pwc-archive/papers-with-abstracts huggingface dataset:
Discovery and recognition of motion primitives in human activities
Authors: Marta Sanzari, Valsamis Ntouskos, Fiora Pirri
Date: 2017-09-29 00:00:00
Tasks: Motion Generation

We present a novel framework for the automatic discovery and recognition of
motion primitives in videos of human activities. Given the 3D pose of a human
in a video, human motion primitives are discovered by optimizing the `motion
flux', a quantity which captures the motion variation of a group of skeletal
joints. A normalization of the primitives is proposed in order to make them
invariant with respect to a subject anatomical variations and data sampling
rate. The discovered primitives are unknown and unlabeled and are
unsupervisedly collected into classes via a hierarchical non-parametric Bayes
mixture model. Once classes are determined and labeled they are further
analyzed for establishing models for recognizing discovered primitives. Each
primitive model is defined by a set of learned parameters.
  Given new video data and given the estimated pose of the subject appearing on
the video, the motion is segmented into primitives, which are recognized with a
probability given according to the parameters of the learned models.
  Using our framework we build a publicly available dataset of human motion
primitives, using sequences taken from well-known motion capture datasets. We
expect that our framework, by providing an objective way for discovering and
categorizing human motion, will be a useful tool in numerous research fields
including video analysis, human inspired motion generation, learning by
demonstration, intuitive human-robot interaction, and human behavior analysis.
Sparse Stochastic Zeroth-Order Optimization with an Application to Bandit Structured Prediction
Authors: Artem Sokolov, Julian Hitschler, Mayumi Ohta, Stefan Riezler
Date: 2018-06-12 00:00:00
Tasks: Prediction, Structured Prediction

Stochastic zeroth-order (SZO), or gradient-free, optimization allows to optimize arbitrary functions by relying only on function evaluations under parameter perturbations, however, the iteration complexity of SZO methods suffers a factor proportional to the dimensionality of the perturbed function. We show that in scenarios with natural sparsity patterns as in structured prediction applications, this factor can be reduced to the expected number of active features over input-output pairs. We give a general proof that applies sparse SZO optimization to Lipschitz-continuous, nonconvex, stochastic objectives, and present an experimental evaluation on linear bandit structured prediction tasks with sparse word-based feature representations that confirm our theoretical results.
Impersonation: Modeling Persona in Smart Responses to Email
Authors: Rajeev Gupta, Ranganath Kondapally, Chakrapani Ravi Kiran
Date: 2018-06-12 00:00:00

In this paper, we present design, implementation, and effectiveness of
generating personalized suggestions for email replies. To personalize email
responses based on users style and personality, we model the users persona
based on her past responses to emails. This model is added to the
language-based model created across users using past responses of the all user
emails.
  A users model captures the typical responses of the user given a particular
context. The context includes the email received, recipient of the email, and
other external signals such as calendar activities, preferences, etc. The
context along with users personality (e.g., extrovert, formal, reserved, etc.)
is used to suggest responses. These responses can be a mixture of multiple
modes: email replies (textual), audio clips, etc. This helps in making
responses mimic the user as much as possible and helps the user to be more
productive while retaining her mark in the responses.
Convergence of Gradient Descent on Separable Data
Authors: Mor Shpigel Nacson, Jason D. Lee, Suriya Gunasekar, Pedro H. P. Savarese, Nathan Srebro, Daniel Soudry
Date: 2018-03-05 00:00:00

We provide a detailed study on the implicit bias of gradient descent when
optimizing loss functions with strictly monotone tails, such as the logistic
loss, over separable datasets. We look at two basic questions: (a) what are the
conditions on the tail of the loss function under which gradient descent
converges in the direction of the $L_2$ maximum-margin separator? (b) how does
the rate of margin convergence depend on the tail of the loss function and the
choice of the step size? We show that for a large family of super-polynomial
tailed losses, gradient descent iterates on linear networks of any depth
converge in the direction of $L_2$ maximum-margin solution, while this does not
hold for losses with heavier tails. Within this family, for simple linear
models we show that the optimal rates with fixed step size is indeed obtained
for the commonly used exponentially tailed losses such as logistic loss.
However, with a fixed step size the optimal convergence rate is extremely slow
as $1/\log(t)$, as also proved in Soudry et al. (2018). For linear models with
exponential loss, we further prove that the convergence rate could be improved
to $\log (t) /\sqrt{t}$ by using aggressive step sizes that compensates for the
rapidly vanishing gradients. Numerical results suggest this method might be
useful for deep networks.
An Ensemble Model for Sentiment Analysis of Hindi-English Code-Mixed Data
Authors: Madan Gopal Jhanwar, Arpita Das
Date: 2018-06-12 00:00:00
Tasks: Sentiment Analysis

In multilingual societies like India, code-mixed social media texts comprise
the majority of the Internet. Detecting the sentiment of the code-mixed user
opinions plays a crucial role in understanding social, economic and political
trends. In this paper, we propose an ensemble of character-trigrams based LSTM
model and word-ngrams based Multinomial Naive Bayes (MNB) model to identify the
sentiments of Hindi-English (Hi-En) code-mixed data. The ensemble model
combines the strengths of rich sequential patterns from the LSTM model and
polarity of keywords from the probabilistic ngram model to identify sentiments
in sparse and inconsistent code-mixed data. Experiments on reallife user
code-mixed data reveals that our approach yields state-of-the-art results as
compared to several baselines and other deep learning based proposed methods.
ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors
Authors: Mikhail Zaslavskiy, Simon Jégou, Eric W. Tramel, Gilles Wainrib
Date: 2018-06-12 00:00:00
Tasks: Drug Discovery, Prediction

Timely assessment of compound toxicity is one of the biggest challenges
facing the pharmaceutical industry today. A significant proportion of compounds
identified as potential leads are ultimately discarded due to the toxicity they
induce. In this paper, we propose a novel machine learning approach for the
prediction of molecular activity on ToxCast targets. We combine extreme
gradient boosting with fully-connected and graph-convolutional neural network
architectures trained on QSAR physical molecular property descriptors, PubChem
molecular fingerprints, and SMILES sequences. Our ensemble predictor leverages
the strengths of each individual technique, significantly outperforming
existing state-of-the art models on the ToxCast and Tox21 toxicity-prediction
datasets. We provide free access to molecule toxicity prediction using our
model at http://www.owkin.com/toxicblend.
Direct Estimation of Pharmacokinetic Parameters from DCE-MRI using Deep CNN with Forward Physical Model Loss
Authors: Cagdas Ulas, Giles Tetteh, Michael J. Thrippleton, Paul A. Armitage, Stephen D. Makin, Joanna M. Wardlaw, Mike E. Davies, Bjoern H. Menze
Date: 2018-04-08 00:00:00
Tasks: Time Series, Time Series Analysis

Dynamic contrast-enhanced (DCE) MRI is an evolving imaging technique that
provides a quantitative measure of pharmacokinetic (PK) parameters in body
tissues, in which series of T1-weighted images are collected following the
administration of a paramagnetic contrast agent. Unfortunately, in many
applications, conventional clinical DCE-MRI suffers from low spatiotemporal
resolution and insufficient volume coverage. In this paper, we propose a novel
deep learning based approach to directly estimate the PK parameters from
undersampled DCE-MRI data. Specifically, we design a custom loss function where
we incorporate a forward physical model that relates the PK parameters to
corrupted image-time series obtained due to subsampling in k-space. This allows
the network to directly exploit the knowledge of true contrast agent kinetics
in the training phase, and hence provide more accurate restoration of PK
parameters. Experiments on clinical brain DCE datasets demonstrate the efficacy
of our approach in terms of fidelity of PK parameter reconstruction and
significantly faster parameter inference compared to a model-based iterative
reconstruction method.
DeepASL: Kinetic Model Incorporated Loss for Denoising Arterial Spin Labeled MRI via Deep Residual Learning
Authors: Cagdas Ulas, Giles Tetteh, Stephan Kaczmarz, Christine Preibisch, Bjoern H. Menze
Date: 2018-04-08 00:00:00
Tasks: Denoising, parameter estimation

Arterial spin labeling (ASL) allows to quantify the cerebral blood flow (CBF)
by magnetic labeling of the arterial blood water. ASL is increasingly used in
clinical studies due to its noninvasiveness, repeatability and benefits in
quantification. However, ASL suffers from an inherently low-signal-to-noise
ratio (SNR) requiring repeated measurements of control/spin-labeled (C/L) pairs
to achieve a reasonable image quality, which in return increases motion
sensitivity. This leads to clinically prolonged scanning times increasing the
risk of motion artifacts. Thus, there is an immense need of advanced imaging
and processing techniques in ASL. In this paper, we propose a novel deep
learning based approach to improve the perfusion-weighted image quality
obtained from a subset of all available pairwise C/L subtractions.
Specifically, we train a deep fully convolutional network (FCN) to learn a
mapping from noisy perfusion-weighted image and its subtraction (residual) from
the clean image. Additionally, we incorporate the CBF estimation model in the
loss function during training, which enables the network to produce high
quality images while simultaneously enforcing the CBF estimates to be as close
as reference CBF values. Extensive experiments on synthetic and clinical ASL
datasets demonstrate the effectiveness of our method in terms of improved ASL
image quality, accurate CBF parameter estimation and considerably small
computation time during testing.
Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation
Authors: Haoyang Wen, Yijia Liu, Wanxiang Che, Libo Qin, Ting Liu
Date: 2018-06-12 00:00:00
Proceeding: COLING 2018 8
Tasks: Task-Oriented Dialogue Systems

Classic pipeline models for task-oriented dialogue system require explicit
modeling the dialogue states and hand-crafted action spaces to query a
domain-specific knowledge base. Conversely, sequence-to-sequence models learn
to map dialogue history to the response in current turn without explicit
knowledge base querying. In this work, we propose a novel framework that
leverages the advantages of classic pipeline and sequence-to-sequence models.
Our framework models a dialogue state as a fixed-size distributed
representation and use this representation to query a knowledge base via an
attention mechanism. Experiment on Stanford Multi-turn Multi-domain
Task-oriented Dialogue Dataset shows that our framework significantly
outperforms other sequence-to-sequence based baseline models on both automatic
and human evaluation.
ML + FV = $\heartsuit$? A Survey on the Application of Machine Learning to Formal Verification
Authors: Moussa Amrani, Levi Lúcio, Adrien Bibal
Date: 2018-06-10 00:00:00
Tasks: Automated Theorem Proving, BIG-bench Machine Learning

Formal Verification (FV) and Machine Learning (ML) can seem incompatible due
to their opposite mathematical foundations and their use in real-life problems:
FV mostly relies on discrete mathematics and aims at ensuring correctness; ML
often relies on probabilistic models and consists of learning patterns from
training data. In this paper, we postulate that they are complementary in
practice, and explore how ML helps FV in its classical approaches: static
analysis, model-checking, theorem-proving, and SAT solving. We draw a landscape
of the current practice and catalog some of the most prominent uses of ML
inside FV tools, thus offering a new perspective on FV techniques that can help
researchers and practitioners to better locate the possible synergies. We
discuss lessons learned from our work, point to possible improvements and offer
visions for the future of the domain in the light of the science of software
and systems modeling.
Analyzing Uncertainty in Neural Machine Translation
Authors: Myle Ott, Michael Auli, David Grangier, Marc'Aurelio Ranzato
Date: 2018-02-28 00:00:00
Proceeding: ICML 2018 7
Tasks: Diversity, Machine Translation, Sentence, Translation, valid

Machine translation is a popular test bed for research in neural
sequence-to-sequence models but despite much recent research, there is still a
lack of understanding of these models. Practitioners report performance
degradation with large beams, the under-estimation of rare words and a lack of
diversity in the final translations. Our study relates some of these issues to
the inherent uncertainty of the task, due to the existence of multiple valid
translations for a single source sentence, and to the extrinsic uncertainty
caused by noisy training data. We propose tools and metrics to assess how
uncertainty in the data is captured by the model distribution and how it
affects search strategies that generate translations. Our results show that
search works remarkably well but that models tend to spread too much
probability mass over the hypothesis space. Next, we propose tools to assess
model calibration and show how to easily fix some shortcomings of current
models. As part of this study, we release multiple human reference translations
for two popular benchmarks.
Ask No More: Deciding when to guess in referential visual dialogue
Authors: Ravi Shekhar, Tim Baumgartner, Aashish Venkatesh, Elia Bruni, Raffaella Bernardi, Raquel Fernandez
Date: 2018-05-17 00:00:00
Proceeding: COLING 2018 8
Tasks: Decision Making, Visual Dialog

Our goal is to explore how the abilities brought in by a dialogue manager can
be included in end-to-end visually grounded conversational agents. We make
initial steps towards this general goal by augmenting a task-oriented visual
dialogue model with a decision-making component that decides whether to ask a
follow-up question to identify a target referent in an image, or to stop the
conversation to make a guess. Our analyses show that adding a decision making
component produces dialogues that are less repetitive and that include fewer
unnecessary questions, thus potentially leading to more efficient and less
unnatural interactions.
U-SegNet: Fully Convolutional Neural Network based Automated Brain tissue segmentation Tool
Authors: Pulkit Kumar, Pravin Nagar, Chetan Arora, Anubha Gupta
Date: 2018-06-12 00:00:00
Tasks: Segmentation

Automated brain tissue segmentation into white matter (WM), gray matter (GM),
and cerebro-spinal fluid (CSF) from magnetic resonance images (MRI) is helpful
in the diagnosis of neuro-disorders such as epilepsy, Alzheimer's, multiple
sclerosis, etc. However, thin GM structures at the periphery of cortex and
smooth transitions on tissue boundaries such as between GM and WM, or WM and
CSF pose difficulty in building a reliable segmentation tool. This paper
proposes a Fully Convolutional Neural Network (FCN) tool, that is a hybrid of
two widely used deep learning segmentation architectures SegNet and U-Net, for
improved brain tissue segmentation. We propose a skip connection inspired from
U-Net, in the SegNet architetcure, to incorporate fine multiscale information
for better tissue boundary identification. We show that the proposed U-SegNet
architecture, improves segmentation performance, as measured by average dice
ratio, to 89.74% on the widely used IBSR dataset consisting of T-1 weighted MRI
volumes of 18 subjects.
Sample Dropout for Audio Scene Classification Using Multi-Scale Dense Connected Convolutional Neural Network
Authors: Dawei Feng, Kele Xu, Haibo Mi, Feifan Liao, Yan Zhou
Date: 2018-06-12 00:00:00
Tasks: Acoustic Scene Classification, Classification, Data Augmentation, General Classification, Scene Classification

Acoustic scene classification is an intricate problem for a machine. As an
emerging field of research, deep Convolutional Neural Networks (CNN) achieve
convincing results. In this paper, we explore the use of multi-scale Dense
connected convolutional neural network (DenseNet) for the classification task,
with the goal to improve the classification performance as multi-scale features
can be extracted from the time-frequency representation of the audio signal. On
the other hand, most of previous CNN-based audio scene classification
approaches aim to improve the classification accuracy, by employing different
regularization techniques, such as the dropout of hidden units and data
augmentation, to reduce overfitting. It is widely known that outliers in the
training set have a high negative influence on the trained model, and culling
the outliers may improve the classification performance, while it is often
under-explored in previous studies. In this paper, inspired by the silence
removal in the speech signal processing, a novel sample dropout approach is
proposed, which aims to remove outliers in the training dataset. Using the
DCASE 2017 audio scene classification datasets, the experimental results
demonstrates the proposed multi-scale DenseNet providing a superior performance
than the traditional single-scale DenseNet, while the sample dropout method can
further improve the classification robustness of multi-scale DenseNet.
Using Chaos in Grey Wolf Optimizer and Application to Prime Factorization
Authors: Harshit Mehrotra, Dr. Saibal K. Pal
Date: 2018-06-12 00:00:00

The Grey Wolf Optimizer (GWO) is a swarm intelligence meta-heuristic
algorithm inspired by the hunting behaviour and social hierarchy of grey wolves
in nature. This paper analyses the use of chaos theory in this algorithm to
improve its ability to escape local optima by replacing the key parameters by
chaotic variables. The optimal choice of chaotic maps is then used to apply the
Chaotic Grey Wolf Optimizer (CGWO) to the problem of factoring a large semi
prime into its prime factors. Assuming the number of digits of the factors to
be equal, this is a computationally difficult task upon which the
RSA-cryptosystem relies. This work proposes the use of a new objective function
to solve the problem and uses the CGWO to optimize it and compute the factors.
It is shown that this function performs better than its predecessor for large
semi primes and CGWO is an efficient algorithm to optimize it.
Quaternion Recurrent Neural Networks
Authors: Titouan Parcollet, Mirco Ravanelli, Mohamed Morchid, Georges Linarès, Chiheb Trabelsi, Renato de Mori, Yoshua Bengio
Date: 2018-06-12 00:00:00
Proceeding: ICLR 2019 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Recurrent neural networks (RNNs) are powerful architectures to model
sequential data, due to their capability to learn short and long-term
dependencies between the basic elements of a sequence. Nonetheless, popular
tasks such as speech or images recognition, involve multi-dimensional input
features that are characterized by strong internal dependencies between the
dimensions of the input vector. We propose a novel quaternion recurrent neural
network (QRNN), alongside with a quaternion long-short term memory neural
network (QLSTM), that take into account both the external relations and these
internal structural dependencies with the quaternion algebra. Similarly to
capsules, quaternions allow the QRNN to code internal dependencies by composing
and processing multidimensional features as single entities, while the
recurrent operation reveals correlations between the elements composing the
sequence. We show that both QRNN and QLSTM achieve better performances than RNN
and LSTM in a realistic application of automatic speech recognition. Finally,
we show that QRNN and QLSTM reduce by a maximum factor of 3.3x the number of
free parameters needed, compared to real-valued RNNs and LSTMs to reach better
results, leading to a more compact representation of the relevant information.
Markerless Inside-Out Tracking for Interventional Applications
Authors: Benjamin Busam, Patrick Ruhkamp, Salvatore Virga, Beatrice Lentes, Julia Rackerseder, Nassir Navab, Christoph Hennersperger
Date: 2018-04-05 00:00:00
Tasks: Translation

Tracking of rotation and translation of medical instruments plays a
substantial role in many modern interventions. Traditional external optical
tracking systems are often subject to line-of-sight issues, in particular when
the region of interest is difficult to access or the procedure allows only for
limited rigid body markers. The introduction of inside-out tracking systems
aims to overcome these issues. We propose a marker-less tracking system based
on visual SLAM to enable tracking of instruments in an interventional scenario.
To achieve this goal, we mount a miniature multi-modal (monocular, stereo,
active depth) vision system on the object of interest and relocalize its pose
within an adaptive map of the operating room. We compare state-of-the-art
algorithmic pipelines and apply the idea to transrectal 3D Ultrasound (TRUS)
compounding of the prostate. Obtained volumes are compared to reconstruction
using a commercial optical tracking system as well as a robotic manipulator.
Feature-based binocular SLAM is identified as the most promising method and is
tested extensively in challenging clinical environment under severe occlusion
and for the use case of prostate US biopsies.
Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction
Authors: Adriano Pinto, Sergio Pereira, Raphael Meier, Victor Alves, Roland Wiest, Carlos A. Silva, Mauricio Reyes
Date: 2018-06-12 00:00:00
Tasks: Decision Making, Prediction

Stroke is the second most common cause of death in developed countries, where
rapid clinical intervention can have a major impact on a patient's life. To
perform the revascularization procedure, the decision making of physicians
considers its risks and benefits based on multi-modal MRI and clinical
experience. Therefore, automatic prediction of the ischemic stroke lesion
outcome has the potential to assist the physician towards a better stroke
assessment and information about tissue outcome. Typically, automatic methods
consider the information of the standard kinetic models of diffusion and
perfusion MRI (e.g. Tmax, TTP, MTT, rCBF, rCBV) to perform lesion outcome
prediction. In this work, we propose a deep learning method to fuse this
information with an automated data selection of the raw 4D PWI image
information, followed by a data-driven deep-learning modeling of the underlying
blood flow hemodynamics. We demonstrate the ability of the proposed approach to
improve prediction of tissue at risk before therapy, as compared to only using
the standard clinical perfusion maps, hence suggesting on the potential
benefits of the proposed data-driven raw perfusion data modelling approach.