pwc-archive/papers-with-abstracts huggingface dataset:
Machine learning applied to single-shot x-ray diagnostics in an XFEL
Authors: A. Sanchez-Gonzalez, P. Micaelli, C. Olivier, T. R. Barillot, M. Ilchen, A. A. Lutman, A. Marinelli, T. Maxwell, A. Achner, M. Agåker, N. Berrah, C. Bostedt, J. Buck, P. H. Bucksbaum, S. Carron Montero, B. Cooper, J. P. Cryan, M. Dong, R. Feifel, L. J. Frasinski, H. Fukuzawa, A. Galler, G. Hartmann, N. Hartmann, W. Helml, A. S. Johnson, A. Knie, A. O. Lindahl, J. Liu, K. Motomura, M. Mucke, C. O'Grady, J-E. Rubensson, E. R. Simpson, R. J. Squibb, C. Såthe, K. Ueda, M. Vacher, D. J. Walke, V. Zhaunerchyk, R. N. Coffee, J. P. Marangos
Date: 2016-10-11 00:00:00
Tasks: BIG-bench Machine Learning, Prediction

X-ray free-electron lasers (XFELs) are the only sources currently able to
produce bright few-fs pulses with tunable photon energies from 100 eV to more
than 10 keV. Due to the stochastic SASE operating principles and other
technical issues the output pulses are subject to large fluctuations, making it
necessary to characterize the x-ray pulses on every shot for data sorting
purposes. We present a technique that applies machine learning tools to predict
x-ray pulse properties using simple electron beam and x-ray parameters as
input. Using this technique at the Linac Coherent Light Source (LCLS), we
report mean errors below 0.3 eV for the prediction of the photon energy at 530
eV and below 1.6 fs for the prediction of the delay between two x-ray pulses.
We also demonstrate spectral shape prediction with a mean agreement of 97%.
This approach could potentially be used at the next generation of
high-repetition-rate XFELs to provide accurate knowledge of complex x-ray
pulses at the full repetition rate.
DOTmark - A Benchmark for Discrete Optimal Transport
Authors: Jörn Schrieber, Dominic Schuhmacher, Carsten Gottschlich
Date: 2016-10-11 00:00:00

The Wasserstein metric or earth mover's distance (EMD) is a useful tool in
statistics, machine learning and computer science with many applications to
biological or medical imaging, among others. Especially in the light of
increasingly complex data, the computation of these distances via optimal
transport is often the limiting factor. Inspired by this challenge, a variety
of new approaches to optimal transport has been proposed in recent years and
along with these new methods comes the need for a meaningful comparison.
  In this paper, we introduce a benchmark for discrete optimal transport,
called DOTmark, which is designed to serve as a neutral collection of problems,
where discrete optimal transport methods can be tested, compared to one
another, and brought to their limits on large-scale instances. It consists of a
variety of grayscale images, in various resolutions and classes, such as
several types of randomly generated images, classical test images and real data
from microscopy.
  Along with the DOTmark we present a survey and a performance test for a cross
section of established methods ranging from more traditional algorithms, such
as the transportation simplex, to recently developed approaches, such as the
shielding neighborhood method, and including also a comparison with commercial
solvers.
Simple Question Answering by Attentive Convolutional Neural Network
Authors: Wenpeng Yin, Mo Yu, Bing Xiang, Bo-Wen Zhou, Hinrich Schütze
Date: 2016-06-10 00:00:00
Proceeding: COLING 2016 12
Tasks: Entity Linking, Fact Selection, Question Answering

This work focuses on answering single-relation factoid questions over
Freebase. Each question can acquire the answer from a single fact of form
(subject, predicate, object) in Freebase. This task, simple question answering
(SimpleQA), can be addressed via a two-step pipeline: entity linking and fact
selection. In fact selection, we match the subject entity in a fact candidate
with the entity mention in the question by a character-level convolutional
neural network (char-CNN), and match the predicate in that fact with the
question by a word-level CNN (word-CNN). This work makes two main
contributions. (i) A simple and effective entity linker over Freebase is
proposed. Our entity linker outperforms the state-of-the-art entity linker over
SimpleQA task. (ii) A novel attentive maxpooling is stacked over word-CNN, so
that the predicate representation can be matched with the predicate-focused
question representation more effectively. Experiments show that our system sets
new state-of-the-art in this task.
Survey on the Use of Typological Information in Natural Language Processing
Authors: Helen O'Horan, Yevgeni Berzak, Ivan Vulić, Roi Reichart, Anna Korhonen
Date: 2016-10-11 00:00:00
Proceeding: COLING 2016 12
Tasks: Multilingual NLP, Survey

In recent years linguistic typology, which classifies the world's languages
according to their functional and structural properties, has been widely used
to support multilingual NLP. While the growing importance of typological
information in supporting multilingual tasks has been recognised, no systematic
survey of existing typological resources and their use in NLP has been
published. This paper provides such a survey as well as discussion which we
hope will both inform and inspire future work in the area.
From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning
Authors: Lieke Gelderloos, Grzegorz Chrupała
Date: 2016-10-11 00:00:00
Proceeding: COLING 2016 12
Tasks: Form, Grounded language learning, Image Description

We present a model of visually-grounded language learning based on stacked
gated recurrent neural networks which learns to predict visual features given
an image description in the form of a sequence of phonemes. The learning task
resembles that faced by human language learners who need to discover both
structure and meaning from noisy and ambiguous data across modalities. We show
that our model indeed learns to predict features of the visual context given
phonetically transcribed image descriptions, and show that it represents
linguistic information in a hierarchy of levels: lower layers in the stack are
comparatively more sensitive to form, whereas higher layers are more sensitive
to meaning.
A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion
Authors: Daniel L. Pimentel-Alarcón, Nigel Boston, Robert D. Nowak
Date: 2015-03-09 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

Low-rank matrix completion (LRMC) problems arise in a wide variety of
applications. Previous theory mainly provides conditions for completion under
missing-at-random samplings. This paper studies deterministic conditions for
completion. An incomplete $d \times N$ matrix is finitely rank-$r$ completable
if there are at most finitely many rank-$r$ matrices that agree with all its
observed entries. Finite completability is the tipping point in LRMC, as a few
additional samples of a finitely completable matrix guarantee its unique
completability. The main contribution of this paper is a deterministic sampling
condition for finite completability. We use this to also derive deterministic
sampling conditions for unique completability that can be efficiently verified.
We also show that under uniform random sampling schemes, these conditions are
satisfied with high probability if $O(\max\{r,\log d\})$ entries per column are
observed. These findings have several implications on LRMC regarding lower
bounds, sample and computational complexity, the role of coherence, adaptive
settings and the validation of any completion algorithm. We complement our
theoretical results with experiments that support our findings and motivate
future analysis of uncharted sampling regimes.
Keystroke dynamics as signal for shallow syntactic parsing
Authors: Barbara Plank
Date: 2016-10-11 00:00:00
Proceeding: COLING 2016 12
Tasks: CCG Supertagging, Chunking

Keystroke dynamics have been extensively used in psycholinguistic and writing
research to gain insights into cognitive processing. But do keystroke logs
contain actual signal that can be used to learn better natural language
processing models?
  We postulate that keystroke dynamics contain information about syntactic
structure that can inform shallow syntactic parsing. To test this hypothesis,
we explore labels derived from keystroke logs as auxiliary task in a multi-task
bidirectional Long Short-Term Memory (bi-LSTM). Our results show promising
results on two shallow syntactic parsing tasks, chunking and CCG supertagging.
Our model is simple, has the advantage that data can come from distinct
sources, and produces models that are significantly better than models trained
on the text annotations alone.
A Greedy Approach for Budgeted Maximum Inner Product Search
Authors: Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei, Inderjit S. Dhillon
Date: 2016-10-11 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Recommendation Systems

Maximum Inner Product Search (MIPS) is an important task in many machine
learning applications such as the prediction phase of a low-rank matrix
factorization model for a recommender system. There have been some works on how
to perform MIPS in sub-linear time recently. However, most of them do not have
the flexibility to control the trade-off between search efficient and search
quality. In this paper, we study the MIPS problem with a computational budget.
By carefully studying the problem structure of MIPS, we develop a novel
Greedy-MIPS algorithm, which can handle budgeted MIPS by design. While simple
and intuitive, Greedy-MIPS yields surprisingly superior performance compared to
state-of-the-art approaches. As a specific example, on a candidate set
containing half a million vectors of dimension 200, Greedy-MIPS runs 200x
faster than the naive approach while yielding search results with the top-5
precision greater than 75\%.
Counting Grid Aggregation for Event Retrieval and Recognition
Authors: Zhanning Gao, Gang Hua, Dongqing Zhang, Jianru Xue, Nanning Zheng
Date: 2016-04-05 00:00:00
Tasks: Retrieval

Event retrieval and recognition in a large corpus of videos necessitates a
holistic fixed-size visual representation at the video clip level that is
comprehensive, compact, and yet discriminative. It shall comprehensively
aggregate information across relevant video frames, while suppress redundant
information, leading to a compact representation that can effectively
differentiate among different visual events. In search for such a
representation, we propose to build a spatially consistent counting grid model
to aggregate together deep features extracted from different video frames. The
spatial consistency of the counting grid model is achieved by introducing a
prior model estimated from a large corpus of video data. The counting grid
model produces an intermediate tensor representation for each video, which
automatically identifies and removes the feature redundancy across the
different frames. The tensor representation is subsequently reduced to a
fixed-size vector representation by averaging over the counting grid. When
compared to existing methods on both event retrieval and event classification
benchmarks, we achieve significantly better accuracy with much more compact
representation.
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving
Authors: Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua
Date: 2016-10-11 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Trajectory Planning

Autonomous driving is a multi-agent setting where the host vehicle must apply
sophisticated negotiation skills with other road users when overtaking, giving
way, merging, taking left and right turns and while pushing ahead in
unstructured urban roadways. Since there are many possible scenarios, manually
tackling all possible cases will likely yield a too simplistic policy.
Moreover, one must balance between unexpected behavior of other
drivers/pedestrians and at the same time not to be too defensive so that normal
traffic flow is maintained.
  In this paper we apply deep reinforcement learning to the problem of forming
long term driving strategies. We note that there are two major challenges that
make autonomous driving different from other robotic tasks. First, is the
necessity for ensuring functional safety - something that machine learning has
difficulty with given that performance is optimized at the level of an
expectation over many instances. Second, the Markov Decision Process model
often used in robotics is problematic in our case because of unpredictable
behavior of other agents in this multi-agent scenario. We make three
contributions in our work. First, we show how policy gradient iterations can be
used without Markovian assumptions. Second, we decompose the problem into a
composition of a Policy for Desires (which is to be learned) and trajectory
planning with hard constraints (which is not learned). The goal of Desires is
to enable comfort of driving, while hard constraints guarantees the safety of
driving. Third, we introduce a hierarchical temporal abstraction we call an
"Option Graph" with a gating mechanism that significantly reduces the effective
horizon and thereby reducing the variance of the gradient estimation even
further.
Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations
Authors: Arkaitz Zubiaga, Elena Kochkina, Maria Liakata, Rob Procter, Michal Lukasik
Date: 2016-09-28 00:00:00
Proceeding: COLING 2016 12
Tasks: General Classification, Rumour Detection, Stance Classification

Rumour stance classification, the task that determines if each tweet in a
collection discussing a rumour is supporting, denying, questioning or simply
commenting on the rumour, has been attracting substantial interest. Here we
introduce a novel approach that makes use of the sequence of transitions
observed in tree-structured conversation threads in Twitter. The conversation
threads are formed by harvesting users' replies to one another, which results
in a nested tree-like structure. Previous work addressing the stance
classification task has treated each tweet as a separate unit. Here we analyse
tweets by virtue of their position in a sequence and test two sequential
classifiers, Linear-Chain CRF and Tree CRF, each of which makes different
assumptions about the conversational structure. We experiment with eight
Twitter datasets, collected during breaking news, and show that exploiting the
sequential structure of Twitter conversations achieves significant improvements
over the non-sequential methods. Our work is the first to model Twitter
conversations as a tree structure in this manner, introducing a novel way of
tackling NLP tasks on Twitter conversations.
Machine learning based data mining for Milky Way filamentary structures reconstruction
Authors: Giuseppe Riccio, Stefano Cavuoti, Eugenio Schisano, Massimo Brescia, Amata Mercurio, Davide Elia, Milena Benedettini, Stefano Pezzuto, Sergio Molinari, Anna Maria Di Giorgio
Date: 2015-05-25 00:00:00
Tasks: BIG-bench Machine Learning

We present an innovative method called FilExSeC (Filaments Extraction,
Selection and Classification), a data mining tool developed to investigate the
possibility to refine and optimize the shape reconstruction of filamentary
structures detected with a consolidated method based on the flux derivative
analysis, through the column-density maps computed from Herschel infrared
Galactic Plane Survey (Hi-GAL) observations of the Galactic plane. The present
methodology is based on a feature extraction module followed by a machine
learning model (Random Forest) dedicated to select features and to classify the
pixels of the input images. From tests on both simulations and real
observations the method appears reliable and robust with respect to the
variability of shape and distribution of filaments. In the cases of highly
defined filament structures, the presented method is able to bridge the gaps
among the detected fragments, thus improving their shape reconstruction. From a
preliminary "a posteriori" analysis of derived filament physical parameters,
the method appears potentially able to add a sufficient contribution to
complete and refine the filament reconstruction.
Assisted Dictionary Learning for fMRI Data Analysis
Authors: Manuel Morante Moreno, Yannis Kopsinis, Eleftherios Kofidis, Christos Chatzichristos, Sergios Theodoridis
Date: 2016-10-11 00:00:00
Tasks: Dictionary Learning

Extracting information from functional magnetic resonance (fMRI) images has
been a major area of research for more than two decades. The goal of this work
is to present a new method for the analysis of fMRI data sets, that is capable
to incorporate a priori available information, via an efficient optimization
framework. Tests on synthetic data sets demonstrate significant performance
gains over existing methods of this kind.
GMM-Free Flat Start Sequence-Discriminative DNN Training
Authors: Gábor Gosztolya, Tamás Grósz, László Tóth
Date: 2016-10-11 00:00:00
Tasks: Clustering

Recently, attempts have been made to remove Gaussian mixture models (GMM)
from the training process of deep neural network-based hidden Markov models
(HMM/DNN). For the GMM-free training of a HMM/DNN hybrid we have to solve two
problems, namely the initial alignment of the frame-level state labels and the
creation of context-dependent states. Although flat-start training via
iteratively realigning and retraining the DNN using a frame-level error
function is viable, it is quite cumbersome. Here, we propose to use a
sequence-discriminative training criterion for flat start. While
sequence-discriminative training is routinely applied only in the final phase
of model training, we show that with proper caution it is also suitable for
getting an alignment of context-independent DNN models. For the construction of
tied states we apply a recently proposed KL-divergence-based state clustering
method, hence our whole training process is GMM-free. In the experimental
evaluation we found that the sequence-discriminative flat start training method
is not only significantly faster than the straightforward approach of iterative
retraining and realignment, but the word error rates attained are slightly
better as well.
Toward a new instances of NELL
Authors: Maisa C. Duarte, Pierre Maret
Date: 2016-10-11 00:00:00

We are developing the method to start new instances of NELL in various
languages and develop then NELL multilingualism. We base our method on our
experience on NELL Portuguese and NELL French. This reports explain our method
and develops some research perspectives.
Network of Bandits insure Privacy of end-users
Authors: Raphaël Féraud
Date: 2016-02-11 00:00:00

In order to distribute the best arm identification task as close as possible
to the user's devices, on the edge of the Radio Access Network, we propose a
new problem setting, where distributed players collaborate to find the best
arm. This architecture guarantees privacy to end-users since no events are
stored. The only thing that can be observed by an adversary through the core
network is aggregated information across users. We provide a first algorithm,
Distributed Median Elimination, which is optimal in term of number of
transmitted bits and near optimal in term of speed-up factor with respect to an
optimal algorithm run independently on each player. In practice, this first
algorithm cannot handle the trade-off between the communication cost and the
speed-up factor, and requires some knowledge about the distribution of players.
Extended Distributed Median Elimination overcomes these limitations, by playing
in parallel different instances of Distributed Median Elimination and selecting
the best one. Experiments illustrate and complete the analysis. According to
the analysis, in comparison to Median Elimination performed on each player, the
proposed algorithm shows significant practical improvements.
An active efficient coding model of the optokinetic nystagmus
Authors: Chong Zhang, Jochen Triesch, Bertram E. Shi
Date: 2016-06-21 00:00:00

Optokinetic nystagmus (OKN) is an involuntary eye movement responsible for
stabilizing retinal images in the presence of relative motion between an
observer and the environment. Fully understanding the development of
optokinetic nystagmus requires a neurally plausible computational model that
accounts for the neural development and the behavior. To date, work in this
area has been limited. We propose a neurally plausible framework for the joint
development of disparity and motion tuning in the visual cortex, the
optokinetic and vergence eye movements. This framework models the joint
emergence of both perception and behavior, and accounts for the importance of
the development of normal vergence control and binocular vision in achieving
normal monocular OKN (mOKN) behaviors. Because the model includes behavior, we
can simulate the same perturbations as performed in past experiments, such as
artificially induced strabismus. The proposed model agrees both qualitatively
and quantitatively with a number of findings from the literature on both
binocular vision as well as the optokinetic reflex. Finally, our model also
makes quantitative predictions about the OKN behavior using the same methods
used to characterize the OKN in the experimental literature.
Natural Scene Image Segmentation Based on Multi-Layer Feature Extraction
Authors: Fariba Zohrizadeh, Mohsen Kheirandishfard, Farhad Kamangar
Date: 2016-05-24 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

This paper addresses the problem of natural image segmentation by extracting
information from a multi-layer array which is constructed based on color,
gradient, and statistical properties of the local neighborhoods in an image. A
Gaussian Mixture Model (GMM) is used to improve the effectiveness of local
spectral histogram features. Grouping these features leads to forming a rough
initial over-segmented layer which contains coherent regions of pixels. The
regions are merged by using two proposed functions for calculating the distance
between two neighboring regions and making decisions about their merging.
Extensive experiments are performed on the Berkeley Segmentation Dataset to
evaluate the performance of our proposed method and compare the results with
the recent state-of-the-art methods. The experimental results indicate that our
method achieves higher level of accuracy for natural images compared to recent
methods.
An Empirical Exploration of Skip Connections for Sequential Tagging
Authors: Huijia Wu, Jiajun Zhang, Cheng-qing Zong
Date: 2016-10-11 00:00:00
Proceeding: COLING 2016 12
Tasks: CCG Supertagging, POS, POS Tagging

In this paper, we empirically explore the effects of various kinds of skip
connections in stacked bidirectional LSTMs for sequential tagging. We
investigate three kinds of skip connections connecting to LSTM cells: (a) skip
connections to the gates, (b) skip connections to the internal states and (c)
skip connections to the cell outputs. We present comprehensive experiments
showing that skip connections to cell outputs outperform the remaining two.
Furthermore, we observe that using gated identity functions as skip mappings
works pretty well. Based on this novel skip connections, we successfully train
deep stacked bidirectional LSTM models and obtain state-of-the-art results on
CCG supertagging and comparable results on POS tagging.