pwc-archive/papers-with-abstracts huggingface dataset:
Short-Term Prediction and Multi-Camera Fusion on Semantic Grids
Authors: Lukas Hoyer, Patrick Kesper, Anna Khoreva, Volker Fischer
Date: 2019-03-21 00:00:00
Tasks: Decision Making, Decoder, Semantic Segmentation, Sensor Fusion

An environment representation (ER) is a substantial part of every autonomous system. It introduces a common interface between perception and other system components, such as decision making, and allows downstream algorithms to deal with abstracted data without knowledge of the used sensor. In this work, we propose and evaluate a novel architecture that generates an egocentric, grid-based, predictive, and semantically-interpretable ER. In particular, we provide a proof of concept for the spatio-temporal fusion of multiple camera sequences and short-term prediction in such an ER. Our design utilizes a strong semantic segmentation network together with depth and egomotion estimates to first extract semantic information from multiple camera streams and then transform these separately into egocentric temporally-aligned bird's-eye view grids. A deep encoder-decoder network is trained to fuse a stack of these grids into a unified semantic grid representation and to predict the dynamics of its surrounding. We evaluate this representation on real-world sequences of the Cityscapes dataset and show that our architecture can make accurate predictions in complex sensor fusion scenarios and significantly outperforms a model-driven baseline in a category-based evaluation.
Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer
Authors: Ting-Rui Chiang, Chao-Wei Huang, Shang-Yu Su, Yun-Nung Chen
Date: 2019-03-21 00:00:00
Tasks: Response Generation, Sentence

With the increasing research interest in dialogue response generation, there
is an emerging branch formulating this task as selecting next sentences, where
given the partial dialogue contexts, the goal is to determine the most probable
next sentence. Following the recent success of the Transformer model, this
paper proposes (1) a new variant of attention mechanism based on multi-head
attention, called highway attention, and (2) a recurrent model based on
transformer and the proposed highway attention, so-called Highway Recurrent
Transformer. Experiments on the response selection task in the seventh Dialog
System Technology Challenge (DSTC7) show the capability of the proposed model
of modeling both utterance-level and dialogue-level information; the
effectiveness of each module is further analyzed as well.
Improving Machine Hearing on Limited Data Sets
Authors: Pavol Harar, Roswitha Bammer, Anna Breger, Monika Dörfler, Zdenek Smekal
Date: 2019-03-21 00:00:00
Tasks: Information Retrieval, Music Information Retrieval, Retrieval

Convolutional neural network (CNN) architectures have originated and revolutionized machine learning for images. In order to take advantage of CNNs in predictive modeling with audio data, standard FFT-based signal processing methods are often applied to convert the raw audio waveforms into an image-like representations (e.g. spectrograms). Even though conventional images and spectrograms differ in their feature properties, this kind of pre-processing reduces the amount of training data necessary for successful training. In this contribution we investigate how input and target representations interplay with the amount of available training data in a music information retrieval setting. We compare the standard mel-spectrogram inputs with a newly proposed representation, called Mel scattering. Furthermore, we investigate the impact of additional target data representations by using an augmented target loss function which incorporates unused available information. We observe that all proposed methods outperform the standard mel-transform representation when using a limited data set and discuss their strengths and limitations. The source code for reproducibility of our experiments as well as intermediate results and model checkpoints are available in an online repository.
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
Authors: Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei zhang, Abraham Bernstein, Huajun Chen
Date: 2019-03-21 00:00:00
Tasks: Entity Embeddings, Knowledge Graphs, Link Prediction

Reasoning is essential for the development of large knowledge graphs,
especially for completion, which aims to infer new triples based on existing
ones. Both rules and embeddings can be used for knowledge graph reasoning and
they have their own advantages and difficulties. Rule-based reasoning is
accurate and explainable but rule learning with searching over the graph always
suffers from efficiency due to huge search space. Embedding-based reasoning is
more scalable and efficient as the reasoning is conducted via computation
between embeddings, but it has difficulty learning good representations for
sparse entities because a good embedding relies heavily on data richness. Based
on this observation, in this paper we explore how embedding and rule learning
can be combined together and complement each other's difficulties with their
advantages. We propose a novel framework IterE iteratively learning embeddings
and rules, in which rules are learned from embeddings with proper pruning
strategy and embeddings are learned from existing triples and new triples
inferred by rules. Evaluations on embedding qualities of IterE show that rules
help improve the quality of sparse entity embeddings and their link prediction
results. We also evaluate the efficiency of rule learning and quality of rules
from IterE compared with AMIE+, showing that IterE is capable of generating
high quality rules more efficiently. Experiments show that iteratively learning
embeddings and rules benefit each other during learning and prediction.
The CASE Dataset of Candidate Spaces for Advert Implantation
Authors: Soumyabrata Dev, Murhaf Hossari, Matthew Nicholson, Killian McCabe, Atul Nautiyal, Clare Conran, Jian Tang, Wei Xu, François Pitié
Date: 2019-03-21 00:00:00
Tasks: Marketing, Semantic Segmentation

With the advent of faster internet services and growth of multimedia content,
we observe a massive growth in the number of online videos. The users generate
these video contents at an unprecedented rate, owing to the use of smart-phones
and other hand-held video capturing devices. This creates immense potential for
the advertising and marketing agencies to create personalized content for the
users. In this paper, we attempt to assist the video editors to generate
augmented video content, by proposing candidate spaces in video frames. We
propose and release a large-scale dataset of outdoor scenes, along with
manually annotated maps for candidate spaces. We also benchmark several
deep-learning based semantic segmentation algorithms on this proposed dataset.
Biasing MCTS with Features for General Games
Authors: Dennis J. N. J. Soemers, Éric Piette, Cameron Browne
Date: 2019-03-21 00:00:00
Tasks: Board Games

This paper proposes using a linear function approximator, rather than a deep
neural network (DNN), to bias a Monte Carlo tree search (MCTS) player for
general games. This is unlikely to match the potential raw playing strength of
DNNs, but has advantages in terms of generality, interpretability and resources
(time and hardware) required for training. Features describing local patterns
are used as inputs. The features are formulated in such a way that they are
easily interpretable and applicable to a wide range of general games, and might
encode simple local strategies. We gradually create new features during the
same self-play training process used to learn feature weights. We evaluate the
playing strength of an MCTS player biased by learnt features against a standard
upper confidence bounds for trees (UCT) player in multiple different board
games, and demonstrate significantly improved playing strength in the majority
of them after a small number of self-play training games.
Dynamic Power Management for Neuromorphic Many-Core Systems
Authors: Sebastian Hoeppner, Bernhard Vogginger, Yexin Yan, Andreas Dixius, Stefan Scholze, Johannes Partzsch, Felix Neumaerker, Stephan Hartmann, Stefan Schiefer, Georg Ellguth, Love Cederstroem, Luis Plana, Jim Garside, Steve Furber, Christian Mayr
Date: 2019-03-21 00:00:00
Tasks: Management

This work presents a dynamic power management architecture for neuromorphic
many core systems such as SpiNNaker. A fast dynamic voltage and frequency
scaling (DVFS) technique is presented which allows the processing elements (PE)
to change their supply voltage and clock frequency individually and
autonomously within less than 100 ns. This is employed by the neuromorphic
simulation software flow, which defines the performance level (PL) of the PE
based on the actual workload within each simulation cycle. A test chip in 28 nm
SLP CMOS technology has been implemented. It includes 4 PEs which can be scaled
from 0.7 V to 1.0 V with frequencies from 125 MHz to 500 MHz at three distinct
PLs. By measurement of three neuromorphic benchmarks it is shown that the total
PE power consumption can be reduced by 75%, with 80% baseline power reduction
and a 50% reduction of energy per neuron and synapse computation, all while
maintaining temporary peak system performance to achieve biological real-time
operation of the system. A numerical model of this power management model is
derived which allows DVFS architecture exploration for neuromorphics. The
proposed technique is to be used for the second generation SpiNNaker
neuromorphic many core system.
Learning with Batch-wise Optimal Transport Loss for 3D Shape Recognition
Authors: Lin Xu, Han Sun, Yuai Liu
Date: 2019-03-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Shape Recognition, Metric Learning, Retrieval, Triplet

Deep metric learning is essential for visual recognition. The widely used
pair-wise (or triplet) based loss objectives cannot make full use of semantical
information in training samples or give enough attention to those hard samples
during optimization. Thus, they often suffer from a slow convergence rate and
inferior performance. In this paper, we show how to learn an importance-driven
distance metric via optimal transport programming from batches of samples. It
can automatically emphasize hard examples and lead to significant improvements
in convergence. We propose a new batch-wise optimal transport loss and combine
it in an end-to-end deep metric learning manner. We use it to learn the
distance metric and deep feature representation jointly for recognition.
Empirical results on visual retrieval and classification tasks with six
benchmark datasets, i.e., MNIST, CIFAR10, SHREC13, SHREC14, ModelNet10, and
ModelNet40, demonstrate the superiority of the proposed method. It can
accelerate the convergence rate significantly while achieving a
state-of-the-art recognition performance. For example, in 3D shape recognition
experiments, we show that our method can achieve better recognition performance
within only 5 epochs than what can be obtained by mainstream 3D shape
recognition approaches after 200 epochs.
PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram
Authors: Shyam A, Vignesh Ravichandran, Preejith S. P, Jayaraj Joseph, Mohanasankar Sivaprakasam
Date: 2019-03-21 00:00:00
Tasks: Heart rate estimation, Transfer Learning

Photoplethysmogram (PPG) is increasingly used to provide monitoring of the
cardiovascular system under ambulatory conditions. Wearable devices like
smartwatches use PPG to allow long term unobtrusive monitoring of heart rate in
free living conditions. PPG based heart rate measurement is unfortunately
highly susceptible to motion artifacts, particularly when measured from the
wrist. Traditional machine learning and deep learning approaches rely on
tri-axial accelerometer data along with PPG to perform heart rate estimation.
The conventional learning based approaches have not addressed the need for
device-specific modeling due to differences in hardware design among PPG
devices. In this paper, we propose a novel end to end deep learning model to
perform heart rate estimation using 8 second length input PPG signal. We
evaluate the proposed model on the IEEE SPC 2015 dataset, achieving a mean
absolute error of 3.36+-4.1BPM for HR estimation on 12 subjects without
requiring patient specific training. We also studied the feasibility of
applying transfer learning along with sparse retraining from a comprehensive in
house PPG dataset for heart rate estimation across PPG devices with different
hardware design.
RAP-Net: Recurrent Attention Pooling Networks for Dialogue Response Selection
Authors: Chao-Wei Huang, Ting-Rui Chiang, Shang-Yu Su, Yun-Nung Chen
Date: 2019-03-21 00:00:00
Tasks: Diversity

The response selection has been an emerging research topic due to the growing
interest in dialogue modeling, where the goal of the task is to select an
appropriate response for continuing dialogues. To further push the end-to-end
dialogue model toward real-world scenarios, the seventh Dialog System
Technology Challenge (DSTC7) proposed a challenging track based on real chatlog
datasets. The competition focuses on dialogue modeling with several advanced
characteristics: (1) natural language diversity, (2) capability of precisely
selecting a proper response from a large set of candidates or the scenario
without any correct answer, and (3) knowledge grounding. This paper introduces
recurrent attention pooling networks (RAP-Net), a novel framework for response
selection, which can well estimate the relevance between the dialogue contexts
and the candidates. The proposed RAP-Net is shown to be effective and can be
generalized across different datasets and settings in the DSTC7 experiments.
Transferability of Operational Status Classification Models Among Different Wind Turbine Typesq
Authors: Z. Trstanova, A. Martinsson, C. Matthews, S. Jimenez, B. Leimkuhler, T. Van Delft, M. Wilkinson
Date: 2019-03-21 00:00:00
Tasks: Classification, General Classification

A detailed understanding of wind turbine performance status classification
can improve operations and maintenance in the wind energy industry. Due to
different engineering properties of wind turbines, the standard supervised
learning models used for classification do not generalize across data sets
obtained from different wind sites. We propose two methods to deal with the
transferability of the trained models: first, data normalization in the form of
power curve alignment, and second, a robust method based on convolutional
neural networks and feature-space extension. We demonstrate the success of our
methods on real-world data sets with industrial applications.
Towards Characterizing Divergence in Deep Q-Learning
Authors: Joshua Achiam, Ethan Knight, Pieter Abbeel
Date: 2019-03-21 00:00:00
Tasks: continuous-control, Continuous Control, MuJoCo, OpenAI Gym, Q-Learning, Reinforcement Learning

Deep Q-Learning (DQL), a family of temporal difference algorithms for
control, employs three techniques collectively known as the `deadly triad' in
reinforcement learning: bootstrapping, off-policy learning, and function
approximation. Prior work has demonstrated that together these can lead to
divergence in Q-learning algorithms, but the conditions under which divergence
occurs are not well-understood. In this note, we give a simple analysis based
on a linear approximation to the Q-value updates, which we believe provides
insight into divergence under the deadly triad. The central point in our
analysis is to consider when the leading order approximation to the deep-Q
update is or is not a contraction in the sup norm. Based on this analysis, we
develop an algorithm which permits stable deep Q-learning for continuous
control without any of the tricks conventionally used (such as target networks,
adaptive gradient optimizers, or using multiple Q functions). We demonstrate
that our algorithm performs above or near state-of-the-art on standard MuJoCo
benchmarks from the OpenAI Gym.
Context-Constrained Accurate Contour Extraction for Occlusion Edge Detection
Authors: Rui Lu, Menghan Zhou, Anlong Ming, Yu Zhou
Date: 2019-03-21 00:00:00
Tasks: Edge Detection

Occlusion edge detection requires both accurate locations and context
constraints of the contour. Existing CNN-based pipeline does not utilize
adaptive methods to filter the noise introduced by low-level features. To
address this dilemma, we propose a novel Context-constrained accurate Contour
Extraction Network (CCENet). Spatial details are retained and contour-sensitive
context is augmented through two extraction blocks, respectively. Then, an
elaborately designed fusion module is available to integrate features, which
plays a complementary role to restore details and remove clutter. Weight
response of attention mechanism is eventually utilized to enhance occluded
contours and suppress noise. The proposed CCENet significantly surpasses
state-of-the-art methods on PIOD and BSDS ownership dataset of object edge
detection and occlusion orientation detection.
Node Embedding over Temporal Graphs
Authors: Uriel Singer, Ido Guy, Kira Radinsky
Date: 2019-03-21 00:00:00
Tasks: Clustering, Link Prediction, Node Classification, Prediction

In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph's nodes and edges over time and incorporates this dynamics in a temporal node embedding framework for different graph prediction tasks. We present a joint loss function that creates a temporal embedding of a node by learning to combine its historical temporal embeddings, such that it optimizes per given task (e.g., link prediction). The algorithm is initialized using static node embeddings, which are then aligned over the representations of a node at different time points, and eventually adapted for the given task in a joint optimization. We evaluate the effectiveness of our approach over a variety of temporal graphs for the two fundamental tasks of temporal link prediction and multi-label node classification, comparing to competitive baselines and algorithmic alternatives. Our algorithm shows performance improvements across many of the datasets and baselines and is found particularly effective for graphs that are less cohesive, with a lower clustering coefficient.
Tensor-Ring Nuclear Norm Minimization and Application for Visual Data Completion
Authors: Jinshi Yu, Chao Li, Qibin Zhao, Guoxu Zhou
Date: 2019-03-21 00:00:00
Tasks: Missing Values

Tensor ring (TR) decomposition has been successfully used to obtain the
state-of-the-art performance in the visual data completion problem. However,
the existing TR-based completion methods are severely non-convex and
computationally demanding. In addition, the determination of the optimal TR
rank is a tough work in practice. To overcome these drawbacks, we first
introduce a class of new tensor nuclear norms by using tensor circular
unfolding. Then we theoretically establish connection between the rank of the
circularly-unfolded matrices and the TR ranks. We also develop an efficient
tensor completion algorithm by minimizing the proposed tensor nuclear norm.
Extensive experimental results demonstrate that our proposed tensor completion
method outperforms the conventional tensor completion methods in the
image/video in-painting problem with striped missing values.
Individualized Multilayer Tensor Learning with An Application in Imaging Analysis
Authors: Xiwei Tang, Xuan Bi, Annie Qu
Date: 2019-03-21 00:00:00
Tasks: Dimensionality Reduction, Tensor Decomposition

This work is motivated by multimodality breast cancer imaging data, which is
quite challenging in that the signals of discrete tumor-associated
microvesicles (TMVs) are randomly distributed with heterogeneous patterns. This
imposes a significant challenge for conventional imaging regression and
dimension reduction models assuming a homogeneous feature structure. We develop
an innovative multilayer tensor learning method to incorporate heterogeneity to
a higher-order tensor decomposition and predict disease status effectively
through utilizing subject-wise imaging features and multimodality information.
Specifically, we construct a multilayer decomposition which leverages an
individualized imaging layer in addition to a modality-specific tensor
structure. One major advantage of our approach is that we are able to
efficiently capture the heterogeneous spatial features of signals that are not
characterized by a population structure as well as integrating multimodality
information simultaneously. To achieve scalable computing, we develop a new
bi-level block improvement algorithm. In theory, we investigate both the
algorithm convergence property, tensor signal recovery error bound and
asymptotic consistency for prediction model estimation. We also apply the
proposed method for simulated and human breast cancer imaging data. Numerical
results demonstrate that the proposed method outperforms other existing
competing methods.
Convolutional neural network for detection and classification of seizures in clinical data
Authors: Tomas Iesmantas, Robertas Alzbutas
Date: 2019-03-21 00:00:00
Tasks: General Classification, Seizure Detection, Sensitivity, Specificity

Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usually are patient non-specific. Epilepsy patients suffer from severe detrimental effects like physical injury or depression due to unpredictable seizures. However, even in hospitals due to the high rate of false positives, the seizure alert systems are of poor help for patients as tools of seizure detection are mostly trained on unrealistically clean data, containing little noise and obtained under controlled laboratory conditions, where patient groups are homogeneous, e.g. in terms of age or type of seizures. In this study authors present the approach for detection and classification of a seizure using clinical data of electroencephalograms and a convolutional neural network trained on features of brain synchronisation and power spectrum. Various deep learning methods were applied, and the network was trained on a very heterogeneous clinical electroencephalogram dataset. In total, eight different types of seizures were considered, and the patients were of various ages, health conditions and they were observed under clinical conditions. Despite this, the classifier presented in this paper achieved sensitivity and specificity equal to 0.68 and 0.67, accordingly, which is a significant improvement as compared to the known results for clinical data.
Learning Disentangled Representations of Satellite Image Time Series
Authors: Eduardo Sanchez, Mathieu Serrurier, Mathias Ortner
Date: 2019-03-21 00:00:00
Tasks: Change Detection, image-classification, Image Classification, Image Retrieval, Image Segmentation, Image-to-Image Translation, Retrieval, Semantic Segmentation, Time Series, Time Series Analysis, Translation

In this paper, we investigate how to learn a suitable representation of
satellite image time series in an unsupervised manner by leveraging large
amounts of unlabeled data. Additionally , we aim to disentangle the
representation of time series into two representations: a shared representation
that captures the common information between the images of a time series and an
exclusive representation that contains the specific information of each image
of the time series. To address these issues, we propose a model that combines a
novel component called cross-domain autoencoders with the variational
autoencoder (VAE) and generative ad-versarial network (GAN) methods. In order
to learn disentangled representations of time series, our model learns the
multimodal image-to-image translation task. We train our model using satellite
image time series from the Sentinel-2 mission. Several experiments are carried
out to evaluate the obtained representations. We show that these disentangled
representations can be very useful to perform multiple tasks such as image
classification, image retrieval, image segmentation and change detection.