pwc-archive/papers-with-abstracts huggingface dataset:
Discriminative Representation Combinations for Accurate Face Spoofing Detection
Authors: Xiao Song, Xu Zhao, Liangji Fang, Tianwei Lin
Date: 2018-08-27 00:00:00
Tasks: Face Presentation Attack Detection

Three discriminative representations for face presentation attack detection
are introduced in this paper. Firstly we design a descriptor called spatial
pyramid coding micro-texture (SPMT) feature to characterize local appearance
information. Secondly we utilize the SSD, which is a deep learning framework
for detection, to excavate context cues and conduct end-to-end face
presentation attack detection. Finally we design a descriptor called template
face matched binocular depth (TFBD) feature to characterize stereo structures
of real and fake faces. For accurate presentation attack detection, we also
design two kinds of representation combinations. Firstly, we propose a
decision-level cascade strategy to combine SPMT with SSD. Secondly, we use a
simple score fusion strategy to combine face structure cues (TFBD) with local
micro-texture features (SPMT). To demonstrate the effectiveness of our design,
we evaluate the representation combination of SPMT and SSD on three public
datasets, which outperforms all other state-of-the-art methods. In addition, we
evaluate the representation combination of SPMT and TFBD on our dataset and
excellent performance is also achieved.
Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problems
Authors: Filipe Rodrigues, Francisco C. Pereira
Date: 2018-08-27 00:00:00
Tasks: Multi-Task Learning, quantile regression, regression

Spatio-temporal problems are ubiquitous and of vital importance in many
research fields. Despite the potential already demonstrated by deep learning
methods in modeling spatio-temporal data, typical approaches tend to focus
solely on conditional expectations of the output variables being modeled. In
this paper, we propose a multi-output multi-quantile deep learning approach for
jointly modeling several conditional quantiles together with the conditional
expectation as a way to provide a more complete "picture" of the predictive
density in spatio-temporal problems. Using two large-scale datasets from the
transportation domain, we empirically demonstrate that, by approaching the
quantile regression problem from a multi-task learning perspective, it is
possible to solve the embarrassing quantile crossings problem, while
simultaneously significantly outperforming state-of-the-art quantile regression
methods. Moreover, we show that jointly modeling the mean and several
conditional quantiles not only provides a rich description about the predictive
density that can capture heteroscedastic properties at a neglectable
computational overhead, but also leads to improved predictions of the
conditional expectation due to the extra information and a regularization
effect induced by the added quantiles.
Theoretical Foundations of the A2RD Project: Part I
Authors: Juliao Braga, Joao Nuno Silva, Patricia Takako Endo, Nizam Omar
Date: 2018-08-27 00:00:00

This article identifies and discusses the theoretical foundations that were
considered in the design of the A2RD model. In addition to the points
considered, references are made to the studies available and considered in the
approach.
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation
Authors: Liangchen Luo, Jingjing Xu, Junyang Lin, Qi Zeng, Xu sun
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Dialogue Generation, Text Generation

Generating semantically coherent responses is still a major challenge in
dialogue generation. Different from conventional text generation tasks, the
mapping between inputs and responses in conversations is more complicated,
which highly demands the understanding of utterance-level semantic dependency,
a relation between the whole meanings of inputs and outputs. To address this
problem, we propose an Auto-Encoder Matching (AEM) model to learn such
dependency. The model contains two auto-encoders and one mapping module. The
auto-encoders learn the semantic representations of inputs and responses, and
the mapping module learns to connect the utterance-level representations.
Experimental results from automatic and human evaluations demonstrate that our
model is capable of generating responses of high coherence and fluency compared
to baseline models. The code is available at https://github.com/lancopku/AMM
Sparsity in Deep Neural Networks - An Empirical Investigation with TensorQuant
Authors: Dominik Marek Loroch, Franz-Josef Pfreundt, Norbert Wehn, Janis Keuper
Date: 2018-08-27 00:00:00
Tasks: Autonomous Driving

Deep learning is finding its way into the embedded world with applications
such as autonomous driving, smart sensors and aug- mented reality. However, the
computation of deep neural networks is demanding in energy, compute power and
memory. Various approaches have been investigated to reduce the necessary
resources, one of which is to leverage the sparsity occurring in deep neural
networks due to the high levels of redundancy in the network parameters. It has
been shown that sparsity can be promoted specifically and the achieved sparsity
can be very high. But in many cases the methods are evaluated on rather small
topologies. It is not clear if the results transfer onto deeper topologies. In
this paper, the TensorQuant toolbox has been extended to offer a platform to
investigate sparsity, especially in deeper models. Several practical relevant
topologies for varying classification problem sizes are investigated to show
the differences in sparsity for activations, weights and gradients.
Amobee at IEST 2018: Transfer Learning from Language Models
Authors: Alon Rozental, Daniel Fleischer, Zohar Kelrich
Date: 2018-08-27 00:00:00
Proceeding: WS 2018 10
Tasks: Transfer Learning

This paper describes the system developed at Amobee for the WASSA 2018
implicit emotions shared task (IEST). The goal of this task was to predict the
emotion expressed by missing words in tweets without an explicit mention of
those words. We developed an ensemble system consisting of language models
together with LSTM-based networks containing a CNN attention mechanism. Our
approach represents a novel use of language models (specifically trained on a
large Twitter dataset) to predict and classify emotions. Our system reached 1st
place with a macro $\text{F}_1$ score of 0.7145.
Improving Cross-Lingual Word Embeddings by Meeting in the Middle
Authors: Yerai Doval, Jose Camacho-Collados, Luis Espinosa-Anke, Steven Schockaert
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Cross-Lingual Word Embeddings, Multilingual NLP, Word Embeddings

Cross-lingual word embeddings are becoming increasingly important in
multilingual NLP. Recently, it has been shown that these embeddings can be
effectively learned by aligning two disjoint monolingual vector spaces through
linear transformations, using no more than a small bilingual dictionary as
supervision. In this work, we propose to apply an additional transformation
after the initial alignment step, which moves cross-lingual synonyms towards a
middle point between them. By applying this transformation our aim is to obtain
a better cross-lingual integration of the vector spaces. In addition, and
perhaps surprisingly, the monolingual spaces also improve by this
transformation. This is in contrast to the original alignment, which is
typically learned such that the structure of the monolingual spaces is
preserved. Our experiments confirm that the resulting cross-lingual embeddings
outperform state-of-the-art models in both monolingual and cross-lingual
evaluation tasks.
Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization
Authors: Liu Liu, Hongdong Li, Yuchao Dai
Date: 2018-08-27 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image-Based Localization, Representation Learning, Retrieval, Triplet

This paper tackles the problem of large-scale image-based localization (IBL) where the spatial location of a query image is determined by finding out the most similar reference images in a large database. For solving this problem, a critical task is to learn discriminative image representation that captures informative information relevant for localization. We propose a novel representation learning method having higher location-discriminating power. It provides the following contributions: 1) we represent a place (location) as a set of exemplar images depicting the same landmarks and aim to maximize similarities among intra-place images while minimizing similarities among inter-place images; 2) we model a similarity measure as a probability distribution on L_2-metric distances between intra-place and inter-place image representations; 3) we propose a new Stochastic Attraction and Repulsion Embedding (SARE) loss function minimizing the KL divergence between the learned and the actual probability distributions; 4) we give theoretical comparisons between SARE, triplet ranking and contrastive losses. It provides insights into why SARE is better by analyzing gradients. Our SARE loss is easy to implement and pluggable to any CNN. Experiments show that our proposed method improves the localization performance on standard benchmarks by a large margin. Demonstrating the broad applicability of our method, we obtained the third place out of 209 teams in the 2018 Google Landmark Retrieval Challenge. Our code and model are available at https://github.com/Liumouliu/deepIBL.
Adaptive Structural Learning of Deep Belief Network for Medical Examination Data and Its Knowledge Extraction by using C4.5
Authors: Shin Kamada, Takumi Ichimura, Toshihide Harada
Date: 2018-08-27 00:00:00

Deep Learning has a hierarchical network architecture to represent the
complicated feature of input patterns. The adaptive structural learning method
of Deep Belief Network (DBN) has been developed. The method can discover an
optimal number of hidden neurons for given input data in a Restricted Boltzmann
Machine (RBM) by neuron generation-annihilation algorithm, and generate a new
hidden layer in DBN by the extension of the algorithm. In this paper, the
proposed adaptive structural learning of DBN was applied to the comprehensive
medical examination data for the cancer prediction. The prediction system shows
higher classification accuracy (99.8% for training and 95.5% for test) than the
traditional DBN. Moreover, the explicit knowledge with respect to the relation
between input and output patterns was extracted from the trained DBN network by
C4.5. Some characteristics extracted in the form of IF-THEN rules to find an
initial cancer at the early stage were reported in this paper.
Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach
Authors: Pratik Jawanpuria, Arjun Balgovind, Anoop Kunchukuttan, Bamdev Mishra
Date: 2018-08-27 00:00:00
Proceeding: TACL 2019 3
Tasks: Bilingual Lexicon Induction, Multilingual Word Embeddings, Recommendation Systems, Translation, Word Embeddings, Word Similarity, Word Translation

We propose a novel geometric approach for learning bilingual mappings given
monolingual embeddings and a bilingual dictionary. Our approach decouples
learning the transformation from the source language to the target language
into (a) learning rotations for language-specific embeddings to align them to a
common space, and (b) learning a similarity metric in the common space to model
similarities between the embeddings. We model the bilingual mapping problem as
an optimization problem on smooth Riemannian manifolds. We show that our
approach outperforms previous approaches on the bilingual lexicon induction and
cross-lingual word similarity tasks. We also generalize our framework to
represent multiple languages in a common latent space. In particular, the
latent space representations for several languages are learned jointly, given
bilingual dictionaries for multiple language pairs. We illustrate the
effectiveness of joint learning for multiple languages in zero-shot word
translation setting. Our implementation is available at
https://github.com/anoopkunchukuttan/geomm .
Learning behavioral context recognition with multi-stream temporal convolutional networks
Authors: Aaqib Saeed, Tanir Ozcelebi, Stojan Trajanovski, Johan Lukkien
Date: 2018-08-27 00:00:00
Tasks: Feature Engineering, Multi-Task Learning

Smart devices of everyday use (such as smartphones and wearables) are
increasingly integrated with sensors that provide immense amounts of
information about a person's daily life such as behavior and context. The
automatic and unobtrusive sensing of behavioral context can help develop
solutions for assisted living, fitness tracking, sleep monitoring, and several
other fields. Towards addressing this issue, we raise the question: can a
machine learn to recognize a diverse set of contexts and activities in a
real-life through joint learning from raw multi-modal signals (e.g.
accelerometer, gyroscope and audio etc.)? In this paper, we propose a
multi-stream temporal convolutional network to address the problem of
multi-label behavioral context recognition. A four-stream network architecture
handles learning from each modality with a contextualization module which
incorporates extracted representations to infer a user's context. Our empirical
evaluation suggests that a deep convolutional network trained end-to-end
achieves an optimal recognition rate. Furthermore, the presented architecture
can be extended to include similar sensors for performance improvements and
handles missing modalities through multi-task learning without any manual
feature engineering on highly imbalanced and sparsely labeled dataset.
Identifiability of Complete Dictionary Learning
Authors: Jérémy E. Cohen, Nicolas Gillis
Date: 2018-08-27 00:00:00
Tasks: Dictionary Learning

Sparse component analysis (SCA), also known as complete dictionary learning,
is the following problem: Given an input matrix $M$ and an integer $r$, find a
dictionary $D$ with $r$ columns and a matrix $B$ with $k$-sparse columns (that
is, each column of $B$ has at most $k$ non-zero entries) such that $M \approx
DB$. A key issue in SCA is identifiability, that is, characterizing the
conditions under which $D$ and $B$ are essentially unique (that is, they are
unique up to permutation and scaling of the columns of $D$ and rows of $B$).
Although SCA has been vastly investigated in the last two decades, only a few
works have tackled this issue in the deterministic scenario, and no work
provides reasonable bounds in the minimum number of samples (that is, columns
of $M$) that leads to identifiability. In this work, we provide new results in
the deterministic scenario when the data has a low-rank structure, that is,
when $D$ is (under)complete. While previous bounds feature a combinatorial term
$r \choose k$, we exhibit a sufficient condition involving
$\mathcal{O}(r^3/(r-k)^2)$ samples that yields an essentially unique
decomposition, as long as these data points are well spread among the subspaces
spanned by $r-1$ columns of $D$. We also exhibit a necessary lower bound on the
number of samples that contradicts previous results in the literature when $k$
equals $r-1$. Our bounds provide a drastic improvement compared to the state of
the art, and imply for example that for a fixed proportion of zeros (constant
and independent of $r$, e.g., 10\% of zero entries in $B$), one only requires
$\mathcal{O}(r)$ data points to guarantee identifiability.
On the convergence of optimistic policy iteration for stochastic shortest path problem
Authors: Yuanlong Chen
Date: 2018-08-27 00:00:00

In this paper, we prove some convergence results of a special case of
optimistic policy iteration algorithm for stochastic shortest path problem. We
consider both Monte Carlo and $TD(\lambda)$ methods for the policy evaluation
step under the condition that the termination state will eventually be reached
almost surely.
Sentence Embeddings in NLI with Iterative Refinement Encoders
Authors: Aarne Talman, Anssi Yli-Jyrä, Jörg Tiedemann
Date: 2018-08-27 00:00:00
Tasks: Natural Language Inference, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings, Transfer Learning

Sentence-level representations are necessary for various NLP tasks. Recurrent neural networks have proven to be very effective in learning distributed representations and can be trained efficiently on natural language inference tasks. We build on top of one such model and propose a hierarchy of BiLSTM and max pooling layers that implements an iterative refinement strategy and yields state of the art results on the SciTail dataset as well as strong results for SNLI and MultiNLI. We can show that the sentence embeddings learned in this way can be utilized in a wide variety of transfer learning tasks, outperforming InferSent on 7 out of 10 and SkipThought on 8 out of 9 SentEval sentence embedding evaluation tasks. Furthermore, our model beats the InferSent model in 8 out of 10 recently published SentEval probing tasks designed to evaluate sentence embeddings' ability to capture some of the important linguistic properties of sentences.
Learning from Positive and Unlabeled Data under the Selected At Random Assumption
Authors: Jessa Bekker, Jesse Davis
Date: 2018-08-27 00:00:00
Tasks: General Classification, Medical Diagnosis

For many interesting tasks, such as medical diagnosis and web page
classification, a learner only has access to some positively labeled examples
and many unlabeled examples. Learning from this type of data requires making
assumptions about the true distribution of the classes and/or the mechanism
that was used to select the positive examples to be labeled. The commonly made
assumptions, separability of the classes and positive examples being selected
completely at random, are very strong. This paper proposes a weaker assumption
that assumes the positive examples to be selected at random, conditioned on
some of the attributes. To learn under this assumption, an EM method is
proposed. Experiments show that our method is not only very capable of learning
under this assumption, but it also outperforms the state of the art for
learning under the selected completely at random assumption.
What Makes Natural Scene Memorable?
Authors: Jiaxin Lu, Mai Xu, Ren Yang, Zulin Wang
Date: 2018-08-27 00:00:00

Recent studies on image memorability have shed light on the visual features
that make generic images, object images or face photographs memorable. However,
a clear understanding and reliable estimation of natural scene memorability
remain elusive. In this paper, we provide an attempt to answer: "what exactly
makes natural scene memorable". Specifically, we first build LNSIM, a
large-scale natural scene image memorability database (containing 2,632 images
and memorability annotations). Then, we mine our database to investigate how
low-, middle- and high-level handcrafted features affect the memorability of
natural scene. In particular, we find that high-level feature of scene category
is rather correlated with natural scene memorability. Thus, we propose a deep
neural network based natural scene memorability (DeepNSM) predictor, which
takes advantage of scene category. Finally, the experimental results validate
the effectiveness of DeepNSM.
Generalisation in humans and deep neural networks
Authors: Robert Geirhos, Carlos R. Medina Temme, Jonas Rauber, Heiko H. Schütt, Matthias Bethge, Felix A. Wichmann
Date: 2018-08-27 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Object Recognition

We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations. First, using three well known DNNs (ResNet-152, VGG-19, GoogLeNet) we find the human visual system to be more robust to nearly all of the tested image manipulations, and we observe progressively diverging classification error-patterns between humans and DNNs when the signal gets weaker. Secondly, we show that DNNs trained directly on distorted images consistently surpass human performance on the exact distortion types they were trained on, yet they display extremely poor generalisation abilities when tested on other distortion types. For example, training on salt-and-pepper noise does not imply robustness on uniform white noise and vice versa. Thus, changes in the noise distribution between training and testing constitutes a crucial challenge to deep learning vision systems that can be systematically addressed in a lifelong machine learning approach. Our new dataset consisting of 83K carefully measured human psychophysical trials provide a useful reference for lifelong robustness against image degradations set by the human visual system.
Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
Authors: Shashi Narayan, Shay B. Cohen, Mirella Lapata
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Articles, Document Summarization, Extreme Summarization, Sentence, Text Summarization

We introduce extreme summarization, a new single-document summarization task
which does not favor extractive strategies and calls for an abstractive
modeling approach. The idea is to create a short, one-sentence news summary
answering the question "What is the article about?". We collect a real-world,
large-scale dataset for this task by harvesting online articles from the
British Broadcasting Corporation (BBC). We propose a novel abstractive model
which is conditioned on the article's topics and based entirely on
convolutional neural networks. We demonstrate experimentally that this
architecture captures long-range dependencies in a document and recognizes
pertinent content, outperforming an oracle extractive system and
state-of-the-art abstractive approaches when evaluated automatically and by
humans.
Comparing Attention-based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension
Authors: Matthias Blohm, Glorianna Jagfeld, Ekta Sood, Xiang Yu, Ngoc Thang Vu
Date: 2018-08-27 00:00:00
Proceeding: CONLL 2018 10
Tasks: Machine Reading Comprehension, Question Answering, Reading Comprehension

We propose a machine reading comprehension model based on the
compare-aggregate framework with two-staged attention that achieves
state-of-the-art results on the MovieQA question answering dataset. To
investigate the limitations of our model as well as the behavioral difference
between convolutional and recurrent neural networks, we generate adversarial
examples to confuse the model and compare to human performance. Furthermore, we
assess the generalizability of our model by analyzing its differences to human
inference,
simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions
Authors: Fenglin Liu, Xuancheng Ren, Yuanxin Liu, Houfeng Wang, Xu sun
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, Image Captioning

The encode-decoder framework has shown recent success in image captioning.
Visual attention, which is good at detailedness, and semantic attention, which
is good at comprehensiveness, have been separately proposed to ground the
caption on the image. In this paper, we propose the Stepwise Image-Topic
Merging Network (simNet) that makes use of the two kinds of attention at the
same time. At each time step when generating the caption, the decoder
adaptively merges the attentive information in the extracted topics and the
image according to the generated context, so that the visual information and
the semantic information can be effectively combined. The proposed approach is
evaluated on two benchmark datasets and reaches the state-of-the-art
performances.(The code is available at https://github.com/lancopku/simNet)