments on the Gigaword benchmark dataset demonstrate that
our model can greatly reduce fake summaries by 80%. Notably, the fact
descriptions also bring significant improvement on informativeness since they
often condense the meaning of the source text.
A User Simulator for Task-Completion Dialogues
Authors: Xiujun Li, Zachary C. Lipton, Bhuwan Dhingra, Lihong Li, Jianfeng Gao, Yun-Nung Chen
Date: 2016-12-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Task-Oriented Dialogue Systems, User Simulation

Despite widespread interests in reinforcement-learning for task-oriented
dialogue systems, several obstacles can frustrate research and development
progress. First, reinforcement learners typically require interaction with the
environment, so conventional dialogue corpora cannot be used directly. Second,
each task presents specific challenges, requiring separate corpus of
task-specific annotated data. Third, collecting and annotating human-machine or
human-human conversations for task-oriented dialogues requires extensive domain
knowledge. Because building an appropriate dataset can be both financially
costly and time-consuming, one popular approach is to build a user simulator
based upon a corpus of example dialogues. Then, one can train reinforcement
learning agents in an online fashion as they interact with the simulator.
Dialogue agents trained on these simulators can serve as an effective starting
point. Once agents master the simulator, they may be deployed in a real
environment to interact with humans, and continue to be trained online. To ease
empirical algorithmic comparisons in dialogues, this paper introduces a new,
publicly available simulation framework, where our simulator, designed for the
movie-booking domain, leverages both rules and collected data. The simulator
supports two tasks: movie ticket booking and movie seeking. Finally, we
demonstrate several agents and detail the procedure to add and test your own
agent in the proposed framework.
3D Shape Segmentation with Projective Convolutional Networks
Authors: Evangelos Kalogerakis, Melinos Averkiou, Subhransu Maji, Siddhartha Chaudhuri
Date: 2016-12-08 00:00:00
Proceeding: CVPR 2017 7
Tasks: Segmentation

This paper introduces a deep architecture for segmenting 3D objects into
their labeled semantic parts. Our architecture combines image-based Fully
Convolutional Networks (FCNs) and surface-based Conditional Random Fields
(CRFs) to yield coherent segmentations of 3D shapes. The image-based FCNs are
used for efficient view-based reasoning about 3D object parts. Through a
special projection layer, FCN outputs are effectively aggregated across
multiple views and scales, then are projected onto the 3D object surfaces.
Finally, a surface-based CRF combines the projected outputs with geometric
consistency cues to yield coherent segmentations. The whole architecture
(multi-view FCNs and CRF) is trained end-to-end. Our approach significantly
outperforms the existing state-of-the-art methods in the currently largest
segmentation benchmark (ShapeNet). Finally, we demonstrate promising
segmentation results on noisy 3D shapes acquired from consumer-grade depth
cameras.