pwc-archive/papers-with-abstracts huggingface dataset:
Global sensitivity analysis for optimization with variable selection
Authors: Adrien Spagnol, Rodolphe Le Riche, Sebastien Da Veiga
Date: 2018-11-12 00:00:00
Tasks: Sensitivity, Variable Selection

The optimization of high dimensional functions is a key issue in engineering
problems but it frequently comes at a cost that is not acceptable since it
usually involves a complex and expensive computer code. Engineers often
overcome this limitation by first identifying which parameters drive the most
the function variations: non-influential variables are set to a fixed value and
the optimization procedure is carried out with the remaining influential
variables. Such variable selection is performed through influence measures that
are meaningful for regression problems. However it does not account for the
specific structure of optimization problems where we would like to identify
which variables most lead to constraints satisfaction and low values of the
objective function. In this paper, we propose a new sensitivity analysis that
accounts for the specific aspects of optimization problems. In particular, we
introduce an influence measure based on the Hilbert-Schmidt Independence
Criterion to characterize whether a design variable matters to reach low values
of the objective function and to satisfy the constraints. This sensitivity
measure makes it possible to sort the inputs and reduce the problem dimension.
We compare a random and a greedy strategies to set the values of the
non-influential variables before conducting a local optimization. Applications
to several test-cases show that this variable selection and the greedy strategy
significantly reduce the number of function evaluations at a limited cost in
terms of solution performance.
Hallucinating very low-resolution and obscured face images
Authors: Lianping Yang, Bin Shao, Ting Sun, Song Ding, Xiangde Zhang
Date: 2018-11-12 00:00:00
Tasks: Face Hallucination, Facial Landmark Detection, Hallucination

Most of the face hallucination methods are designed for complete inputs. They
will not work well if the inputs are very tiny or contaminated by large
occlusion. Inspired by this fact, we propose an obscured face hallucination
network(OFHNet). The OFHNet consists of four parts: an inpainting network, an
upsampling network, a discriminative network, and a fixed facial landmark
detection network. The inpainting network restores the low-resolution(LR)
obscured face images. The following upsampling network is to upsample the
output of inpainting network. In order to ensure the generated
high-resolution(HR) face images more photo-realistic, we utilize the
discriminative network and the facial landmark detection network to better the
result of upsampling network. In addition, we present a semantic structure
loss, which makes the generated HR face images more pleasing. Extensive
experiments show that our framework can restore the appealing HR face images
from 1/4 missing area LR face images with a challenging scaling factor of 8x.
Blind Over-the-Air Computation and Data Fusion via Provable Wirtinger Flow
Authors: Jialin Dong, Yuanming Shi, Zhi Ding
Date: 2018-11-12 00:00:00

Over-the-air computation (AirComp) shows great promise to support fast data
fusion in Internet-of-Things (IoT) networks. AirComp typically computes desired
functions of distributed sensing data by exploiting superposed data
transmission in multiple access channels. To overcome its reliance on channel
station information (CSI), this work proposes a novel blind over-the-air
computation (BlairComp) without requiring CSI access, particularly for low
complexity and low latency IoT networks. To solve the resulting non-convex
optimization problem without the initialization dependency exhibited by the
solutions of a number of recently proposed efficient algorithms, we develop a
Wirtinger flow solution to the BlairComp problem based on random
initialization. To analyze the resulting efficiency, we prove its statistical
optimality and global convergence guarantee. Specifically, in the first stage
of the algorithm, the iteration of randomly initialized Wirtinger flow given
sufficient data samples can enter a local region that enjoys strong convexity
and strong smoothness within a few iterations. We also prove the estimation
error of BlairComp in the local region to be sufficiently small. We show that,
at the second stage of the algorithm, its estimation error decays exponentially
at a linear convergence rate.
Extending Pretrained Segmentation Networks with Additional Anatomical Structures
Authors: Firat Ozdemir, Orcun Goksel
Date: 2018-11-12 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Incremental Learning, Segmentation

Comprehensive surgical planning require complex patient-specific anatomical models. For instance, functional muskuloskeletal simulations necessitate all relevant structures to be segmented, which could be performed in real-time using deep neural networks given sufficient annotated samples. Such large datasets of multiple structure annotations are costly to procure and are often unavailable in practice. Nevertheless, annotations from different studies and centers can be readily available, or become available in the future in an incremental fashion. We propose a class-incremental segmentation framework for extending a deep network trained for some anatomical structure to yet another structure using a small incremental annotation set. Through distilling knowledge from the current state of the framework, we bypass the need for a full retraining. This is a meta-method to extend any choice of desired deep segmentation network with only a minor addition per structure, which makes it suitable for lifelong class-incremental learning and applicable also for future deep neural network architectures. We evaluated our methods on a public knee dataset of 100 MR volumes. Through varying amount of incremental annotation ratios, we show how our proposed method can retain the previous anatomical structure segmentation performance superior to the conventional finetuning approach. In addition, our framework inherently exploits transferable knowledge from previously trained structures to incremental tasks, demonstrated by superior results compared to non-incremental training. With the presented method, new anatomical structures can be learned without catastrophic forgetting of older structures and without extensive increase of memory and complexity.
Angry or Climbing Stairs? Towards Physiological Emotion Recognition in the Wild
Authors: Judith S. Heinisch, Christoph Anderson, Klaus David
Date: 2018-11-12 00:00:00
Tasks: Emotion Recognition

Inferring emotions from physiological signals has gained much traction in the
last years. Physiological responses to emotions, however, are commonly
interfered and overlapped by physical activities, posing a challenge towards
emotion recognition in the wild. In this paper, we address this challenge by
investigating new features and machine-learning models for emotion recognition,
non-sensitive to physical-based interferences. We recorded physiological
signals from 18 participants that were exposed to emotions before and while
performing physical activities to assess the performance of non-sensitive
emotion recognition models. We trained models with the least exhaustive
physical activity (sitting) and tested with the remaining, more exhausting
activities. For three different emotion categories, we achieve classification
accuracies ranging from 47.88% - 73.35% for selected feature sets and per
participant. Furthermore, we investigate the performance across all
participants and of each activity individually. In this regard, we achieve
similar results, between 55.17% and 67.41%, indicating the viability of emotion
recognition models not being influenced by single physical activities.
Parameterized Synthetic Image Data Set for Fisheye Lens
Authors: Zhen Chen, Anthimos Georgiadis
Date: 2018-11-12 00:00:00
Tasks: Diversity

Based on different projection geometry, a fisheye image can be presented as a
parameterized non-rectilinear image. Deep neural networks(DNN) is one of the
solutions to extract parameters for fisheye image feature description. However,
a large number of images are required for training a reasonable prediction
model for DNN. In this paper, we propose to extend the scale of the training
dataset using parameterized synthetic images. It effectively boosts the
diversity of images and avoids the data scale limitation. To simulate different
viewing angles and distances, we adopt controllable parameterized projection
processes on transformation. The reliability of the proposed method is proved
by testing images captured by our fisheye camera. The synthetic dataset is the
first dataset that is able to extend to a big scale labeled fisheye image
dataset. It is accessible via: http://www2.leuphana.de/misl/fisheye-data-set/.
Importance Weighted Evolution Strategies
Authors: Víctor Campos, Xavier Giro-i-Nieto, Jordi Torres
Date: 2018-11-12 00:00:00
Tasks: CPU, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Evolution Strategies (ES) emerged as a scalable alternative to popular
Reinforcement Learning (RL) techniques, providing an almost perfect speedup
when distributed across hundreds of CPU cores thanks to a reduced communication
overhead. Despite providing large improvements in wall-clock time, ES is data
inefficient when compared to competing RL methods. One of the main causes of
such inefficiency is the collection of large batches of experience, which are
discarded after each policy update. In this work, we study how to perform more
than one update per batch of experience by means of Importance Sampling while
preserving the scalability of the original method. The proposed method,
Importance Weighted Evolution Strategies (IW-ES), shows promising results and
is a first step towards designing efficient ES algorithms.
Fine-tuning of Language Models with Discriminator
Authors: Vadim Popov, Mikhail Kudinov
Date: 2018-11-12 00:00:00
Tasks: Language Modeling, Language Modelling

Cross-entropy loss is a common choice when it comes to multiclass
classification tasks and language modeling in particular. Minimizing this loss
results in language models of very good quality. We show that it is possible to
fine-tune these models and make them perform even better if they are fine-tuned
with sum of cross-entropy loss and reverse Kullback-Leibler divergence. The
latter is estimated using discriminator network that we train in advance.
During fine-tuning probabilities of rare words that are usually underestimated
by language models become bigger. The novel approach that we propose allows us
to reach state-of-the-art quality on Penn Treebank: perplexity decreases from
52.4 to 52.1. Our fine-tuning algorithm is rather fast, scales well to
different architectures and datasets and requires almost no hyperparameter
tuning: the only hyperparameter that needs to be tuned is learning rate.
Depth Image Upsampling based on Guided Filter with Low Gradient Minimization
Authors: Hang Yang, Zhongbo Zhang
Date: 2018-11-12 00:00:00
Tasks: Depth Image Upsampling

In this paper, we present a novel upsampling framework to enhance the spatial
resolution of the depth image. In our framework, the upscaling of a
low-resolution depth image is guided by a corresponding intensity images, we
formulate it as a cost aggregation problem with the guided filter. However, the
guided filter does not make full use of the properties of the depth image.
Since depth images have quite sparse gradients, it inspires us to regularize
the gradients for improving depth upscaling results. Statistics show a special
property of depth images, that is, there is a non-ignorable part of pixels
whose horizontal or vertical derivatives are equal to $\pm 1$. Considering this
special property, we propose a low gradient regularization method which reduces
the penalty for horizontal or vertical derivative $\pm1$. The proposed low
gradient regularization is integrated with the guided filter into the depth
image upsampling method. Experimental results demonstrate the effectiveness of
our proposed approach both qualitatively and quantitatively compared with the
state-of-the-art methods.
Matrix Product Operator Restricted Boltzmann Machines
Authors: Cong Chen, Kim Batselier, Ching-Yun Ko, Ngai Wong
Date: 2018-11-12 00:00:00
Tasks: Denoising, Dimensionality Reduction, General Classification

A restricted Boltzmann machine (RBM) learns a probability distribution over
its input samples and has numerous uses like dimensionality reduction,
classification and generative modeling. Conventional RBMs accept vectorized
data that dismisses potentially important structural information in the
original tensor (multi-way) input. Matrix-variate and tensor-variate RBMs,
named MvRBM and TvRBM, have been proposed but are all restrictive by model
construction, which leads to a weak model expression power. This work presents
the matrix product operator RBM (MPORBM) that utilizes a tensor network
generalization of Mv/TvRBM, preserves input formats in both the visible and
hidden layers, and results in higher expressive power. A novel training
algorithm integrating contrastive divergence and an alternating optimization
procedure is also developed. Numerical experiments compare the MPORBM with the
traditional RBM and MvRBM for data classification and image completion and
denoising tasks. The expressive power of the MPORBM as a function of the
MPO-rank is also investigated.
Learning Personalized End-to-End Goal-Oriented Dialog
Authors: Liangchen Luo, Wenhao Huang, Qi Zeng, Zaiqing Nie, Xu sun
Date: 2018-11-12 00:00:00
Tasks: Goal-Oriented Dialog

Most existing works on dialog systems only consider conversation content
while neglecting the personality of the user the bot is interacting with, which
begets several unsolved issues. In this paper, we present a personalized
end-to-end model in an attempt to leverage personalization in goal-oriented
dialogs. We first introduce a Profile Model which encodes user profiles into
distributed embeddings and refers to conversation history from other similar
users. Then a Preference Model captures user preferences over knowledge base
entities to handle the ambiguity in user requests. The two models are combined
into the Personalized MemN2N. Experiments show that the proposed model achieves
qualitative performance improvements over state-of-the-art methods. As for
human evaluation, it also outperforms other approaches in terms of task
completion rate and user satisfaction.
Learning The Invisible: A Hybrid Deep Learning-Shearlet Framework for Limited Angle Computed Tomography
Authors: T. A. Bubba, G. Kutyniok, M. Lassas, M. März, W. Samek, S. Siltanen, V. Srinivasan
Date: 2018-11-12 00:00:00

The high complexity of various inverse problems poses a significant challenge
to model-based reconstruction schemes, which in such situations often reach
their limits. At the same time, we witness an exceptional success of data-based
methodologies such as deep learning. However, in the context of inverse
problems, deep neural networks mostly act as black box routines, used for
instance for a somewhat unspecified removal of artifacts in classical image
reconstructions. In this paper, we will focus on the severely ill-posed inverse
problem of limited angle computed tomography, in which entire boundary sections
are not captured in the measurements. We will develop a hybrid reconstruction
framework that fuses model-based sparse regularization with data-driven deep
learning. Our method is reliable in the sense that we only learn the part that
can provably not be handled by model-based methods, while applying the
theoretically controllable sparse regularization technique to the remaining
parts. Such a decomposition into visible and invisible segments is achieved by
means of the shearlet transform that allows to resolve wavefront sets in the
phase space. Furthermore, this split enables us to assign the clear task of
inferring unknown shearlet coefficients to the neural network and thereby
offering an interpretation of its performance in the context of limited angle
computed tomography. Our numerical experiments show that our algorithm
significantly surpasses both pure model- and more data-based reconstruction
methods.
The Cinderella Complex: Word Embeddings Reveal ender Stereotypes in Movies and Books
Authors: Huimin Xu, Zhang Zhang, Lingfei Wu, Cheng-Jun Wang
Date: 2018-11-12 00:00:00
Tasks: Word Embeddings

Our analysis of thousands of movies and books reveals how these cultural products weave stereotypical gender roles into morality tales and perpetuate gender inequality through storytelling. Using the word embedding techniques, we reveal the constructed emotional dependency of female characters on male characters in stories.
Holistic Multi-modal Memory Network for Movie Question Answering
Authors: Anran Wang, Anh Tuan Luu, Chuan-Sheng Foo, Hongyuan Zhu, Yi Tay, Vijay Chandrasekhar
Date: 2018-11-12 00:00:00
Tasks: Question Answering, Retrieval, Video Question Answering

Answering questions according to multi-modal context is a challenging problem
as it requires a deep integration of different data sources. Existing
approaches only employ partial interactions among data sources in one attention
hop. In this paper, we present the Holistic Multi-modal Memory Network (HMMN)
framework which fully considers the interactions between different input
sources (multi-modal context, question) in each hop. In addition, it takes
answer choices into consideration during the context retrieval stage.
Therefore, the proposed framework effectively integrates multi-modal context,
question, and answer information, which leads to more informative context
retrieved for question answering. Our HMMN framework achieves state-of-the-art
accuracy on MovieQA dataset. Extensive ablation studies show the importance of
holistic reasoning and contributions of different attention strategies.
Differentiating Concepts and Instances for Knowledge Graph Embedding
Authors: Xin Lv, Lei Hou, Juanzi Li, Zhiyuan Liu
Date: 2018-11-12 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction, Triple Classification

Concepts, which represent a group of different instances sharing common
properties, are essential information in knowledge representation. Most
conventional knowledge embedding methods encode both entities (concepts and
instances) and relations as vectors in a low dimensional semantic space
equally, ignoring the difference between concepts and instances. In this paper,
we propose a novel knowledge graph embedding model named TransC by
differentiating concepts and instances. Specifically, TransC encodes each
concept in knowledge graph as a sphere and each instance as a vector in the
same semantic space. We use the relative positions to model the relations
between concepts and instances (i.e., instanceOf), and the relations between
concepts and sub-concepts (i.e., subClassOf). We evaluate our model on both
link prediction and triple classification tasks on the dataset based on YAGO.
Experimental results show that TransC outperforms state-of-the-art methods, and
captures the semantic transitivity for instanceOf and subClassOf relation. Our
codes and datasets can be obtained from https:// github.com/davidlvxin/TransC.
Navigating Assistance System for Quadcopter with Deep Reinforcement Learning
Authors: Tung-Cheng Wu, Shau-Yin Tseng, Chin-Feng Lai, Chia-Yu Ho, Ying-Hsun Lai
Date: 2018-11-12 00:00:00
Tasks: Collision Avoidance, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we present a deep reinforcement learning method for quadcopter
bypassing the obstacle on the flying path. In the past study, the algorithm
only controls the forward direction about quadcopter. In this letter, we use
two functions to control quadcopter. One is quadcopter navigating function. It
is based on calculating coordination point and find the straight path to the
goal. The other function is collision avoidance function. It is implemented by
deep Q-network model. Both two function will output rotating degree, the agent
will combine both output and turn direct. Besides, deep Q-network can also make
quadcopter fly up and down to bypass the obstacle and arrive at the goal. Our
experimental result shows that the collision rate is 14% after 500 flights.
Based on this work, we will train more complex sense and transfer model to the
real quadcopter.
Forecasting People's Needs in Hurricane Events from Social Network
Authors: Long Nguyen, Zhou Yang, Jia Li, Guofeng Cao, Fang Jin
Date: 2018-11-12 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, Management

Social networks can serve as a valuable communication channel for calls for help, offering assistance, and coordinating rescue activities in disaster. Social networks such as Twitter allow users to continuously update relevant information, which is especially useful during a crisis, where the rapidly changing conditions make it crucial to be able to access accurate information promptly. Social media helps those directly affected to inform others of conditions on the ground in real time and thus enables rescue workers to coordinate their efforts more effectively, better meeting the survivors' need. This paper presents a new sequence to sequence based framework for forecasting people's needs during disasters using social media and weather data. It consists of two Long Short-Term Memory (LSTM) models, one of which encodes input sequences of weather information and the other plays as a conditional decoder that decodes the encoded vector and forecasts the survivors' needs. Case studies utilizing data collected during Hurricane Sandy in 2012, Hurricane Harvey and Hurricane Irma in 2017 were analyzed and the results compared with those obtained using a statistical language model n-gram and an LSTM generative model. Our proposed sequence to sequence method forecast people's needs more successfully than either of the other models. This new approach shows great promise for enhancing disaster management activities such as evacuation planning and commodity flow management.
Estimation of Dimensions Contributing to Detected Anomalies with Variational Autoencoders
Authors: Yasuhiro Ikeda, Kengo Tajiri, Yuusuke Nakano, Keishiro Watanabe, Keisuke Ishibashi
Date: 2018-11-12 00:00:00
Tasks: Anomaly Detection, Dimensionality Reduction

Anomaly detection using dimensionality reduction has been an essential
technique for monitoring multidimensional data. Although deep learning-based
methods have been well studied for their remarkable detection performance,
their interpretability is still a problem. In this paper, we propose a novel
algorithm for estimating the dimensions contributing to the detected anomalies
by using variational autoencoders (VAEs). Our algorithm is based on an
approximative probabilistic model that considers the existence of anomalies in
the data, and by maximizing the log-likelihood, we estimate which dimensions
contribute to determining data as an anomaly. The experiments results with
benchmark datasets show that our algorithm extracts the contributing dimensions
more accurately than baseline methods.