pwc-archive/papers-with-abstracts huggingface dataset:
Attentional Encoder Network for Targeted Sentiment Classification
Authors: Youwei Song, Jiahai Wang, Tao Jiang, Zhiyue Liu, Yanghui Rao
Date: 2019-02-25 00:00:00
Tasks: Aspect-Based Sentiment Analysis (ABSA), Classification, General Classification, Sentiment Analysis, Sentiment Classification

Targeted sentiment classification aims at determining the sentimental
tendency towards specific targets. Most of the previous approaches model
context and target words with RNN and attention. However, RNNs are difficult to
parallelize and truncated backpropagation through time brings difficulty in
remembering long-term patterns. To address this issue, this paper proposes an
Attentional Encoder Network (AEN) which eschews recurrence and employs
attention based encoders for the modeling between context and target. We raise
the label unreliability issue and introduce label smoothing regularization. We
also apply pre-trained BERT to this task and obtain new state-of-the-art
results. Experiments and analysis demonstrate the effectiveness and lightweight
of our model.
End-to-end Hand Mesh Recovery from a Monocular RGB Image
Authors: Xiong Zhang, Qiang Li, Hong Mo, Wenbo Zhang, Wen Zheng
Date: 2019-02-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Hand Pose Estimation, Hand Pose Estimation, Pose Estimation

In this paper, we present a HAnd Mesh Recovery (HAMR) framework to tackle the problem of reconstructing the full 3D mesh of a human hand from a single RGB image. In contrast to existing research on 2D or 3D hand pose estimation from RGB or/and depth image data, HAMR can provide a more expressive and useful mesh representation for monocular hand image understanding. In particular, the mesh representation is achieved by parameterizing a generic 3D hand model with shape and relative 3D joint angles. By utilizing this mesh representation, we can easily compute the 3D joint locations via linear interpolations between the vertexes of the mesh, while obtain the 2D joint locations with a projection of the 3D joints.To this end, a differentiable re-projection loss can be defined in terms of the derived representations and the ground-truth labels, thus making our framework end-to-end trainable.Qualitative experiments show that our framework is capable of recovering appealing 3D hand mesh even in the presence of severe occlusions.Quantitatively, our approach also outperforms the state-of-the-art methods for both 2D and 3D hand pose estimation from a monocular RGB image on several benchmark datasets.
Multi-Label Network Classification via Weighted Personalized Factorizations
Authors: Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme
Date: 2019-02-25 00:00:00
Tasks: Classification, General Classification, Implicit Relations, Relational Reasoning

Multi-label network classification is a well-known task that is being used in
a wide variety of web-based and non-web-based domains. It can be formalized as
a multi-relational learning task for predicting nodes labels based on their
relations within the network. In sparse networks, this prediction task can be
very challenging when only implicit feedback information is available such as
in predicting user interests in social networks. Current approaches rely on
learning per-node latent representations by utilizing the network structure,
however, implicit feedback relations are naturally sparse and contain only
positive observed feedbacks which mean that these approaches will treat all
observed relations as equally important. This is not necessarily the case in
real-world scenarios as implicit relations might have semantic weights which
reflect the strength of those relations. If those weights can be approximated,
the models can be trained to differentiate between strong and weak relations.
In this paper, we propose a weighted personalized two-stage multi-relational
matrix factorization model with Bayesian personalized ranking loss for network
classification that utilizes basic transitive node similarity function for
weighting implicit feedback relations. Experiments show that the proposed model
significantly outperforms the state-of-art models on three different real-world
web-based datasets and a biology-based dataset.
MIRA: A Computational Neuro-Based Cognitive Architecture Applied to Movie Recommender Systems
Authors: Mariana B. Santos, Amanda M. Lima, Lucas A. Silva, Felipe S. Vargas, Guilherme A. Wachs-Lopes, Paulo S. Rodrigues
Date: 2019-02-25 00:00:00
Tasks: Movie Recommendation, Recommendation Systems, Small Data Image Classification

The human mind is still an unknown process of neuroscience in many aspects.
Nevertheless, for decades the scientific community has proposed computational
models that try to simulate their parts, specific applications, or their
behavior in different situations. The most complete model in this line is
undoubtedly the LIDA model, proposed by Stan Franklin with the aim of serving
as a generic computational architecture for several applications. The present
project is inspired by the LIDA model to apply it to the process of movie
recommendation, the model called MIRA (Movie Intelligent Recommender Agent)
presented percentages of precision similar to a traditional model when
submitted to the same assay conditions. Moreover, the proposed model reinforced
the precision indexes when submitted to tests with volunteers, proving once
again its performance as a cognitive model, when executed with small data
volumes. Considering that the proposed model achieved a similar behavior to the
traditional models under conditions expected to be similar for natural systems,
it can be said that MIRA reinforces the applicability of LIDA as a path to be
followed for the study and generation of computational agents inspired by
neural behaviors.
Adversarial attacks hidden in plain sight
Authors: Jan Philip Göpfert, André Artelt, Heiko Wersing, Barbara Hammer
Date: 2019-02-25 00:00:00
Tasks: General Classification

Convolutional neural networks have been used to achieve a string of successes during recent years, but their lack of interpretability remains a serious issue. Adversarial examples are designed to deliberately fool neural networks into making any desired incorrect classification, potentially with very high certainty. Several defensive approaches increase robustness against adversarial attacks, demanding attacks of greater magnitude, which lead to visible artifacts. By considering human visual perception, we compose a technique that allows to hide such adversarial attacks in regions of high complexity, such that they are imperceptible even to an astute observer. We carry out a user study on classifying adversarially modified images to validate the perceptual quality of our approach and find significant evidence for its concealment with regards to human visual perception.
Separating the EoR Signal with a Convolutional Denoising Autoencoder: A Deep-learning-based Method
Authors: Weitian Li, Haiguang Xu, Zhixian Ma, Ruimin Zhu, Dan Hu, Zhenghao Zhu, Junhua Gu, Chenxi Shan, Jie Zhu, Xiang-Ping Wu
Date: 2019-02-25 00:00:00
Tasks: Denoising

When applying the foreground removal methods to uncover the faint
cosmological signal from the epoch of reionization (EoR), the foreground
spectra are assumed to be smooth. However, this assumption can be seriously
violated in practice since the unresolved or mis-subtracted foreground sources,
which are further complicated by the frequency-dependent beam effects of
interferometers, will generate significant fluctuations along the frequency
dimension. To address this issue, we propose a novel deep-learning-based method
that uses a 9-layer convolutional denoising autoencoder (CDAE) to separate the
EoR signal. After being trained on the SKA images simulated with realistic beam
effects, the CDAE achieves excellent performance as the mean correlation
coefficient ($\bar{\rho}$) between the reconstructed and input EoR signals
reaches $0.929 \pm 0.045$. In comparison, the two representative traditional
methods, namely the polynomial fitting method and the continuous wavelet
transform method, both have difficulties in modelling and removing the
foreground emission complicated with the beam effects, yielding only
$\bar{\rho}_{\text{poly}} = 0.296 \pm 0.121$ and $\bar{\rho}_{\text{cwt}} =
0.198 \pm 0.160$, respectively. We conclude that, by hierarchically learning
sophisticated features through multiple convolutional layers, the CDAE is a
powerful tool that can be used to overcome the complicated beam effects and
accurately separate the EoR signal. Our results also exhibit the great
potential of deep-learning-based methods in future EoR experiments.
Relation Extraction using Explicit Context Conditioning
Authors: Gaurav Singh, Parminder Bhatia
Date: 2019-02-25 00:00:00
Proceeding: NAACL 2019 6
Tasks: Relation, Relation Extraction, Sentence

Relation Extraction (RE) aims to label relations between groups of marked
entities in raw text. Most current RE models learn context-aware
representations of the target entities that are then used to establish relation
between them. This works well for intra-sentence RE and we call them
first-order relations. However, this methodology can sometimes fail to capture
complex and long dependencies. To address this, we hypothesize that at times
two target entities can be explicitly connected via a context token. We refer
to such indirect relations as second-order relations and describe an efficient
implementation for computing them. These second-order relation scores are then
combined with first-order relation scores. Our empirical results show that the
proposed method leads to state-of-the-art performance over two biomedical
datasets.
Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference
Authors: Xianfeng Tang, Boqing Gong, Yanwei Yu, Huaxiu Yao, Yandong Li, Haiyong Xie, Xiaoyu Wang
Date: 2019-02-25 00:00:00
Tasks: Deep Reinforcement Learning, Graph Embedding, Reinforcement Learning

Real-time traffic volume inference is key to an intelligent city. It is a
challenging task because accurate traffic volumes on the roads can only be
measured at certain locations where sensors are installed. Moreover, the
traffic evolves over time due to the influences of weather, events, holidays,
etc. Existing solutions to the traffic volume inference problem often rely on
dense GPS trajectories, which inevitably fail to account for the vehicles which
carry no GPS devices or have them turned off. Consequently, the results are
biased to taxicabs because they are almost always online for GPS tracking. In
this paper, we propose a novel framework for the citywide traffic volume
inference using both dense GPS trajectories and incomplete trajectories
captured by camera surveillance systems. Our approach employs a high-fidelity
traffic simulator and deep reinforcement learning to recover full vehicle
movements from the incomplete trajectories. In order to jointly model the
recovered trajectories and dense GPS trajectories, we construct spatiotemporal
graphs and use multi-view graph embedding to encode the multi-hop correlations
between road segments into real-valued vectors. Finally, we infer the citywide
traffic volumes by propagating the traffic values of monitored road segments to
the unmonitored ones through masked pairwise similarities. Extensive
experiments with two big regions in a provincial capital city in China verify
the effectiveness of our approach.
Audio Caption: Listen and Tell
Authors: Mengyue Wu, Heinrich Dinkel, Kai Yu
Date: 2019-02-25 00:00:00
Tasks: Decoder, General Classification

Increasing amount of research has shed light on machine perception of audio events, most of which concerns detection and classification tasks. However, human-like perception of audio scenes involves not only detecting and classifying audio sounds, but also summarizing the relationship between different audio events. Comparable research such as image caption has been conducted, yet the audio field is still quite barren. This paper introduces a manually-annotated dataset for audio caption. The purpose is to automatically generate natural sentences for audio scene description and to bridge the gap between machine perception of audio and image. The whole dataset is labelled in Mandarin and we also include translated English annotations. A baseline encoder-decoder model is provided for both English and Mandarin. Similar BLEU scores are derived for both languages: our model can generate understandable and data-related captions based on the dataset.
Resource-constrained multi-project scheduling with activity and time flexibility
Authors: Viktoria A. Hauder, Andreas Beham, Sebastian Raggl, Sophie N. Parragh, Michael Affenzeller
Date: 2019-02-25 00:00:00
Tasks: Scheduling

Project scheduling in manufacturing environments often requires flexibility in terms of the selection and the exact length of alternative production activities. Moreover, the simultaneous scheduling of multiple lots is mandatory in many production planning applications. To meet these requirements, a new resource-constrained project scheduling problem (RCPSP) is introduced where both decisions (activity flexibility and time flexibility) are integrated. Besides the minimization of makespan, two new alternative objectives are presented: maximization of balanced length of selected activities (time balance) and maximization of balanced resource utilization (resource balance). New mixed integer and constraint programming (CP) models are proposed for the developed integrated flexible project scheduling problem. Benchmark instances on an already existing flexible RCPSP and the newly developed problem are solved to optimality. The real-world applicability of the suggested CP models is shown by additionally solving a large industry case.
Pretraining-Based Natural Language Generation for Text Summarization
Authors: Haoyu Zhang, Jianjun Xu, Ji Wang
Date: 2019-02-25 00:00:00
Proceeding: CONLL 2019 11
Tasks: Abstractive Text Summarization, Decoder, Text Generation, Text Summarization

In this paper, we propose a novel pretraining-based encoder-decoder
framework, which can generate the output sequence based on the input sequence
in a two-stage manner. For the encoder of our model, we encode the input
sequence into context representations using BERT. For the decoder, there are
two stages in our model, in the first stage, we use a Transformer-based decoder
to generate a draft output sequence. In the second stage, we mask each word of
the draft sequence and feed it to BERT, then by combining the input sequence
and the draft representation generated by BERT, we use a Transformer-based
decoder to predict the refined word for each masked position. To the best of
our knowledge, our approach is the first method which applies the BERT into
text generation tasks. As the first step in this direction, we evaluate our
proposed method on the text summarization task. Experimental results show that
our model achieves new state-of-the-art on both CNN/Daily Mail and New York
Times datasets.
Modularity as a Means for Complexity Management in Neural Networks Learning
Authors: David Castillo-Bolado, Cayetano Guerra-Artal, Mario Hernandez-Tejera
Date: 2019-02-25 00:00:00
Tasks: Management

Training a Neural Network (NN) with lots of parameters or intricate
architectures creates undesired phenomena that complicate the optimization
process. To address this issue we propose a first modular approach to NN
design, wherein the NN is decomposed into a control module and several
functional modules, implementing primitive operations. We illustrate the
modular concept by comparing performances between a monolithic and a modular NN
on a list sorting problem and show the benefits in terms of training speed,
training stability and maintainability. We also discuss some questions that
arise in modular NNs.
The MBPEP: a deep ensemble pruning algorithm providing high quality uncertainty prediction
Authors: Ruihan Hu, Qijun Huang, Sheng Chang, Hao Wang, Jin He
Date: 2019-02-25 00:00:00
Tasks: BIG-bench Machine Learning, Ensemble Pruning, Prediction

Machine learning algorithms have been effectively applied into various real
world tasks. However, it is difficult to provide high-quality machine learning
solutions to accommodate an unknown distribution of input datasets; this
difficulty is called the uncertainty prediction problems. In this paper, a
margin-based Pareto deep ensemble pruning (MBPEP) model is proposed. It
achieves the high-quality uncertainty estimation with a small value of the
prediction interval width (MPIW) and a high confidence of prediction interval
coverage probability (PICP) by using deep ensemble networks. In addition to
these networks, unique loss functions are proposed, and these functions make
the sub-learners available for standard gradient descent learning. Furthermore,
the margin criterion fine-tuning-based Pareto pruning method is introduced to
optimize the ensembles. Several experiments including predicting uncertainties
of classification and regression are conducted to analyze the performance of
MBPEP. The experimental results show that MBPEP achieves a small interval width
and a low learning error with an optimal number of ensembles. For the
real-world problems, MBPEP performs well on input datasets with unknown
distributions datasets incomings and improves learning performance on a multi
task problem when compared to that of each single model.
A Theoretical Analysis of Contrastive Unsupervised Representation Learning
Authors: Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, Nikunj Saunshi
Date: 2019-02-25 00:00:00
Tasks: Contrastive Learning, General Classification, Representation Learning

Recent empirical works have successfully used unlabeled data to learn feature
representations that are broadly useful in downstream classification tasks.
Several of these methods are reminiscent of the well-known word2vec embedding
algorithm: leveraging availability of pairs of semantically "similar" data
points and "negative samples," the learner forces the inner product of
representations of similar pairs with each other to be higher on average than
with negative samples. The current paper uses the term contrastive learning for
such algorithms and presents a theoretical framework for analyzing them by
introducing latent classes and hypothesizing that semantically similar points
are sampled from the same latent class. This framework allows us to show
provable guarantees on the performance of the learned representations on the
average classification task that is comprised of a subset of the same set of
latent classes. Our generalization bound also shows that learned
representations can reduce (labeled) sample complexity on downstream tasks. We
conduct controlled experiments in both the text and image domains to support
the theory.
Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation
Authors: Soochan Lee, Junsoo Ha, Gunhee Kim
Date: 2019-02-25 00:00:00
Proceeding: ICLR 2019 5
Tasks: Conditional Image Generation, Diversity, Image Generation, Image Inpainting, Image-to-Image Translation, Super-Resolution, Translation

Recent advances in conditional image generation tasks, such as image-to-image
translation and image inpainting, are largely accounted to the success of
conditional GAN models, which are often optimized by the joint use of the GAN
loss with the reconstruction loss. However, we reveal that this training recipe
shared by almost all existing methods causes one critical side effect: lack of
diversity in output samples. In order to accomplish both training stability and
multimodal output generation, we propose novel training schemes with a new set
of losses named moment reconstruction losses that simply replace the
reconstruction loss. We show that our approach is applicable to any conditional
generation tasks by performing thorough experiments on image-to-image
translation, super-resolution and image inpainting using Cityscapes and CelebA
dataset. Quantitative evaluations also confirm that our methods achieve a great
diversity in outputs while retaining or even improving the visual fidelity of
generated samples.
Revealing quantum chaos with machine learning
Authors: Y. A. Kharkov, V. E. Sotskov, A. A. Karazeev, E. O. Kiktenko, A. K. Fedorov
Date: 2019-02-25 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

Understanding properties of quantum matter is an outstanding challenge in science. In this paper, we demonstrate how machine-learning methods can be successfully applied for the classification of various regimes in single-particle and many-body systems. We realize neural network algorithms that perform a classification between regular and chaotic behavior in quantum billiard models with remarkably high accuracy. We use the variational autoencoder for autosupervised classification of regular/chaotic wave functions, as well as demonstrating that variational autoencoders could be used as a tool for detection of anomalous quantum states, such as quantum scars. By taking this method further, we show that machine learning techniques allow us to pin down the transition from integrability to many-body quantum chaos in Heisenberg XXZ spin chains. For both cases, we confirm the existence of universal W shapes that characterize the transition. Our results pave the way for exploring the power of machine learning tools for revealing exotic phenomena in quantum many-body systems.
Faster Genetic Programming GPquick via multicore and Advanced Vector Extensions
Authors: W. B. Langdon, W. Banzhaf
Date: 2019-02-25 00:00:00

We evolve floating point Sextic polynomial populations of genetic programming
binary trees for up to a million generations. Programs with almost four hundred
million instructions are created by crossover. To support unbounded Long-Term
Evolution Experiment LTEE GP we use both SIMD parallel AVX 512 bit instructions
and 48 threads to yield performance of up to 139 billion GP operations per
second, 139 giga GPops, on a single Intel Xeon Gold 6126 2.60GHz server.
Deep High-Resolution Representation Learning for Human Pose Estimation
Authors: Ke Sun, Bin Xiao, Dong Liu, Jingdong Wang
Date: 2019-02-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: 2D Human Pose Estimation, 2D Pose Estimation, 3D Human Pose Estimation, 3D Pose Estimation, Instance Segmentation, Keypoint Detection, Multi-Person Pose Estimation, Object Detection, Pose Estimation, Pose Tracking, Representation Learning, Vocal Bursts Intensity Prediction

This is an official pytorch implementation of Deep High-Resolution
Representation Learning for Human Pose Estimation. In this work, we are
interested in the human pose estimation problem with a focus on learning
reliable high-resolution representations. Most existing methods recover
high-resolution representations from low-resolution representations produced by
a high-to-low resolution network. Instead, our proposed network maintains
high-resolution representations through the whole process. We start from a
high-resolution subnetwork as the first stage, gradually add high-to-low
resolution subnetworks one by one to form more stages, and connect the
mutli-resolution subnetworks in parallel. We conduct repeated multi-scale
fusions such that each of the high-to-low resolution representations receives
information from other parallel representations over and over, leading to rich
high-resolution representations. As a result, the predicted keypoint heatmap is
potentially more accurate and spatially more precise. We empirically
demonstrate the effectiveness of our network through the superior pose
estimation results over two benchmark datasets: the COCO keypoint detection
dataset and the MPII Human Pose dataset. The code and models have been publicly
available at
\url{https://github.com/leoxiaobin/deep-high-resolution-net.pytorch}.