pwc-archive/papers-with-abstracts huggingface dataset:
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits
Authors: Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan
Date: 2019-01-24 00:00:00
Tasks: Multi-Armed Bandits

We consider the problem of identifying any $k$ out of the best $m$ arms in an
$n$-armed stochastic multi-armed bandit. Framed in the PAC setting, this
particular problem generalises both the problem of `best subset selection' and
that of selecting `one out of the best m' arms [arcsk 2017]. In applications
such as crowd-sourcing and drug-designing, identifying a single good solution
is often not sufficient. Moreover, finding the best subset might be hard due to
the presence of many indistinguishably close solutions. Our generalisation of
identifying exactly $k$ arms out of the best $m$, where $1 \leq k \leq m$,
serves as a more effective alternative. We present a lower bound on the
worst-case sample complexity for general $k$, and a fully sequential PAC
algorithm, \GLUCB, which is more sample-efficient on easy instances. Also,
extending our analysis to infinite-armed bandits, we present a PAC algorithm
that is independent of $n$, which identifies an arm from the best $\rho$
fraction of arms using at most an additive poly-log number of samples than
compared to the lower bound, thereby improving over [arcsk 2017] and
[Aziz+AKA:2018]. The problem of identifying $k > 1$ distinct arms from the best
$\rho$ fraction is not always well-defined; for a special class of this
problem, we present lower and upper bounds. Finally, through a reduction, we
establish a relation between upper bounds for the `one out of the best $\rho$'
problem for infinite instances and the `one out of the best $m$' problem for
finite instances. We conjecture that it is more efficient to solve `small'
finite instances using the latter formulation, rather than going through the
former.
Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
Authors: Philipp Seeböck, David Romo-Bucheli, Sebastian Waldstein, Hrvoje Bogunović, José Ignacio Orlando, Bianca S. Gerendas, Georg Langs, Ursula Schmidt-Erfurth
Date: 2019-01-24 00:00:00

Optical coherence tomography (OCT) has become the most important imaging
modality in ophthalmology. A substantial amount of research has recently been
devoted to the development of machine learning (ML) models for the
identification and quantification of pathological features in OCT images. Among
the several sources of variability the ML models have to deal with, a major
factor is the acquisition device, which can limit the ML model's
generalizability. In this paper, we propose to reduce the image variability
across different OCT devices (Spectralis and Cirrus) by using CycleGAN, an
unsupervised unpaired image transformation algorithm. The usefulness of this
approach is evaluated in the setting of retinal fluid segmentation, namely
intraretinal cystoid fluid (IRC) and subretinal fluid (SRF). First, we train a
segmentation model on images acquired with a source OCT device. Then we
evaluate the model on (1) source, (2) target and (3) transformed versions of
the target OCT images. The presented transformation strategy shows an F1 score
of 0.4 (0.51) for IRC (SRF) segmentations. Compared with traditional
transformation approaches, this means an F1 score gain of 0.2 (0.12).
Three-dimensional Backbone Network for 3D Object Detection in Traffic Scenes
Authors: Xuesong Li, Jose Guivant, Ngaiming Kwok, Yongzhi Xu, Ruowei Li, Hongkun Wu
Date: 2019-01-24 00:00:00
Tasks: 2D Object Detection, 3D Object Detection, Object, object-detection, Object Detection

The task of detecting 3D objects in traffic scenes has a pivotal role in many real-world applications. However, the performance of 3D object detection is lower than that of 2D object detection due to the lack of powerful 3D feature extraction methods. To address this issue, this study proposes a 3D backbone network to acquire comprehensive 3D feature maps for 3D object detection. It primarily consists of sparse 3D convolutional neural network operations in the point cloud. The 3D backbone network can inherently learn 3D features from the raw data without compressing the point cloud into multiple 2D images. The sparse 3D convolutional neural network takes full advantage of the sparsity in the 3D point cloud to accelerate computation and save memory, which makes the 3D backbone network feasible in a real-world application. Empirical experiments were conducted on the KITTI benchmark and comparable results were obtained with respect to the state-of-the-art performance for 3D object detection.
Deep Reasoning with Multi-Scale Context for Salient Object Detection
Authors: Zun Li, Congyan Lang, Yunpeng Chen, Junhao Liew, Jiashi Feng
Date: 2019-01-24 00:00:00
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Saliency Prediction, Salient Object Detection

To detect salient objects accurately, existing methods usually design complex
backbone network architectures to learn and fuse powerful features. However,
the saliency inference module that performs saliency prediction from the fused
features receives much less attention on its architecture design and typically
adopts only a few fully convolutional layers. In this paper, we find the
limited capacity of the saliency inference module indeed makes a fundamental
performance bottleneck, and enhancing its capacity is critical for obtaining
better saliency prediction. Correspondingly, we propose a deep yet light-weight
saliency inference module that adopts a multi-dilated depth-wise convolution
architecture. Such a deep inference module, though with simple architecture,
can directly perform reasoning about salient objects from the multi-scale
convolutional features fast, and give superior salient object detection
performance with less computational cost. To our best knowledge, we are the
first to reveal the importance of the inference module for salient object
detection, and present a novel architecture design with attractive efficiency
and accuracy. Extensive experimental evaluations demonstrate that our simple
framework performs favorably compared with the state-of-the-art methods with
complex backbone design.
Learning Global Pairwise Interactions with Bayesian Neural Networks
Authors: Tianyu Cui, Pekka Marttinen, Samuel Kaski
Date: 2019-01-24 00:00:00

Estimating global pairwise interaction effects, i.e., the difference between the joint effect and the sum of marginal effects of two input features, with uncertainty properly quantified, is centrally important in science applications. We propose a non-parametric probabilistic method for detecting interaction effects of unknown form. First, the relationship between the features and the output is modelled using a Bayesian neural network, capable of representing complex interactions and principled uncertainty. Second, interaction effects and their uncertainty are estimated from the trained model. For the second step, we propose an intuitive global interaction measure: Bayesian Group Expected Hessian (GEH), which aggregates information of local interactions as captured by the Hessian. GEH provides a natural trade-off between type I and type II error and, moreover, comes with theoretical guarantees ensuring that the estimated interaction effects and their uncertainty can be improved by training a more accurate BNN. The method empirically outperforms available non-probabilistic alternatives on simulated and real-world data. Finally, we demonstrate its ability to detect interpretable interactions between higher-level features (at deeper layers of the neural network).
Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples
Authors: Kamil Nar, Orhan Ocal, S. Shankar Sastry, Kannan Ramchandran
Date: 2019-01-24 00:00:00
Tasks: Binary Classification

State-of-the-art neural networks are vulnerable to adversarial examples; they
can easily misclassify inputs that are imperceptibly different than their
training and test data. In this work, we establish that the use of
cross-entropy loss function and the low-rank features of the training data have
responsibility for the existence of these inputs. Based on this observation, we
suggest that addressing adversarial examples requires rethinking the use of
cross-entropy loss function and looking for an alternative that is more suited
for minimization with low-rank features. In this direction, we present a
training scheme called differential training, which uses a loss function
defined on the differences between the features of points from opposite
classes. We show that differential training can ensure a large margin between
the decision boundary of the neural network and the points in the training
dataset. This larger margin increases the amount of perturbation needed to flip
the prediction of the classifier and makes it harder to find an adversarial
example with small perturbations. We test differential training on a binary
classification task with CIFAR-10 dataset and demonstrate that it radically
reduces the ratio of images for which an adversarial example could be found --
not only in the training dataset, but in the test dataset as well.
Extracting PICO elements from RCT abstracts using 1-2gram analysis and multitask classification
Authors: Xia Yuan, Liao xiaoli, Li Shilei, Shi Qinwen, Wu Jinfa, Li Ke
Date: 2019-01-24 00:00:00
Tasks: Classification, Feature Engineering, General Classification, PICO

The core of evidence-based medicine is to read and analyze numerous papers in
the medical literature on a specific clinical problem and summarize the
authoritative answers to that problem. Currently, to formulate a clear and
focused clinical problem, the popular PICO framework is usually adopted, in
which each clinical problem is considered to consist of four parts:
patient/problem (P), intervention (I), comparison (C) and outcome (O). In this
study, we compared several classification models that are commonly used in
traditional machine learning. Next, we developed a multitask classification
model based on a soft-margin SVM with a specialized feature engineering method
that combines 1-2gram analysis with TF-IDF analysis. Finally, we trained and
tested several generic models on an open-source data set from BioNLP 2018. The
results show that the proposed multitask SVM classification model based on
1-2gram TF-IDF features exhibits the best performance among the tested models.
On Local Optimizers of Acquisition Functions in Bayesian Optimization
Authors: Jungtaek Kim, Seungjin Choi
Date: 2019-01-24 00:00:00
Tasks: Bayesian Optimization, valid

Bayesian optimization is a sample-efficient method for finding a global optimum of an expensive-to-evaluate black-box function. A global solution is found by accumulating a pair of query point and its function value, repeating these two procedures: (i) modeling a surrogate function; (ii) maximizing an acquisition function to determine where next to query. Convergence guarantees are only valid when the global optimizer of the acquisition function is found at each round and selected as the next query point. In practice, however, local optimizers of an acquisition function are also used, since searching for the global optimizer is often a non-trivial or time-consuming task. In this paper we consider three popular acquisition functions, PI, EI, and GP-UCB induced by Gaussian process regression. Then we present a performance analysis on the behavior of local optimizers of those acquisition functions, in terms of {\em instantaneous regrets} over global optimizers. We also introduce an analysis, allowing a local optimization method to start from multiple different initial conditions. Numerical experiments confirm the validity of our theoretical analysis.
Semantic Matching by Weakly Supervised 2D Point Set Registration
Authors: Zakaria Laskar, Hamed R. -Tavakoli, Juho Kannala
Date: 2019-01-24 00:00:00

In this paper we address the problem of establishing correspondences between
different instances of the same object. The problem is posed as finding the
geometric transformation that aligns a given image pair. We use a convolutional
neural network (CNN) to directly regress the parameters of the transformation
model. The alignment problem is defined in the setting where an unordered set
of semantic key-points per image are available, but, without the correspondence
information. To this end we propose a novel loss function based on cyclic
consistency that solves this 2D point set registration problem by inferring the
optimal geometric transformation model parameters. We train and test our
approach on a standard benchmark dataset Proposal-Flow
(PF-PASCAL)\cite{proposal_flow}. The proposed approach achieves
state-of-the-art results demonstrating the effectiveness of the method. In
addition, we show our approach further benefits from additional training
samples in PF-PASCAL generated by using category level information.
Semi-Supervised Semantic Matching
Authors: Zakaria Laskar, Juho Kannala
Date: 2019-01-24 00:00:00

Convolutional neural networks (CNNs) have been successfully applied to solve
the problem of correspondence estimation between semantically related images.
Due to non-availability of large training datasets, existing methods resort to
self-supervised or unsupervised training paradigm. In this paper we propose a
semi-supervised learning framework that imposes cyclic consistency constraint
on unlabeled image pairs. Together with the supervised loss the proposed model
achieves state-of-the-art on a benchmark semantic matching dataset.
Overcomplete Independent Component Analysis via SDP
Authors: Anastasia Podosinnikova, Amelia Perry, Alexander Wein, Francis Bach, Alexandre d'Aspremont, David Sontag
Date: 2019-01-24 00:00:00

We present a novel algorithm for overcomplete independent components analysis
(ICA), where the number of latent sources k exceeds the dimension p of observed
variables. Previous algorithms either suffer from high computational complexity
or make strong assumptions about the form of the mixing matrix. Our algorithm
does not make any sparsity assumption yet enjoys favorable computational and
theoretical properties. Our algorithm consists of two main steps: (a)
estimation of the Hessians of the cumulant generating function (as opposed to
the fourth and higher order cumulants used by most algorithms) and (b) a novel
semi-definite programming (SDP) relaxation for recovering a mixing component.
We show that this relaxation can be efficiently solved with a projected
accelerated gradient descent method, which makes the whole algorithm
computationally practical. Moreover, we conjecture that the proposed program
recovers a mixing component at the rate k < p^2/4 and prove that a mixing
component can be recovered with high probability when k < (2 - epsilon) p log p
when the original components are sampled uniformly at random on the hyper
sphere. Experiments are provided on synthetic data and the CIFAR-10 dataset of
real images.
A review of sentiment computation methods with R packages
Authors: Maurizio Naldi
Date: 2019-01-24 00:00:00
Tasks: Sentiment Analysis

Four packages in R are analyzed to carry out sentiment analysis. All packages
allow to define custom dictionaries. Just one - Sentiment R - properly accounts
for the presence of negators.
Whole slide image registration for the study of tumor heterogeneity
Authors: Leslie Solorzano, Gabriela M. Almeida, Bárbara Mesquita, Diana Martins, Carla Oliveira, Carolina Wählby
Date: 2019-01-24 00:00:00
Tasks: feature selection, Image Registration, whole slide images

Consecutive thin sections of tissue samples make it possible to study local
variation in e.g. protein expression and tumor heterogeneity by staining for a
new protein in each section. In order to compare and correlate patterns of
different proteins, the images have to be registered with high accuracy. The
problem we want to solve is registration of gigapixel whole slide images (WSI).
This presents 3 challenges: (i) Images are very large; (ii) Thin sections
result in artifacts that make global affine registration prone to very large
local errors; (iii) Local affine registration is required to preserve correct
tissue morphology (local size, shape and texture). In our approach we compare
WSI registration based on automatic and manual feature selection on either the
full image or natural sub-regions (as opposed to square tiles). Working with
natural sub-regions, in an interactive tool makes it possible to exclude
regions containing scientifically irrelevant information. We also present a new
way to visualize local registration quality by a Registration Confidence Map
(RCM). With this method, intra-tumor heterogeneity and charateristics of the
tumor microenvironment can be observed and quantified.
Deep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back
Authors: Martin Simonovsky
Date: 2019-01-24 00:00:00
Tasks: Graph Matching

A graph is a powerful concept for representation of relations between pairs
of entities. Data with underlying graph structure can be found across many
disciplines and there is a natural desire for understanding such data better.
Deep learning (DL) has achieved significant breakthroughs in a variety of
machine learning tasks in recent years, especially where data is structured on
a grid, such as in text, speech, or image understanding. However, surprisingly
little has been done to explore the applicability of DL on arbitrary
graph-structured data directly.
  The goal of this thesis is to investigate architectures for DL on graphs and
study how to transfer, adapt or generalize concepts that work well on
sequential and image data to this domain. We concentrate on two important
primitives: embedding graphs or their nodes into a continuous vector space
representation (encoding) and, conversely, generating graphs from such vectors
back (decoding). To that end, we make the following contributions.
  First, we introduce Edge-Conditioned Convolutions (ECC), a convolution-like
operation on graphs performed in the spatial domain where filters are
dynamically generated based on edge attributes. The method is used to encode
graphs with arbitrary and varying structure.
  Second, we propose SuperPoint Graph, an intermediate point cloud
representation with rich edge attributes encoding the contextual relationship
between object parts. Based on this representation, ECC is employed to segment
large-scale point clouds without major sacrifice in fine details.
  Third, we present GraphVAE, a graph generator allowing us to decode graphs
with variable but upper-bounded number of nodes making use of approximate graph
matching for aligning the predictions of an autoencoder with its inputs. The
method is applied to the task of molecule generation.
Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey
Authors: Santhosh Kelathodi Kumaran, Debi Prosad Dogra, Partha Pratim Roy
Date: 2019-01-24 00:00:00
Tasks: Anomaly Detection

Computer vision has evolved in the last decade as a key technology for
numerous applications replacing human supervision. In this paper, we present a
survey on relevant visual surveillance related researches for anomaly detection
in public places, focusing primarily on roads. Firstly, we revisit the surveys
done in the last 10 years in this field. Since the underlying building block of
a typical anomaly detection is learning, we emphasize more on learning methods
applied on video scenes. We then summarize the important contributions made
during last six years on anomaly detection primarily focusing on features,
underlying techniques, applied scenarios and types of anomalies using single
static camera. Finally, we discuss the challenges in the computer vision
related anomaly detection techniques and some of the important future
possibilities.
Faking Fairness via Stealthily Biased Sampling
Authors: Kazuto Fukuchi, Satoshi Hara, Takanori Maehara
Date: 2019-01-24 00:00:00
Tasks: Fairness

Auditing fairness of decision-makers is now in high demand. To respond to this social demand, several fairness auditing tools have been developed. The focus of this study is to raise an awareness of the risk of malicious decision-makers who fake fairness by abusing the auditing tools and thereby deceiving the social communities. The question is whether such a fraud of the decision-maker is detectable so that the society can avoid the risk of fake fairness. In this study, we answer this question negatively. We specifically put our focus on a situation where the decision-maker publishes a benchmark dataset as the evidence of his/her fairness and attempts to deceive a person who uses an auditing tool that computes a fairness metric. To assess the (un)detectability of the fraud, we explicitly construct an algorithm, the stealthily biased sampling, that can deliberately construct an evil benchmark dataset via subsampling. We show that the fraud made by the stealthily based sampling is indeed difficult to detect both theoretically and empirically.
Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data
Authors: Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis
Date: 2019-01-24 00:00:00
Tasks: Density Estimation, Time Series, Time Series Analysis, Time Series Forecasting

Time series forecasting is a crucial component of many important
applications, ranging from forecasting the stock markets to energy load
prediction. The high-dimensionality, velocity and variety of the data collected
in these applications pose significant and unique challenges that must be
carefully addressed for each of them. In this work, a novel Temporal Logistic
Neural Bag-of-Features approach, that can be used to tackle these challenges,
is proposed. The proposed method can be effectively combined with deep neural
networks, leading to powerful deep learning models for time series analysis.
However, combining existing BoF formulations with deep feature extractors pose
significant challenges: the distribution of the input features is not
stationary, tuning the hyper-parameters of the model can be especially
difficult and the normalizations involved in the BoF model can cause
significant instabilities during the training process. The proposed method is
capable of overcoming these limitations by a employing a novel adaptive scaling
mechanism and replacing the classical Gaussian-based density estimation
involved in the regular BoF model with a logistic kernel. The effectiveness of
the proposed approach is demonstrated using extensive experiments on a
large-scale financial time series dataset that consists of more than 4 million
limit orders.