pwc-archive/papers-with-abstracts huggingface dataset:
Multi-level SVM Based CAD Tool for Classifying Structural MRIs
Authors: Jerrin Thomas Panachakel, Jeena R. S.
Date: 2017-06-26 00:00:00
Tasks: Classification, General Classification, Specificity

The revolutionary developments in the field of supervised machine learning
have paved way to the development of CAD tools for assisting doctors in
diagnosis. Recently, the former has been employed in the prediction of
neurological disorders such as Alzheimer's disease. We propose a CAD (Computer
Aided Diagnosis tool for differentiating neural lesions caused by CVA
(Cerebrovascular Accident) from the lesions caused by other neural disorders by
using Non-negative Matrix Factorisation (NMF) and Haralick features for feature
extraction and SVM (Support Vector Machine) for pattern recognition. We also
introduce a multi-level classification system that has better classification
efficiency, sensitivity and specificity when compared to systems using NMF or
Haralick features alone as features for classification. Cross-validation was
performed using LOOCV (Leave-One-Out Cross Validation) method and our proposed
system has a classification accuracy of over 86%.
YouTube-8M Video Understanding Challenge Approach and Applications
Authors: Edward Chen
Date: 2017-06-26 00:00:00
Tasks: Ensemble Learning, Video Understanding

This paper introduces the YouTube-8M Video Understanding Challenge hosted as
a Kaggle competition and also describes my approach to experimenting with
various models. For each of my experiments, I provide the score result as well
as possible improvements to be made. Towards the end of the paper, I discuss
the various ensemble learning techniques that I applied on the dataset which
significantly boosted my overall competition score. At last, I discuss the
exciting future of video understanding research and also the many applications
that such research could significantly improve.
YoTube: Searching Action Proposal via Recurrent and Static Regression Networks
Authors: Hongyuan Zhu, Romain Vial, Shijian Lu, Yonghong Tian, Xian-Bin Cao
Date: 2017-06-26 00:00:00
Tasks: Optical Flow Estimation, regression

In this paper, we present YoTube-a novel network fusion framework for
searching action proposals in untrimmed videos, where each action proposal
corresponds to a spatialtemporal video tube that potentially locates one human
action. Our method consists of a recurrent YoTube detector and a static YoTube
detector, where the recurrent YoTube explores the regression capability of RNN
for candidate bounding boxes predictions using learnt temporal dynamics and the
static YoTube produces the bounding boxes using rich appearance cues in a
single frame. Both networks are trained using rgb and optical flow in order to
fully exploit the rich appearance, motion and temporal context, and their
outputs are fused to produce accurate and robust proposal boxes. Action
proposals are finally constructed by linking these boxes using dynamic
programming with a novel trimming method to handle the untrimmed video
effectively and efficiently. Extensive experiments on the challenging UCF-101
and UCF-Sports datasets show that our proposed technique obtains superior
performance compared with the state-of-the-art.
An Effective Way to Improve YouTube-8M Classification Accuracy in Google Cloud Platform
Authors: Zhenzhen Zhong, Shujiao Huang, Cheng Zhan, Licheng Zhang, Zhiwei Xiao, Chang-Chun Wang, Pei Yang
Date: 2017-06-26 00:00:00
Tasks: Classification, Deep Learning, General Classification, Video Classification, Video Understanding

Large-scale datasets have played a significant role in progress of neural
network and deep learning areas. YouTube-8M is such a benchmark dataset for
general multi-label video classification. It was created from over 7 million
YouTube videos (450,000 hours of video) and includes video labels from a
vocabulary of 4716 classes (3.4 labels/video on average). It also comes with
pre-extracted audio & visual features from every second of video (3.2 billion
feature vectors in total). Google cloud recently released the datasets and
organized 'Google Cloud & YouTube-8M Video Understanding Challenge' on Kaggle.
Competitors are challenged to develop classification algorithms that assign
video-level labels using the new and improved Youtube-8M V2 dataset. Inspired
by the competition, we started exploration of audio understanding and
classification using deep learning algorithms and ensemble methods. We built
several baseline predictions according to the benchmark paper and public github
tensorflow code. Furthermore, we improved global prediction accuracy (GAP) from
base level 77% to 80.7% through approaches of ensemble.
End-to-end Learning of Image based Lane-Change Decision
Authors: Seong-Gyun Jeong, Jiwon Kim, Sujung Kim, Jaesik Min
Date: 2017-06-26 00:00:00
Tasks: Autonomous Vehicles, object-detection, Object Detection

We propose an image based end-to-end learning framework that helps
lane-change decisions for human drivers and autonomous vehicles. The proposed
system, Safe Lane-Change Aid Network (SLCAN), trains a deep convolutional
neural network to classify the status of adjacent lanes from rear view images
acquired by cameras mounted on both sides of the vehicle. Rather than depending
on any explicit object detection or tracking scheme, SLCAN reads the whole
input image and directly decides whether initiation of the lane-change at the
moment is safe or not. We collected and annotated 77,273 rear side view images
to train and test SLCAN. Experimental results show that the proposed framework
achieves 96.98% classification accuracy although the test images are from
unseen roadways. We also visualize the saliency map to understand which part of
image SLCAN looks at for correct decisions.
In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling
Authors: Neil G. Marchant, Benjamin I. P. Rubinstein
Date: 2017-03-02 00:00:00
Tasks: Entity Resolution

Entity resolution (ER) presents unique challenges for evaluation methodology.
While crowdsourcing platforms acquire ground truth, sound approaches to
sampling must drive labelling efforts. In ER, extreme class imbalance between
matching and non-matching records can lead to enormous labelling requirements
when seeking statistically consistent estimates for rigorous evaluation. This
paper addresses this important challenge with the OASIS algorithm: a sampler
and F-measure estimator for ER evaluation. OASIS draws samples from a (biased)
instrumental distribution, chosen to ensure estimators with optimal asymptotic
variance. As new labels are collected OASIS updates this instrumental
distribution via a Bayesian latent variable model of the annotator oracle, to
quickly focus on unlabelled items providing more information. We prove that
resulting estimates of F-measure, precision, recall converge to the true
population values. Thorough comparisons of sampling methods on a variety of ER
datasets demonstrate significant labelling reductions of up to 83% without loss
to estimate accuracy.
English-Japanese Neural Machine Translation with Encoder-Decoder-Reconstructor
Authors: Yukio Matsumura, Takayuki Sato, Mamoru Komachi
Date: 2017-06-26 00:00:00
Tasks: Decoder, Machine Translation, NMT, Translation

Neural machine translation (NMT) has recently become popular in the field of
machine translation. However, NMT suffers from the problem of repeating or
missing words in the translation. To address this problem, Tu et al. (2017)
proposed an encoder-decoder-reconstructor framework for NMT using
back-translation. In this method, they selected the best forward translation
model in the same manner as Bahdanau et al. (2015), and then trained a
bi-directional translation model as fine-tuning. Their experiments show that it
offers significant improvement in BLEU scores in Chinese-English translation
task. We confirm that our re-implementation also shows the same tendency and
alleviates the problem of repeating and missing words in the translation on a
English-Japanese task too. In addition, we evaluate the effectiveness of
pre-training by comparing it with a jointly-trained model of forward
translation and back-translation.
Device Placement Optimization with Reinforcement Learning
Authors: Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean
Date: 2017-06-13 00:00:00
Proceeding: ICML 2017 8
Tasks: Language Modeling, Language Modelling, Machine Translation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Translation

The past few years have witnessed a growth in size and computational
requirements for training and inference with neural networks. Currently, a
common approach to address these requirements is to use a heterogeneous
distributed environment with a mixture of hardware devices such as CPUs and
GPUs. Importantly, the decision of placing parts of the neural models on
devices is often made by human experts based on simple heuristics and
intuitions. In this paper, we propose a method which learns to optimize device
placement for TensorFlow computational graphs. Key to our method is the use of
a sequence-to-sequence model to predict which subsets of operations in a
TensorFlow graph should run on which of the available devices. The execution
time of the predicted placements is then used as the reward signal to optimize
the parameters of the sequence-to-sequence model. Our main result is that on
Inception-V3 for ImageNet classification, and on RNN LSTM, for language
modeling and neural machine translation, our model finds non-trivial device
placements that outperform hand-crafted heuristics and traditional algorithmic
methods.
Automatic Synonym Discovery with Knowledge Bases
Authors: Meng Qu, Xiang Ren, Jiawei Han
Date: 2017-06-25 00:00:00
Tasks: Articles

Recognizing entity synonyms from text has become a crucial task in many
entity-leveraging applications. However, discovering entity synonyms from
domain-specific text corpora (e.g., news articles, scientific papers) is rather
challenging. Current systems take an entity name string as input to find out
other names that are synonymous, ignoring the fact that often times a name
string can refer to multiple entities (e.g., "apple" could refer to both Apple
Inc and the fruit apple). Moreover, most existing methods require training data
manually created by domain experts to construct supervised-learning systems. In
this paper, we study the problem of automatic synonym discovery with knowledge
bases, that is, identifying synonyms for knowledge base entities in a given
domain-specific corpus. The manually-curated synonyms for each entity stored in
a knowledge base not only form a set of name strings to disambiguate the
meaning for each other, but also can serve as "distant" supervision to help
determine important features for the task. We propose a novel framework, called
DPE, to integrate two kinds of mutually-complementing signals for synonym
discovery, i.e., distributional features based on corpus-level statistics and
textual patterns based on local contexts. In particular, DPE jointly optimizes
the two kinds of signals in conjunction with distant supervision, so that they
can mutually enhance each other in the training stage. At the inference stage,
both signals will be utilized to discover synonyms for the given entities.
Experimental results prove the effectiveness of the proposed framework.
Automated text summarisation and evidence-based medicine: A survey of two domains
Authors: Abeed Sarker, Diego Molla, Cecile Paris
Date: 2017-06-25 00:00:00
Tasks: Survey

The practice of evidence-based medicine (EBM) urges medical practitioners to
utilise the latest research evidence when making clinical decisions. Because of
the massive and growing volume of published research on various medical topics,
practitioners often find themselves overloaded with information. As such,
natural language processing research has recently commenced exploring
techniques for performing medical domain-specific automated text summarisation
(ATS) techniques-- targeted towards the task of condensing large medical texts.
However, the development of effective summarisation techniques for this task
requires cross-domain knowledge. We present a survey of EBM, the
domain-specific needs for EBM, automated summarisation techniques, and how they
have been applied hitherto. We envision that this survey will serve as a first
resource for the development of future operational text summarisation
techniques for EBM.
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context
Authors: Shyam Upadhyay, Kai-Wei Chang, Matt Taddy, Adam Kalai, James Zou
Date: 2017-06-25 00:00:00
Proceeding: WS 2017 8
Tasks: Representation Learning, Word Embeddings

Word embeddings, which represent a word as a point in a vector space, have
become ubiquitous to several NLP tasks. A recent line of work uses bilingual
(two languages) corpora to learn a different vector for each sense of a word,
by exploiting crosslingual signals to aid sense identification. We present a
multi-view Bayesian non-parametric algorithm which improves multi-sense word
embeddings by (a) using multilingual (i.e., more than two languages) corpora to
significantly improve sense embeddings beyond what one achieves with bilingual
information, and (b) uses a principled approach to learn a variable number of
senses per word, in a data-driven manner. Ours is the first approach with the
ability to leverage multilingual corpora efficiently for multi-sense
representation learning. Experiments show that multilingual training
significantly improves performance over monolingual and bilingual training, by
allowing us to combine different parallel corpora to leverage multilingual
context. Multilingual training yields comparable performance to a state of the
art mono-lingual model trained on five times more training data.
Photometric Stereo by Hemispherical Metric Embedding
Authors: Ofer Bartal, Nati Ofir, Yaron Lipman, Ronen Basri
Date: 2017-06-25 00:00:00

Photometric Stereo methods seek to reconstruct the 3d shape of an object from
motionless images obtained with varying illumination. Most existing methods
solve a restricted problem where the physical reflectance model, such as
Lambertian reflectance, is known in advance. In contrast, we do not restrict
ourselves to a specific reflectance model. Instead, we offer a method that
works on a wide variety of reflectances. Our approach uses a simple yet
uncommonly used property of the problem - the sought after normals are points
on a unit hemisphere. We present a novel embedding method that maps pixels to
normals on the unit hemisphere. Our experiments demonstrate that this approach
outperforms existing manifold learning methods for the task of hemisphere
embedding. We further show successful reconstructions of objects from a wide
variety of reflectances including smooth, rough, diffuse and specular surfaces,
even in the presence of significant attached shadows. Finally, we empirically
prove that under these challenging settings we obtain more accurate shape
reconstructions than existing methods.
Hamiltonian operator for spectral shape analysis
Authors: Yoni Choukroun, Alon Shtern, Alex Bronstein, Ron Kimmel
Date: 2016-11-07 00:00:00

Many shape analysis methods treat the geometry of an object as a metric space
that can be captured by the Laplace-Beltrami operator. In this paper, we
propose to adapt the classical Hamiltonian operator from quantum mechanics to
the field of shape analysis. To this end we study the addition of a potential
function to the Laplacian as a generator for dual spaces in which shape
processing is performed. We present a general optimization approach for solving
variational problems involving the basis defined by the Hamiltonian using
perturbation theory for its eigenvectors. The suggested operator is shown to
produce better functional spaces to operate with, as demonstrated on different
shape analysis tasks.
A Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints
Authors: Bin Hu, Peter Seiler, Anders Rantzer
Date: 2017-06-25 00:00:00
Tasks: Stochastic Optimization

We develop a simple routine unifying the analysis of several important
recently-developed stochastic optimization methods including SAGA, Finito, and
stochastic dual coordinate ascent (SDCA). First, we show an intrinsic
connection between stochastic optimization methods and dynamic jump systems,
and propose a general jump system model for stochastic optimization methods.
Our proposed model recovers SAGA, SDCA, Finito, and SAG as special cases. Then
we combine jump system theory with several simple quadratic inequalities to
derive sufficient conditions for convergence rate certifications of the
proposed jump system model under various assumptions (with or without
individual convexity, etc). The derived conditions are linear matrix
inequalities (LMIs) whose sizes roughly scale with the size of the training
set. We make use of the symmetry in the stochastic optimization methods and
reduce these LMIs to some equivalent small LMIs whose sizes are at most 3 by 3.
We solve these small LMIs to provide analytical proofs of new convergence rates
for SAGA, Finito and SDCA (with or without individual convexity). We also
explain why our proposed LMI fails in analyzing SAG. We reveal a key difference
between SAG and other methods, and briefly discuss how to extend our LMI
analysis for SAG. An advantage of our approach is that the proposed analysis
can be automated for a large class of stochastic methods under various
assumptions (with or without individual convexity, etc).
Learning a Static Analyzer from Data
Authors: Pavol Bielik, Veselin Raychev, Martin Vechev
Date: 2016-11-06 00:00:00

To be practically useful, modern static analyzers must precisely model the
effect of both, statements in the programming language as well as frameworks
used by the program under analysis. While important, manually addressing these
challenges is difficult for at least two reasons: (i) the effects on the
overall analysis can be non-trivial, and (ii) as the size and complexity of
modern libraries increase, so is the number of cases the analysis must handle.
  In this paper we present a new, automated approach for creating static
analyzers: instead of manually providing the various inference rules of the
analyzer, the key idea is to learn these rules from a dataset of programs. Our
method consists of two ingredients: (i) a synthesis algorithm capable of
learning a candidate analyzer from a given dataset, and (ii) a counter-example
guided learning procedure which generates new programs beyond those in the
initial dataset, critical for discovering corner cases and ensuring the learned
analysis generalizes to unseen programs.
  We implemented and instantiated our approach to the task of learning
JavaScript static analysis rules for a subset of points-to analysis and for
allocation sites analysis. These are challenging yet important problems that
have received significant research attention. We show that our approach is
effective: our system automatically discovered practical and useful inference
rules for many cases that are tricky to manually identify and are missed by
state-of-the-art, manually tuned analyzers.
Scalable multimodal convolutional networks for brain tumour segmentation
Authors: Lucas Fidon, Wenqi Li, Luis C. Garcia-Peraza-Herrera, Jinendra Ekanayake, Neil Kitchen, Sebastien Ourselin, Tom Vercauteren
Date: 2017-06-25 00:00:00
Tasks: Multimodal Deep Learning, Segmentation

Brain tumour segmentation plays a key role in computer-assisted surgery. Deep
neural networks have increased the accuracy of automatic segmentation
significantly, however these models tend to generalise poorly to different
imaging modalities than those for which they have been designed, thereby
limiting their applications. For example, a network architecture initially
designed for brain parcellation of monomodal T1 MRI can not be easily
translated into an efficient tumour segmentation network that jointly utilises
T1, T1c, Flair and T2 MRI. To tackle this, we propose a novel scalable
multimodal deep learning architecture using new nested structures that
explicitly leverage deep features within or across modalities. This aims at
making the early layers of the architecture structured and sparse so that the
final architecture becomes scalable to the number of modalities. We evaluate
the scalable architecture for brain tumour segmentation and give evidence of
its regularisation effect compared to the conventional concatenation approach.
Well-supported phylogenies using largest subsets of core-genes by discrete particle swarm optimization
Authors: Reem Alsrraj, Bassam AlKindy, Christophe Guyeux, Laurent Philippe, Jean-François Couchot
Date: 2017-06-25 00:00:00

The number of complete chloroplastic genomes increases day after day, making
it possible to rethink plants phylogeny at the biomolecular era. Given a set of
close plants sharing in the order of one hundred of core chloroplastic genes,
this article focuses on how to extract the largest subset of sequences in order
to obtain the most supported species tree. Due to computational complexity, a
discrete and distributed Particle Swarm Optimization (DPSO) is proposed. It is
finally applied to the core genes of Rosales order.
Generate Identity-Preserving Faces by Generative Adversarial Networks
Authors: Zhigang Li, Yupin Luo
Date: 2017-06-10 00:00:00

Generating identity-preserving faces aims to generate various face images
keeping the same identity given a target face image. Although considerable
generative models have been developed in recent years, it is still challenging
to simultaneously acquire high quality of facial images and preserve the
identity. Here we propose a compelling method using generative adversarial
networks (GAN). Concretely, we leverage the generator of trained GAN to
generate plausible faces and FaceNet as an identity-similarity discriminator to
ensure the identity. Experimental results show that our method is qualified to
generate both plausible and identity-preserving faces with high quality. In
addition, our method provides a universal framework which can be realized in
various ways by combining different face generators and identity-similarity
discriminator.
Detekcja upadku i wybranych akcji na sekwencjach obrazów cyfrowych
Authors: Michal Kepski
Date: 2017-06-25 00:00:00
Tasks: Action Recognition, Human Detection, Privacy Preserving, Specificity, Temporal Action Localization

In recent years a growing interest on action recognition is observed,
including detection of fall accident for the elderly. However, despite many
efforts undertaken, the existing technology is not widely used by elderly,
mainly because of its flaws like low precision, large number of false alarms,
inadequate privacy preserving during data acquisition and processing. This
research work meets these expectations. The work is empirical and it is
situated in the field of computer vision systems. The main part of the work
situates itself in the area of action and behavior recognition. Efficient
algorithms for fall detection were developed, tested and implemented using
image sequences and wireless inertial sensor worn by a monitored person. A set
of descriptors for depth maps has been elaborated to permit classification of
pose as well as the action of a person. Experimental research was carried out
based on the prepared data repository consisting of synchronized depth and
accelerometric data. The study was carried out in the scenario with a static
camera facing the scene and an active camera observing the scene from above.
The experimental results showed that the developed algorithms for fall
detection have high sensitivity and specificity. The algorithm were designed
with regard to low computational demands and possibility to run on ARM
platforms. Several experiments including person detection, tracking and fall
detection in real-time were carried out to show efficiency and reliability of
the proposed solutions.