pwc-archive/papers-with-abstracts huggingface dataset:
Lessons from the Bible on Modern Topics: Low-Resource Multilingual Topic Model Evaluation
Authors: Shudong Hao, Jordan Boyd-Graber, Michael J. Paul
Date: 2018-04-26 00:00:00
Proceeding: NAACL 2018 6
Tasks: Topic Models

Multilingual topic models enable document analysis across languages through
coherent multilingual summaries of the data. However, there is no standard and
effective metric to evaluate the quality of multilingual topics. We introduce a
new intrinsic evaluation of multilingual topic models that correlates well with
human judgments of multilingual topic coherence as well as performance in
downstream applications. Importantly, we also study evaluation for low-resource
languages. Because standard metrics fail to accurately measure topic quality
when robust external resources are unavailable, we propose an adaptation model
that improves the accuracy and reliability of these metrics in low-resource
settings.
Adaptive Performance Assessment For Drivers Through Behavioral Advantage
Authors: Dicong Qiu, Karthik Paga
Date: 2018-04-23 00:00:00
Tasks: Autonomous Driving

The potential positive impact of autonomous driving and driver assistance
technolo- gies have been a major impetus over the last decade. On the flip
side, it has been a challenging problem to analyze the performance of human
drivers or autonomous driving agents quantitatively. In this work, we propose a
generic method that compares the performance of drivers or autonomous driving
agents even if the environmental conditions are different, by using the driver
behavioral advantage instead of absolute metrics, which efficiently removes the
environmental factors. A concrete application of the method is also presented,
where the performance of more than 100 truck drivers was evaluated and ranked
in terms of fuel efficiency, covering more than 90,000 trips spanning an
average of 300 miles in a variety of driving conditions and environments.
Capsule networks for low-data transfer learning
Authors: Andrew Gritsevskiy, Maksym Korablyov
Date: 2018-04-26 00:00:00
Tasks: Transfer Learning

We propose a capsule network-based architecture for generalizing learning to
new data with few examples. Using both generative and non-generative capsule
networks with intermediate routing, we are able to generalize to new
information over 25 times faster than a similar convolutional neural network.
We train the networks on the multiMNIST dataset lacking one digit. After the
networks reach their maximum accuracy, we inject 1-100 examples of the missing
digit into the training set, and measure the number of batches needed to return
to a comparable level of accuracy. We then discuss the improvement in low-data
transfer learning that capsule networks bring, and propose future directions
for capsule research.
BEST : A decision tree algorithm that handles missing values
Authors: Cédric Beaulac, Jeffrey S. Rosenthal
Date: 2018-04-26 00:00:00
Tasks: General Classification, Missing Values

The main contribution of this paper is the development of a new decision tree algorithm. The proposed approach allows users to guide the algorithm through the data partitioning process. We believe this feature has many applications but in this paper we demonstrate how to utilize this algorithm to analyse data sets containing missing values. We tested our algorithm against simulated data sets with various missing data structures and a real data set. The results demonstrate that this new classification procedure efficiently handles missing values and produces results that are slightly more accurate and more interpretable than most common procedures without any imputations or pre-processing.
fMRI: preprocessing, classification and pattern recognition
Authors: Maxim Sharaev, Alexander Andreev, Alexey Artemov, Alexander Bernstein, Evgeny Burnaev, Ekaterina Kondratyeva, Svetlana Sushchinskaya, Renat Akzhigitov
Date: 2018-04-26 00:00:00
Tasks: Classification, General Classification

As machine learning continues to gain momentum in the neuroscience community,
we witness the emergence of novel applications such as diagnostics,
characterization, and treatment outcome prediction for psychiatric and
neurological disorders, for instance, epilepsy and depression. Systematic
research into these mental disorders increasingly involves drawing clinical
conclusions on the basis of data-driven approaches; to this end, structural and
functional neuroimaging serve as key source modalities. Identification of
informative neuroimaging markers requires establishing a comprehensive
preparation pipeline for data which may be severely corrupted by artifactual
signal fluctuations. In this work, we review a large body of literature to
provide ample evidence for the advantages of pattern recognition approaches in
clinical applications, overview advanced graph-based pattern recognition
approaches, and propose a noise-aware neuroimaging data processing pipeline. To
demonstrate the effectiveness of our approach, we provide results from a pilot
study, which show a significant improvement in classification accuracy,
indicating a promising research direction.
Machine Learning pipeline for discovering neuroimaging-based biomarkers in neurology and psychiatry
Authors: Alexander Bernstein, Evgeny Burnaev, Ekaterina Kondratyeva, Svetlana Sushchinskaya, Maxim Sharaev, Alexander Andreev, Alexey Artemov, Renat Akzhigitov
Date: 2018-04-26 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Diagnostic, General Classification

We consider a problem of diagnostic pattern recognition/classification from
neuroimaging data. We propose a common data analysis pipeline for
neuroimaging-based diagnostic classification problems using various ML
algorithms and processing toolboxes for brain imaging. We illustrate the
pipeline application by discovering new biomarkers for diagnostics of epilepsy
and depression based on clinical and MRI/fMRI data for patients and healthy
volunteers.
CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learning
Authors: Yuxin Peng, Jinwei Qi, Yuxin Yuan
Date: 2017-10-14 00:00:00
Tasks: Cross-Modal Retrieval, Representation Learning, Retrieval

It is known that the inconsistent distribution and representation of
different modalities, such as image and text, cause the heterogeneity gap that
makes it challenging to correlate such heterogeneous data. Generative
adversarial networks (GANs) have shown its strong ability of modeling data
distribution and learning discriminative representation, existing GANs-based
works mainly focus on generative problem to generate new data. We have
different goal, aim to correlate heterogeneous data, by utilizing the power of
GANs to model cross-modal joint distribution. Thus, we propose Cross-modal GANs
to learn discriminative common representation for bridging heterogeneity gap.
The main contributions are: (1) Cross-modal GANs architecture is proposed to
model joint distribution over data of different modalities. The inter-modality
and intra-modality correlation can be explored simultaneously in generative and
discriminative models. Both of them beat each other to promote cross-modal
correlation learning. (2) Cross-modal convolutional autoencoders with
weight-sharing constraint are proposed to form generative model. They can not
only exploit cross-modal correlation for learning common representation, but
also preserve reconstruction information for capturing semantic consistency
within each modality. (3) Cross-modal adversarial mechanism is proposed, which
utilizes two kinds of discriminative models to simultaneously conduct
intra-modality and inter-modality discrimination. They can mutually boost to
make common representation more discriminative by adversarial training process.
To the best of our knowledge, our proposed CM-GANs approach is the first to
utilize GANs to perform cross-modal common representation learning. Experiments
are conducted to verify the performance of our proposed approach on cross-modal
retrieval paradigm, compared with 10 methods on 3 cross-modal datasets.
Two-Stream Binocular Network: Accurate Near Field Finger Detection Based On Binocular Images
Authors: Yi Wei, Guijin Wang, Cairong Zhang, Hengkai Guo, Xinghao Chen, Huazhong Yang
Date: 2018-04-26 00:00:00
Tasks: Fingertip Detection, Hand Pose Estimation, Pose Estimation

Fingertip detection plays an important role in human computer interaction.
Previous works transform binocular images into depth images. Then depth-based
hand pose estimation methods are used to predict 3D positions of fingertips.
Different from previous works, we propose a new framework, named Two-Stream
Binocular Network (TSBnet) to detect fingertips from binocular images directly.
TSBnet first shares convolutional layers for low level features of right and
left images. Then it extracts high level features in two-stream convolutional
networks separately. Further, we add a new layer: binocular distance
measurement layer to improve performance of our model. To verify our scheme, we
build a binocular hand image dataset, containing about 117k pairs of images in
training set and 10k pairs of images in test set. Our methods achieve an
average error of 10.9mm on our test set, outperforming previous work by 5.9mm
(relatively 35.1%).
Associative Compression Networks for Representation Learning
Authors: Alex Graves, Jacob Menick, Aaron van den Oord
Date: 2018-04-06 00:00:00
Tasks: Representation Learning

This paper introduces Associative Compression Networks (ACNs), a new
framework for variational autoencoding with neural networks. The system differs
from existing variational autoencoders (VAEs) in that the prior distribution
used to model each code is conditioned on a similar code from the dataset. In
compression terms this equates to sequentially transmitting the dataset using
an ordering determined by proximity in latent space. Since the prior need only
account for local, rather than global variations in the latent space, the
coding cost is greatly reduced, leading to rich, informative codes. Crucially,
the codes remain informative when powerful, autoregressive decoders are used,
which we argue is fundamentally difficult with normal VAEs. Experimental
results on MNIST, CIFAR-10, ImageNet and CelebA show that ACNs discover
high-level latent features such as object class, writing style, pose and facial
expression, which can be used to cluster and classify the data, as well as to
generate diverse and convincing samples. We conclude that ACNs are a promising
new direction for representation learning: one that steps away from IID
modelling, and towards learning a structured description of the dataset as a
whole.
Detection-Tracking for Efficient Person Analysis: The DetTA Pipeline
Authors: Stefan Breuers, Lucas Beyer, Umer Rafi, Bastian Leibe
Date: 2018-04-26 00:00:00
Tasks: Attribute, GPU

In the past decade many robots were deployed in the wild, and people
detection and tracking is an important component of such deployments. On top of
that, one often needs to run modules which analyze persons and extract higher
level attributes such as age and gender, or dynamic information like gaze and
pose. The latter ones are especially necessary for building a reactive, social
robot-person interaction.
  In this paper, we combine those components in a fully modular
detection-tracking-analysis pipeline, called DetTA. We investigate the benefits
of such an integration on the example of head and skeleton pose, by using the
consistent track ID for a temporal filtering of the analysis modules'
observations, showing a slight improvement in a challenging real-world
scenario. We also study the potential of a so-called "free-flight" mode, where
the analysis of a person attribute only relies on the filter's predictions for
certain frames. Here, our study shows that this boosts the runtime
dramatically, while the prediction quality remains stable. This insight is
especially important for reducing power consumption and sharing precious
(GPU-)memory when running many analysis components on a mobile platform,
especially so in the era of expensive deep learning methods.
IamNN: Iterative and Adaptive Mobile Neural Network for Efficient Image Classification
Authors: Sam Leroux, Pavlo Molchanov, Pieter Simoens, Bart Dhoedt, Thomas Breuel, Jan Kautz
Date: 2018-04-26 00:00:00
Tasks: General Classification, image-classification, Image Classification

Deep residual networks (ResNets) made a recent breakthrough in deep learning.
The core idea of ResNets is to have shortcut connections between layers that
allow the network to be much deeper while still being easy to optimize avoiding
vanishing gradients. These shortcut connections have interesting side-effects
that make ResNets behave differently from other typical network architectures.
In this work we use these properties to design a network based on a ResNet but
with parameter sharing and with adaptive computation time. The resulting
network is much smaller than the original network and can adapt the
computational cost to the complexity of the input image.
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
Authors: Shuohang Wang, Mo Yu, Jing Jiang, Wei zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, Murray Campbell
Date: 2017-11-14 00:00:00
Proceeding: ICLR 2018 1
Tasks: Open-Domain Question Answering, Question Answering, Reading Comprehension, Reranking, Re-Ranking, TriviaQA

A popular recent approach to answering open-domain questions is to first
search for question-related passages and then apply reading comprehension
models to extract answers. Existing methods usually extract answers from single
passages independently. But some questions require a combination of evidence
from across different sources to answer correctly. In this paper, we propose
two models which make use of multiple passages to generate their answers. Both
use an answer-reranking approach which reorders the answer candidates generated
by an existing state-of-the-art QA model. We propose two methods, namely,
strength-based re-ranking and coverage-based re-ranking, to make use of the
aggregated evidence from different passages to better determine the answer. Our
models have achieved state-of-the-art results on three public open-domain QA
datasets: Quasar-T, SearchQA and the open-domain version of TriviaQA, with
about 8 percentage points of improvement over the former two datasets.
Generative Steganography by Sampling
Authors: Jia Liu, Yu Lei, Yan Ke, Jun Li, Min-qing Zhang, Xiaoyuan Yan
Date: 2018-04-26 00:00:00
Tasks: Image Inpainting

In this paper, a new data-driven information hiding scheme called generative
steganography by sampling (GSS) is proposed. The stego is directly sampled by a
powerful generator without an explicit cover. Secret key shared by both parties
is used for message embedding and extraction, respectively. Jensen-Shannon
Divergence is introduced as new criteria for evaluation of the security of the
generative steganography. Based on these principles, a simple practical
generative steganography method is proposed using semantic image inpainting.
Experiments demonstrate the potential of the framework through qualitative and
quantitative evaluation of the generated stego images.
Visual Estimation of Building Condition with Patch-level ConvNets
Authors: David Koch, Miroslav Despotovic, Muntaha Sakeena, Mario Döller, Matthias Zeppelzauer
Date: 2018-04-26 00:00:00

The condition of a building is an important factor for real estate valuation.
Currently, the estimation of condition is determined by real estate appraisers
which makes it subjective to a certain degree. We propose a novel vision-based
approach for the assessment of the building condition from exterior views of
the building. To this end, we develop a multi-scale patch-based pattern
extraction approach and combine it with convolutional neural networks to
estimate building condition from visual clues. Our evaluation shows that
visually estimated building condition can serve as a proxy for condition
estimates by appraisers.
English-Catalan Neural Machine Translation in the Biomedical Domain through the cascade approach
Authors: Marta R. Costa-jussà, Noe Casas, Maite Melero
Date: 2018-03-19 00:00:00
Tasks: Machine Translation, Translation

This paper describes the methodology followed to build a neural machine
translation system in the biomedical domain for the English-Catalan language
pair. This task can be considered a low-resourced task from the point of view
of the domain and the language pair. To face this task, this paper reports
experiments on a cascade pivot strategy through Spanish for the neural machine
translation using the English-Spanish SCIELO and Spanish-Catalan El Peri\'odico
database. To test the final performance of the system, we have created a new
test data set for English-Catalan in the biomedical domain which is freely
available on request.
Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimation
Authors: Markus Oberweger, Mahdi Rad, Vincent Lepetit
Date: 2018-04-11 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object, Pose Estimation

We introduce a novel method for robust and accurate 3D object pose estimation
from a single color image under large occlusions. Following recent approaches,
we first predict the 2D projections of 3D points related to the target object
and then compute the 3D pose from these correspondences using a geometric
method. Unfortunately, as the results of our experiments show, predicting these
2D projections using a regular CNN or a Convolutional Pose Machine is highly
sensitive to partial occlusions, even when these methods are trained with
partially occluded examples. Our solution is to predict heatmaps from multiple
small patches independently and to accumulate the results to obtain accurate
and robust predictions. Training subsequently becomes challenging because
patches with similar appearances but different positions on the object
correspond to different heatmaps. However, we provide a simple yet effective
solution to deal with such ambiguities. We show that our approach outperforms
existing methods on two challenging datasets: The Occluded LineMOD dataset and
the YCB-Video dataset, both exhibiting cluttered scenes with highly occluded
objects. Project website:
https://www.tugraz.at/institute/icg/research/team-lepetit/research-projects/robust-object-pose-estimation/
Deep Learning for Predicting Asset Returns
Authors: Guanhao Feng, Jingyu He, Nicholas G. Polson
Date: 2018-04-25 00:00:00
Tasks: Deep Learning, Time Series, Time Series Analysis

Deep learning searches for nonlinear factors for predicting asset returns.
Predictability is achieved via multiple layers of composite factors as opposed
to additive ones. Viewed in this way, asset pricing studies can be revisited
using multi-layer deep learners, such as rectified linear units (ReLU) or
long-short-term-memory (LSTM) for time-series effects. State-of-the-art
algorithms including stochastic gradient descent (SGD), TensorFlow and dropout
design provide imple- mentation and efficient factor exploration. To illustrate
our methodology, we revisit the equity market risk premium dataset of Welch and
Goyal (2008). We find the existence of nonlinear factors which explain
predictability of returns, in particular at the extremes of the characteristic
space. Finally, we conclude with directions for future research.
Domain Adaptation through Synthesis for Unsupervised Person Re-identification
Authors: Slawomir Bak, Peter Carr, Jean-Francois Lalonde
Date: 2018-04-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Diversity, Domain Adaptation, Person Re-Identification, Unsupervised Person Re-Identification

Drastic variations in illumination across surveillance cameras make the
person re-identification problem extremely challenging. Current large scale
re-identification datasets have a significant number of training subjects, but
lack diversity in lighting conditions. As a result, a trained model requires
fine-tuning to become effective under an unseen illumination condition. To
alleviate this problem, we introduce a new synthetic dataset that contains
hundreds of illumination conditions. Specifically, we use 100 virtual humans
illuminated with multiple HDR environment maps which accurately model realistic
indoor and outdoor lighting. To achieve better accuracy in unseen illumination
conditions we propose a novel domain adaptation technique that takes advantage
of our synthetic data and performs fine-tuning in a completely unsupervised
way. Our approach yields significantly higher accuracy than semi-supervised and
unsupervised state-of-the-art methods, and is very competitive with supervised
techniques.
Learn To Pay Attention
Authors: Saumya Jetley, Nicholas A. Lord, Namhoon Lee, Philip H. S. Torr
Date: 2018-04-06 00:00:00
Proceeding: ICLR 2018 1
Tasks: Adversarial Attack, General Classification, image-classification, Image Classification, Object Discovery, Weakly supervised segmentation

We propose an end-to-end-trainable attention module for convolutional neural
network (CNN) architectures built for image classification. The module takes as
input the 2D feature vector maps which form the intermediate representations of
the input image at different stages in the CNN pipeline, and outputs a 2D
matrix of scores for each map. Standard CNN architectures are modified through
the incorporation of this module, and trained under the constraint that a
convex combination of the intermediate 2D feature vectors, as parameterised by
the score matrices, must \textit{alone} be used for classification.
Incentivised to amplify the relevant and suppress the irrelevant or misleading,
the scores thus assume the role of attention values. Our experimental
observations provide clear evidence to this effect: the learned attention maps
neatly highlight the regions of interest while suppressing background clutter.
Consequently, the proposed function is able to bootstrap standard CNN
architectures for the task of image classification, demonstrating superior
generalisation over 6 unseen benchmark datasets. When binarised, our attention
maps outperform other CNN-based attention maps, traditional saliency maps, and
top object proposals for weakly supervised segmentation as demonstrated on the
Object Discovery dataset. We also demonstrate improved robustness against the
fast gradient sign method of adversarial attack.
On deep speaker embeddings for text-independent speaker recognition
Authors: Sergey Novoselov, Andrey Shulipa, Ivan Kremnev, Alexandr Kozlov, Vadim Shchemelinin
Date: 2018-04-26 00:00:00
Tasks: General Classification, Metric Learning, Speaker Recognition, Speaker Verification, Text-Independent Speaker Recognition

We investigate deep neural network performance in the textindependent speaker
recognition task. We demonstrate that using angular softmax activation at the
last classification layer of a classification neural network instead of a
simple softmax activation allows to train a more generalized discriminative
speaker embedding extractor. Cosine similarity is an effective metric for
speaker verification in this embedding space. We also address the problem of
choosing an architecture for the extractor. We found that deep networks with
residual frame level connections outperform wide but relatively shallow
architectures. This paper also proposes several improvements for previous
DNN-based extractor systems to increase the speaker recognition accuracy. We
show that the discriminatively trained similarity metric learning approach
outperforms the standard LDA-PLDA method as an embedding backend. The results
obtained on Speakers in the Wild and NIST SRE 2016 evaluation sets demonstrate
robustness of the proposed systems when dealing with close to real-life
conditions.