pwc-archive/papers-with-abstracts huggingface dataset:
Fully automated primary particle size analysis of agglomerates on transmission electron microscopy images via artificial neural networks
Authors: Max Frei, Frank Einar Kruis
Date: 2018-06-08 00:00:00

There is a high demand for fully automated methods for the analysis of
primary particle size distributions of agglomerates on transmission electron
microscopy images. Therefore, a novel method, based on the utilization of
artificial neural networks, was proposed, implemented and validated. The
training of the artificial neural networks requires large quantities (up to
several hundreds of thousands) of transmission electron microscopy images of
agglomerates consisting of primary particles with known sizes. Since the manual
evaluation of such large amounts of transmission electron microscopy images is
not feasible, a synthesis of lifelike transmission electron microscopy images
as training data was implemented. The proposed method can compete with
state-of-the-art automated imaging particle size methods like the Hough
transformation, ultimate erosion and watershed transformation and is in some
cases even able to outperform these methods. It is however still outperformed
by the manual analysis.
Text Classification based on Word Subspace with Term-Frequency
Authors: Erica K. Shimomoto, Lincon S. Souza, Bernardo B. Gatto, Kazuhiro Fukui
Date: 2018-06-08 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

Text classification has become indispensable due to the rapid increase of
text in digital form. Over the past three decades, efforts have been made to
approach this task using various learning algorithms and statistical models
based on bag-of-words (BOW) features. Despite its simple implementation, BOW
features lack semantic meaning representation. To solve this problem, neural
networks started to be employed to learn word vectors, such as the word2vec.
Word2vec embeds word semantic structure into vectors, where the angle between
vectors indicates the meaningful similarity between words. To measure the
similarity between texts, we propose the novel concept of word subspace, which
can represent the intrinsic variability of features in a set of word vectors.
Through this concept, it is possible to model text from word vectors while
holding semantic information. To incorporate the word frequency directly in the
subspace model, we further extend the word subspace to the term-frequency (TF)
weighted word subspace. Based on these new concepts, text classification can be
performed under the mutual subspace method (MSM) framework. The validity of our
modeling is shown through experiments on the Reuters text database, comparing
the results to various state-of-art algorithms.
Machine Learning CICY Threefolds
Authors: Kieran Bull, Yang-Hui He, Vishnu Jejjala, Challenger Mishra
Date: 2018-06-08 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic

The latest techniques from Neural Networks and Support Vector Machines (SVM)
are used to investigate geometric properties of Complete Intersection
Calabi-Yau (CICY) threefolds, a class of manifolds that facilitate string model
building. An advanced neural network classifier and SVM are employed to (1)
learn Hodge numbers and report a remarkable improvement over previous efforts,
(2) query for favourability, and (3) predict discrete symmetries, a highly
imbalanced problem to which both Synthetic Minority Oversampling Technique
(SMOTE) and permutations of the CICY matrix are used to decrease the class
imbalance and improve performance. In each case study, we employ a genetic
algorithm to optimise the hyperparameters of the neural network. We demonstrate
that our approach provides quick diagnostic tools capable of shortlisting
quasi-realistic string models based on compactification over smooth CICYs and
further supports the paradigm that classes of problems in algebraic geometry
can be machine learned.
Noisy subspace clustering via matching pursuits
Authors: Michael Tschannen, Helmut Bölcskei
Date: 2016-12-11 00:00:00
Tasks: Clustering

Sparsity-based subspace clustering algorithms have attracted significant
attention thanks to their excellent performance in practical applications. A
prominent example is the sparse subspace clustering (SSC) algorithm by
Elhamifar and Vidal, which performs spectral clustering based on an adjacency
matrix obtained by sparsely representing each data point in terms of all the
other data points via the Lasso. When the number of data points is large or the
dimension of the ambient space is high, the computational complexity of SSC
quickly becomes prohibitive. Dyer et al. observed that SSC-OMP obtained by
replacing the Lasso by the greedy orthogonal matching pursuit (OMP) algorithm
results in significantly lower computational complexity, while often yielding
comparable performance. The central goal of this paper is an analytical
performance characterization of SSC-OMP for noisy data. Moreover, we introduce
and analyze the SSC-MP algorithm, which employs matching pursuit (MP) in lieu
of OMP. Both SSC-OMP and SSC-MP are proven to succeed even when the subspaces
intersect and when the data points are contaminated by severe noise. The
clustering conditions we obtain for SSC-OMP and SSC-MP are similar to those for
SSC and for the thresholding-based subspace clustering (TSC) algorithm due to
Heckel and B\"olcskei. Analytical results in combination with numerical results
indicate that both SSC-OMP and SSC-MP with a data-dependent stopping criterion
automatically detect the dimensions of the subspaces underlying the data.
Moreover, experiments on synthetic and on real data show that SSC-MP compares
very favorably to SSC, SSC-OMP, TSC, and the nearest subspace neighbor
algorithm, both in terms of clustering performance and running time. In
addition, we find that, in contrast to SSC-OMP, the performance of SSC-MP is
very robust with respect to the choice of parameters in the stopping criteria.
VTrails: Inferring Vessels with Geodesic Connectivity Trees
Authors: Stefano Moriconi, Maria A. Zuluaga, H. Rolf Jäger, Parashkev Nachev, Sébastien Ourselin, M. Jorge Cardoso
Date: 2018-06-08 00:00:00

The analysis of vessel morphology and connectivity has an impact on a number
of cardiovascular and neurovascular applications by providing patient-specific
high-level quantitative features such as spatial location, direction and scale.
In this paper we present an end-to-end approach to extract an acyclic vascular
tree from angiographic data by solving a connectivity-enforcing anisotropic
fast marching over a voxel-wise tensor field representing the orientation of
the underlying vascular tree. The method is validated using synthetic and real
vascular images. We compare VTrails against classical and state-of-the-art
ridge detectors for tubular structures by assessing the connectedness of the
vesselness map and inspecting the synthesized tensor field as proof of concept.
VTrails performance is evaluated on images with different levels of
degradation: we verify that the extracted vascular network is an acyclic graph
(i.e. a tree), and we report the extraction accuracy, precision and recall.
Rotation Equivariant CNNs for Digital Pathology
Authors: Bastiaan S. Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, Max Welling
Date: 2018-06-08 00:00:00
Tasks: BIG-bench Machine Learning, Breast Tumour Classification

We propose a new model for digital pathology segmentation, based on the
observation that histopathology images are inherently symmetric under rotation
and reflection. Utilizing recent findings on rotation equivariant CNNs, the
proposed model leverages these symmetries in a principled manner. We present a
visual analysis showing improved stability on predictions, and demonstrate that
exploiting rotation equivariance significantly improves tumor detection
performance on a challenging lymph node metastases dataset. We further present
a novel derived dataset to enable principled comparison of machine learning
models, in combination with an initial benchmark. Through this dataset, the
task of histopathology diagnosis becomes accessible as a challenging benchmark
for fundamental machine learning research.
Automated Curriculum Learning by Rewarding Temporally Rare Events
Authors: Niels Justesen, Sebastian Risi
Date: 2018-03-19 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Reward shaping allows reinforcement learning (RL) agents to accelerate
learning by receiving additional reward signals. However, these signals can be
difficult to design manually, especially for complex RL tasks. We propose a
simple and general approach that determines the reward of pre-defined events by
their rarity alone. Here events become less rewarding as they are experienced
more often, which encourages the agent to continually explore new types of
events as it learns. The adaptiveness of this reward function results in a form
of automated curriculum learning that does not have to be specified by the
experimenter. We demonstrate that this \emph{Rarity of Events} (RoE) approach
enables the agent to succeed in challenging VizDoom scenarios without access to
the extrinsic reward from the environment. Furthermore, the results demonstrate
that RoE learns a more versatile policy that adapts well to critical changes in
the environment. Rewarding events based on their rarity could help in many
unsolved RL environments that are characterized by sparse extrinsic rewards but
a plethora of known event types.
Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation
Authors: Alain Jungo, Raphael Meier, Ekin Ermis, Evelyn Herrmann, Mauricio Reyes
Date: 2018-06-08 00:00:00
Tasks: Segmentation

Uncertainty estimates of modern neuronal networks provide additional
information next to the computed predictions and are thus expected to improve
the understanding of the underlying model. Reliable uncertainties are
particularly interesting for safety-critical computer-assisted applications in
medicine, e.g., neurosurgical interventions and radiotherapy planning. We
propose an uncertainty-driven sanity check for the identification of
segmentation results that need particular expert review. Our method uses a
fully-convolutional neural network and computes uncertainty estimates by the
principle of Monte Carlo dropout. We evaluate the performance of the proposed
method on a clinical dataset with 30 postoperative brain tumor images. The
method can segment the highly inhomogeneous resection cavities accurately (Dice
coefficients 0.792 $\pm$ 0.154). Furthermore, the proposed sanity check is able
to detect the worst segmentation and three out of the four outliers. The
results highlight the potential of using the additional information from the
model's parameter uncertainty to validate the segmentation performance of a
deep learning model.
Contextual Hourglass Networks for Segmentation and Density Estimation
Authors: Daniel Oñoro-Rubio, Mathias Niepert
Date: 2018-06-08 00:00:00
Tasks: Density Estimation, Image Segmentation, Medical Image Segmentation, Object Counting, Segmentation, Semantic Segmentation

Hourglass networks such as the U-Net and V-Net are popular neural
architectures for medical image segmentation and counting problems. Typical
instances of hourglass networks contain shortcut connections between mirroring
layers. These shortcut connections improve the performance and it is
hypothesized that this is due to mitigating effects on the vanishing gradient
problem and the ability of the model to combine feature maps from earlier and
later layers. We propose a method for not only combining feature maps of
mirroring layers but also feature maps of layers with different spatial
dimensions. For instance, the method enables the integration of the bottleneck
feature map with those of the reconstruction layers. The proposed approach is
applicable to any hourglass architecture. We evaluated the contextual hourglass
networks on image segmentation and object counting problems in the medical
domain. We achieve competitive results outperforming popular hourglass networks
by up to 17 percentage points.
The Uncertainty Bellman Equation and Exploration
Authors: Brendan O'Donoghue, Ian Osband, Remi Munos, Volodymyr Mnih
Date: 2017-09-15 00:00:00
Proceeding: ICML 2018 7
Tasks: Reinforcement Learning

We consider the exploration/exploitation problem in reinforcement learning.
For exploitation, it is well known that the Bellman equation connects the value
at any time-step to the expected value at subsequent time-steps. In this paper
we consider a similar \textit{uncertainty} Bellman equation (UBE), which
connects the uncertainty at any time-step to the expected uncertainties at
subsequent time-steps, thereby extending the potential exploratory benefit of a
policy beyond individual time-steps. We prove that the unique fixed point of
the UBE yields an upper bound on the variance of the posterior distribution of
the Q-values induced by any policy. This bound can be much tighter than
traditional count-based bonuses that compound standard deviation rather than
variance. Importantly, and unlike several existing approaches to optimism, this
method scales naturally to large systems with complex generalization.
Substituting our UBE-exploration strategy for $\epsilon$-greedy improves DQN
performance on 51 out of 57 games in the Atari suite.
Deep Extreme Multi-label Learning
Authors: Wenjie Zhang, Junchi Yan, Xiangfeng Wang, Hongyuan Zha
Date: 2017-04-12 00:00:00
Tasks: Classification, Extreme Multi-Label Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Learning

Extreme multi-label learning (XML) or classification has been a practical and
important problem since the boom of big data. The main challenge lies in the
exponential label space which involves $2^L$ possible label sets especially
when the label dimension $L$ is huge, e.g., in millions for Wikipedia labels.
This paper is motivated to better explore the label space by originally
establishing an explicit label graph. In the meanwhile, deep learning has been
widely studied and used in various classification problems including
multi-label classification, however it has not been properly introduced to XML,
where the label space can be as large as in millions. In this paper, we propose
a practical deep embedding method for extreme multi-label classification, which
harvests the ideas of non-linear embedding and graph priors-based label space
modeling simultaneously. Extensive experiments on public datasets for XML show
that our method performs competitive against state-of-the-art result.
A Stein variational Newton method
Authors: Gianluca Detommaso, Tiangang Cui, Alessio Spantini, Youssef Marzouk, Robert Scheichl
Date: 2018-06-08 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Variational Inference

Stein variational gradient descent (SVGD) was recently proposed as a general
purpose nonparametric variational inference algorithm [Liu & Wang, NIPS 2016]:
it minimizes the Kullback-Leibler divergence between the target distribution
and its approximation by implementing a form of functional gradient descent on
a reproducing kernel Hilbert space. In this paper, we accelerate and generalize
the SVGD algorithm by including second-order information, thereby approximating
a Newton-like iteration in function space. We also show how second-order
information can lead to more effective choices of kernel. We observe
significant computational gains over the original SVGD algorithm in multiple
test cases.
Unifying Identification and Context Learning for Person Recognition
Authors: Qingqiu Huang, Yu Xiong, Dahua Lin
Date: 2018-06-08 00:00:00
Proceeding: CVPR 2018 6
Tasks: Face Recognition, Person Recognition

Despite the great success of face recognition techniques, recognizing persons
under unconstrained settings remains challenging. Issues like profile views,
unfavorable lighting, and occlusions can cause substantial difficulties.
Previous works have attempted to tackle this problem by exploiting the context,
e.g. clothes and social relations. While showing promising improvement, they
are usually limited in two important aspects, relying on simple heuristics to
combine different cues and separating the construction of context from people
identities. In this work, we aim to move beyond such limitations and propose a
new framework to leverage context for person recognition. In particular, we
propose a Region Attention Network, which is learned to adaptively combine
visual cues with instance-dependent weights. We also develop a unified
formulation, where the social contexts are learned along with the reasoning of
people identities. These models substantially improve the robustness when
working with the complex contextual relations in unconstrained environments. On
two large datasets, PIPA and Cast In Movies (CIM), a new dataset proposed in
this work, our method consistently achieves state-of-the-art performance under
multiple evaluation policies.
StrassenNets: Deep Learning with a Multiplication Budget
Authors: Michael Tschannen, Aran Khanna, Anima Anandkumar
Date: 2017-12-11 00:00:00
Proceeding: ICML 2018 7
Tasks: Deep Learning, image-classification, Image Classification, Knowledge Distillation, Language Modeling, Language Modelling, Model Compression, Neural Network Compression

A large fraction of the arithmetic operations required to evaluate deep
neural networks (DNNs) consists of matrix multiplications, in both convolution
and fully connected layers. We perform end-to-end learning of low-cost
approximations of matrix multiplications in DNN layers by casting matrix
multiplications as 2-layer sum-product networks (SPNs) (arithmetic circuits)
and learning their (ternary) edge weights from data. The SPNs disentangle
multiplication and addition operations and enable us to impose a budget on the
number of multiplication operations. Combining our method with knowledge
distillation and applying it to image classification DNNs (trained on ImageNet)
and language modeling DNNs (using LSTMs), we obtain a first-of-a-kind reduction
in number of multiplications (over 99.5%) while maintaining the predictive
performance of the full-precision models. Finally, we demonstrate that the
proposed framework is able to rediscover Strassen's matrix multiplication
algorithm, learning to multiply $2 \times 2$ matrices using only 7
multiplications instead of 8.
Conditional Time Series Forecasting with Convolutional Neural Networks
Authors: Anastasia Borovykh, Sander Bohte, Cornelis W. Oosterlee
Date: 2017-03-14 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

We present a method for conditional time series forecasting based on an
adaptation of the recent deep convolutional WaveNet architecture. The proposed
network contains stacks of dilated convolutions that allow it to access a broad
range of history when forecasting, a ReLU activation function and conditioning
is performed by applying multiple convolutional filters in parallel to separate
time series which allows for the fast processing of data and the exploitation
of the correlation structure between the multivariate time series. We test and
analyze the performance of the convolutional network both unconditionally as
well as conditionally for financial time series forecasting using the S&P500,
the volatility index, the CBOE interest rate and several exchange rates and
extensively compare it to the performance of the well-known autoregressive
model and a long-short term memory network. We show that a convolutional
network is well-suited for regression-type problems and is able to effectively
learn dependencies in and between the series without the need for long
historical time series, is a time-efficient and easy to implement alternative
to recurrent-type networks and tends to outperform linear and recurrent models.
Inference for $L_2$-Boosting
Authors: David Rügamer, Sonja Greven
Date: 2018-05-04 00:00:00
Tasks: Variable Selection

We propose a statistical inference framework for the component-wise functional gradient descent algorithm (CFGD) under normality assumption for model errors, also known as $L_2$-Boosting. The CFGD is one of the most versatile tools to analyze data, because it scales well to high-dimensional data sets, allows for a very flexible definition of additive regression models and incorporates inbuilt variable selection. Due to the variable selection, we build on recent proposals for post-selection inference. However, the iterative nature of component-wise boosting, which can repeatedly select the same component to update, necessitates adaptations and extensions to existing approaches. We propose tests and confidence intervals for linear, grouped and penalized additive model components selected by $L_2$-Boosting. Our concepts also transfer to slow-learning algorithms more generally, and to other selection techniques which restrict the response space to more complex sets than polyhedra. We apply our framework to an additive model for sales prices of residential apartments and investigate the properties of our concepts in simulation studies.
Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
Authors: Hongyang Li, Yu Liu, Wanli Ouyang, Xiaogang Wang
Date: 2017-09-13 00:00:00
Tasks: object-detection, Object Detection, Region Proposal

In this paper, we propose a zoom-out-and-in network for generating object
proposals. A key observation is that it is difficult to classify anchors of
different sizes with the same set of features. Anchors of different sizes
should be placed accordingly based on different depth within a network: smaller
boxes on high-resolution layers with a smaller stride while larger boxes on
low-resolution counterparts with a larger stride. Inspired by the conv/deconv
structure, we fully leverage the low-level local details and high-level
regional semantics from two feature map streams, which are complimentary to
each other, to identify the objectness in an image. A map attention decision
(MAD) unit is further proposed to aggressively search for neuron activations
among two streams and attend the most contributive ones on the feature learning
of the final loss. The unit serves as a decisionmaker to adaptively activate
maps along certain channels with the solely purpose of optimizing the overall
training loss. One advantage of MAD is that the learned weights enforced on
each feature channel is predicted on-the-fly based on the input context, which
is more suitable than the fixed enforcement of a convolutional kernel.
Experimental results on three datasets, including PASCAL VOC 2007, ImageNet
DET, MS COCO, demonstrate the effectiveness of our proposed algorithm over
other state-of-the-arts, in terms of average recall (AR) for region proposal
and average precision (AP) for object detection.