pwc-archive/papers-with-abstracts huggingface dataset:
Dialogue Learning With Human-In-The-Loop
Authors: Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston
Date: 2016-11-29 00:00:00
Tasks: Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

An important aspect of developing conversational agents is to give a bot the
ability to improve through communicating with humans and to learn from the
mistakes that it makes. Most research has focused on learning from fixed
training sets of labeled data rather than interacting with a dialogue partner
in an online fashion. In this paper we explore this direction in a
reinforcement learning setting where the bot improves its question-answering
ability from feedback a teacher gives following its generated responses. We
build a simulator that tests various aspects of such learning in a synthetic
environment, and introduce models that work in this regime. Finally, real
experiments with Mechanical Turk validate the approach.
Belief Revision, Minimal Change and Relaxation: A General Framework based on Satisfaction Systems, and Applications to Description Logics
Authors: Marc Aiguier, Jamal Atif, Isabelle Bloch, Céline Hudelot
Date: 2015-02-08 00:00:00

Belief revision of knowledge bases represented by a set of sentences in a
given logic has been extensively studied but for specific logics, mainly
propositional, and also recently Horn and description logics. Here, we propose
to generalize this operation from a model-theoretic point of view, by defining
revision in an abstract model theory known under the name of satisfaction
systems. In this framework, we generalize to any satisfaction systems the
characterization of the well known AGM postulates given by Katsuno and
Mendelzon for propositional logic in terms of minimal change among
interpretations. Moreover, we study how to define revision, satisfying the AGM
postulates, from relaxation notions that have been first introduced in
description logics to define dissimilarity measures between concepts, and the
consequence of which is to relax the set of models of the old belief until it
becomes consistent with the new pieces of knowledge. We show how the proposed
general framework can be instantiated in different logics such as
propositional, first-order, description and Horn logics. In particular for
description logics, we introduce several concrete relaxation operators tailored
for the description logic $\ALC{}$ and its fragments $\EL{}$ and $\ELext{}$,
discuss their properties and provide some illustrative examples.
Tumour Ellipsification in Ultrasound Images for Treatment Prediction in Breast Cancer
Authors: Mehrdad J. Gangeh, Hamid. R. Tizhoosh, Kan Wu, Dun Huang, Hadi Tadayyon, Gregory J. Czarnota
Date: 2017-01-13 00:00:00

Recent advances in using quantitative ultrasound (QUS) methods have provided
a promising framework to non-invasively and inexpensively monitor or predict
the effectiveness of therapeutic cancer responses. One of the earliest steps in
using QUS methods is contouring a region of interest (ROI) inside the tumour in
ultrasound B-mode images. While manual segmentation is a very time-consuming
and tedious task for human experts, auto-contouring is also an extremely
difficult task for computers due to the poor quality of ultrasound B-mode
images. However, for the purpose of cancer response prediction, a rough
boundary of the tumour as an ROI is only needed. In this research, a
semi-automated tumour localization approach is proposed for ROI estimation in
ultrasound B-mode images acquired from patients with locally advanced breast
cancer (LABC). The proposed approach comprised several modules, including 1)
feature extraction using keypoint descriptors, 2) augmenting the feature
descriptors with the distance of the keypoints to the user-input pixel as the
centre of the tumour, 3) supervised learning using a support vector machine
(SVM) to classify keypoints as "tumour" or "non-tumour", and 4) computation of
an ellipse as an outline of the ROI representing the tumour. Experiments with
33 B-mode images from 10 LABC patients yielded promising results with an
accuracy of 76.7% based on the Dice coefficient performance measure. The
results demonstrated that the proposed method can potentially be used as the
first stage in a computer-assisted cancer response prediction system for
semi-automated contouring of breast tumours.
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
Authors: Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo, Matthew C. H. Lee, Hugh Salimbeni, Kai Arulkumaran, Murray Shanahan
Date: 2016-11-08 00:00:00
Tasks: Clustering, Human Pose Forecasting

We study a variant of the variational autoencoder model (VAE) with a Gaussian
mixture as a prior distribution, with the goal of performing unsupervised
clustering through deep generative models. We observe that the known problem of
over-regularisation that has been shown to arise in regular VAEs also manifests
itself in our model and leads to cluster degeneracy. We show that a heuristic
called minimum information constraint that has been shown to mitigate this
effect in VAEs can also be applied to improve unsupervised clustering
performance with our model. Furthermore we analyse the effect of this heuristic
and provide an intuition of the various processes with the help of
visualizations. Finally, we demonstrate the performance of our model on
synthetic data, MNIST and SVHN, showing that the obtained clusters are
distinct, interpretable and result in achieving competitive performance on
unsupervised clustering to the state-of-the-art results.
What Can I Do Now? Guiding Users in a World of Automated Decisions
Authors: Matthias Gallé
Date: 2017-01-13 00:00:00

More and more processes governing our lives use in some part an automatic
decision step, where -- based on a feature vector derived from an applicant --
an algorithm has the decision power over the final outcome. Here we present a
simple idea which gives some of the power back to the applicant by providing
her with alternatives which would make the decision algorithm decide
differently. It is based on a formalization reminiscent of methods used for
evasion attacks, and consists in enumerating the subspaces where the
classifiers decides the desired output. This has been implemented for the
specific case of decision forests (ensemble methods based on decision trees),
mapping the problem to an iterative version of enumerating $k$-cliques.
Truncation-free Hybrid Inference for DPMM
Authors: Arnim Bleier
Date: 2017-01-13 00:00:00
Tasks: Variational Inference

Dirichlet process mixture models (DPMM) are a cornerstone of Bayesian
non-parametrics. While these models free from choosing the number of components
a-priori, computationally attractive variational inference often reintroduces
the need to do so, via a truncation on the variational distribution. In this
paper we present a truncation-free hybrid inference for DPMM, combining the
advantages of sampling-based MCMC and variational methods. The proposed
hybridization enables more efficient variational updates, while increasing
model complexity only if needed. We evaluate the properties of the hybrid
updates and their empirical performance in single- as well as mixed-membership
models. Our method is easy to implement and performs favorably compared to
existing schemas.
On the links between argumentation-based reasoning and nonmonotonic reasoning
Authors: Zimi Li, Nir Oren, Simon Parsons
Date: 2017-01-13 00:00:00

In this paper we investigate the links between instantiated argumentation
systems and the axioms for non-monotonic reasoning described in [9] with the
aim of characterising the nature of argument based reasoning. In doing so, we
consider two possible interpretations of the consequence relation, and describe
which axioms are met by ASPIC+ under each of these interpretations. We then
consider the links between these axioms and the rationality postulates. Our
results indicate that argument based reasoning as characterised by ASPIC+ is -
according to the axioms of [9] - non-cumulative and non-monotonic, and
therefore weaker than the weakest non-monotonic reasoning systems they
considered possible. This weakness underpins ASPIC+'s success in modelling
other reasoning systems, and we conclude by considering the relationship
between ASPIC+ and other weak logical systems.
End-to-End ASR-free Keyword Search from Speech
Authors: Kartik Audhkhasi, Andrew Rosenberg, Abhinav Sethy, Bhuvana Ramabhadran, Brian Kingsbury
Date: 2017-01-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

End-to-end (E2E) systems have achieved competitive results compared to
conventional hybrid hidden Markov model (HMM)-deep neural network based
automatic speech recognition (ASR) systems. Such E2E systems are attractive due
to the lack of dependence on alignments between input acoustic and output
grapheme or HMM state sequence during training. This paper explores the design
of an ASR-free end-to-end system for text query-based keyword search (KWS) from
speech trained with minimal supervision. Our E2E KWS system consists of three
sub-systems. The first sub-system is a recurrent neural network (RNN)-based
acoustic auto-encoder trained to reconstruct the audio through a
finite-dimensional representation. The second sub-system is a character-level
RNN language model using embeddings learned from a convolutional neural
network. Since the acoustic and text query embeddings occupy different
representation spaces, they are input to a third feed-forward neural network
that predicts whether the query occurs in the acoustic utterance or not. This
E2E ASR-free KWS system performs respectably despite lacking a conventional ASR
system and trains much faster.
LIDE: Language Identification from Text Documents
Authors: Priyank Mathur, Arkajyoti Misra, Emrah Budur
Date: 2017-01-13 00:00:00
Tasks: Language Identification

The increase in the use of microblogging came along with the rapid growth on
short linguistic data. On the other hand deep learning is considered to be the
new frontier to extract meaningful information out of large amount of raw data
in an automated manner. In this study, we engaged these two emerging fields to
come up with a robust language identifier on demand, namely Language
Identification Engine (LIDE). As a result, we achieved 95.12% accuracy in
Discriminating between Similar Languages (DSL) Shared Task 2015 dataset, which
is comparable to the maximum reported accuracy of 95.54% achieved so far.
Restricted Boltzmann Machines with Gaussian Visible Units Guided by Pairwise Constraints
Authors: Jielei Chu, Hongjun Wang, Hua Meng, Peng Jin, Tianrui Li
Date: 2017-01-13 00:00:00
Tasks: Clustering

Restricted Boltzmann machines (RBMs) and their variants are usually trained
by contrastive divergence (CD) learning, but the training procedure is an
unsupervised learning approach, without any guidances of the background
knowledge. To enhance the expression ability of traditional RBMs, in this
paper, we propose pairwise constraints restricted Boltzmann machine with
Gaussian visible units (pcGRBM) model, in which the learning procedure is
guided by pairwise constraints and the process of encoding is conducted under
these guidances. The pairwise constraints are encoded in hidden layer features
of pcGRBM. Then, some pairwise hidden features of pcGRBM flock together and
another part of them are separated by the guidances. In order to deal with
real-valued data, the binary visible units are replaced by linear units with
Gausian noise in the pcGRBM model. In the learning process of pcGRBM, the
pairwise constraints are iterated transitions between visible and hidden units
during CD learning procedure. Then, the proposed model is inferred by
approximative gradient descent method and the corresponding learning algorithm
is designed in this paper. In order to compare the availability of pcGRBM and
traditional RBMs with Gaussian visible units, the features of the pcGRBM and
RBMs hidden layer are used as input 'data' for K-means, spectral clustering
(SP) and affinity propagation (AP) algorithms, respectively. A thorough
experimental evaluation is performed with sixteen image datasets of Microsoft
Research Asia Multimedia (MSRA-MM). The experimental results show that the
clustering performance of K-means, SP and AP algorithms based on pcGRBM model
are significantly better than traditional RBMs. In addition, the pcGRBM model
for clustering task shows better performance than some semi-supervised
clustering algorithms.
Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks
Authors: Julius Hannink, Thomas Kautz, Cristian F. Pasluosta, Karl-Günter Gaßmann, Jochen Klucken, Bjoern M. Eskofier
Date: 2016-09-12 00:00:00

Measurement of stride-related, biomechanical parameters is the common
rationale for objective gait impairment scoring. State-of-the-art double
integration approaches to extract these parameters from inertial sensor data
are, however, limited in their clinical applicability due to the underlying
assumptions. To overcome this, we present a method to translate the abstract
information provided by wearable sensors to context-related expert features
based on deep convolutional neural networks. Regarding mobile gait analysis,
this enables integration-free and data-driven extraction of a set of 8
spatio-temporal stride parameters. To this end, two modelling approaches are
compared: A combined network estimating all parameters of interest and an
ensemble approach that spawns less complex networks for each parameter
individually. The ensemble approach is outperforming the combined modelling in
the current application. On a clinically relevant and publicly available
benchmark dataset, we estimate stride length, width and medio-lateral change in
foot angle up to ${-0.15\pm6.09}$ cm, ${-0.09\pm4.22}$ cm and ${0.13 \pm
3.78^\circ}$ respectively. Stride, swing and stance time as well as heel and
toe contact times are estimated up to ${\pm 0.07}$, ${\pm0.05}$, ${\pm 0.07}$,
${\pm0.07}$ and ${\pm0.12}$ s respectively. This is comparable to and in parts
outperforming or defining state-of-the-art. Our results further indicate that
the proposed change in methodology could substitute assumption-driven
double-integration methods and enable mobile assessment of spatio-temporal
stride parameters in clinically critical situations as e.g. in the case of
spastic gait impairments.
Inferring Cognitive Models from Data using Approximate Bayesian Computation
Authors: Antti Kangasrääsiö, Kumaripaba Athukorala, Andrew Howes, Jukka Corander, Samuel Kaski, Antti Oulasvirta
Date: 2016-12-02 00:00:00

An important problem for HCI researchers is to estimate the parameter values
of a cognitive model from behavioral data. This is a difficult problem, because
of the substantial complexity and variety in human behavioral strategies. We
report an investigation into a new approach using approximate Bayesian
computation (ABC) to condition model parameters to data and prior knowledge. As
the case study we examine menu interaction, where we have click time data only
to infer a cognitive model that implements a search behaviour with parameters
such as fixation duration and recall probability. Our results demonstrate that
ABC (i) improves estimates of model parameter values, (ii) enables meaningful
comparisons between model variants, and (iii) supports fitting models to
individual users. ABC provides ample opportunities for theoretical HCI research
by allowing principled inference of model parameter values and their
uncertainty.
A dissimilarity-based approach to predictive maintenance with application to HVAC systems
Authors: Riccardo Satta, Stefano Cavallari, Eraldo Pomponi, Daniele Grasselli, Davide Picheo, Carlo Annis
Date: 2017-01-13 00:00:00

The goal of predictive maintenance is to forecast the occurrence of faults of
an appliance, in order to proactively take the necessary actions to ensure its
availability. In many application scenarios, predictive maintenance is applied
to a set of homogeneous appliances. In this paper, we firstly review taxonomies
and main methodologies currently used for condition-based maintenance;
secondly, we argue that the mutual dissimilarities of the behaviours of all
appliances of this set (the "cohort") can be exploited to detect upcoming
faults. Specifically, inspired by dissimilarity-based representations, we
propose a novel machine learning approach based on the analysis of concurrent
mutual differences of the measurements coming from the cohort. We evaluate our
method over one year of historical data from a cohort of 17 HVAC (Heating,
Ventilation and Air Conditioning) systems installed in an Italian hospital. We
show that certain kinds of faults can be foreseen with an accuracy, measured in
terms of area under the ROC curve, as high as 0.96.
Learning to Navigate in Complex Environments
Authors: Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andrew J. Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent SIfre, Koray Kavukcuoglu, Dharshan Kumaran, Raia Hadsell
Date: 2016-11-11 00:00:00
Tasks: Depth Estimation, Depth Prediction, General Classification, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning to navigate in complex environments with dynamic elements is an
important milestone in developing AI agents. In this work we formulate the
navigation question as a reinforcement learning problem and show that data
efficiency and task performance can be dramatically improved by relying on
additional auxiliary tasks leveraging multimodal sensory inputs. In particular
we consider jointly learning the goal-driven reinforcement learning problem
with auxiliary depth prediction and loop closure classification tasks. This
approach can learn to navigate from raw sensory input in complicated 3D mazes,
approaching human-level performance even under conditions where the goal
location changes frequently. We provide detailed analysis of the agent
behaviour, its ability to localise, and its network activity dynamics, showing
that the agent implicitly learns key navigation abilities.
Diffusion-based nonlinear filtering for multimodal data fusion with application to sleep stage assessment
Authors: Ori Katz, Ronen Talmon, Yu-Lun Lo, Hau-Tieng Wu
Date: 2017-01-13 00:00:00

The problem of information fusion from multiple data-sets acquired by
multimodal sensors has drawn significant research attention over the years. In
this paper, we focus on a particular problem setting consisting of a physical
phenomenon or a system of interest observed by multiple sensors. We assume that
all sensors measure some aspects of the system of interest with additional
sensor-specific and irrelevant components. Our goal is to recover the variables
relevant to the observed system and to filter out the nuisance effects of the
sensor-specific variables. We propose an approach based on manifold learning,
which is particularly suitable for problems with multiple modalities, since it
aims to capture the intrinsic structure of the data and relies on minimal prior
model knowledge. Specifically, we propose a nonlinear filtering scheme, which
extracts the hidden sources of variability captured by two or more sensors,
that are independent of the sensor-specific components. In addition to
presenting a theoretical analysis, we demonstrate our technique on real
measured data for the purpose of sleep stage assessment based on multiple,
multimodal sensor measurements. We show that without prior knowledge on the
different modalities and on the measured system, our method gives rise to a
data-driven representation that is well correlated with the underlying sleep
process and is robust to noise and sensor-specific effects.
Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network
Authors: Seunghyun Yoon, Hyeongu Yun, Yuna Kim, Gyu-tae Park, Kyomin Jung
Date: 2017-01-13 00:00:00
Tasks: Language Modeling, Language Modelling, Transfer Learning

In this paper, we propose an efficient transfer leaning methods for training
a personalized language model using a recurrent neural network with long
short-term memory architecture. With our proposed fast transfer learning
schemes, a general language model is updated to a personalized language model
with a small amount of user data and a limited computing resource. These
methods are especially useful for a mobile device environment while the data is
prevented from transferring out of the device for privacy purposes. Through
experiments on dialogue data in a drama, it is verified that our transfer
learning methods have successfully generated the personalized language model,
whose output is more similar to the personal language style in both qualitative
and quantitative aspects.
Kernel Approximation Methods for Speech Recognition
Authors: Avner May, Alireza Bagheri Garakani, Zhiyun Lu, Dong Guo, Kuan Liu, Aurélien Bellet, Linxi Fan, Michael Collins, Daniel Hsu, Brian Kingsbury, Michael Picheny, Fei Sha
Date: 2017-01-13 00:00:00
Tasks: feature selection, speech-recognition, Speech Recognition

We study large-scale kernel methods for acoustic modeling in speech
recognition and compare their performance to deep neural networks (DNNs). We
perform experiments on four speech recognition datasets, including the TIMIT
and Broadcast News benchmark tasks, and compare these two types of models on
frame-level performance metrics (accuracy, cross-entropy), as well as on
recognition metrics (word/character error rate). In order to scale kernel
methods to these large datasets, we use the random Fourier feature method of
Rahimi and Recht (2007). We propose two novel techniques for improving the
performance of kernel acoustic models. First, in order to reduce the number of
random features required by kernel models, we propose a simple but effective
method for feature selection. The method is able to explore a large number of
non-linear features while maintaining a compact model more efficiently than
existing approaches. Second, we present a number of frame-level metrics which
correlate very strongly with recognition performance when computed on the
heldout set; we take advantage of these correlations by monitoring these
metrics during training in order to decide when to stop learning. This
technique can noticeably improve the recognition performance of both DNN and
kernel models, while narrowing the gap between them. Additionally, we show that
the linear bottleneck method of Sainath et al. (2013) improves the performance
of our kernel models significantly, in addition to speeding up training and
making the models more compact. Together, these three methods dramatically
improve the performance of kernel acoustic models, making their performance
comparable to DNNs on the tasks we explored.
Real-Time Optical flow-based Video Stabilization for Unmanned Aerial Vehicles
Authors: Anli Lim, Bharath Ramesh, Yue Yang, Cheng Xiang, Zhi Gao, Feng Lin
Date: 2017-01-13 00:00:00
Tasks: Optical Flow Estimation, Video Stabilization

This paper describes the development of a novel algorithm to tackle the
problem of real-time video stabilization for unmanned aerial vehicles (UAVs).
There are two main components in the algorithm: (1) By designing a suitable
model for the global motion of UAV, the proposed algorithm avoids the necessity
of estimating the most general motion model, projective transformation, and
considers simpler motion models, such as rigid transformation and similarity
transformation. (2) To achieve a high processing speed, optical-flow based
tracking is employed in lieu of conventional tracking and matching methods used
by state-of-the-art algorithms. These two new ideas resulted in a real-time
stabilization algorithm, developed over two phases. Stage I considers
processing the whole sequence of frames in the video while achieving an average
processing speed of 50fps on several publicly available benchmark videos. Next,
Stage II undertakes the task of real-time video stabilization using a
multi-threading implementation of the algorithm designed in Stage I.