pwc-archive/papers-with-abstracts huggingface dataset:
Object detection at 200 Frames Per Second
Authors: Rakesh Mehta, Cemalettin Ozturk
Date: 2018-05-16 00:00:00
Tasks: Object, object-detection, Object Detection

In this paper, we propose an efficient and fast object detector which can
process hundreds of frames per second. To achieve this goal we investigate
three main aspects of the object detection framework: network architecture,
loss function and training data (labeled and unlabeled). In order to obtain
compact network architecture, we introduce various improvements, based on
recent work, to develop an architecture which is computationally light-weight
and achieves a reasonable performance. To further improve the performance,
while keeping the complexity same, we utilize distillation loss function. Using
distillation loss we transfer the knowledge of a more accurate teacher network
to proposed light-weight student network. We propose various innovations to
make distillation efficient for the proposed one stage detector pipeline:
objectness scaled distillation loss, feature map non-maximal suppression and a
single unified distillation loss function for detection. Finally, building upon
the distillation loss, we explore how much can we push the performance by
utilizing the unlabeled data. We train our model with unlabeled data using the
soft labels of the teacher network. Our final network consists of 10x fewer
parameters than the VGG based object detection network and it achieves a speed
of more than 200 FPS and proposed changes improve the detection accuracy by 14
mAP over the baseline on Pascal dataset.
Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks
Authors: Timothy J. O'Shea, Tamoghna Roy, Nathan West
Date: 2018-05-16 00:00:00

Channel modeling is a critical topic when considering designing, learning, or
evaluating the performance of any communications system. Most prior work in
designing or learning new modulation schemes has focused on using highly
simplified analytic channel models such as additive white Gaussian noise
(AWGN), Rayleigh fading channels or similar. Recently, we proposed the usage of
a generative adversarial networks (GANs) to jointly approximate a wireless
channel response model (e.g. from real black box measurements) and optimize for
an efficient modulation scheme over it using machine learning. This approach
worked to some degree, but was unable to produce accurate probability
distribution functions (PDFs) representing the stochastic channel response. In
this paper, we focus specifically on the problem of accurately learning a
channel PDF using a variational GAN, introducing an architecture and loss
function which can accurately capture stochastic behavior. We illustrate where
our prior method failed and share results capturing the performance of such as
system over a range of realistic channel distributions.
Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks
Authors: Charley Gros, Benjamin De Leener, Atef Badji, Josefina Maranzano, Dominique Eden, Sara M. Dupont, Jason Talbott, Ren Zhuoquiong, Yaou Liu, Tobias Granberg, Russell Ouellette, Yasuhiko Tachibana, Masaaki Hori, Kouhei Kamiya, Lydia Chougar, Leszek Stawiarz, Jan Hillert, Elise Bannier, Anne Kerbrat, Gilles Edan, Pierre Labauge, Virginie Callot, Jean Pelletier, Bertrand Audoin, Henitsoa Rasoanandrianina, Jean-Christophe Brisset, Paola Valsasina, Maria A. Rocca, Massimo Filippi, Rohit Bakshi, Shahamat Tauhid, Ferran Prados, Marios Yiannakas, Hugh Kearney, Olga Ciccarelli, Seth Smith, Constantina Andrada Treaba, Caterina Mainero, Jennifer Lefeuvre, Daniel S. Reich, Govind Nair, Vincent Auclair, Donald G. McLaren, Allan R. Martin, Michael G. Fehlings, Shahabeddin Vahdat, Ali Khatibi, Julien Doyon, Timothy Shepherd, Erik Charlson, Sridar Narayanan, Julien Cohen-Adad
Date: 2018-05-16 00:00:00
Tasks: Lesion Segmentation, Prognosis, Segmentation

The spinal cord is frequently affected by atrophy and/or lesions in multiple
sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI
data provides measures of damage, which are key criteria for the diagnosis,
prognosis, and longitudinal monitoring in MS. Automating this operation
eliminates inter-rater variability and increases the efficiency of
large-throughput analysis pipelines. Robust and reliable segmentation across
multi-site spinal cord data is challenging because of the large variability
related to acquisition parameters and image artifacts. The goal of this study
was to develop a fully-automatic framework, robust to variability in both image
parameters and clinical condition, for segmentation of the spinal cord and
intramedullary MS lesions from conventional MRI data. Scans of 1,042 subjects
(459 healthy controls, 471 MS patients, and 112 with other spinal pathologies)
were included in this multi-site study (n=30). Data spanned three contrasts
(T1-, T2-, and T2*-weighted) for a total of 1,943 volumes. The proposed cord
and lesion automatic segmentation approach is based on a sequence of two
Convolutional Neural Networks (CNNs). To deal with the very small proportion of
spinal cord and/or lesion voxels compared to the rest of the volume, a first
CNN with 2D dilated convolutions detects the spinal cord centerline, followed
by a second CNN with 3D convolutions that segments the spinal cord and/or
lesions. When compared against manual segmentation, our CNN-based approach
showed a median Dice of 95% vs. 88% for PropSeg, a state-of-the-art spinal cord
segmentation method. Regarding lesion segmentation on MS data, our framework
provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise
detection sensitivity and precision of 83% and 77%, respectively. The proposed
framework is open-source and readily available in the Spinal Cord Toolbox.
ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time
Authors: Rudra P. K. Poudel, Ujwal Bonde, Stephan Liwicki, Christopher Zach
Date: 2018-05-11 00:00:00
Tasks: Segmentation, Semantic Segmentation

Modern deep learning architectures produce highly accurate results on many
challenging semantic segmentation datasets. State-of-the-art methods are,
however, not directly transferable to real-time applications or embedded
devices, since naive adaptation of such systems to reduce computational cost
(speed, memory and energy) causes a significant drop in accuracy. We propose
ContextNet, a new deep neural network architecture which builds on factorized
convolution, network compression and pyramid representation to produce
competitive semantic segmentation in real-time with low memory requirement.
ContextNet combines a deep network branch at low resolution that captures
global context information efficiently with a shallow branch that focuses on
high-resolution segmentation details. We analyse our network in a thorough
ablation study and present results on the Cityscapes dataset, achieving 66.1%
accuracy at 18.3 frames per second at full (1024x2048) resolution (41.9 fps
with pipelined computations for streamed data).
Entanglement-guided architectures of machine learning by quantum tensor network
Authors: Yuhan Liu, Xiao Zhang, Maciej Lewenstein, Shi-Ju Ran
Date: 2018-03-24 00:00:00
Tasks: BIG-bench Machine Learning

It is a fundamental, but still elusive question whether the schemes based on
quantum mechanics, in particular on quantum entanglement, can be used for
classical information processing and machine learning. Even partial answer to
this question would bring important insights to both fields of machine learning
and quantum mechanics. In this work, we implement simple numerical experiments,
related to pattern/images classification, in which we represent the classifiers
by many-qubit quantum states written in the matrix product states (MPS).
Classical machine learning algorithm is applied to these quantum states to
learn the classical data. We explicitly show how quantum entanglement (i.e.,
single-site and bipartite entanglement) can emerge in such represented images.
Entanglement characterizes here the importance of data, and such information
are practically used to guide the architecture of MPS, and improve the
efficiency. The number of needed qubits can be reduced to less than 1/10 of the
original number, which is within the access of the state-of-the-art quantum
computers. We expect such numerical experiments could open new paths in
charactering classical machine learning algorithms, and at the same time shed
lights on the generic quantum simulations/computations of machine learning
tasks.
Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography
Authors: Chang Liu, Xiangrui Zeng, Kaiwen Wang, Qiang Guo, Min Xu
Date: 2018-05-16 00:00:00
Tasks: Classification, Diversity, General Classification, Multi-Task Learning, Segmentation, Semantic Segmentation

Cellular Electron Cryo-Tomography (CECT) is a powerful 3D imaging tool for
studying the native structure and organization of macromolecules inside single
cells. For systematic recognition and recovery of macromolecular structures
captured by CECT, methods for several important tasks such as subtomogram
classification and semantic segmentation have been developed. However, the
recognition and recovery of macromolecular structures are still very difficult
due to high molecular structural diversity, crowding molecular environment, and
the imaging limitations of CECT. In this paper, we propose a novel multi-task
3D convolutional neural network model for simultaneous classification,
segmentation, and coarse structural recovery of macromolecules of interest in
subtomograms. In our model, the learned image features of one task are shared
and thereby mutually reinforce the learning of other tasks. Evaluated on
realistically simulated and experimental CECT data, our multi-task learning
model outperformed all single-task learning methods for classification and
segmentation. In addition, we demonstrate that our model can generalize to
discover, segment and recover novel structures that do not exist in the
training data.
Constructing Narrative Event Evolutionary Graph for Script Event Prediction
Authors: Zhongyang Li, Xiao Ding, Ting Liu
Date: 2018-05-14 00:00:00
Tasks: Graph Neural Network, Multiple-choice

Script event prediction requires a model to predict the subsequent event
given an existing event context. Previous models based on event pairs or event
chains cannot make full use of dense event connections, which may limit their
capability of event prediction. To remedy this, we propose constructing an
event graph to better utilize the event network information for script event
prediction. In particular, we first extract narrative event chains from large
quantities of news corpus, and then construct a narrative event evolutionary
graph (NEEG) based on the extracted chains. NEEG can be seen as a knowledge
base that describes event evolutionary principles and patterns. To solve the
inference problem on NEEG, we present a scaled graph neural network (SGNN) to
model event interactions and learn better event representations. Instead of
computing the representations on the whole graph, SGNN processes only the
concerned nodes each time, which makes our model feasible to large-scale
graphs. By comparing the similarity between input context event representations
and candidate event representations, we can choose the most reasonable
subsequent event. Experimental results on widely used New York Times corpus
demonstrate that our model significantly outperforms state-of-the-art baseline
methods, by using standard multiple choice narrative cloze evaluation.
Robust 6D Object Pose Estimation with Stochastic Congruent Sets
Authors: Chaitanya Mitash, Abdeslam Boularias, Kostas Bekris
Date: 2018-05-16 00:00:00
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, Object, Pose Estimation, Segmentation, Semantic Segmentation, Stochastic Optimization

Object pose estimation is frequently achieved by first segmenting an RGB
image and then, given depth data, registering the corresponding point cloud
segment against the object's 3D model. Despite the progress due to CNNs,
semantic segmentation output can be noisy, especially when the CNN is only
trained on synthetic data. This causes registration methods to fail in
estimating a good object pose. This work proposes a novel stochastic
optimization process that treats the segmentation output of CNNs as a
confidence probability. The algorithm, called Stochastic Congruent Sets
(StoCS), samples pointsets on the point cloud according to the soft
segmentation distribution and so as to agree with the object's known geometry.
The pointsets are then matched to congruent sets on the 3D object model to
generate pose estimates. StoCS is shown to be robust on an APC dataset, despite
the fact the CNN is trained only on synthetic data. In the YCB dataset, StoCS
outperforms a recent network for 6D pose estimation and alternative pointset
matching techniques.
DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction
Authors: Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He, Zhenhua Dong
Date: 2018-04-12 00:00:00
Tasks: Click-Through Rate Prediction, Feature Engineering, Recommendation Systems

Learning sophisticated feature interactions behind user behaviors is critical
in maximizing CTR for recommender systems. Despite great progress, existing
methods have a strong bias towards low- or high-order interactions, or rely on
expertise feature engineering. In this paper, we show that it is possible to
derive an end-to-end learning model that emphasizes both low- and high-order
feature interactions. The proposed framework, DeepFM, combines the power of
factorization machines for recommendation and deep learning for feature
learning in a new neural network architecture. Compared to the latest Wide &
Deep model from Google, DeepFM has a shared raw feature input to both its
"wide" and "deep" components, with no need of feature engineering besides raw
features. DeepFM, as a general learning framework, can incorporate various
network architectures in its deep component. In this paper, we study two
instances of DeepFM where its "deep" component is DNN and PNN respectively, for
which we denote as DeepFM-D and DeepFM-P. Comprehensive experiments are
conducted to demonstrate the effectiveness of DeepFM-D and DeepFM-P over the
existing models for CTR prediction, on both benchmark data and commercial data.
We conduct online A/B test in Huawei App Market, which reveals that DeepFM-D
leads to more than 10% improvement of click-through rate in the production
environment, compared to a well-engineered LR model. We also covered related
practice in deploying our framework in Huawei App Market.
Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds
Authors: Daniele Grattarola, Daniele Zambon, Cesare Alippi, Lorenzo Livi
Date: 2018-05-16 00:00:00
Tasks: Change Detection, Functional Connectivity, Seizure Detection

The space of graphs is often characterised by a non-trivial geometry, which
complicates learning and inference in practical applications. A common approach
is to use embedding techniques to represent graphs as points in a conventional
Euclidean space, but non-Euclidean spaces have often been shown to be better
suited for embedding graphs. Among these, constant-curvature Riemannian
manifolds (CCMs) offer embedding spaces suitable for studying the statistical
properties of a graph distribution, as they provide ways to easily compute
metric geodesic distances. In this paper, we focus on the problem of detecting
changes in stationarity in a stream of attributed graphs. To this end, we
introduce a novel change detection framework based on neural networks and CCMs,
that takes into account the non-Euclidean nature of graphs. Our contribution in
this work is twofold. First, via a novel approach based on adversarial
learning, we compute graph embeddings by training an autoencoder to represent
graphs on CCMs. Second, we introduce two novel change detection tests operating
on CCMs. We perform experiments on synthetic data, as well as two real-world
application scenarios: the detection of epileptic seizures using functional
connectivity brain networks, and the detection of hostility between two
subjects, using human skeletal graphs. Results show that the proposed methods
are able to detect even small changes in a graph-generating process,
consistently outperforming approaches based on Euclidean embeddings.
Clustering, Coding, and the Concept of Similarity
Authors: L. Thorne McCarty
Date: 2014-01-10 00:00:00
Tasks: Clustering

This paper develops a theory of clustering and coding which combines a
geometric model with a probabilistic model in a principled way. The geometric
model is a Riemannian manifold with a Riemannian metric, ${g}_{ij}({\bf x})$,
which we interpret as a measure of dissimilarity. The probabilistic model
consists of a stochastic process with an invariant probability measure which
matches the density of the sample input data. The link between the two models
is a potential function, $U({\bf x})$, and its gradient, $\nabla U({\bf x})$.
We use the gradient to define the dissimilarity metric, which guarantees that
our measure of dissimilarity will depend on the probability measure. Finally,
we use the dissimilarity metric to define a coordinate system on the embedded
Riemannian manifold, which gives us a low-dimensional encoding of our original
data.
A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks
Authors: Chandrakant Bothe, Cornelius Weber, Sven Magg, Stefan Wermter
Date: 2018-05-16 00:00:00
Proceeding: LREC 2018 5
Tasks: Dialogue Act Classification, General Classification, Language Modeling, Language Modelling, Natural Language Understanding

Dialogue act recognition is an important part of natural language
understanding. We investigate the way dialogue act corpora are annotated and
the learning approaches used so far. We find that the dialogue act is
context-sensitive within the conversation for most of the classes.
Nevertheless, previous models of dialogue act classification work on the
utterance-level and only very few consider context. We propose a novel
context-based learning method to classify dialogue acts using a character-level
language model utterance representation, and we notice significant improvement.
We evaluate this method on the Switchboard Dialogue Act corpus, and our results
show that the consideration of the preceding utterances as a context of the
current utterance improves dialogue act detection.
Image Colorization with Generative Adversarial Networks
Authors: Kamyar Nazeri, Eric Ng, Mehran Ebrahimi
Date: 2018-03-14 00:00:00
Tasks: Colorization, Generative Adversarial Network, Image Colorization

Over the last decade, the process of automatic image colorization has been of
significant interest for several application areas including restoration of
aged or degraded images. This problem is highly ill-posed due to the large
degrees of freedom during the assignment of color information. Many of the
recent developments in automatic colorization involve images that contain a
common theme or require highly processed data such as semantic maps as input.
In our approach, we attempt to fully generalize the colorization procedure
using a conditional Deep Convolutional Generative Adversarial Network (DCGAN),
extend current methods to high-resolution images and suggest training
strategies that speed up the process and greatly stabilize it. The network is
trained over datasets that are publicly available such as CIFAR-10 and
Places365. The results of the generative model and traditional deep neural
networks are compared.
Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces
Authors: Stefan Klus, Ingmar Schuster, Krikamol Muandet
Date: 2017-12-05 00:00:00

Transfer operators such as the Perron--Frobenius or Koopman operator play an important role in the global analysis of complex dynamical systems. The eigenfunctions of these operators can be used to detect metastable sets, to project the dynamics onto the dominant slow processes, or to separate superimposed signals. We extend transfer operator theory to reproducing kernel Hilbert spaces and show that these operators are related to Hilbert space representations of conditional distributions, known as conditional mean embeddings in the machine learning community. Moreover, numerical methods to compute empirical estimates of these embeddings are akin to data-driven methods for the approximation of transfer operators such as extended dynamic mode decomposition and its variants. One main benefit of the presented kernel-based approaches is that these methods can be applied to any domain where a similarity measure given by a kernel is available. We illustrate the results with the aid of guiding examples and highlight potential applications in molecular dynamics as well as video and text data analysis.
Unbiased split variable selection for random survival forests using maximally selected rank statistics
Authors: Marvin N. Wright, Theresa Dankowski, Andreas Ziegler
Date: 2016-05-11 00:00:00
Tasks: Selection bias, Survival Prediction, Variable Selection

The most popular approach for analyzing survival data is the Cox regression
model. The Cox model may, however, be misspecified, and its proportionality
assumption may not always be fulfilled. An alternative approach for survival
prediction is random forests for survival outcomes. The standard split
criterion for random survival forests is the log-rank test statistics, which
favors splitting variables with many possible split points. Conditional
inference forests avoid this split variable selection bias. However, linear
rank statistics are utilized by default in conditional inference forests to
select the optimal splitting variable, which cannot detect non-linear effects
in the independent variables. An alternative is to use maximally selected rank
statistics for the split point selection. As in conditional inference forests,
splitting variables are compared on the p-value scale. However, instead of the
conditional Monte-Carlo approach used in conditional inference forests, p-value
approximations are employed. We describe several p-value approximations and the
implementation of the proposed random forest approach. A simulation study
demonstrates that unbiased split variable selection is possible. However, there
is a trade-off between unbiased split variable selection and runtime. In
benchmark studies of prediction performance on simulated and real datasets the
new method performs better than random survival forests if informative
dichotomous variables are combined with uninformative variables with more
categories and better than conditional inference forests if non-linear
covariate effects are included. In a runtime comparison the method proves to be
computationally faster than both alternatives, if a simple p-value
approximation is used.
A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss
Authors: Wan-Ting Hsu, Chieh-Kai Lin, Ming-Ying Lee, Kerui Min, Jing Tang, Min Sun
Date: 2018-05-16 00:00:00
Proceeding: ACL 2018 7
Tasks: Abstractive Text Summarization, Sentence

We propose a unified model combining the strength of extractive and
abstractive summarization. On the one hand, a simple extractive model can
obtain sentence-level attention with high ROUGE scores but less readable. On
the other hand, a more complicated abstractive model can obtain word-level
dynamic attention to generate a more readable paragraph. In our model,
sentence-level attention is used to modulate the word-level attention such that
words in less attended sentences are less likely to be generated. Moreover, a
novel inconsistency loss function is introduced to penalize the inconsistency
between two levels of attentions. By end-to-end training our model with the
inconsistency loss and original losses of extractive and abstractive models, we
achieve state-of-the-art ROUGE scores while being the most informative and
readable summarization on the CNN/Daily Mail dataset in a solid human
evaluation.