pwc-archive/papers-with-abstracts huggingface dataset:
On-Demand Learning for Deep Image Restoration
Authors: Ruohan Gao, Kristen Grauman
Date: 2016-12-05 00:00:00
Proceeding: ICCV 2017 10
Tasks: Deblurring, Denoising, Image Deblurring, Image Denoising, Image Inpainting, Image Restoration

While machine learning approaches to image restoration offer great promise,
current methods risk training models fixated on performing well only for image
corruption of a particular level of difficulty---such as a certain level of
noise or blur. First, we examine the weakness of conventional "fixated" models
and demonstrate that training general models to handle arbitrary levels of
corruption is indeed non-trivial. Then, we propose an on-demand learning
algorithm for training image restoration models with deep convolutional neural
networks. The main idea is to exploit a feedback mechanism to self-generate
training instances where they are needed most, thereby learning models that can
generalize across difficulty levels. On four restoration tasks---image
inpainting, pixel interpolation, image deblurring, and image denoising---and
three diverse datasets, our approach consistently outperforms both the status
quo training procedure and curriculum learning alternatives.
Streaming kernel regression with provably adaptive mean, variance, and regularization
Authors: Audrey Durand, Odalric-Ambrym Maillard, Joelle Pineau
Date: 2017-08-02 00:00:00
Tasks: regression, Thompson Sampling, valid

We consider the problem of streaming kernel regression, when the observations
arrive sequentially and the goal is to recover the underlying mean function,
assumed to belong to an RKHS. The variance of the noise is not assumed to be
known. In this context, we tackle the problem of tuning the regularization
parameter adaptively at each time step, while maintaining tight confidence
bounds estimates on the value of the mean function at each point. To this end,
we first generalize existing results for finite-dimensional linear regression
with fixed regularization and known variance to the kernel setup with a
regularization parameter allowed to be a measurable function of past
observations. Then, using appropriate self-normalized inequalities we build
upper and lower bound estimates for the variance, leading to Bersntein-like
concentration bounds. The later is used in order to define the adaptive
regularization. The bounds resulting from our technique are valid uniformly
over all observation points and all time steps, and are compared against the
literature with numerical experiments. Finally, the potential of these tools is
illustrated by an application to kernelized bandits, where we revisit the
Kernel UCB and Kernel Thompson Sampling procedures, and show the benefits of
the novel adaptive kernel tuning strategy.
Fairness-aware machine learning: a perspective
Authors: Indre Zliobaite
Date: 2017-08-02 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Fairness

Algorithms learned from data are increasingly used for deciding many aspects
in our life: from movies we see, to prices we pay, or medicine we get. Yet
there is growing evidence that decision making by inappropriately trained
algorithms may unintentionally discriminate people. For example, in automated
matching of candidate CVs with job descriptions, algorithms may capture and
propagate ethnicity related biases. Several repairs for selected algorithms
have already been proposed, but the underlying mechanisms how such
discrimination happens from the computational perspective are not yet
scientifically understood. We need to develop theoretical understanding how
algorithms may become discriminatory, and establish fundamental machine
learning principles for prevention. We need to analyze machine learning process
as a whole to systematically explain the roots of discrimination occurrence,
which will allow to devise global machine learning optimization criteria for
guaranteed prevention, as opposed to pushing empirical constraints into
existing algorithms case-by-case. As a result, the state-of-the-art will
advance from heuristic repairing, to proactive and theoretically supported
prevention. This is needed not only because law requires to protect vulnerable
people. Penetration of big data initiatives will only increase, and computer
science needs to provide solid explanations and accountability to the public,
before public concerns lead to unnecessarily restrictive regulations against
machine learning.
Rapid Exact Signal Scanning with Deep Convolutional Neural Networks
Authors: Markus Thom, Franz Gritschneder
Date: 2015-08-27 00:00:00

A rigorous formulation of the dynamics of a signal processing scheme aimed at
dense signal scanning without any loss in accuracy is introduced and analyzed.
Related methods proposed in the recent past lack a satisfactory analysis of
whether they actually fulfill any exactness constraints. This is improved
through an exact characterization of the requirements for a sound sliding
window approach. The tools developed in this paper are especially beneficial if
Convolutional Neural Networks are employed, but can also be used as a more
general framework to validate related approaches to signal scanning. The
proposed theory helps to eliminate redundant computations and renders special
case treatment unnecessary, resulting in a dramatic boost in efficiency
particularly on massively parallel processors. This is demonstrated both
theoretically in a computational complexity analysis and empirically on modern
parallel processors.
Helping AI to Play Hearthstone: AAIA'17 Data Mining Challenge
Authors: Andrzej Janusz, Maciej Świechowski, Tomasz Tajmajer
Date: 2017-08-02 00:00:00

This paper summarizes the AAIA'17 Data Mining Challenge: Helping AI to Play
Hearthstone which was held between March 23, and May 15, 2017 at the Knowledge
Pit platform. We briefly describe the scope and background of this competition
in the context of a more general project related to the development of an AI
engine for video games, called Grail. We also discuss the outcomes of this
challenge and demonstrate how predictive models for the assessment of player's
winning chances can be utilized in a construction of an intelligent agent for
playing Hearthstone. Finally, we show a few selected machine learning
approaches for modeling state and action values in Hearthstone. We provide
evaluation for a few promising solutions that may be used to create more
advanced types of agents, especially in conjunction with Monte Carlo Tree
Search algorithms.
The University of Edinburgh's Neural MT Systems for WMT17
Authors: Rico Sennrich, Alexandra Birch, Anna Currey, Ulrich Germann, Barry Haddow, Kenneth Heafield, Antonio Valerio Miceli Barone, Philip Williams
Date: 2017-08-02 00:00:00
Proceeding: WS 2017 9
Tasks: Decoder, Machine Translation, Translation

This paper describes the University of Edinburgh's submissions to the WMT17
shared news translation and biomedical translation tasks. We participated in 12
translation directions for news, translating between English and Czech, German,
Latvian, Russian, Turkish and Chinese. For the biomedical task we submitted
systems for English to Czech, German, Polish and Romanian. Our systems are
neural machine translation systems trained with Nematus, an attentional
encoder-decoder. We follow our setup from last year and build BPE-based models
with parallel and back-translated monolingual training data. Novelties this
year include the use of deep architectures, layer normalization, and more
compact models due to weight tying and improvements in BPE segmentations. We
perform extensive ablative experiments, reporting on the effectivenes of layer
normalization, deep architectures, and different ensembling techniques.
An Empirical Study of Language CNN for Image Captioning
Authors: Jiuxiang Gu, Gang Wang, Jianfei Cai, Tsuhan Chen
Date: 2016-12-21 00:00:00
Proceeding: ICCV 2017 10
Tasks: Caption Generation, Image Captioning, Language Modeling, Language Modelling

Language Models based on recurrent neural networks have dominated recent
image caption generation tasks. In this paper, we introduce a Language CNN
model which is suitable for statistical language modeling tasks and shows
competitive performance in image captioning. In contrast to previous models
which predict next word based on one previous word and hidden state, our
language CNN is fed with all the previous words and can model the long-range
dependencies of history words, which are critical for image captioning. The
effectiveness of our approach is validated on two datasets MS COCO and
Flickr30K. Our extensive experimental results show that our method outperforms
the vanilla recurrent neural network based language models and is competitive
with the state-of-the-art methods.
Dynamic Data Selection for Neural Machine Translation
Authors: Marlies van der Wees, Arianna Bisazza, Christof Monz
Date: 2017-08-02 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Machine Translation, NMT, Translation

Intelligent selection of training data has proven a successful technique to
simultaneously increase training efficiency and translation performance for
phrase-based machine translation (PBMT). With the recent increase in popularity
of neural machine translation (NMT), we explore in this paper to what extent
and how NMT can also benefit from data selection. While state-of-the-art data
selection (Axelrod et al., 2011) consistently performs well for PBMT, we show
that gains are substantially lower for NMT. Next, we introduce dynamic data
selection for NMT, a method in which we vary the selected subset of training
data between different training epochs. Our experiments show that the best
results are achieved when applying a technique we call gradual fine-tuning,
with improvements up to +2.6 BLEU over the original data selection approach and
up to +3.1 BLEU over a general baseline.
Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks
Authors: Sangheum Hwang, Sunggyun Park
Date: 2017-08-02 00:00:00
Tasks: Segmentation

We introduce an accurate lung segmentation model for chest radiographs based
on deep convolutional neural networks. Our model is based on atrous
convolutional layers to increase the field-of-view of filters efficiently. To
improve segmentation performances further, we also propose a multi-stage
training strategy, network-wise training, which the current stage network is
fed with both input images and the outputs from pre-stage network. It is shown
that this strategy has an ability to reduce falsely predicted labels and
produce smooth boundaries of lung fields. We evaluate the proposed model on a
common benchmark dataset, JSRT, and achieve the state-of-the-art segmentation
performances with much fewer model parameters.
Interpatient Respiratory Motion Model Transfer for Virtual Reality Simulations of Liver Punctures
Authors: Andre Mastmeyer, Matthias Wilms, Heinz Handels
Date: 2017-07-26 00:00:00

Current virtual reality (VR) training simulators of liver punctures often
rely on static 3D patient data and use an unrealistic (sinusoidal) periodic
animation of the respiratory movement. Existing methods for the animation of
breathing motion support simple mathematical or patient-specific, estimated
breathing models. However with personalized breathing models for each new
patient, a heavily dose relevant or expensive 4D data acquisition is mandatory
for keyframe-based motion modeling. Given the reference 4D data, first a model
building stage using linear regression motion field modeling takes place. Then
the methodology shown here allows the transfer of existing reference
respiratory motion models of a 4D reference patient to a new static 3D patient.
This goal is achieved by using non-linear inter-patient registration to warp
one personalized 4D motion field model to new 3D patient data. This cost- and
dose-saving new method is shown here visually in a qualitative proof-of-concept
study.
OmniArt: Multi-task Deep Learning for Artistic Data Analysis
Authors: Gjorgji Strezoski, Marcel Worring
Date: 2017-08-02 00:00:00
Tasks: Art Period Estimation (544 Artists), Deep Learning, Multi-Task Learning, Period Estimation

Vast amounts of artistic data is scattered on-line from both museums and art
applications. Collecting, processing and studying it with respect to all
accompanying attributes is an expensive process. With a motivation to speed up
and improve the quality of categorical analysis in the artistic domain, in this
paper we propose an efficient and accurate method for multi-task learning with
a shared representation applied in the artistic domain. We continue to show how
different multi-task configurations of our method behave on artistic data and
outperform handcrafted feature approaches as well as convolutional neural
networks. In addition to the method and analysis, we propose a challenge like
nature to the new aggregated data set with almost half a million samples and
structured meta-data to encourage further research and societal engagement.
Deep Detection of People and their Mobility Aids for a Hospital Robot
Authors: Andres Vasquez, Marina Kollmitz, Andreas Eitel, Wolfram Burgard
Date: 2017-08-02 00:00:00
Tasks: object-detection, Object Detection, Position, Region Proposal

Robots operating in populated environments encounter many different types of
people, some of whom might have an advanced need for cautious interaction,
because of physical impairments or their advanced age. Robots therefore need to
recognize such advanced demands to provide appropriate assistance, guidance or
other forms of support. In this paper, we propose a depth-based perception
pipeline that estimates the position and velocity of people in the environment
and categorizes them according to the mobility aids they use: pedestrian,
person in wheelchair, person in a wheelchair with a person pushing them, person
with crutches and person using a walker. We present a fast region proposal
method that feeds a Region-based Convolutional Network (Fast R-CNN). With this,
we speed up the object detection process by a factor of seven compared to a
dense sliding window approach. We furthermore propose a probabilistic position,
velocity and class estimator to smooth the CNN's detections and account for
occlusions and misclassifications. In addition, we introduce a new hospital
dataset with over 17,000 annotated RGB-D images. Extensive experiments confirm
that our pipeline successfully keeps track of people and their mobility aids,
even in challenging situations with multiple people from different categories
and frequent occlusions. Videos of our experiments and the dataset are
available at http://www2.informatik.uni-freiburg.de/~kollmitz/MobilityAids
Action recognition by learning pose representations
Authors: Alessia Saggese, Nicola Strisciuglio, Mario Vento, Nicolai Petkov
Date: 2017-08-02 00:00:00
Tasks: Action Classification, Action Recognition, Position, Temporal Action Localization

Pose detection is one of the fundamental steps for the recognition of human
actions. In this paper we propose a novel trainable detector for recognizing
human poses based on the analysis of the skeleton. The main idea is that a
skeleton pose can be described by the spatial arrangements of its joints.
Starting from this consideration, we propose a trainable pose detector, that
can be configured on a prototype skeleton in an automatic configuration
process. The result of the configuration is a model of the position of the
joints in the concerned skeleton. In the application phase, the joint positions
contained in the model are compared with the ones of their homologous joints in
the skeleton under test. The similarity of two skeletons is computed as a
combination of the position scores achieved by homologous joints. In this paper
we describe an action classification method based on the use of the proposed
trainable detectors to extract features from the skeletons. We performed
experiments on the publicly available MSDRA data set and the achieved results
confirm the effectiveness of the proposed approach.
Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings
Authors: Takuya Hiraoka, Masaaki Tsuchida, Yotaro Watanabe
Date: 2017-08-02 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper is the first attempt to learn the policy of an inquiry dialog
system (IDS) by using deep reinforcement learning (DRL). Most IDS frameworks
represent dialog states and dialog acts with logical formulae. In order to make
learning inquiry dialog policies more effective, we introduce a logical formula
embedding framework based on a recursive neural network. The results of
experiments to evaluate the effect of 1) the DRL and 2) the logical formula
embedding framework show that the combination of the two are as effective or
even better than existing rule-based methods for inquiry dialog policies.
Temporal Dynamic Graph LSTM for Action-driven Video Object Detection
Authors: Yuan Yuan, Xiaodan Liang, Xiaolong Wang, Dit-yan Yeung, Abhinav Gupta
Date: 2017-08-02 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, object-detection, Object Detection, Object Recognition, Video Object Detection, Weakly Supervised Object Detection

In this paper, we investigate a weakly-supervised object detection framework.
Most existing frameworks focus on using static images to learn object
detectors. However, these detectors often fail to generalize to videos because
of the existing domain shift. Therefore, we investigate learning these
detectors directly from boring videos of daily activities. Instead of using
bounding boxes, we explore the use of action descriptions as supervision since
they are relatively easy to gather. A common issue, however, is that objects of
interest that are not involved in human actions are often absent in global
action descriptions known as "missing label". To tackle this problem, we
propose a novel temporal dynamic graph Long Short-Term Memory network (TD-Graph
LSTM). TD-Graph LSTM enables global temporal reasoning by constructing a
dynamic graph that is based on temporal correlations of object proposals and
spans the entire video. The missing label issue for each individual frame can
thus be significantly alleviated by transferring knowledge across correlated
objects proposals in the whole video. Extensive evaluations on a large-scale
daily-life action dataset (i.e., Charades) demonstrates the superiority of our
proposed method. We also release object bounding-box annotations for more than
5,000 frames in Charades. We believe this annotated data can also benefit other
research on video-based object recognition in the future.
Active Decision Boundary Annotation with Deep Generative Models
Authors: Miriam W. Huijser, Jan C. van Gemert
Date: 2017-03-20 00:00:00
Proceeding: ICCV 2017 10
Tasks: Active Learning

This paper is on active learning where the goal is to reduce the data
annotation burden by interacting with a (human) oracle during training.
Standard active learning methods ask the oracle to annotate data samples.
Instead, we take a profoundly different approach: we ask for annotations of the
decision boundary. We achieve this using a deep generative model to create
novel instances along a 1d line. A point on the decision boundary is revealed
where the instances change class. Experimentally we show on three data sets
that our method can be plugged-in to other active learning schemes, that human
oracles can effectively annotate points on the decision boundary, that our
method is robust to annotation noise, and that decision boundary annotations
improve over annotating data samples.
Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks
Authors: Ruediger Ehlers
Date: 2017-05-03 00:00:00
Tasks: Collision Avoidance, Handwritten Digit Recognition

We present an approach for the verification of feed-forward neural networks
in which all nodes have a piece-wise linear activation function. Such networks
are often used in deep learning and have been shown to be hard to verify for
modern satisfiability modulo theory (SMT) and integer linear programming (ILP)
solvers.
  The starting point of our approach is the addition of a global linear
approximation of the overall network behavior to the verification problem that
helps with SMT-like reasoning over the network behavior. We present a
specialized verification algorithm that employs this approximation in a search
process in which it infers additional node phases for the non-linear nodes in
the network from partial node phase assignments, similar to unit propagation in
classical SAT solving. We also show how to infer additional conflict clauses
and safe node fixtures from the results of the analysis steps performed during
the search. The resulting approach is evaluated on collision avoidance and
handwritten digit recognition case studies.
Recurrent Neural Network Based Modeling of Gene Regulatory Network Using Bat Algorithm
Authors: Sudip Mandal, Goutam Saha, Rajat K. Pal
Date: 2015-08-21 00:00:00
Tasks: Time Series, Time Series Analysis

Correct inference of genetic regulations inside a cell is one of the greatest
challenges in post genomic era for the biologist and researchers. Several
intelligent techniques and models were already proposed to identify the
regulatory relations among genes from the biological database like time series
microarray data. Recurrent Neural Network (RNN) is one of the most popular and
simple approach to model the dynamics as well as to infer correct dependencies
among genes. In this paper, Bat Algorithm (BA) is applied to optimize the model
parameters of RNN model of Gene Regulatory Network (GRN). Initially the
proposed method is tested against small artificial network without any noise
and the efficiency is observed in term of number of iteration, number of
population and BA optimization parameters. The model is also validated in
presence of different level of random noise for the small artificial network
and that proved its ability to infer the correct inferences in presence of
noise like real world dataset. In the next phase of this research, BA based RNN
is applied to real world benchmark time series microarray dataset of E. coli.
The results prove that it can able to identify the maximum number of true
positive regulation but also include some false positive regulations.
Therefore, BA is very suitable for identifying biological plausible GRN with
the help RNN model.
Generation of High Dynamic Range Illumination from a Single Image for the Enhancement of Undesirably Illuminated Images
Authors: Jae Sung Park, Nam Ik Cho
Date: 2017-08-02 00:00:00

This paper presents an algorithm that enhances undesirably illuminated images
by generating and fusing multi-level illuminations from a single image.The
input image is first decomposed into illumination and reflectance components by
using an edge-preserving smoothing filter. Then the reflectance component is
scaled up to improve the image details in bright areas. The illumination
component is scaled up and down to generate several illumination images that
correspond to certain camera exposure values different from the original. The
virtual multi-exposure illuminations are blended into an enhanced illumination,
where we also propose a method to generate appropriate weight maps for the tone
fusion. Finally, an enhanced image is obtained by multiplying the equalized
illumination and enhanced reflectance. Experiments show that the proposed
algorithm produces visually pleasing output and also yields comparable
objective results to the conventional enhancement methods, while requiring
modest computational loads.