pwc-archive/papers-with-abstracts huggingface dataset:
Using phase instead of optical flow for action recognition
Authors: Omar Hommos, Silvia L. Pintea, Pascal S. M. Mettes, Jan C. van Gemert
Date: 2018-09-10 00:00:00
Tasks: Action Recognition, Motion Magnification, Optical Flow Estimation, Temporal Action Localization, Video Frame Interpolation

Currently, the most common motion representation for action recognition is
optical flow. Optical flow is based on particle tracking which adheres to a
Lagrangian perspective on dynamics. In contrast to the Lagrangian perspective,
the Eulerian model of dynamics does not track, but describes local changes. For
video, an Eulerian phase-based motion representation, using complex steerable
filters, has been successfully employed recently for motion magnification and
video frame interpolation. Inspired by these previous works, here, we proposes
learning Eulerian motion representations in a deep architecture for action
recognition. We learn filters in the complex domain in an end-to-end manner. We
design these complex filters to resemble complex Gabor filters, typically
employed for phase-information extraction. We propose a phase-information
extraction module, based on these complex filters, that can be used in any
network architecture for extracting Eulerian representations. We experimentally
analyze the added value of Eulerian motion representations, as extracted by our
proposed phase extraction module, and compare with existing motion
representations based on optical flow, on the UCF101 dataset.
Finding Better Topologies for Deep Convolutional Neural Networks by Evolution
Authors: Honglei Zhang, Serkan Kiranyaz, Moncef Gabbouj
Date: 2018-09-10 00:00:00
Tasks: Evolutionary Algorithms, image-classification, Image Classification

Due to the nonlinearity of artificial neural networks, designing topologies
for deep convolutional neural networks (CNN) is a challenging task and often
only heuristic approach, such as trial and error, can be applied. An
evolutionary algorithm can solve optimization problems where the fitness
landscape is unknown. However, evolutionary algorithms are computing resource
intensive, which makes it difficult for problems when deep CNNs are involved.
In this paper, we propose an evolutionary strategy to find better topologies
for deep CNNs. Incorporating the concept of knowledge inheritance and knowledge
learning, our evolutionary algorithm can be executed with limited computing
resources. We applied the proposed algorithm in finding effective topologies of
deep CNNs for the image classification task using CIFAR-10 dataset. After the
evolution, we analyzed the topologies that performed well for this task. Our
studies verify the techniques that have been commonly used in human designed
deep CNNs. We also discovered that some of the graph properties greatly affect
the system performance. We applied the guidelines learned from the evolution
and designed new network topologies that outperform Residual Net with less
layers on CIFAR-10, CIFAR-100, and SVHN dataset.
Multi-Context Deep Network for Angle-Closure Glaucoma Screening in Anterior Segment OCT
Authors: Huazhu Fu, Yanwu Xu, Stephen Lin, Damon Wing Kee Wong, Baskaran Mani, Meenakshi Mahesh, Tin Aung, Jiang Liu
Date: 2018-09-10 00:00:00
Tasks: Diagnostic, General Classification

A major cause of irreversible visual impairment is angle-closure glaucoma,
which can be screened through imagery from Anterior Segment Optical Coherence
Tomography (AS-OCT). Previous computational diagnostic techniques address this
screening problem by extracting specific clinical measurements or handcrafted
visual features from the images for classification. In this paper, we instead
propose to learn from training data a discriminative representation that may
capture subtle visual cues not modeled by predefined features. Based on
clinical priors, we formulate this learning with a presented Multi-Context Deep
Network (MCDN) architecture, in which parallel Convolutional Neural Networks
are applied to particular image regions and at corresponding scales known to be
informative for clinically diagnosing angle-closure glaucoma. The output
feature maps of the parallel streams are merged into a classification layer to
produce the deep screening result. Moreover, we incorporate estimated clinical
parameters to further enhance performance. On a clinical AS-OCT dataset, our
system is validated through comparisons to previous screening methods.
Gait learning for soft microrobots controlled by light fields
Authors: Alexander von Rohr, Sebastian Trimpe, Alonso Marco, Peer Fischer, Stefano Palagi
Date: 2018-09-10 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes

Soft microrobots based on photoresponsive materials and controlled by light
fields can generate a variety of different gaits. This inherent flexibility can
be exploited to maximize their locomotion performance in a given environment
and used to adapt them to changing conditions. Albeit, because of the lack of
accurate locomotion models, and given the intrinsic variability among
microrobots, analytical control design is not possible. Common data-driven
approaches, on the other hand, require running prohibitive numbers of
experiments and lead to very sample-specific results. Here we propose a
probabilistic learning approach for light-controlled soft microrobots based on
Bayesian Optimization (BO) and Gaussian Processes (GPs). The proposed approach
results in a learning scheme that is data-efficient, enabling gait optimization
with a limited experimental budget, and robust against differences among
microrobot samples. These features are obtained by designing the learning
scheme through the comparison of different GP priors and BO settings on a
semi-synthetic data set. The developed learning scheme is validated in
microrobot experiments, resulting in a 115% improvement in a microrobot's
locomotion performance with an experimental budget of only 20 tests. These
encouraging results lead the way toward self-adaptive microrobotic systems
based on light-controlled soft microrobots and probabilistic learning control.
Hand-tremor frequency estimation in videos
Authors: Silvia L. Pintea, Jian Zheng, XiLin Li, Paulina J. M. Bank, Jacobus J. van Hilten, Jan C. van Gemert
Date: 2018-09-10 00:00:00

We focus on the problem of estimating human hand-tremor frequency from input
RGB video data. Estimating tremors from video is important for non-invasive
monitoring, analyzing and diagnosing patients suffering from motor-disorders
such as Parkinson's disease. We consider two approaches for hand-tremor
frequency estimation: (a) a Lagrangian approach where we detect the hand at
every frame in the video, and estimate the tremor frequency along the
trajectory; and (b) an Eulerian approach where we first localize the hand, we
subsequently remove the large motion along the movement trajectory of the hand,
and we use the video information over time encoded as intensity values or phase
information to estimate the tremor frequency.
  We estimate hand tremors on a new human tremor dataset, TIM-Tremor,
containing static tasks as well as a multitude of more dynamic tasks, involving
larger motion of the hands. The dataset has 55 tremor patient recordings
together with: associated ground truth accelerometer data from the most
affected hand, RGB video data, and aligned depth data.
Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States
Authors: Peter Wolf, Karl Kurzer, Tobias Wingert, Florian Kuhnt, J. Marius Zöllner
Date: 2018-09-10 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

Making the right decision in traffic is a challenging task that is highly
dependent on individual preferences as well as the surrounding environment.
Therefore it is hard to model solely based on expert knowledge. In this work we
use Deep Reinforcement Learning to learn maneuver decisions based on a compact
semantic state representation. This ensures a consistent model of the
environment across scenarios as well as a behavior adaptation function,
enabling on-line changes of desired behaviors without re-training. The input
for the neural network is a simulated object list similar to that of Radar or
Lidar sensors, superimposed by a relational semantic scene description. The
state as well as the reward are extended by a behavior adaptation function and
a parameterization respectively. With little expert knowledge and a set of
mid-level actions, it can be seen that the agent is capable to adhere to
traffic rules and learns to drive safely in a variety of situations.
Towards JointUD: Part-of-speech Tagging and Lemmatization using Recurrent Neural Networks
Authors: Gor Arakelyan, Karen Hambardzumyan, Hrant Khachatrian
Date: 2018-09-10 00:00:00
Proceeding: CONLL 2018 10
Tasks: Dependency Parsing, Lemmatization, Part-Of-Speech Tagging, Sentence, Sentence segmentation

This paper describes our submission to CoNLL 2018 UD Shared Task. We have
extended an LSTM-based neural network designed for sequence tagging to
additionally generate character-level sequences. The network was jointly
trained to produce lemmas, part-of-speech tags and morphological features.
Sentence segmentation, tokenization and dependency parsing were handled by
UDPipe 1.2 baseline. The results demonstrate the viability of the proposed
multitask architecture, although its performance still remains far from
state-of-the-art.
Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Data
Authors: Jessa Bekker, Pieter Robberechts, Jesse Davis
Date: 2018-09-10 00:00:00
Tasks: Selection bias

Most positive and unlabeled data is subject to selection biases. The labeled examples can, for example, be selected from the positive set because they are easier to obtain or more obviously positive. This paper investigates how learning can be ena BHbled in this setting. We propose and theoretically analyze an empirical-risk-based method for incorporating the labeling mechanism. Additionally, we investigate under which assumptions learning is possible when the labeling mechanism is not fully understood and propose a practical method to enable this. Our empirical analysis supports the theoretical results and shows that taking into account the possibility of a selection bias, even when the labeling mechanism is unknown, improves the trained classifiers.
Learning Sequence Encoders for Temporal Knowledge Graph Completion
Authors: Alberto García-Durán, Sebastijan Dumančić, Mathias Niepert
Date: 2018-09-10 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Knowledge Graph Completion, Knowledge Graphs, Link Prediction, Relation, Temporal Knowledge Graph Completion

Research on link prediction in knowledge graphs has mainly focused on static
multi-relational data. In this work we consider temporal knowledge graphs where
relations between entities may only hold for a time interval or a specific
point in time. In line with previous work on static knowledge graphs, we
propose to address this problem by learning latent entity and relation type
representations. To incorporate temporal information, we utilize recurrent
neural networks to learn time-aware representations of relation types which can
be used in conjunction with existing latent factorization methods. The proposed
approach is shown to be robust to common challenges in real-world KGs: the
sparsity and heterogeneity of temporal expressions. Experiments show the
benefits of our approach on four temporal KGs. The data sets are available
under a permissive BSD-3 license 1.
Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search
Authors: Karl Kurzer, Florian Engelhorn, J. Marius Zöllner
Date: 2018-09-10 00:00:00

Urban traffic scenarios often require a high degree of cooperation between
traffic participants to ensure safety and efficiency. Observing the behavior of
others, humans infer whether or not others are cooperating. This work aims to
extend the capabilities of automated vehicles, enabling them to cooperate
implicitly in heterogeneous environments. Continuous actions allow for
arbitrary trajectories and hence are applicable to a much wider class of
problems than existing cooperative approaches with discrete action spaces.
Based on cooperative modeling of other agents, Monte Carlo Tree Search (MCTS)
in conjunction with Decoupled-UCT evaluates the action-values of each agent in
a cooperative and decentralized way, respecting the interdependence of actions
among traffic participants. The extension to continuous action spaces is
addressed by incorporating novel MCTS-specific enhancements for efficient
search space exploration. The proposed algorithm is evaluated under different
scenarios, showing that the algorithm is able to achieve effective cooperative
planning and generate solutions egocentric planning fails to identify.
Learning to Generate Structured Queries from Natural Language with Indirect Supervision
Authors: Ziwei Bai, Bo Yu, Bowen Wu, Zhuoran Wang, Baoxun Wang
Date: 2018-09-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Generating structured query language (SQL) from natural language is an
emerging research topic. This paper presents a new learning paradigm from
indirect supervision of the answers to natural language questions, instead of
SQL queries. This paradigm facilitates the acquisition of training data due to
the abundant resources of question-answer pairs for various domains in the
Internet, and expels the difficult SQL annotation job. An end-to-end neural
model integrating with reinforcement learning is proposed to learn SQL
generation policy within the answer-driven learning paradigm. The model is
evaluated on datasets of different domains, including movie and academic
publication. Experimental results show that our model outperforms the baseline
models.
Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge
Authors: Debanjan Chaudhuri, Agustinus Kristiadi, Jens Lehmann, Asja Fischer
Date: 2018-09-10 00:00:00
Proceeding: CONLL 2018 10

Building systems that can communicate with humans is a core problem in
Artificial Intelligence. This work proposes a novel neural network architecture
for response selection in an end-to-end multi-turn conversational dialogue
setting. The architecture applies context level attention and incorporates
additional external knowledge provided by descriptions of domain-specific
words. It uses a bi-directional Gated Recurrent Unit (GRU) for encoding context
and responses and learns to attend over the context words given the latent
response representation and vice versa.In addition, it incorporates external
domain specific information using another GRU for encoding the domain keyword
descriptions. This allows better representation of domain-specific keywords in
responses and hence improves the overall performance. Experimental results show
that our model outperforms all other state-of-the-art methods for response
selection in multi-turn conversations.
Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis
Authors: Francesco La Rosa, Mário João Fartaria, Tobias Kober, Jonas Richiardi, Cristina Granziera, Jean-Philippe Thiran, Meritxell Bach Cuadra
Date: 2018-09-10 00:00:00
Tasks: Lesion Segmentation, Segmentation

In this work, we present a comparison of a shallow and a deep learning
architecture for the automated segmentation of white matter lesions in MR
images of multiple sclerosis patients. In particular, we train and test both
methods on early stage disease patients, to verify their performance in
challenging conditions, more similar to a clinical setting than what is
typically provided in multiple sclerosis segmentation challenges. Furthermore,
we evaluate a prototype naive combination of the two methods, which refines the
final segmentation. All methods were trained on 32 patients, and the evaluation
was performed on a pure test set of 73 cases. Results show low lesion-wise
false positives (30%) for the deep learning architecture, whereas the shallow
architecture yields the best Dice coefficient (63%) and volume difference
(19%). Combining both shallow and deep architectures further improves the
lesion-wise metrics (69% and 26% lesion-wise true and false positive rate,
respectively).
Towards one-shot learning for rare-word translation with external experts
Authors: Ngoc-Quan Pham, Jan Niehues, Alex Waibel
Date: 2018-09-10 00:00:00
Proceeding: WS 2018 7
Tasks: Machine Translation, NMT, One-Shot Learning, Reinforcement Learning, Reinforcement Learning (RL), Translation, Word Translation

Neural machine translation (NMT) has significantly improved the quality of
automatic translation models. One of the main challenges in current systems is
the translation of rare words. We present a generic approach to address this
weakness by having external models annotate the training data as Experts, and
control the model-expert interaction with a pointer network and reinforcement
learning. Our experiments using phrase-based models to simulate Experts to
complement neural machine translation models show that the model can be trained
to copy the annotations into the output consistently. We demonstrate the
benefit of our proposed framework in outof-domain translation scenarios with
only lexical resources, improving more than 1.0 BLEU point in both translation
directions English to Spanish and German to English
Geoseg: A Computer Vision Package for Automatic Building Segmentation and Outline Extraction
Authors: Guangming Wu, Zhiling Guo
Date: 2018-09-10 00:00:00
Tasks: Computational Efficiency, Deep Learning

Recently, deep learning algorithms, especially fully convolutional network
based methods, are becoming very popular in the field of remote sensing.
However, these methods are implemented and evaluated through various datasets
and deep learning frameworks. There has not been a package that covers these
methods in a unifying manner. In this study, we introduce a computer vision
package termed Geoseg that focus on building segmentation and outline
extraction. Geoseg implements over nine state-of-the-art models as well as
utility scripts needed to conduct model training, logging, evaluating and
visualization. The implementation of Geoseg emphasizes unification, simplicity,
and flexibility. The performance and computational efficiency of all
implemented methods are evaluated by comparison experiment through a unified,
high-quality aerial image dataset.
The AAU Multimodal Annotation Toolboxes: Annotating Objects in Images and Videos
Authors: Chris H. Bahnsen, Andreas Møgelmose, Thomas B. Moeslund
Date: 2018-09-10 00:00:00
Tasks: General Classification, TAG

This tech report gives an introduction to two annotation toolboxes that
enable the creation of pixel and polygon-based masks as well as bounding boxes
around objects of interest. Both toolboxes support the annotation of sequential
images in the RGB and thermal modalities. Each annotated object is assigned a
classification tag, a unique ID, and one or more optional meta data tags. The
toolboxes are written in C++ with the OpenCV and Qt libraries and are operated
by using the visual interface and the extensive range of keyboard shortcuts.
Pre-built binaries are available for Windows and MacOS and the tools can be
built from source under Linux as well. So far, tens of thousands of frames have
been annotated using the toolboxes.
Short-Term Meaning Shift: A Distributional Exploration
Authors: Marco Del Tredici, Raquel Fernández, Gemma Boleda
Date: 2018-09-10 00:00:00
Proceeding: NAACL 2019 6

We present the first exploration of meaning shift over short periods of time
in online communities using distributional representations. We create a small
annotated dataset and use it to assess the performance of a standard model for
meaning shift detection on short-term meaning shift. We find that the model has
problems distinguishing meaning shift from referential phenomena, and propose a
measure of contextual variability to remedy this.
Learning Time Dependent Choice
Authors: Zachary Chase, Siddharth Prasad
Date: 2018-09-10 00:00:00
Tasks: Active Learning, PAC learning

We explore questions dealing with the learnability of models of choice over
time. We present a large class of preference models defined by a structural
criterion for which we are able to obtain an exponential improvement over
previously known learning bounds for more general preference models. This in
particular implies that the three most important discounted utility models of
intertemporal choice -- exponential, hyperbolic, and quasi-hyperbolic
discounting -- are learnable in the PAC setting with VC dimension that grows
logarithmically in the number of time periods. We also examine these models in
the framework of active learning. We find that the commonly studied
stream-based setting is in general difficult to analyze for preference models,
but we provide a redeeming situation in which the learner can indeed improve
upon the guarantees provided by PAC learning. In contrast to the stream-based
setting, we show that if the learner is given full power over the data he
learns from -- in the form of learning via membership queries -- even very
naive algorithms significantly outperform the guarantees provided by higher
level active learning algorithms.
A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising
Authors: Di Wu, Cheng Chen, Xun Yang, Xiujun Chen, Qing Tan, Jian Xu, Kun Gai
Date: 2018-09-10 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In online display advertising, guaranteed contracts and real-time bidding
(RTB) are two major ways to sell impressions for a publisher. Despite the
increasing popularity of RTB, there is still half of online display advertising
revenue generated from guaranteed contracts. Therefore, simultaneously selling
impressions through both guaranteed contracts and RTB is a straightforward
choice for a publisher to maximize its yield. However, deriving the optimal
strategy to allocate impressions is not a trivial task, especially when the
environment is unstable in real-world applications. In this paper, we formulate
the impression allocation problem as an auction problem where each contract can
submit virtual bids for individual impressions. With this formulation, we
derive the optimal impression allocation strategy by solving the optimal
bidding functions for contracts. Since the bids from contracts are decided by
the publisher, we propose a multi-agent reinforcement learning (MARL) approach
to derive cooperative policies for the publisher to maximize its yield in an
unstable environment. The proposed approach also resolves the common challenges
in MARL such as input dimension explosion, reward credit assignment, and
non-stationary environment. Experimental evaluations on large-scale real
datasets demonstrate the effectiveness of our approach.