pwc-archive/papers-with-abstracts huggingface dataset:
On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference
Authors: Adam Poliak, Yonatan Belinkov, James Glass, Benjamin Van Durme
Date: 2018-04-25 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, Natural Language Inference, NMT, Sentence, Translation, World Knowledge

We propose a process for investigating the extent to which sentence
representations arising from neural machine translation (NMT) systems encode
distinct semantic phenomena. We use these representations as features to train
a natural language inference (NLI) classifier based on datasets recast from
existing semantic annotations. In applying this process to a representative NMT
system, we find its encoder appears most suited to supporting inferences at the
syntax-semantics interface, as compared to anaphora resolution requiring
world-knowledge. We conclude with a discussion on the merits and potential
deficiencies of the existing process, and how it may be improved and extended
as a broader framework for evaluating semantic coverage.
Recurrent Deterministic Policy Gradient Method for Bipedal Locomotion on Rough Terrain Challenge
Authors: Doo Re Song, Chuanyu Yang, Christopher McGreavy, Zhibin Li
Date: 2017-10-08 00:00:00

This paper presents a deep learning framework that is capable of solving partially observable locomotion tasks based on our novel interpretation of Recurrent Deterministic Policy Gradient (RDPG). We study on bias of sampled error measure and its variance induced by the partial observability of environment and subtrajectory sampling, respectively. Three major improvements are introduced in our RDPG based learning framework: tail-step bootstrap of interpolated temporal difference, initialisation of hidden state using past trajectory scanning, and injection of external experiences learned by other agents. The proposed learning framework was implemented to solve the Bipedal-Walker challenge in OpenAI's gym simulation environment where only partial state information is available. Our simulation study shows that the autonomous behaviors generated by the RDPG agent are highly adaptive to a variety of obstacles and enables the agent to effectively traverse rugged terrains for long distance with higher success rate than leading contenders.
Reachability Analysis of Deep Neural Networks with Provable Guarantees
Authors: Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska
Date: 2018-05-06 00:00:00

Verifying correctness of deep neural networks (DNNs) is challenging. We study
a generic reachability problem for feed-forward DNNs which, for a given set of
inputs to the network and a Lipschitz-continuous function over its outputs,
computes the lower and upper bound on the function values. Because the network
and the function are Lipschitz continuous, all values in the interval between
the lower and upper bound are reachable. We show how to obtain the safety
verification problem, the output range analysis problem and a robustness
measure by instantiating the reachability problem. We present a novel algorithm
based on adaptive nested optimisation to solve the reachability problem. The
technique has been implemented and evaluated on a range of DNNs, demonstrating
its efficiency, scalability and ability to handle a broader class of networks
than state-of-the-art verification approaches.
Acquisition and use of knowledge over a restricted domain by intelligent agents
Authors: Juliao Braga, Nizam Omar, Luciana F. Thome
Date: 2018-05-06 00:00:00
Tasks: Position

This short paper provides a description of an architecture to acquisition and
use of knowledge by intelligent agents over a restricted domain of the Internet
Infrastructure. The proposed architecture is added to an intelligent agent
deployment model over a very useful server for Internet Autonomous System
administrators. Such servers, which are heavily dependent on arbitrary and
eventual updates of human beings, become unreliable. This is a position paper
that proposes three research questions that are still in progress.
FASK with Interventional Knowledge Recovers Edges from the Sachs Model
Authors: Joseph Ramsey, Bryan Andrews
Date: 2018-05-06 00:00:00

We report a procedure that, in one step from continuous data with minimal
preparation, recovers the graph found by Sachs et al. \cite{sachs2005causal},
with only a few edges different. The algorithm, Fast Adjacency Skewness (FASK),
relies on a mixture of linear reasoning and reasoning from the skewness of
variables; the Sachs data is a good candidate for this procedure since the
skewness of the variables is quite pronounced. We review the ground truth model
from Sachs et al. as well as some of the fluctuations seen in the protein
abundances in the system, give the Sachs model and the FASK model, and perform
a detailed comparison. Some variation in hyper-parameters is explored, though
the main result uses values at or near the defaults learned from work modeling
fMRI data.
Zero-shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens
Authors: Marek Rei, Anders Søgaard
Date: 2018-05-06 00:00:00
Proceeding: NAACL 2018 6
Tasks: Sentence

Can attention- or gradient-based visualization techniques be used to infer
token-level labels for binary sequence tagging problems, using networks trained
only on sentence-level labels? We construct a neural network architecture based
on soft attention, train it as a binary sentence classifier and evaluate
against token-level annotation on four different datasets. Inferring token
labels from a network provides a method for quantitatively evaluating what the
model is learning, along with generating useful feedback in assistance systems.
Our results indicate that attention-based methods are able to predict
token-level labels more accurately, compared to gradient-based methods,
sometimes even rivaling the supervised oracle network.
Appearance-and-Relation Networks for Video Classification
Authors: Limin Wang, Wei Li, Wen Li, Luc van Gool
Date: 2017-11-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Classification, Action Recognition, Classification, General Classification, Relation, Relation Network, Temporal Action Localization, Video Classification

Spatiotemporal feature learning in videos is a fundamental problem in
computer vision. This paper presents a new architecture, termed as
Appearance-and-Relation Network (ARTNet), to learn video representation in an
end-to-end manner. ARTNets are constructed by stacking multiple generic
building blocks, called as SMART, whose goal is to simultaneously model
appearance and relation from RGB input in a separate and explicit manner.
Specifically, SMART blocks decouple the spatiotemporal learning module into an
appearance branch for spatial modeling and a relation branch for temporal
modeling. The appearance branch is implemented based on the linear combination
of pixels or filter responses in each frame, while the relation branch is
designed based on the multiplicative interactions between pixels or filter
responses across multiple frames. We perform experiments on three action
recognition benchmarks: Kinetics, UCF101, and HMDB51, demonstrating that SMART
blocks obtain an evident improvement over 3D convolutions for spatiotemporal
feature learning. Under the same training setting, ARTNets achieve superior
performance on these three datasets to the existing state-of-the-art methods.
Dynamic and Static Topic Model for Analyzing Time-Series Document Collections
Authors: Rem Hida, Naoya Takeishi, Takehisa Yairi, Koichi Hori
Date: 2018-05-06 00:00:00
Proceeding: ACL 2018 7
Tasks: Articles, Time Series, Time Series Analysis

For extracting meaningful topics from texts, their structures should be
considered properly. In this paper, we aim to analyze structured time-series
documents such as a collection of news articles and a series of scientific
papers, wherein topics evolve along time depending on multiple topics in the
past and are also related to each other at each time. To this end, we propose a
dynamic and static topic model, which simultaneously considers the dynamic
structures of the temporal topic evolution and the static structures of the
topic hierarchy at each time. We show the results of experiments on collections
of scientific papers, in which the proposed method outperformed conventional
models. Moreover, we show an example of extracted topic structures, which we
found helpful for analyzing research activities.
A review of neuro-fuzzy systems based on intelligent control
Authors: Fatemeh Zahedi, Zahra Zahedi
Date: 2018-05-06 00:00:00

The system's ability to adapt and self-organize are two key factors when it
comes to how well the system can survive the changes to the environment and the
plant they work within. Intelligent control improves these two factors in
controllers. Considering the increasing complexity of dynamic systems along
with their need for feedback controls, using more complicated controls has
become necessary and intelligent control can be a suitable response to this
necessity. This paper briefly describes the structure of intelligent control
and provides a review on fuzzy logic and neural networks which are some of the
base methods for intelligent control. The different aspects of these two
methods are then compared together and an example of a combined method is
presented.
Decomposition Strategies for Constructive Preference Elicitation
Authors: Paolo Dragone, Stefano Teso, Mohit Kumar, Andrea Passerini
Date: 2017-11-22 00:00:00

We tackle the problem of constructive preference elicitation, that is the
problem of learning user preferences over very large decision problems,
involving a combinatorial space of possible outcomes. In this setting, the
suggested configuration is synthesized on-the-fly by solving a constrained
optimization problem, while the preferences are learned itera tively by
interacting with the user. Previous work has shown that Coactive Learning is a
suitable method for learning user preferences in constructive scenarios. In
Coactive Learning the user provides feedback to the algorithm in the form of an
improvement to a suggested configuration. When the problem involves many
decision variables and constraints, this type of interaction poses a
significant cognitive burden on the user. We propose a decomposition technique
for large preference-based decision problems relying exclusively on inference
and feedback over partial configurations. This has the clear advantage of
drastically reducing the user cognitive load. Additionally, part-wise inference
can be (up to exponentially) less computationally demanding than inference over
full configurations. We discuss the theoretical implications of working with
parts and present promising empirical results on one synthetic and two
realistic constructive problems.
Context Spaces as the Cornerstone of a Near-Transparent & Self-Reorganizing Semantic Desktop
Authors: Christian Jilek, Markus Schröder, Sven Schwarz, Heiko Maus, Andreas Dengel
Date: 2018-05-06 00:00:00

Existing Semantic Desktops are still reproached for being too complicated to
use or not scaling well. Besides, a real "killer app" is still missing. In this
paper, we present a new prototype inspired by NEPOMUK and its successors having
a semantic graph and ontologies as its basis. In addition, we introduce the
idea of context spaces that users can directly interact with and work on. To
make them available in all applications without further ado, the system is
transparently integrated using mostly standard protocols complemented by a
sidebar for advanced features. By exploiting collected context information and
applying Managed Forgetting features (like hiding, condensation or deletion),
the system is able to dynamically reorganize itself, which also includes a kind
of tidy-up-itself functionality. We therefore expect it to be more scalable
while providing new levels of user support. An early prototype has been
implemented and is presented in this demo.
Predicting clinical significance of BRCA1 and BRCA2 single nucleotide substitution variants with unknown clinical significance using probabilistic neural network and deep neural network-stacked autoencoder
Authors: Ehsan Rahmatizad KhajePasha, Mahdi Bazarghan, Hamidreza Kheiri Manjili, Ramin Mohammadkhani, Ruhallah Amandi
Date: 2018-05-06 00:00:00

Non-synonymous single nucleotide polymorphisms (nsSNPs) are single nucleotide
substitution occurring in the coding region of a gene and leads to a change in
amino-acid sequence of protein. The studies have shown these variations may be
associated with disease. Thus, investigating the effects of nsSNPs on protein
function will give a greater insight on how nsSNPs can lead into disease.
Breast cancer is the most common cancer among women causing highest cancer
death every year. BRCA1 and BRCA2 tumor suppressor genes are two main
candidates of which, mutations in them can increase the risk of developing
breast cancer. For prediction and detection of the cancer one can use
experimental or computational methods, but the experimental method is very
costly and time consuming in comparison with the computational method. The
computer and computational methods have been used for more than 30 years. Here
we try to predict the clinical significance of BRCA1 and BRCA2 nsSNPs as well
as the unknown clinical significances. Nearly 500 BRCA1 and BRCA2 nsSNPs with
known clinical significances retrieved from NCBI database. Based on
hydrophobicity or hydrophilicity and their role in proteins' second structure,
they are divided into 6 groups, each assigned with scores. The data are
prepared in the acceptable form to the automated prediction mechanisms,
Probabilistic Neural Network (PNN) and Deep Neural NetworkStacked AutoEncoder
(DNN). With Jackknife cross validation we show that the prediction accuracy
achieved for BRCA1 and BRCA2 using PNN are 87.97% and 82.17% respectively,
while 95.41% and 92.80% accuracies achieved using DNN. The total required
processing time for the training and testing the PNN is 0.9 second and DNN
requires about 7 hours of training and it can predict instantly. both methods
show great improvement in accuracy and speed compared to previous attempts.
An Interval Type-2 Fuzzy Approach to Automatic PDF Generation for Histogram Specification
Authors: Vishal Agarwal, Diwanshu Jain, A. Vamshi Krishna Reddy, Frank Chung-Hoon Rhee
Date: 2018-05-06 00:00:00
Tasks: Image Enhancement

Image enhancement plays an important role in several application in the field
of computer vision and image processing. Histogram specification (HS) is one of
the most widely used techniques for contrast enhancement of an image, which
requires an appropriate probability density function for the transformation. In
this paper, we propose a fuzzy method to find a suitable PDF automatically for
histogram specification using interval type - 2 (IT2) fuzzy approach, based on
the fuzzy membership values obtained from the histogram of input image. The
proposed algorithm works in 5 stages which includes - symmetric Gaussian
fitting on the histogram, extraction of IT2 fuzzy membership functions (MFs)
and therefore, footprint of uncertainty (FOU), obtaining membership value (MV),
generating PDF and application of HS. We have proposed 4 different methods to
find membership values - point-wise method, center of weight method, area
method, and karnik-mendel (KM) method. The framework is sensitive to local
variations in the histogram and chooses the best PDF so as to improve contrast
enhancement. Experimental validity of the methods used is illustrated by
qualitative and quantitative analysis on several images using the image quality
index - Average Information Content (AIC) or Entropy, and by comparison with
the commonly used algorithms such as Histogram Equalization (HE), Recursive
Mean-Separate Histogram Equalization (RMSHE) and Brightness Preserving Fuzzy
Histogram Equalization (BPFHE). It has been found out that on an average, our
algorithm improves the AIC index by 11.5% as compared to the index obtained by
histogram equalisation.
Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks
Authors: Hassan Al Hajj, Mathieu Lamard, Pierre-Henri Conze, Béatrice Cochener, Gwenolé Quellec
Date: 2017-10-04 00:00:00
Tasks: General Classification

This paper investigates the automatic monitoring of tool usage during a
surgery, with potential applications in report generation, surgical training
and real-time decision support. Two surgeries are considered: cataract surgery,
the most common surgical procedure, and cholecystectomy, one of the most common
digestive surgeries. Tool usage is monitored in videos recorded either through
a microscope (cataract surgery) or an endoscope (cholecystectomy). Following
state-of-the-art video analysis solutions, each frame of the video is analyzed
by convolutional neural networks (CNNs) whose outputs are fed to recurrent
neural networks (RNNs) in order to take temporal relationships between events
into account. Novelty lies in the way those CNNs and RNNs are trained.
Computational complexity prevents the end-to-end training of "CNN+RNN" systems.
Therefore, CNNs are usually trained first, independently from the RNNs. This
approach is clearly suboptimal for surgical tool analysis: many tools are very
similar to one another, but they can generally be differentiated based on past
events. CNNs should be trained to extract the most useful visual features in
combination with the temporal context. A novel boosting strategy is proposed to
achieve this goal: the CNN and RNN parts of the system are simultaneously
enriched by progressively adding weak classifiers (either CNNs or RNNs) trained
to improve the overall classification accuracy. Experiments were performed in a
dataset of 50 cataract surgery videos and a dataset of 80 cholecystectomy
videos. Very good classification performance are achieved in both datasets:
tool usage could be labeled with an average area under the ROC curve of $A_z =
0.9961$ and $A_z = 0.9939$, respectively, in offline mode (using past, present
and future information), and $A_z = 0.9957$ and $A_z = 0.9936$, respectively,
in online mode (using past and present information only).
Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network
Authors: Liyan Sun, Zhiwen Fan, Yue Huang, Xinghao Ding, John Paisley
Date: 2018-05-06 00:00:00
Tasks: compressed sensing, Decoder, Image Reconstruction, MRI Reconstruction, MRI segmentation, Segmentation

The need for fast acquisition and automatic analysis of MRI data is growing
in the age of big data. Although compressed sensing magnetic resonance imaging
(CS-MRI) has been studied to accelerate MRI by reducing k-space measurements,
in current CS-MRI techniques MRI applications such as segmentation are
overlooked when doing image reconstruction. In this paper, we test the utility
of CS-MRI methods in automatic segmentation models and propose a unified deep
neural network architecture called SegNetMRI which we apply to the combined
CS-MRI reconstruction and segmentation problem. SegNetMRI is built upon a MRI
reconstruction network with multiple cascaded blocks each containing an
encoder-decoder unit and a data fidelity unit, and MRI segmentation networks
having the same encoder-decoder structure. The two subnetworks are pre-trained
and fine-tuned with shared reconstruction encoders. The outputs are merged into
the final segmentation. Our experiments show that SegNetMRI can improve both
the reconstruction and segmentation performance when using compressive
measurements.
Multi-Scale Face Restoration with Sequential Gating Ensemble Network
Authors: Jianxin Lin, Tiankuang Zhou, Zhibo Chen
Date: 2018-05-06 00:00:00
Tasks: Ensemble Learning, Face Recognition, Image Restoration

Restoring face images from distortions is important in face recognition
applications and is challenged by multiple scale issues, which is still not
well-solved in research area. In this paper, we present a Sequential Gating
Ensemble Network (SGEN) for multi-scale face restoration issue. We first employ
the principle of ensemble learning into SGEN architecture design to reinforce
predictive performance of the network. The SGEN aggregates multi-level
base-encoders and base-decoders into the network, which enables the network to
contain multiple scales of receptive field. Instead of combining these
base-en/decoders directly with non-sequential operations, the SGEN takes
base-en/decoders from different levels as sequential data. Specifically, the
SGEN learns to sequentially extract high level information from base-encoders
in bottom-up manner and restore low level information from base-decoders in
top-down manner. Besides, we propose to realize bottom-up and top-down
information combination and selection with Sequential Gating Unit (SGU). The
SGU sequentially takes two inputs from different levels and decides the output
based on one active input. Experiment results demonstrate that our SGEN is more
effective at multi-scale human face restoration with more image details and
less noise than state-of-the-art image restoration models. By using adversarial
training, SGEN also produces more visually preferred results than other models
through subjective evaluation.
Branching embedding: A heuristic dimensionality reduction algorithm based on hierarchical clustering
Authors: Makito Oku
Date: 2018-05-06 00:00:00
Tasks: Clustering, Dimensionality Reduction

This paper proposes a new dimensionality reduction algorithm named branching
embedding (BE). It converts a dendrogram to a two-dimensional scatter plot, and
visualizes the inherent structures of the original high-dimensional data. Since
the conversion part is not computationally demanding, the BE algorithm would be
beneficial for the case where hierarchical clustering is already performed.
Numerical experiments revealed that the outputs of the algorithm moderately
preserve the original hierarchical structures.
Protein Folding Optimization using Differential Evolution Extended with Local Search and Component Reinitialization
Authors: Borko Bošković, Janez Brest
Date: 2017-10-19 00:00:00
Tasks: Protein Folding

This paper presents a novel Differential Evolution algorithm for protein
folding optimization that is applied to a three-dimensional AB off-lattice
model. The proposed algorithm includes two new mechanisms. A local search is
used to improve convergence speed and to reduce the runtime complexity of the
energy calculation. For this purpose, a local movement is introduced within the
local search. The designed evolutionary algorithm has fast convergence speed
and, therefore, when it is trapped into the local optimum or a relatively good
solution is located, it is hard to locate a better similar solution. The
similar solution is different from the good solution in only a few components.
A component reinitialization method is designed to mitigate this problem. Both
the new mechanisms and the proposed algorithm were analyzed on well-known amino
acid sequences that are used frequently in the literature. Experimental results
show that the employed new mechanisms improve the efficiency of our algorithm
and that the proposed algorithm is superior to other state-of-the-art
algorithms. It obtained a hit ratio of 100% for sequences up to 18 monomers,
within a budget of $10^{11}$ solution evaluations. New best-known solutions
were obtained for most of the sequences. The existence of the symmetric
best-known solutions is also demonstrated in the paper.