pwc-archive/papers-with-abstracts huggingface dataset:
Crosslingual Document Embedding as Reduced-Rank Ridge Regression
Authors: Martin Josifoski, Ivan S. Paskov, Hristo S. Paskov, Martin Jaggi, Robert West
Date: 2019-04-08 00:00:00
Tasks: Document Embedding, regression, Retrieval, Sentence

There has recently been much interest in extending vector-based word
representations to multiple languages, such that words can be compared across
languages. In this paper, we shift the focus from words to documents and
introduce a method for embedding documents written in any language into a
single, language-independent vector space. For training, our approach leverages
a multilingual corpus where the same concept is covered in multiple languages
(but not necessarily via exact translations), such as Wikipedia. Our method,
Cr5 (Crosslingual reduced-rank ridge regression), starts by training a
ridge-regression-based classifier that uses language-specific bag-of-word
features in order to predict the concept that a given document is about. We
show that, when constraining the learned weight matrix to be of low rank, it
can be factored to obtain the desired mappings from language-specific
bags-of-words to language-independent embeddings. As opposed to most prior
methods, which use pretrained monolingual word vectors, postprocess them to
make them crosslingual, and finally average word vectors to obtain document
vectors, Cr5 is trained end-to-end and is thus natively crosslingual as well as
document-level. Moreover, since our algorithm uses the singular value
decomposition as its core operation, it is highly scalable. Experiments show
that our method achieves state-of-the-art performance on a crosslingual
document retrieval task. Finally, although not trained for embedding sentences
and words, it also achieves competitive performance on crosslingual sentence
and word retrieval tasks.
Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification
Authors: Yong Luo, DaCheng Tao, Chang Xu, Chao Xu, Hong Liu, Yonggang Wen
Date: 2019-04-08 00:00:00
Tasks: General Classification, image-classification, Image Classification, Multi-Label Image Classification

In computer vision, image datasets used for classification are naturally
associated with multiple labels and comprised of multiple views, because each
image may contain several objects (e.g. pedestrian, bicycle and tree) and is
properly characterized by multiple visual features (e.g. color, texture and
shape). Currently available tools ignore either the label relationship or the
view complementary. Motivated by the success of the vector-valued function that
constructs matrix-valued kernels to explore the multi-label structure in the
output space, we introduce multi-view vector-valued manifold regularization
(MV$\mathbf{^3}$MR) to integrate multiple features. MV$\mathbf{^3}$MR exploits
the complementary property of different features and discovers the intrinsic
local geometry of the compact support shared by different features under the
theme of manifold regularization. We conducted extensive experiments on two
challenging, but popular datasets, PASCAL VOC' 07 (VOC) and MIR Flickr (MIR),
and validated the effectiveness of the proposed MV$\mathbf{^3}$MR for image
classification.
A Generalization Bound for Online Variational Inference
Authors: Badr-Eddine Chérief-Abdellatif, Pierre Alquier, Mohammad Emtiyaz Khan
Date: 2019-04-08 00:00:00
Tasks: Bayesian Inference, Generalization Bounds, Variational Inference

Bayesian inference provides an attractive online-learning framework to analyze sequential data, and offers generalization guarantees which hold even with model mismatch and adversaries. Unfortunately, exact Bayesian inference is rarely feasible in practice and approximation methods are usually employed, but do such methods preserve the generalization properties of Bayesian inference ? In this paper, we show that this is indeed the case for some variational inference (VI) algorithms. We consider a few existing online, tempered VI algorithms, as well as a new algorithm, and derive their generalization bounds. Our theoretical result relies on the convexity of the variational objective, but we argue that the result should hold more generally and present empirical evidence in support of this. Our work in this paper presents theoretical justifications in favor of online algorithms relying on approximate Bayesian methods.
On Learning Density Aware Embeddings
Authors: Soumyadeep Ghosh, Richa Singh, Mayank Vatsa
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: Face Recognition, Metric Learning, Object Recognition

Deep metric learning algorithms have been utilized to learn discriminative
and generalizable models which are effective for classifying unseen classes. In
this paper, a novel noise tolerant deep metric learning algorithm is proposed.
The proposed method, termed as Density Aware Metric Learning, enforces the
model to learn embeddings that are pulled towards the most dense region of the
clusters for each class. It is achieved by iteratively shifting the estimate of
the center towards the dense region of the cluster thereby leading to faster
convergence and higher generalizability. In addition to this, the approach is
robust to noisy samples in the training data, often present as outliers.
Detailed experiments and analysis on two challenging cross-modal face
recognition databases and two popular object recognition databases exhibit the
efficacy of the proposed approach. It has superior convergence, requires lesser
training time, and yields better accuracies than several popular deep metric
learning methods.
The EntOptLayout Cytoscape plug-in for the efficient visualization of major protein complexes in protein-protein interaction and signalling networks
Authors: Bence Agg, Andrea Csaszar, Mate Szalay-Beko, Daniel V. Veres, Reka Mizsei, Peter Ferdinandy, Peter Csermely, Istvan A. Kovacs
Date: 2019-04-08 00:00:00

Motivation: Network visualizations of complex biological datasets usually result in 'hairball' images, which do not discriminate network modules. Results: We present the EntOptLayout Cytoscape plug-in based on a recently developed network representation theory. The plug-in provides an efficient visualization of network modules, which represent major protein complexes in protein-protein interaction and signalling networks. Importantly, the tool gives a quality score of the network visualization by calculating the information loss between the input data and the visual representation showing a 3- to 25-fold improvement over conventional methods. Availability and implementation: The plug-in (running on Windows, Linux, or Mac OS) and its tutorial (both in written and video forms) can be downloaded freely under the terms of the MIT license from: http://apps.cytoscape.org/apps/entoptlayout. Supplementary data are available at Bioinformatics online. Contact: csermely.peter@med.semmelweis-univ.hu
Generalized active learning and design of statistical experiments for manifold-valued data
Authors: Mikhail A. Langovoy
Date: 2019-04-08 00:00:00
Tasks: Active Learning, BRDF estimation

Characterizing the appearance of real-world surfaces is a fundamental problem
in multidimensional reflectometry, computer vision and computer graphics. For
many applications, appearance is sufficiently well characterized by the
bidirectional reflectance distribution function (BRDF). We treat BRDF
measurements as samples of points from high-dimensional non-linear non-convex
manifolds. BRDF manifolds form an infinite-dimensional space, but typically the
available measurements are very scarce for complicated problems such as BRDF
estimation. Therefore, an efficient learning strategy is crucial when
performing the measurements.
  In this paper, we build the foundation of a mathematical framework that
allows to develop and apply new techniques within statistical design of
experiments and generalized proactive learning, in order to establish more
efficient sampling and measurement strategies for BRDF data manifolds.
Deep Learning Based Computed Tomography Whys and Wherefores
Authors: Shabab Bazrafkan, Vincent Van Nieuwenhove, Joris Soons, Jan De Beenhouwer, Jan Sijbers
Date: 2019-04-08 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction, Deep Learning

This is an article about the Computed Tomography (CT) and how Deep Learning
influences CT reconstruction pipeline, especially in low dose scenarios.
Multi-View Matrix Completion for Multi-Label Image Classification
Authors: Yong Luo, Tongliang Liu, DaCheng Tao, Chao Xu
Date: 2019-04-08 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Image Retrieval, Matrix Completion, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Image Classification, Retrieval

There is growing interest in multi-label image classification due to its
critical role in web-based image analytics-based applications, such as
large-scale image retrieval and browsing. Matrix completion has recently been
introduced as a method for transductive (semi-supervised) multi-label
classification, and has several distinct advantages, including robustness to
missing data and background noise in both feature and label space. However, it
is limited by only considering data represented by a single-view feature, which
cannot precisely characterize images containing several semantic concepts. To
utilize multiple features taken from different views, we have to concatenate
the different features as a long vector. But this concatenation is prone to
over-fitting and often leads to very high time complexity in MC based image
classification. Therefore, we propose to weightedly combine the MC outputs of
different views, and present the multi-view matrix completion (MVMC) framework
for transductive multi-label image classification. To learn the view
combination weights effectively, we apply a cross validation strategy on the
labeled set. In the learning process, we adopt the average precision (AP) loss,
which is particular suitable for multi-label image classification. A least
squares loss formulation is also presented for the sake of efficiency, and the
robustness of the algorithm based on the AP loss compared with the other losses
is investigated. Experimental evaluation on two real world datasets (PASCAL
VOC' 07 and MIR Flickr) demonstrate the effectiveness of MVMC for transductive
(semi-supervised) multi-label image classification, and show that MVMC can
exploit complementary properties of different features and output-consistent
labels for improved multi-label image classification.
Semi-Supervised Few-Shot Learning for Dual Question-Answer Extraction
Authors: Jue Wang, Ke Chen, Lidan Shou, Sai Wu, Sharad Mehrotra
Date: 2019-04-08 00:00:00
Tasks: Clustering, Few-Shot Learning, Sentence

This paper addresses the problem of key phrase extraction from sentences.
Existing state-of-the-art supervised methods require large amounts of annotated
data to achieve good performance and generalization. Collecting labeled data
is, however, often expensive. In this paper, we redefine the problem as
question-answer extraction, and present SAMIE: Self-Asking Model for
Information Ixtraction, a semi-supervised model which dually learns to ask and
to answer questions by itself. Briefly, given a sentence $s$ and an answer $a$,
the model needs to choose the most appropriate question $\hat q$; meanwhile,
for the given sentence $s$ and same question $\hat q$ selected in the previous
step, the model will predict an answer $\hat a$. The model can support few-shot
learning with very limited supervision. It can also be used to perform
clustering analysis when no supervision is provided. Experimental results show
that the proposed method outperforms typical supervised methods especially when
given little labeled data.
"Jam Me If You Can'': Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications
Authors: Nguyen Van Huynh, Diep N. Nguyen, Dinh Thai Hoang, Eryk Dutkiewicz
Date: 2019-04-08 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

With conventional anti-jamming solutions like frequency hopping or spread
spectrum, legitimate transceivers often tend to "escape" or "hide" themselves
from jammers. These reactive anti-jamming approaches are constrained by the
lack of timely knowledge of jamming attacks. Bringing together the latest
advances in neural network architectures and ambient backscattering
communications, this work allows wireless nodes to effectively "face" the
jammer by first learning its jamming strategy, then adapting the rate or
transmitting information right on the jamming signal. Specifically, to deal
with unknown jamming attacks, existing work often relies on reinforcement
learning algorithms, e.g., Q-learning. However, the Q-learning algorithm is
notorious for its slow convergence to the optimal policy, especially when the
system state and action spaces are large. This makes the Q-learning algorithm
pragmatically inapplicable. To overcome this problem, we design a novel deep
reinforcement learning algorithm using the recent dueling neural network
architecture. Our proposed algorithm allows the transmitter to effectively
learn about the jammer and attain the optimal countermeasures thousand times
faster than that of the conventional Q-learning algorithm. Through extensive
simulation results, we show that our design (using ambient backscattering and
the deep dueling neural network architecture) can improve the average
throughput by up to 426% and reduce the packet loss by 24%. By augmenting the
ambient backscattering capability on devices and using our algorithm, it is
interesting to observe that the (successful) transmission rate increases with
the jamming power. Our proposed solution can find its applications in both
civil (e.g., ultra-reliable and low-latency communications or URLLC) and
military scenarios (to combat both inadvertent and deliberate jamming).
Sim-Real Joint Reinforcement Transfer for 3D Indoor Navigation
Authors: Fengda Zhu, Linchao Zhu, Yi Yang
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6

There has been an increasing interest in 3D indoor navigation, where a robot
in an environment moves to a target according to an instruction. To deploy a
robot for navigation in the physical world, lots of training data is required
to learn an effective policy. It is quite labour intensive to obtain sufficient
real environment data for training robots while synthetic data is much easier
to construct by rendering. Though it is promising to utilize the synthetic
environments to facilitate navigation training in the real world, real
environment are heterogeneous from synthetic environment in two aspects. First,
the visual representation of the two environments have significant variances.
Second, the houseplans of these two environments are quite different. Therefore
two types of information, i.e. visual representation and policy behavior, need
to be adapted in the reinforcement model. The learning procedure of visual
representation and that of policy behavior are presumably reciprocal. We
propose to jointly adapt visual representation and policy behavior to leverage
the mutual impacts of environment and policy. Specifically, our method employs
an adversarial feature adaptation model for visual representation transfer and
a policy mimic strategy for policy behavior imitation. Experiment shows that
our method outperforms the baseline by 19.47% without any additional human
annotations.
From Patch to Image Segmentation using Fully Convolutional Networks -- Application to Retinal Images
Authors: Taibou Birgui Sekou, Moncef Hidane, Julien Olivier, Hubert Cardot
Date: 2019-04-08 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation, Transfer Learning

Deep learning based models, generally, require a large number of samples for appropriate training, a requirement that is difficult to satisfy in the medical field. This issue can usually be avoided with a proper initialization of the weights. On the task of medical image segmentation in general, two techniques are oftentimes employed to tackle the training of a deep network $f_T$. The first one consists in reusing some weights of a network $f_S$ pre-trained on a large scale database ($e.g.$ ImageNet). This procedure, also known as $transfer$ $learning$, happens to reduce the flexibility when it comes to new network design since $f_T$ is constrained to match some parts of $f_S$. The second commonly used technique consists in working on image patches to benefit from the large number of available patches. This paper brings together these two techniques and propose to train $arbitrarily$ $designed$ $networks$ that segment an image in one forward pass, with a focus on relatively small databases. An experimental work have been carried out on the tasks of retinal blood vessel segmentation and the optic disc one, using four publicly available databases. Furthermore, three types of network are considered, going from a very light weighted network to a densely connected one. The final results show the efficiency of the proposed framework along with state of the art results on all the databases.
Eliciting New Wikipedia Users' Interests via Automatically Mined Questionnaires: For a Warm Welcome, Not a Cold Start
Authors: Ramtin Yazdanian, Leila Zia, Jonathan Morgan, Bahodir Mansurov, Robert West
Date: 2019-04-08 00:00:00
Tasks: Articles, Recommendation Systems

Every day, thousands of users sign up as new Wikipedia contributors. Once
joined, these users have to decide which articles to contribute to, which users
to seek out and learn from or collaborate with, etc. Any such task is a hard
and potentially frustrating one given the sheer size of Wikipedia. Supporting
newcomers in their first steps by recommending articles they would enjoy
editing or editors they would enjoy collaborating with is thus a promising
route toward converting them into long-term contributors. Standard recommender
systems, however, rely on users' histories of previous interactions with the
platform. As such, these systems cannot make high-quality recommendations to
newcomers without any previous interactions -- the so-called cold-start
problem. The present paper addresses the cold-start problem on Wikipedia by
developing a method for automatically building short questionnaires that, when
completed by a newly registered Wikipedia user, can be used for a variety of
purposes, including article recommendations that can help new editors get
started. Our questionnaires are constructed based on the text of Wikipedia
articles as well as the history of contributions by the already onboarded
Wikipedia editors. We assess the quality of our questionnaire-based
recommendations in an offline evaluation using historical data, as well as an
online evaluation with hundreds of real Wikipedia newcomers, concluding that
our method provides cohesive, human-readable questions that perform well
against several baselines. By addressing the cold-start problem, this work can
help with the sustainable growth and maintenance of Wikipedia's diverse editor
community.
Referring to Objects in Videos using Spatio-Temporal Identifying Descriptions
Authors: Peratham Wiriyathammabhum, Abhinav Shrivastava, Vlad I. Morariu, Larry S. Davis
Date: 2019-04-08 00:00:00
Proceeding: WS 2019 6

This paper presents a new task, the grounding of spatio-temporal identifying
descriptions in videos. Previous work suggests potential bias in existing
datasets and emphasizes the need for a new data creation schema to better model
linguistic structure. We introduce a new data collection scheme based on
grammatical constraints for surface realization to enable us to investigate the
problem of grounding spatio-temporal identifying descriptions in videos. We
then propose a two-stream modular attention network that learns and grounds
spatio-temporal identifying descriptions based on appearance and motion. We
show that motion modules help to ground motion-related words and also help to
learn in appearance modules because modular neural networks resolve task
interference between modules. Finally, we propose a future challenge and a need
for a robust system arising from replacing ground truth visual annotations with
automatic video object detector and temporal event localization.
Improving Domain Adaptation Translation with Domain Invariant and Specific Information
Authors: Shuhao Gu, Yang Feng, Qun Liu
Date: 2019-04-08 00:00:00
Proceeding: NAACL 2019 6
Tasks: Decoder, Domain Adaptation, Machine Translation, Translation

In domain adaptation for neural machine translation, translation performance can benefit from separating features into domain-specific features and common features. In this paper, we propose a method to explicitly model the two kinds of information in the encoder-decoder framework so as to exploit out-of-domain data in in-domain training. In our method, we maintain a private encoder and a private decoder for each domain which are used to model domain-specific information. In the meantime, we introduce a common encoder and a common decoder shared by all the domains which can only have domain-independent information flow through. Besides, we add a discriminator to the shared encoder and employ adversarial training for the whole model to reinforce the performance of information separation and machine translation simultaneously. Experiment results show that our method can outperform competitive baselines greatly on multiple data sets.
Bayesian Subspace Hidden Markov Model for Acoustic Unit Discovery
Authors: Lucas Ondel, Hari Krishna Vydana, Lukáš Burget, Jan Černocký
Date: 2019-04-08 00:00:00
Tasks: Acoustic Unit Discovery

This work tackles the problem of learning a set of language specific acoustic units from unlabeled speech recordings given a set of labeled recordings from other languages. Our approach may be described by the following two steps procedure: first the model learns the notion of acoustic units from the labelled data and then the model uses its knowledge to find new acoustic units on the target language. We implement this process with the Bayesian Subspace Hidden Markov Model (SHMM), a model akin to the Subspace Gaussian Mixture Model (SGMM) where each low dimensional embedding represents an acoustic unit rather than just a HMM's state. The subspace is trained on 3 languages from the GlobalPhone corpus (German, Polish and Spanish) and the AUs are discovered on the TIMIT corpus. Results, measured in equivalent Phone Error Rate, show that this approach significantly outperforms previous HMM based acoustic units discovery systems and compares favorably with the Variational Auto Encoder-HMM.
Streamlined Dense Video Captioning
Authors: Jonghwan Mun, Linjie Yang, Zhou Ren, Ning Xu, Bohyung Han
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: Dense Video Captioning, Reinforcement Learning, Video Captioning

Dense video captioning is an extremely challenging task since accurate and
coherent description of events in a video requires holistic understanding of
video contents as well as contextual reasoning of individual events. Most
existing approaches handle this problem by first detecting event proposals from
a video and then captioning on a subset of the proposals. As a result, the
generated sentences are prone to be redundant or inconsistent since they fail
to consider temporal dependency between events. To tackle this challenge, we
propose a novel dense video captioning framework, which models temporal
dependency across events in a video explicitly and leverages visual and
linguistic context from prior events for coherent storytelling. This objective
is achieved by 1) integrating an event sequence generation network to select a
sequence of event proposals adaptively, and 2) feeding the sequence of event
proposals to our sequential video captioning network, which is trained by
reinforcement learning with two-level rewards at both event and episode levels
for better context modeling. The proposed technique achieves outstanding
performances on ActivityNet Captions dataset in most metrics.