pwc-archive/papers-with-abstracts huggingface dataset:
Preference-Based Monte Carlo Tree Search
Authors: Tobias Joppen, Christian Wirth, Johannes Fürnkranz
Date: 2018-07-17 00:00:00

Monte Carlo tree search (MCTS) is a popular choice for solving sequential
anytime problems. However, it depends on a numeric feedback signal, which can
be difficult to define. Real-time MCTS is a variant which may only rarely
encounter states with an explicit, extrinsic reward. To deal with such cases,
the experimenter has to supply an additional numeric feedback signal in the
form of a heuristic, which intrinsically guides the agent. Recent work has
shown evidence that in different areas the underlying structure is ordinal and
not numerical. Hence erroneous and biased heuristics are inevitable, especially
in such domains. In this paper, we propose a MCTS variant which only depends on
qualitative feedback, and therefore opens up new applications for MCTS. We also
find indications that translating absolute into ordinal feedback may be
beneficial. Using a puzzle domain, we show that our preference-based MCTS
variant, wich only receives qualitative feedback, is able to reach a
performance level comparable to a regular MCTS baseline, which obtains
quantitative feedback.
Domain Adaptation for Deviating Acquisition Protocols in CNN-based Lesion Classification on Diffusion-Weighted MR Images
Authors: Jennifer Kamphenkel, Paul F. Jaeger, Sebastian Bickelhaupt, Frederik Bernd Laun, Wolfgang Lederer, Heidi Daniel, Tristan Anselm Kuder, Stefan Delorme, Heinz-Peter Schlemmer, Franziska Koenig, Klaus H. Maier-Hein
Date: 2018-07-17 00:00:00
Tasks: Cancer Classification, Domain Adaptation, General Classification, Lesion Classification

End-to-end deep learning improves breast cancer classification on
diffusion-weighted MR images (DWI) using a convolutional neural network (CNN)
architecture. A limitation of CNN as opposed to previous model-based approaches
is the dependence on specific DWI input channels used during training. However,
in the context of large-scale application, methods agnostic towards
heterogeneous inputs are desirable, due to the high deviation of scanning
protocols between clinical sites. We propose model-based domain adaptation to
overcome input dependencies and avoid re-training of networks at clinical sites
by restoring training inputs from altered input channels given during
deployment. We demonstrate the method's significant increase in classification
performance and superiority over implicit domain adaptation provided by
training-schemes operating on model-parameters instead of raw DWI images.
Real-time on-board obstacle avoidance for UAVs based on embedded stereo vision
Authors: Boitumelo Ruf, Sebastian Monka, Matthias Kollmann, Michael Grinberg
Date: 2018-07-17 00:00:00
Tasks: Disparity Estimation, High-Level Synthesis

In order to improve usability and safety, modern unmanned aerial vehicles (UAVs) are equipped with sensors to monitor the environment, such as laser-scanners and cameras. One important aspect in this monitoring process is to detect obstacles in the flight path in order to avoid collisions. Since a large number of consumer UAVs suffer from tight weight and power constraints, our work focuses on obstacle avoidance based on a lightweight stereo camera setup. We use disparity maps, which are computed from the camera images, to locate obstacles and to automatically steer the UAV around them. For disparity map computation we optimize the well-known semi-global matching (SGM) approach for the deployment on an embedded FPGA. The disparity maps are then converted into simpler representations, the so called U-/V-Maps, which are used for obstacle detection. Obstacle avoidance is based on a reactive approach which finds the shortest path around the obstacles as soon as they have a critical distance to the UAV. One of the fundamental goals of our work was the reduction of development costs by closing the gap between application development and hardware optimization. Hence, we aimed at using high-level synthesis (HLS) for porting our algorithms, which are written in C/C++, to the embedded FPGA. We evaluated our implementation of the disparity estimation on the KITTI Stereo 2015 benchmark. The integrity of the overall realtime reactive obstacle avoidance algorithm has been evaluated by using Hardware-in-the-Loop testing in conjunction with two flight simulators.
Bench-Marking Information Extraction in Semi-Structured Historical Handwritten Records
Authors: Animesh Prasad, Hervé Déjean, Jean-Luc Meunier, Max Weidemann, Johannes Michael, Gundram Leifert
Date: 2018-07-17 00:00:00
Tasks: Benchmarking, Handwritten Text Recognition, HTR, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

In this report, we present our findings from benchmarking experiments for
information extraction on historical handwritten marriage records Esposalles
from IEHHR - ICDAR 2017 robust reading competition. The information extraction
is modeled as semantic labeling of the sequence across 2 set of labels. This
can be achieved by sequentially or jointly applying handwritten text
recognition (HTR) and named entity recognition (NER). We deploy a pipeline
approach where first we use state-of-the-art HTR and use its output as input
for NER. We show that given low resource setup and simple structure of the
records, high performance of HTR ensures overall high performance. We explore
the various configurations of conditional random fields and neural networks to
benchmark NER on given certain noisy input. The best model on 10-fold
cross-validation as well as blind test data uses n-gram features with
bidirectional long short-term memory.
On Lebesgue Integral Quadrature
Authors: Vladislav Gennadievich Malyshkin
Date: 2018-07-17 00:00:00
Tasks: Gaussian Processes

A new type of quadrature is developed. The Gaussian quadrature, for a given measure, finds optimal values of a function's argument (nodes) and the corresponding weights. In contrast, the Lebesgue quadrature developed in this paper, finds optimal values of function (value-nodes) and the corresponding weights. The Gaussian quadrature groups sums by function argument; it can be viewed as a $n$-point discrete measure, producing the Riemann integral. The Lebesgue quadrature groups sums by function value; it can be viewed as a $n$-point discrete distribution, producing the Lebesgue integral. Mathematically, the problem is reduced to a generalized eigenvalue problem: Lebesgue quadrature value-nodes are the eigenvalues and the corresponding weights are the square of the averaged eigenvectors. A numerical estimation of an integral as the Lebesgue integral is especially advantageous when analyzing irregular and stochastic processes. The approach separates the outcome (value-nodes) and the probability of the outcome (weight). For this reason, it is especially well-suited for the study of non-Gaussian processes. The software implementing the theory is available from the authors.
Context-adaptive neural network based prediction for image compression
Authors: Thierry Dumas, Aline Roumy, Christine Guillemot
Date: 2018-07-17 00:00:00
Tasks: Decoder, Image Compression, Prediction

This paper describes a set of neural network architectures, called Prediction Neural Networks Set (PNNS), based on both fully-connected and convolutional neural networks, for intra image prediction. The choice of neural network for predicting a given image block depends on the block size, hence does not need to be signalled to the decoder. It is shown that, while fully-connected neural networks give good performance for small block sizes, convolutional neural networks provide better predictions in large blocks with complex textures. Thanks to the use of masks of random sizes during training, the neural networks of PNNS well adapt to the available context that may vary, depending on the position of the image block to be predicted. When integrating PNNS into a H.265 codec, PSNR-rate performance gains going from 1.46% to 5.20% are obtained. These gains are on average 0.99% larger than those of prior neural network based methods. Unlike the H.265 intra prediction modes, which are each specialized in predicting a specific texture, the proposed PNNS can model a large set of complex textures.
Hierarchical Multitask Learning for CTC-based Speech Recognition
Authors: Kalpesh Krishna, Shubham Toshniwal, Karen Livescu
Date: 2018-07-17 00:00:00
Tasks: Decoder, speech-recognition, Speech Recognition

Previous work has shown that neural encoder-decoder speech recognition can be
improved with hierarchical multitask learning, where auxiliary tasks are added
at intermediate layers of a deep encoder. We explore the effect of hierarchical
multitask learning in the context of connectionist temporal classification
(CTC)-based speech recognition, and investigate several aspects of this
approach. Consistent with previous work, we observe performance improvements on
telephone conversational speech recognition (specifically the Eval2000 test
sets) when training a subword-level CTC model with an auxiliary phone loss at
an intermediate layer. We analyze the effects of a number of experimental
variables (like interpolation constant and position of the auxiliary loss
function), performance in lower-resource settings, and the relationship between
pretraining and multitask learning. We observe that the hierarchical multitask
approach improves over standard multitask training in our higher-data
experiments, while in the low-resource settings standard multitask training
works well. The best results are obtained by combining hierarchical multitask
learning and pretraining, which improves word error rates by 3.4% absolute on
the Eval2000 test sets.
Robust Deep Multi-modal Learning Based on Gated Information Fusion Network
Authors: Jaekyum Kim, Junho Koh, Yecheol Kim, Jaehyung Choi, Youngbae Hwang, Jun Won Choi
Date: 2018-07-17 00:00:00
Tasks: Data Augmentation, object-detection, Object Detection

The goal of multi-modal learning is to use complimentary information on the
relevant task provided by the multiple modalities to achieve reliable and
robust performance. Recently, deep learning has led significant improvement in
multi-modal learning by allowing for the information fusion in the intermediate
feature levels. This paper addresses a problem of designing robust deep
multi-modal learning architecture in the presence of imperfect modalities. We
introduce deep fusion architecture for object detection which processes each
modality using the separate convolutional neural network (CNN) and constructs
the joint feature map by combining the intermediate features from the CNNs. In
order to facilitate the robustness to the degraded modalities, we employ the
gated information fusion (GIF) network which weights the contribution from each
modality according to the input feature maps to be fused. The weights are
determined through the convolutional layers followed by a sigmoid function and
trained along with the information fusion network in an end-to-end fashion. Our
experiments show that the proposed GIF network offers the additional
architectural flexibility to achieve robust performance in handling some
degraded modalities, and show a significant performance improvement based on
Single Shot Detector (SSD) for KITTI dataset using the proposed fusion network
and data augmentation schemes.
Genetic algorithms in Forth
Authors: S. I. Khashin, S. E. Vaganov
Date: 2018-07-17 00:00:00

A method for automatically finding a program (bytecode) realizing the given
algorithm is developed. The algorithm is specified as a set of tests
(input\_data) $ \rightarrow $ (output\_data). Genetic methods made it possible
to find the implementation of relatively complex algorithms: sorting, decimal
digits, GCD, LCM, factorial, prime divisors, binomial coefficients, and others.
The algorithms are implemented on a highly simplified version of Forth
language.
RuleMatrix: Visualizing and Understanding Classifiers with Rules
Authors: Yao Ming, Huamin Qu, Enrico Bertini
Date: 2018-07-17 00:00:00
Tasks: BIG-bench Machine Learning, Navigate

With the growing adoption of machine learning techniques, there is a surge of
research interest towards making machine learning systems more transparent and
interpretable. Various visualizations have been developed to help model
developers understand, diagnose, and refine machine learning models. However, a
large number of potential but neglected users are the domain experts with
little knowledge of machine learning but are expected to work with machine
learning systems. In this paper, we present an interactive visualization
technique to help users with little expertise in machine learning to
understand, explore and validate predictive models. By viewing the model as a
black box, we extract a standardized rule-based knowledge representation from
its input-output behavior. We design RuleMatrix, a matrix-based visualization
of rules to help users navigate and verify the rules and the black-box model.
We evaluate the effectiveness of RuleMatrix via two use cases and a usability
study.
Learning Generic Diffusion Processes for Image Restoration
Authors: Peng Qiao, Yong Dou, Yunjin Chen, Wensen Feng
Date: 2018-07-17 00:00:00
Tasks: Denoising, Image Restoration

Image restoration problems are typical ill-posed problems where the
regularization term plays an important role. The regularization term learned
via generative approaches is easy to transfer to various image restoration, but
offers inferior restoration quality compared with that learned via
discriminative approaches. On the contrary, the regularization term learned via
discriminative approaches are usually trained for a specific image restoration
problem, and fail in the problem for which it is not trained. To address this
issue, we propose a generic diffusion process (genericDP) to handle multiple
Gaussian denoising problems based on the Trainable Non-linear Reaction
Diffusion (TNRD) models. Instead of one model, which consists of a diffusion
and a reaction term, for one Gaussian denoising problem in TNRD, we enforce
multiple TNRD models to share one diffusion term. The trained genericDP model
can provide both promising denoising performance and high training efficiency
compared with the original TNRD models. We also transfer the trained diffusion
term to non-blind deconvolution which is unseen in the training phase.
Experiment results show that the trained diffusion term for multiple Gaussian
denoising can be transferred to image non-blind deconvolution as an image prior
and provide competitive performance.
Knockoffs for the mass: new feature importance statistics with false discovery guarantees
Authors: Jaime Roquero Gimenez, Amirata Ghorbani, James Zou
Date: 2018-07-17 00:00:00
Tasks: Feature Importance, valid

An important problem in machine learning and statistics is to identify features that causally affect the outcome. This is often impossible to do from purely observational data, and a natural relaxation is to identify features that are correlated with the outcome even conditioned on all other observed features. For example, we want to identify that smoking really is correlated with cancer conditioned on demographics. The knockoff procedure is a recent breakthrough in statistics that, in theory, can identify truly correlated features while guaranteeing that the false discovery is limited. The idea is to create synthetic data -- knockoffs -- that captures correlations amongst the features. However there are substantial computational and practical challenges to generating and using knockoffs. This paper makes several key advances that enable knockoff application to be more efficient and powerful. We develop an efficient algorithm to generate valid knockoffs from Bayesian Networks. Then we systematically evaluate knockoff test statistics and develop new statistics with improved power. The paper combines new mathematical guarantees with systematic experiments on real and synthetic data.
Low-Resource Contextual Topic Identification on Speech
Authors: Chunxi Liu, Matthew Wiesner, Shinji Watanabe, Craig Harman, Jan Trmal, Najim Dehak, Sanjeev Khudanpur
Date: 2018-07-17 00:00:00
Tasks: General Classification, Topic Classification, Translation

In topic identification (topic ID) on real-world unstructured audio, an audio
instance of variable topic shifts is first broken into sequential segments, and
each segment is independently classified. We first present a general purpose
method for topic ID on spoken segments in low-resource languages, using a
cascade of universal acoustic modeling, translation lexicons to English, and
English-language topic classification. Next, instead of classifying each
segment independently, we demonstrate that exploring the contextual
dependencies across sequential segments can provide large improvements. In
particular, we propose an attention-based contextual model which is able to
leverage the contexts in a selective manner. We test both our contextual and
non-contextual models on four LORELEI languages, and on all but one our
attention-based contextual model significantly outperforms the
context-independent models.
Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data
Authors: E. Kelly Buchanan, Ian Kinsella, Ding Zhou, Rong Zhu, Pengcheng Zhou, Felipe Gerhard, John Ferrante, Ying Ma, Sharon Kim, Mohammed Shaik, Yajie Liang, Rongwen Lu, Jacob Reimer, Paul Fahey, Taliah Muhammad, Graham Dempsey, Elizabeth Hillman, Na Ji, Andreas Tolias, Liam Paninski
Date: 2018-07-17 00:00:00
Tasks: Denoising

Calcium imaging has revolutionized systems neuroscience, providing the
ability to image large neural populations with single-cell resolution. The
resulting datasets are quite large, which has presented a barrier to routine
open sharing of this data, slowing progress in reproducible research. State of
the art methods for analyzing this data are based on non-negative matrix
factorization (NMF); these approaches solve a non-convex optimization problem,
and are effective when good initializations are available, but can break down
in low-SNR settings where common initialization approaches fail. Here we
introduce an approach to compressing and denoising functional imaging data. The
method is based on a spatially-localized penalized matrix decomposition (PMD)
of the data to separate (low-dimensional) signal from (temporally-uncorrelated)
noise. This approach can be applied in parallel on local spatial patches and is
therefore highly scalable, does not impose non-negativity constraints or
require stringent identifiability assumptions (leading to significantly more
robust results compared to NMF), and estimates all parameters directly from the
data, so no hand-tuning is required. We have applied the method to a wide range
of functional imaging data (including one-photon, two-photon, three-photon,
widefield, somatic, axonal, dendritic, calcium, and voltage imaging datasets):
in all cases, we observe ~2-4x increases in SNR and compression rates of
20-300x with minimal visible loss of signal, with no adjustment of
hyperparameters; this in turn facilitates the process of demixing the observed
activity into contributions from individual neurons. We focus on two
challenging applications: dendritic calcium imaging data and voltage imaging
data in the context of optogenetic stimulation. In both cases, we show that our
new approach leads to faster and much more robust extraction of activity from
the data.
Photo-unrealistic Image Enhancement for Subject Placement in Outdoor Photography
Authors: Christian Tendyck, Andrew Haddad, Mireille Boutin
Date: 2018-07-17 00:00:00
Tasks: Image Enhancement, valid

Camera display reflections are an issue in bright light situations, as they
may prevent users from correctly positioning the subject in the picture. We
propose a software solution to this problem, which consists in modifying the
image in the viewer, in real time. In our solution, the user is seeing a
posterized image which roughly represents the contour of the objects. Five
enhancement methods are compared in a user study. Our results indicate that the
problem considered is a valid one, as users had problems locating landmarks
nearly 37% of the time under sunny conditions, and that our proposed
enhancement method using contrasting colors is a practical solution to that
problem.
Privacy-preserving classifiers recognize shared mobility behaviours from WiFi network imperfect data
Authors: Orestes Manzanilla-Salazar, Brunilde Sansò
Date: 2018-07-17 00:00:00
Tasks: Privacy Preserving

This paper proves the concept that it is feasible to accurately recognize
specific human mobility shared patterns, based solely on the connection logs
between portable devices and WiFi Access Points (APs), while preserving user's
privacy. We gathered data from the Eduroam WiFi network of Polytechnique
Montreal, making omission of device tracking or physical layer data. The
behaviors we chose to detect were the movements associated to the end of an
academic class, and the patterns related to the small break periods between
classes. Stringent conditions were self-imposed in our experiments. The data is
known to have errors noise, and be susceptible to information loss. No
countermeasures were adopted to mitigate any of these issues. Data
pre-processing consists of basic statistics that were used in aggregating the
data in time intervals. We obtained accuracy values of 93.7 % and 83.3 % (via
Bagged Trees) when recognizing behaviour patterns of breaks between classes and
end-of-classes, respectively.
A Data-Driven Approach for Predicting Vegetation-Related Outages in Power Distribution Systems
Authors: Milad Doostan, Reza Sohrabi, Badrul Chowdhury
Date: 2018-07-17 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

This paper presents a novel data-driven approach for predicting the number of
vegetation-related outages that occur in power distribution systems on a
monthly basis. In order to develop an approach that is able to successfully
fulfill this objective, there are two main challenges that ought to be
addressed. The first challenge is to define the extent of the target area. An
unsupervised machine learning approach is proposed to overcome this difficulty.
The second challenge is to correctly identify the main causes of
vegetation-related outages and to thoroughly investigate their nature. In this
paper, these outages are categorized into two main groups: growth-related and
weather-related outages, and two types of models, namely time series and
non-linear machine learning regression models are proposed to conduct the
prediction tasks, respectively. Moreover, various features that can explain the
variability in vegetation-related outages are engineered and employed. Actual
outage data, obtained from a major utility in the U.S., in addition to
different types of weather and geographical data are utilized to build the
proposed approach. Finally, by utilizing various time series models and machine
learning methods, a comprehensive case study is carried out to demonstrate how
the proposed approach can be used to successfully predict the number of
vegetation-related outages and to help decision-makers to detect vulnerable
zones in their systems.