pwc-archive/papers-with-abstracts huggingface dataset:
Simplifying Probabilistic Expressions in Causal Inference
Authors: Santtu Tikka, Juha Karvanen
Date: 2018-06-19 00:00:00
Tasks: Causal Inference

Obtaining a non-parametric expression for an interventional distribution is
one of the most fundamental tasks in causal inference. Such an expression can
be obtained for an identifiable causal effect by an algorithm or by manual
application of do-calculus. Often we are left with a complicated expression
which can lead to biased or inefficient estimates when missing data or
measurement errors are involved. We present an automatic simplification
algorithm that seeks to eliminate symbolically unnecessary variables from these
expressions by taking advantage of the structure of the underlying graphical
model. Our method is applicable to all causal effect formulas and is readily
available in the R package causaleffect.
Learning to Update for Object Tracking with Recurrent Meta-learner
Authors: Bi Li, Wenxuan Xie, Wen-Jun Zeng, Wenyu Liu
Date: 2018-06-19 00:00:00
Tasks: GPU, Meta-Learning, Object Tracking, Visual Object Tracking, Visual Tracking

Model update lies at the heart of object tracking. Generally, model update is
formulated as an online learning problem where a target model is learned over
the online training set. Our key innovation is to \emph{formulate the model
update problem in the meta-learning framework and learn the online learning
algorithm itself using large numbers of offline videos}, i.e., \emph{learning
to update}. The learned updater takes as input the online training set and
outputs an updated target model. As a first attempt, we design the learned
updater based on recurrent neural networks (RNNs) and demonstrate its
application in a template-based tracker and a correlation filter-based tracker.
Our learned updater consistently improves the base trackers and runs faster
than realtime on GPU while requiring small memory footprint during testing.
Experiments on standard benchmarks demonstrate that our learned updater
outperforms commonly used update baselines including the efficient exponential
moving average (EMA)-based update and the well-designed stochastic gradient
descent (SGD)-based update. Equipped with our learned updater, the
template-based tracker achieves state-of-the-art performance among realtime
trackers on GPU.
Transfer Learning with Human Corneal Tissues: An Analysis of Optimal Cut-Off Layer
Authors: Nadezhda Prodanova, Johannes Stegmaier, Stephan Allgeier, Sebastian Bohn, Oliver Stachs, Bernd Köhler, Ralf Mikut, Andreas Bartschat
Date: 2018-06-19 00:00:00
Tasks: Classification, General Classification, Transfer Learning

Transfer learning is a powerful tool to adapt trained neural networks to new
tasks. Depending on the similarity of the original task to the new task, the
selection of the cut-off layer is critical. For medical applications like
tissue classification, the last layers of an object classification network
might not be optimal. We found that on real data of human corneal tissues the
best feature representation can be found in the middle layers of the
Inception-v3 and in the rear layers of the VGG-19 architecture.
A New COLD Feature based Handwriting Analysis for Ethnicity/Nationality Identification
Authors: Sauradip Nag, Palaiahnakote Shivakumara, Wu Yirui, Umapada Pal, Tong Lu
Date: 2018-06-19 00:00:00

Identifying crime for forensic investigating teams when crimes involve people
of different nationals is challenging. This paper proposes a new method for
ethnicity (nationality) identification based on Cloud of Line Distribution
(COLD) features of handwriting components. The proposed method, at first,
explores tangent angle for the contour pixels in each row and the mean of
intensity values of each row in an image for segmenting text lines. For
segmented text lines, we use tangent angle and direction of base lines to
remove rule lines in the image. We use polygonal approximation for finding
dominant points for contours of edge components. Then the proposed method
connects the nearest dominant points of every dominant point, which results in
line segments of dominant point pairs. For each line segment, the proposed
method estimates angle and length, which gives a point in polar domain. For all
the line segments, the proposed method generates dense points in polar domain,
which results in COLD distribution. As character component shapes change,
according to nationals, the shape of the distribution changes. This observation
is extracted based on distance from pixels of distribution to Principal Axis of
the distribution. Then the features are subjected to an SVM classifier for
identifying nationals. Experiments are conducted on a complex dataset, which
show the proposed method is effective and outperforms the existing method
Restricted Boltzmann Machines: Introduction and Review
Authors: Guido Montufar
Date: 2018-06-19 00:00:00
Tasks: BIG-bench Machine Learning

The restricted Boltzmann machine is a network of stochastic units with
undirected interactions between pairs of visible and hidden units. This model
was popularized as a building block of deep learning architectures and has
continued to play an important role in applied and theoretical machine
learning. Restricted Boltzmann machines carry a rich structure, with
connections to geometry, applied algebra, probability, statistics, machine
learning, and other areas. The analysis of these models is attractive in its
own right and also as a platform to combine and generalize mathematical tools
for graphical models with hidden variables. This article gives an introduction
to the mathematical analysis of restricted Boltzmann machines, reviews recent
results on the geometry of the sets of probability distributions representable
by these models, and suggests a few directions for further investigation.
Cancer Metastasis Detection With Neural Conditional Random Field
Authors: Yi Li, Wei Ping
Date: 2018-06-19 00:00:00
Tasks: Cancer Metastasis Detection, Medical Image Analysis, whole slide images

Breast cancer diagnosis often requires accurate detection of metastasis in
lymph nodes through Whole-slide Images (WSIs). Recent advances in deep
convolutional neural networks (CNNs) have shown significant successes in
medical image analysis and particularly in computational histopathology.
Because of the outrageous large size of WSIs, most of the methods divide one
slide into lots of small image patches and perform classification on each patch
independently. However, neighboring patches often share spatial correlations,
and ignoring these spatial correlations may result in inconsistent predictions.
In this paper, we propose a neural conditional random field (NCRF) deep
learning framework to detect cancer metastasis in WSIs. NCRF considers the
spatial correlations between neighboring patches through a fully connected CRF
which is directly incorporated on top of a CNN feature extractor. The whole
deep network can be trained end-to-end with standard back-propagation algorithm
with minor computational overhead from the CRF component. The CNN feature
extractor can also benefit from considering spatial correlations via the CRF
component. Compared to the baseline method without considering spatial
correlations, we show that the proposed NCRF framework obtains probability maps
of patch predictions with better visual quality. We also demonstrate that our
method outperforms the baseline in cancer metastasis detection on the
Camelyon16 dataset and achieves an average FROC score of 0.8096 on the test
set. NCRF is open sourced at https://github.com/baidu-research/NCRF.
Effect of Hyper-Parameter Optimization on the Deep Learning Model Proposed for Distributed Attack Detection in Internet of Things Environment
Authors: Md Mohaimenuzzaman, Zahraa Said Abdallah, Joarder Kamruzzaman, Bala Srinivasan
Date: 2018-06-19 00:00:00

This paper studies the effect of various hyper-parameters and their selection
for the best performance of the deep learning model proposed in [1] for
distributed attack detection in the Internet of Things (IoT). The findings show
that there are three hyper-parameters that have more influence on the best
performance achieved by the model. As a consequence, this study shows that the
model's accuracy as reported in the paper is not achievable, based on the best
selections of parameters, which is also supported by another recent publication
[2].
MoE-SPNet: A Mixture-of-Experts Scene Parsing Network
Authors: Huan Fu, Mingming Gong, Chaohui Wang, DaCheng Tao
Date: 2018-06-19 00:00:00
Tasks: Mixture-of-Experts, Scene Parsing

Scene parsing is an indispensable component in understanding the semantics
within a scene. Traditional methods rely on handcrafted local features and
probabilistic graphical models to incorporate local and global cues. Recently,
methods based on fully convolutional neural networks have achieved new records
on scene parsing. An important strategy common to these methods is the
aggregation of hierarchical features yielded by a deep convolutional neural
network. However, typical algorithms usually aggregate hierarchical
convolutional features via concatenation or linear combination, which cannot
sufficiently exploit the diversities of contextual information in multi-scale
features and the spatial inhomogeneity of a scene. In this paper, we propose a
mixture-of-experts scene parsing network (MoE-SPNet) that incorporates a
convolutional mixture-of-experts layer to assess the importance of features
from different levels and at different spatial locations. In addition, we
propose a variant of mixture-of-experts called the adaptive hierarchical
feature aggregation (AHFA) mechanism which can be incorporated into existing
scene parsing networks that use skip-connections to fuse features layer-wisely.
In the proposed networks, different levels of features at each spatial location
are adaptively re-weighted according to the local structure and surrounding
contextual information before aggregation. We demonstrate the effectiveness of
the proposed methods on two scene parsing datasets including PASCAL VOC 2012
and SceneParse150 based on two kinds of baseline models FCN-8s and
DeepLab-ASPP.
Response Generation by Context-aware Prototype Editing
Authors: Yu Wu, Furu Wei, Shaohan Huang, Yunli Wang, Zhoujun Li, Ming Zhou
Date: 2018-06-19 00:00:00
Tasks: Decoder, Diversity, Informativeness, Response Generation, Retrieval

Open domain response generation has achieved remarkable progress in recent
years, but sometimes yields short and uninformative responses. We propose a new
paradigm for response generation, that is response generation by editing, which
significantly increases the diversity and informativeness of the generation
results. Our assumption is that a plausible response can be generated by
slightly revising an existing response prototype. The prototype is retrieved
from a pre-defined index and provides a good start-point for generation because
it is grammatical and informative. We design a response editing model, where an
edit vector is formed by considering differences between a prototype context
and a current context, and then the edit vector is fed to a decoder to revise
the prototype response for the current context. Experiment results on a large
scale dataset demonstrate that the response editing model outperforms
generative and retrieval-based models on various aspects.
EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogue
Authors: Linkai Luo, Haiqing Yang, Francis Y. L. Chin
Date: 2018-06-19 00:00:00
Tasks: Emotion Classification, General Classification

In this paper, we propose a self-attentive bidirectional long short-term
memory (SA-BiLSTM) network to predict multiple emotions for the EmotionX
challenge. The BiLSTM exhibits the power of modeling the word dependencies, and
extracting the most relevant features for emotion classification. Building on
top of BiLSTM, the self-attentive network can model the contextual dependencies
between utterances which are helpful for classifying the ambiguous emotions. We
achieve 59.6 and 55.0 unweighted accuracy scores in the \textit{Friends} and
the \textit{EmotionPush} test sets, respectively.
Translating MFM into FOL: towards plant operation planning
Authors: Shota Motoura, Kazeto Yamamoto, Shumpei Kubosawa, Takashi Onishi
Date: 2018-06-19 00:00:00

This paper proposes a method to translate multilevel flow modeling (MFM) into
a first-order language (FOL), which enables the utilisation of logical
techniques, such as inference engines and abductive reasoners. An example of
this is a planning task for a toy plant that can be solved in FOL using
abduction. In addition, owing to the expressivity of FOL, the language is
capable of describing actions and their preconditions. This allows the
derivation of procedures consisting of multiple actions.
Deep neural network based sparse measurement matrix for image compressed sensing
Authors: Wenxue Cui, Feng Jiang, Xinwei Gao, Wen Tao, Debin Zhao
Date: 2018-06-19 00:00:00
Tasks: compressed sensing, Image Compressed Sensing

Gaussian random matrix (GRM) has been widely used to generate linear
measurements in compressed sensing (CS) of natural images. However, there
actually exist two disadvantages with GRM in practice. One is that GRM has
large memory requirement and high computational complexity, which restrict the
applications of CS. Another is that the CS measurements randomly obtained by
GRM cannot provide sufficient reconstruction performances. In this paper, a
Deep neural network based Sparse Measurement Matrix (DSMM) is learned by the
proposed convolutional network to reduce the sampling computational complexity
and improve the CS reconstruction performance. Two sub networks are included in
the proposed network, which are the sampling sub-network and the reconstruction
sub-network. In the sampling sub-network, the sparsity and the normalization
are both considered by the limitation of the storage and the computational
complexity. In order to improve the CS reconstruction performance, a
reconstruction sub-network are introduced to help enhance the sampling
sub-network. So by the offline iterative training of the proposed end-to-end
network, the DSMM is generated for accurate measurement and excellent
reconstruction. Experimental results demonstrate that the proposed DSMM
outperforms GRM greatly on representative CS reconstruction methods
On the Metric Distortion of Embedding Persistence Diagrams into separable Hilbert spaces
Authors: Mathieu Carriere, Ulrich Bauer
Date: 2018-06-19 00:00:00
Tasks: Topological Data Analysis

Persistence diagrams are important descriptors in Topological Data Analysis. Due to the nonlinearity of the space of persistence diagrams equipped with their {\em diagram distances}, most of the recent attempts at using persistence diagrams in machine learning have been done through kernel methods, i.e., embeddings of persistence diagrams into Reproducing Kernel Hilbert Spaces, in which all computations can be performed easily. Since persistence diagrams enjoy theoretical stability guarantees for the diagram distances, the {\em metric properties} of the feature map, i.e., the relationship between the Hilbert distance and the diagram distances, are of central interest for understanding if the persistence diagram guarantees carry over to the embedding. In this article, we study the possibility of embedding persistence diagrams into separable Hilbert spaces, with bi-Lipschitz maps. In particular, we show that for several stable embeddings into infinite-dimensional Hilbert spaces defined in the literature, any lower bound must depend on the cardinalities of the persistence diagrams, and that when the Hilbert space is finite dimensional, finding a bi-Lipschitz embedding is impossible, even when restricting the persistence diagrams to have bounded cardinalities.
VirtualHome: Simulating Household Activities via Programs
Authors: Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler, Antonio Torralba
Date: 2018-06-19 00:00:00
Proceeding: CVPR 2018 6
Tasks: Video Understanding

In this paper, we are interested in modeling complex activities that occur in
a typical household. We propose to use programs, i.e., sequences of atomic
actions and interactions, as a high level representation of complex tasks.
Programs are interesting because they provide a non-ambiguous representation of
a task, and allow agents to execute them. However, nowadays, there is no
database providing this type of information. Towards this goal, we first
crowd-source programs for a variety of activities that happen in people's
homes, via a game-like interface used for teaching kids how to code. Using the
collected dataset, we show how we can learn to extract programs directly from
natural language descriptions or from videos. We then implement the most common
atomic (inter)actions in the Unity3D game engine, and use our programs to
"drive" an artificial agent to execute tasks in a simulated household
environment. Our VirtualHome simulator allows us to create a large activity
video dataset with rich ground-truth, enabling training and testing of video
understanding models. We further showcase examples of our agent performing
tasks in our VirtualHome based on language descriptions.
Maximally Invariant Data Perturbation as Explanation
Authors: Satoshi Hara, Kouichi Ikeno, Tasuku Soma, Takanori Maehara
Date: 2018-06-19 00:00:00
Tasks: image-classification, Image Classification

While several feature scoring methods are proposed to explain the output of
complex machine learning models, most of them lack formal mathematical
definitions. In this study, we propose a novel definition of the feature score
using the maximally invariant data perturbation, which is inspired from the
idea of adversarial example. In adversarial example, one seeks the smallest
data perturbation that changes the model's output. In our proposed approach, we
consider the opposite: we seek the maximally invariant data perturbation that
does not change the model's output. In this way, we can identify important
input features as the ones with small allowable data perturbations. To find the
maximally invariant data perturbation, we formulate the problem as linear
programming. The experiment on the image classification with VGG16 shows that
the proposed method could identify relevant parts of the images effectively.
Theoretical Analysis of Image-to-Image Translation with Adversarial Learning
Authors: Xudong Pan, Mi Zhang, Daizong Ding
Date: 2018-06-19 00:00:00
Proceeding: ICML 2018 7
Tasks: Image-to-Image Translation, Translation

Recently, a unified model for image-to-image translation tasks within
adversarial learning framework has aroused widespread research interests in
computer vision practitioners. Their reported empirical success however lacks
solid theoretical interpretations for its inherent mechanism. In this paper, we
reformulate their model from a brand-new geometrical perspective and have
eventually reached a full interpretation on some interesting but unclear
empirical phenomenons from their experiments. Furthermore, by extending the
definition of generalization for generative adversarial nets to a broader
sense, we have derived a condition to control the generalization capability of
their model. According to our derived condition, several practical suggestions
have also been proposed on model design and dataset construction as a guidance
for further empirical researches.
A Syntactically Constrained Bidirectional-Asynchronous Approach for Emotional Conversation Generation
Authors: Jingyuan Li, Xiao Sun
Date: 2018-06-19 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Diversity

Traditional neural language models tend to generate generic replies with poor
logic and no emotion. In this paper, a syntactically constrained
bidirectional-asynchronous approach for emotional conversation generation
(E-SCBA) is proposed to address this issue. In our model, pre-generated emotion
keywords and topic keywords are asynchronously introduced into the process of
decoding. It is much different from most existing methods which generate
replies from the first word to the last. Through experiments, the results
indicate that our approach not only improves the diversity of replies, but
gains a boost on both logic and emotion compared with baselines.
Private Text Classification
Authors: Leif W. Hanlen, Richard Nock, Hanna Suominen, Neil Bacon
Date: 2018-06-19 00:00:00
Tasks: Classification, General Classification, Privacy Preserving, text-classification, Text Classification

Confidential text corpora exist in many forms, but do not allow arbitrary
sharing. We explore how to use such private corpora using privacy preserving
text analytics. We construct typical text processing applications using
appropriate privacy preservation techniques (including homomorphic encryption,
Rademacher operators and secure computation). We set out the preliminary
materials from Rademacher operators for binary classifiers, and then construct
basic text processing approaches to match those binary classifiers.
Deep Neural Decision Trees
Authors: Yongxin Yang, Irene Garcia Morillo, Timothy M. Hospedales
Date: 2018-06-19 00:00:00

Deep neural networks have been proven powerful at processing perceptual data,
such as images and audio. However for tabular data, tree-based models are more
popular. A nice property of tree-based models is their natural
interpretability. In this work, we present Deep Neural Decision Trees (DNDT) --
tree models realised by neural networks. A DNDT is intrinsically interpretable,
as it is a tree. Yet as it is also a neural network (NN), it can be easily
implemented in NN toolkits, and trained with gradient descent rather than
greedy splitting. We evaluate DNDT on several tabular datasets, verify its
efficacy, and investigate similarities and differences between DNDT and vanilla
decision trees. Interestingly, DNDT self-prunes at both split and
feature-level.
Fast Multiple Landmark Localisation Using a Patch-based Iterative Network
Authors: Yuanwei Li, Amir Alansary, Juan J. Cerrolaza, Bishesh Khanal, Matthew Sinclair, Jacqueline Matthew, Chandni Gupta, Caroline Knight, Bernhard Kainz, Daniel Rueckert
Date: 2018-06-18 00:00:00
Tasks: Multi-Task Learning

We propose a new Patch-based Iterative Network (PIN) for fast and accurate
landmark localisation in 3D medical volumes. PIN utilises a Convolutional
Neural Network (CNN) to learn the spatial relationship between an image patch
and anatomical landmark positions. During inference, patches are repeatedly
passed to the CNN until the estimated landmark position converges to the true
landmark location. PIN is computationally efficient since the inference stage
only selectively samples a small number of patches in an iterative fashion
rather than a dense sampling at every location in the volume. Our approach
adopts a multi-task learning framework that combines regression and
classification to improve localisation accuracy. We extend PIN to localise
multiple landmarks by using principal component analysis, which models the
global anatomical relationships between landmarks. We have evaluated PIN using
72 3D ultrasound images from fetal screening examinations. PIN achieves
quantitatively an average landmark localisation error of 5.59mm and a runtime
of 0.44s to predict 10 landmarks per volume. Qualitatively, anatomical 2D
standard scan planes derived from the predicted landmark locations are visually
similar to the clinical ground truth. Source code is publicly available at
https://github.com/yuanwei1989/landmark-detection.