pwc-archive/papers-with-abstracts huggingface dataset:
Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
Authors: Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, Jeffrey Dean
Date: 2016-11-14 00:00:00
Proceeding: TACL 2017 1
Tasks: Machine Translation, NMT, Sentence, Transfer Learning, Translation

We propose a simple solution to use a single Neural Machine Translation (NMT)
model to translate between multiple languages. Our solution requires no change
in the model architecture from our base system but instead introduces an
artificial token at the beginning of the input sentence to specify the required
target language. The rest of the model, which includes encoder, decoder and
attention, remains unchanged and is shared across all languages. Using a shared
wordpiece vocabulary, our approach enables Multilingual NMT using a single
model without any increase in parameters, which is significantly simpler than
previous proposals for Multilingual NMT. Our method often improves the
translation quality of all involved language pairs, even while keeping the
total number of model parameters constant. On the WMT'14 benchmarks, a single
multilingual model achieves comparable performance for
English$\rightarrow$French and surpasses state-of-the-art results for
English$\rightarrow$German. Similarly, a single multilingual model surpasses
state-of-the-art results for French$\rightarrow$English and
German$\rightarrow$English on WMT'14 and WMT'15 benchmarks respectively. On
production corpora, multilingual models of up to twelve language pairs allow
for better translation of many individual pairs. In addition to improving the
translation quality of language pairs that the model was trained with, our
models can also learn to perform implicit bridging between language pairs never
seen explicitly during training, showing that transfer learning and zero-shot
translation is possible for neural translation. Finally, we show analyses that
hints at a universal interlingua representation in our models and show some
interesting examples when mixing languages.
CoBe -- Coded Beacons for Localization, Object Tracking, and SLAM Augmentation
Authors: Roman Rabinovich, Ibrahim Jubran, Aaron Wetzler, Ron Kimmel
Date: 2017-08-18 00:00:00
Tasks: Object Tracking, Simultaneous Localization and Mapping

This paper presents a novel beacon light coding protocol, which enables fast and accurate identification of the beacons in an image. The protocol is provably robust to a predefined set of detection and decoding errors, and does not require any synchronization between the beacons themselves and the optical sensor. A detailed guide is then given for developing an optical tracking and localization system, which is based on the suggested protocol and readily available hardware. Such a system operates either as a standalone system for recovering the six degrees of freedom of fast moving objects, or integrated with existing SLAM pipelines providing them with error-free and easily identifiable landmarks. Based on this guide, we implemented a low-cost positional tracking system which can run in real-time on an IoT board. We evaluate our system's accuracy and compare it to other popular methods which utilize the same optical hardware, in experiments where the ground truth is known. A companion video containing multiple real-world experiments demonstrates the accuracy, speed, and applicability of the proposed system in a wide range of environments and real-world tasks. Open source code is provided to encourage further development of low-cost localization systems integrating the suggested technology at its navigation core.
Neuro-RAM Unit with Applications to Similarity Testing and Compression in Spiking Neural Networks
Authors: Nancy Lynch, Cameron Musco, Merav Parter
Date: 2017-06-05 00:00:00

We study distributed algorithms implemented in a simplified biologically
inspired model for stochastic spiking neural networks. We focus on tradeoffs
between computation time and network complexity, along with the role of
randomness in efficient neural computation.
  It is widely accepted that neural computation is inherently stochastic. In
recent work, we explored how this stochasticity could be leveraged to solve the
`winner-take-all' leader election task. Here, we focus on using randomness in
neural algorithms for similarity testing and compression. In the most basic
setting, given two $n$-length patterns of firing neurons, we wish to
distinguish if the patterns are equal or $\epsilon$-far from equal.
  Randomization allows us to solve this task with a very compact network, using
$O \left (\frac{\sqrt{n}\log n}{\epsilon}\right)$ auxiliary neurons, which is
sublinear in the input size. At the heart of our solution is the design of a
$t$-round neural random access memory, or indexing network, which we call a
neuro-RAM. This module can be implemented with $O(n/t)$ auxiliary neurons and
is useful in many applications beyond similarity testing.
  Using a VC dimension-based argument, we show that the tradeoff between
runtime and network size in our neuro-RAM is nearly optimal. Our result has
several implications -- since our neuro-RAM can be implemented with
deterministic threshold gates, it shows that, in contrast to similarity
testing, randomness does not provide significant computational advantages for
this problem. It also establishes a separation between feedforward networks
whose gates spike with sigmoidal probability functions, and well-studied
deterministic sigmoidal networks, whose gates output real number sigmoidal
values, and which can implement a neuro-RAM much more efficiently.
Learning Spread-out Local Feature Descriptors
Authors: Xu Zhang, Felix X. Yu, Sanjiv Kumar, Shih-Fu Chang
Date: 2017-08-21 00:00:00
Proceeding: ICCV 2017 10
Tasks: Triplet

We propose a simple, yet powerful regularization technique that can be used
to significantly improve both the pairwise and triplet losses in learning local
feature descriptors. The idea is that in order to fully utilize the expressive
power of the descriptor space, good local feature descriptors should be
sufficiently "spread-out" over the space. In this work, we propose a
regularization term to maximize the spread in feature descriptor inspired by
the property of uniform distribution. We show that the proposed regularization
with triplet loss outperforms existing Euclidean distance based descriptor
learning techniques by a large margin. As an extension, the proposed
regularization technique can also be used to improve image-level deep feature
embedding.
Employing Weak Annotations for Medical Image Analysis Problems
Authors: Martin Rajchl, Lisa M. Koch, Christian Ledig, Jonathan Passerat-Palmbach, Kazunari Misawa, Kensaku MORI, Daniel Rueckert
Date: 2017-08-21 00:00:00
Tasks: Computed Tomography (CT), Liver Segmentation, Medical Image Analysis, Segmentation

To efficiently establish training databases for machine learning methods,
collaborative and crowdsourcing platforms have been investigated to
collectively tackle the annotation effort. However, when this concept is ported
to the medical imaging domain, reading expertise will have a direct impact on
the annotation accuracy. In this study, we examine the impact of expertise and
the amount of available annotations on the accuracy outcome of a liver
segmentation problem in an abdominal computed tomography (CT) image database.
In controlled experiments, we study this impact for different types of weak
annotations. To address the decrease in accuracy associated with lower
expertise, we propose a method for outlier correction making use of a weakly
labelled atlas. Using this approach, we demonstrate that weak annotations
subject to high error rates can achieve a similarly high accuracy as
state-of-the-art multi-atlas segmentation approaches relying on a large amount
of expert manual segmentations. Annotations of this nature can realistically be
obtained from a non-expert crowd and can potentially enable crowdsourcing of
weak annotation tasks for medical image analysis.
Likelihood-free inference by ratio estimation
Authors: Owen Thomas, Ritabrata Dutta, Jukka Corander, Samuel Kaski, Michael U. Gutmann
Date: 2016-11-30 00:00:00

We consider the problem of parametric statistical inference when likelihood computations are prohibitively expensive but sampling from the model is possible. Several so-called likelihood-free methods have been developed to perform inference in the absence of a likelihood function. The popular synthetic likelihood approach infers the parameters by modelling summary statistics of the data by a Gaussian probability distribution. In another popular approach called approximate Bayesian computation, the inference is performed by identifying parameter values for which the summary statistics of the simulated data are close to those of the observed data. Synthetic likelihood is easier to use as no measure of `closeness' is required but the Gaussianity assumption is often limiting. Moreover, both approaches require judiciously chosen summary statistics. We here present an alternative inference approach that is as easy to use as synthetic likelihood but not as restricted in its assumptions, and that, in a natural way, enables automatic selection of relevant summary statistic from a large set of candidates. The basic idea is to frame the problem of estimating the posterior as a problem of estimating the ratio between the data generating distribution and the marginal distribution. This problem can be solved by logistic regression, and including regularising penalty terms enables automatic selection of the summary statistics relevant to the inference task. We illustrate the general theory on canonical examples and employ it to perform inference for challenging stochastic nonlinear dynamical systems and high-dimensional summary statistics.
Deep vs. Diverse Architectures for Classification Problems
Authors: Colleen M. Farrelly
Date: 2017-08-21 00:00:00
Tasks: Classification, Computational Efficiency, General Classification, regression

This study compares various superlearner and deep learning architectures
(machine-learning-based and neural-network-based) for classification problems
across several simulated and industrial datasets to assess performance and
computational efficiency, as both methods have nice theoretical convergence
properties. Superlearner formulations outperform other methods at small to
moderate sample sizes (500-2500) on nonlinear and mixed linear/nonlinear
predictor relationship datasets, while deep neural networks perform well on
linear predictor relationship datasets of all sizes. This suggests faster
convergence of the superlearner compared to deep neural network architectures
on many messy classification problems for real-world data.
  Superlearners also yield interpretable models, allowing users to examine
important signals in the data; in addition, they offer flexible formulation,
where users can retain good performance with low-computational-cost base
algorithms.
  K-nearest-neighbor (KNN) regression demonstrates improvements using the
superlearner framework, as well; KNN superlearners consistently outperform deep
architectures and KNN regression, suggesting that superlearners may be better
able to capture local and global geometric features through utilizing a variety
of algorithms to probe the data space.
The CARESSES EU-Japan project: making assistive robots culturally competent
Authors: Barbara Bruno, Nak Young Chong, Hiroko Kamide, Sanjeev Kanoria, Jaeryoung Lee, Yuto Lim, Amit Kumar Pandey, Chris Papadopoulos, Irena Papadopoulos, Federico Pecora, Alessandro Saffiotti, Antonio Sgorbissa
Date: 2017-08-21 00:00:00

The nursing literature shows that cultural competence is an important
requirement for effective healthcare. We claim that personal assistive robots
should likewise be culturally competent, that is, they should be aware of
general cultural characteristics and of the different forms they take in
different individuals, and take these into account while perceiving, reasoning,
and acting. The CARESSES project is an Europe-Japan collaborative effort that
aims at designing, developing and evaluating culturally competent assistive
robots. These robots will be able to adapt the way they behave, speak and
interact to the cultural identity of the person they assist. This paper
describes the approach taken in the CARESSES project, its initial steps, and
its future plans.
Probabilistic Relation Induction in Vector Space Embeddings
Authors: Zied Bouraoui, Shoaib Jameel, Steven Schockaert
Date: 2017-08-21 00:00:00
Tasks: Relation, Word Embeddings

Word embeddings have been found to capture a surprisingly rich amount of
syntactic and semantic knowledge. However, it is not yet sufficiently
well-understood how the relational knowledge that is implicitly encoded in word
embeddings can be extracted in a reliable way. In this paper, we propose two
probabilistic models to address this issue. The first model is based on the
common relations-as-translations view, but is cast in a probabilistic setting.
Our second model is based on the much weaker assumption that there is a linear
relationship between the vector representations of related words. Compared to
existing approaches, our models lead to more accurate predictions, and they are
more explicit about what can and cannot be extracted from the word embedding.
Deep Convolutional Neural Networks for Massive MIMO Fingerprint-Based Positioning
Authors: Joao Vieira, Erik Leitinger, Muris Sarajlic, Xuhong Li, Fredrik Tufvesson
Date: 2017-08-21 00:00:00

This paper provides an initial investigation on the application of
convolutional neural networks (CNNs) for fingerprint-based positioning using
measured massive MIMO channels. When represented in appropriate domains,
massive MIMO channels have a sparse structure which can be efficiently learned
by CNNs for positioning purposes. We evaluate the positioning accuracy of
state-of-the-art CNNs with channel fingerprints generated from a channel model
with a rich clustered structure: the COST 2100 channel model. We find that
moderately deep CNNs can achieve fractional-wavelength positioning accuracies,
provided that an enough representative data set is available for training.
Fake News in Social Networks
Authors: Christoph Aymanns, Jakob Foerster, Co-Pierre Georg
Date: 2017-08-21 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

We model the spread of news as a social learning game on a network. Agents
can either endorse or oppose a claim made in a piece of news, which itself may
be either true or false. Agents base their decision on a private signal and
their neighbors' past actions. Given these inputs, agents follow strategies
derived via multi-agent deep reinforcement learning and receive utility from
acting in accordance with the veracity of claims. Our framework yields
strategies with agent utility close to a theoretical, Bayes optimal benchmark,
while remaining flexible to model re-specification. Optimized strategies allow
agents to correctly identify most false claims, when all agents receive
unbiased private signals. However, an adversary's attempt to spread fake news
by targeting a subset of agents with a biased private signal can be successful.
Even more so when the adversary has information about agents' network position
or private signal. When agents are aware of the presence of an adversary they
re-optimize their strategies in the training stage and the adversary's attack
is less effective. Hence, exposing agents to the possibility of fake news can
be an effective way to curtail the spread of fake news in social networks. Our
results also highlight that information about the users' private beliefs and
their social network structure can be extremely valuable to adversaries and
should be well protected.
Preconditioned Spectral Clustering for Stochastic Block Partition Streaming Graph Challenge
Authors: David Zhuzhunashvili, Andrew Knyazev
Date: 2017-08-21 00:00:00
Tasks: Clustering, graph partitioning

Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) is
demonstrated to efficiently solve eigenvalue problems for graph Laplacians that
appear in spectral clustering. For static graph partitioning, 10-20 iterations
of LOBPCG without preconditioning result in ~10x error reduction, enough to
achieve 100% correctness for all Challenge datasets with known truth
partitions, e.g., for graphs with 5K/.1M (50K/1M) Vertices/Edges in 2 (7)
seconds, compared to over 5,000 (30,000) seconds needed by the baseline Python
code. Our Python code 100% correctly determines 98 (160) clusters from the
Challenge static graphs with 0.5M (2M) vertices in 270 (1,700) seconds using
10GB (50GB) of memory. Our single-precision MATLAB code calculates the same
clusters at half time and memory. For streaming graph partitioning, LOBPCG is
initiated with approximate eigenvectors of the graph Laplacian already computed
for the previous graph, in many cases reducing 2-3 times the number of required
LOBPCG iterations, compared to the static case. Our spectral clustering is
generic, i.e. assuming nothing specific of the block model or streaming, used
to generate the graphs for the Challenge, in contrast to the base code.
Nevertheless, in 10-stage streaming comparison with the base code for the 5K
graph, the quality of our clusters is similar or better starting at stage 4 (7)
for emerging edging (snowballing) streaming, while the computations are over
100-1000 faster.
Recognizing Involuntary Actions from 3D Skeleton Data Using Body States
Authors: Mozhgan Mokari, Hoda Mohammadzade, Benyamin Ghojogh
Date: 2017-08-21 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Temporal Action Localization

Human action recognition has been one of the most active fields of research
in computer vision for last years. Two dimensional action recognition methods
are facing serious challenges such as occlusion and missing the third dimension
of data. Development of depth sensors has made it feasible to track positions
of human body joints over time. This paper proposes a novel method of action
recognition which uses temporal 3D skeletal Kinect data. This method introduces
the definition of body states and then every action is modeled as a sequence of
these states. The learning stage uses Fisher Linear Discriminant Analysis (LDA)
to construct discriminant feature space for discriminating the body states.
Moreover, this paper suggests the use of the Mahalonobis distance as an
appropriate distance metric for the classification of the states of involuntary
actions. Hidden Markov Model (HMM) is then used to model the temporal
transition between the body states in each action. According to the results,
this method significantly outperforms other popular methods, with recognition
rate of 88.64% for eight different actions and up to 96.18% for classifying
fall actions.
Action Tubelet Detector for Spatio-Temporal Action Localization
Authors: Vicky Kalogeiton, Philippe Weinzaepfel, Vittorio Ferrari, Cordelia Schmid
Date: 2017-05-04 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Localization, Spatio-Temporal Action Localization, Temporal Action Localization

Current state-of-the-art approaches for spatio-temporal action localization
rely on detections at the frame level that are then linked or tracked across
time. In this paper, we leverage the temporal continuity of videos instead of
operating at the frame level. We propose the ACtion Tubelet detector
(ACT-detector) that takes as input a sequence of frames and outputs tubelets,
i.e., sequences of bounding boxes with associated scores. The same way
state-of-the-art object detectors rely on anchor boxes, our ACT-detector is
based on anchor cuboids. We build upon the SSD framework. Convolutional
features are extracted for each frame, while scores and regressions are based
on the temporal stacking of these features, thus exploiting information from a
sequence. Our experimental results show that leveraging sequences of frames
significantly improves detection performance over using individual frames. The
gain of our tubelet detector can be explained by both more accurate scores and
more precise localization. Our ACT-detector outperforms the state-of-the-art
methods for frame-mAP and video-mAP on the J-HMDB and UCF-101 datasets, in
particular at high overlap thresholds.
On the approximation by single hidden layer feedforward neural networks with fixed weights
Authors: Namig J. Guliyev, Vugar E. Ismailov
Date: 2017-08-21 00:00:00

Feedforward neural networks have wide applicability in various disciplines of
science due to their universal approximation property. Some authors have shown
that single hidden layer feedforward neural networks (SLFNs) with fixed weights
still possess the universal approximation property provided that approximated
functions are univariate. But this phenomenon does not lay any restrictions on
the number of neurons in the hidden layer. The more this number, the more the
probability of the considered network to give precise results. In this note, we
constructively prove that SLFNs with the fixed weight $1$ and two neurons in
the hidden layer can approximate any continuous function on a compact subset of
the real line. The applicability of this result is demonstrated in various
numerical examples. Finally, we show that SLFNs with fixed weights cannot
approximate all continuous multivariate functions.
Story Generation from Sequence of Independent Short Descriptions
Authors: Parag Jain, Priyanka Agrawal, Abhijit Mishra, Mohak Sukhwani, Anirban Laha, Karthik Sankaranarayanan
Date: 2017-07-18 00:00:00
Tasks: Machine Translation, Story Generation, Text Generation, Translation

Existing Natural Language Generation (NLG) systems are weak AI systems and
exhibit limited capabilities when language generation tasks demand higher
levels of creativity, originality and brevity. Effective solutions or, at least
evaluations of modern NLG paradigms for such creative tasks have been elusive,
unfortunately. This paper introduces and addresses the task of coherent story
generation from independent descriptions, describing a scene or an event.
Towards this, we explore along two popular text-generation paradigms -- (1)
Statistical Machine Translation (SMT), posing story generation as a translation
problem and (2) Deep Learning, posing story generation as a sequence to
sequence learning problem. In SMT, we chose two popular methods such as phrase
based SMT (PB-SMT) and syntax based SMT (SYNTAX-SMT) to `translate' the
incoherent input text into stories. We then implement a deep recurrent neural
network (RNN) architecture that encodes sequence of variable length input
descriptions to corresponding latent representations and decodes them to
produce well formed comprehensive story like summaries. The efficacy of the
suggested approaches is demonstrated on a publicly available dataset with the
help of popular machine translation and summarization evaluation metrics.
Learned Multi-Patch Similarity
Authors: Wilfried Hartmann, Silvano Galliani, Michal Havlena, Luc van Gool, Konrad Schindler
Date: 2017-03-26 00:00:00
Proceeding: ICCV 2017 10

Estimating a depth map from multiple views of a scene is a fundamental task
in computer vision. As soon as more than two viewpoints are available, one
faces the very basic question how to measure similarity across >2 image
patches. Surprisingly, no direct solution exists, instead it is common to fall
back to more or less robust averaging of two-view similarities. Encouraged by
the success of machine learning, and in particular convolutional neural
networks, we propose to learn a matching function which directly maps multiple
image patches to a scalar similarity score. Experiments on several multi-view
datasets demonstrate that this approach has advantages over methods based on
pairwise patch similarity.