pwc-archive/papers-with-abstracts huggingface dataset:
DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction
Authors: Zhihui Guo, Junjie Bai, Yi Lu, Xin Wang, Kunlin Cao, Qi Song, Milan Sonka, Youbing Yin
Date: 2019-03-25 00:00:00
Tasks: Object, Semantic Segmentation

A novel centerline extraction framework is reported which combines an
end-to-end trainable multi-task fully convolutional network (FCN) with a
minimal path extractor. The FCN simultaneously computes centerline distance
maps and detects branch endpoints. The method generates single-pixel-wide
centerlines with no spurious branches. It handles arbitrary tree-structured
object with no prior assumption regarding depth of the tree or its bifurcation
pattern. It is also robust to substantial scale changes across different parts
of the target object and minor imperfections of the object's segmentation mask.
To the best of our knowledge, this is the first deep-learning based centerline
extraction method that guarantees single-pixel-wide centerline for a complex
tree-structured object. The proposed method is validated in coronary artery
centerline extraction on a dataset of 620 patients (400 of which used as test
set). This application is challenging due to the large number of coronary
branches, branch tortuosity, and large variations in length, thickness, shape,
etc. The proposed method generates well-positioned centerlines, exhibiting
lower number of missing branches and is more robust in the presence of minor
imperfections of the object segmentation mask. Compared to a state-of-the-art
traditional minimal path approach, our method improves patient-level success
rate of centerline extraction from 54.3% to 88.8% according to independent
human expert review.
Active Learning of Spin Network Models
Authors: Jialong Jiang, David A. Sivak, Matt Thomson
Date: 2019-03-25 00:00:00
Tasks: Active Learning

The inverse statistical problem of finding direct interactions in complex networks is difficult. In the natural sciences, well-controlled perturbation experiments are widely used to probe the structure of complex networks. However, our understanding of how and why perturbations aid inference remains heuristic, and we lack automated procedures that determine network structure by combining inference and perturbation. Therefore, we propose a general mathematical framework to study inference with iteratively applied perturbations. Using the formulation of information geometry, our framework quantifies the difficulty of inference and the information gain from perturbations through the curvature of the underlying parameter manifold, measured by Fisher information. We apply the framework to the inference of spin network models and find that designed perturbations can reduce the sampling complexity by $10^6$-fold across a variety of network architectures. Physically, our framework reveals that perturbations boost inference by causing a network to explore previously inaccessible states. Optimal perturbations break spin-spin correlations within a network, increasing the information available for inference and thus reducing sampling complexity by orders of magnitude. Our active learning framework could be powerful in the analysis of complex networks as well as in the rational design of experiments.
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
Authors: Kjersti Aas, Martin Jullum, Anders Løland
Date: 2019-03-25 00:00:00
Tasks: BIG-bench Machine Learning

Explaining complex or seemingly simple machine learning models is an important practical problem. We want to explain individual predictions from a complex machine learning model by learning simple, interpretable explanations. Shapley values is a game theoretic concept that can be used for this purpose. The Shapley value framework has a series of desirable theoretical properties, and can in principle handle any predictive model. Kernel SHAP is a computationally efficient approximation to Shapley values in higher dimensions. Like several other existing methods, this approach assumes that the features are independent, which may give very wrong explanations. This is the case even if a simple linear model is used for predictions. In this paper, we extend the Kernel SHAP method to handle dependent features. We provide several examples of linear and non-linear models with various degrees of feature dependence, where our method gives more accurate approximations to the true Shapley values. We also propose a method for aggregating individual Shapley values, such that the prediction can be explained by groups of dependent variables.
dpUGC: Learn Differentially Private Representation for User Generated Contents
Authors: Xuan-Son Vu, Son N. Tran, Lili Jiang
Date: 2019-03-25 00:00:00

This paper firstly proposes a simple yet efficient generalized approach to
apply differential privacy to text representation (i.e., word embedding). Based
on it, we propose a user-level approach to learn personalized differentially
private word embedding model on user generated contents (UGC). To our best
knowledge, this is the first work of learning user-level differentially private
word embedding model from text for sharing. The proposed approaches protect the
privacy of the individual from re-identification, especially provide better
trade-off of privacy and data utility on UGC data for sharing. The experimental
results show that the trained embedding models are applicable for the classic
text analysis tasks (e.g., regression). Moreover, the proposed approaches of
learning differentially private embedding models are both framework- and data-
independent, which facilitates the deployment and sharing. The source code is
available at https://github.com/sonvx/dpText.
The functional role of cue-driven feature-based feedback in object recognition
Authors: Sushrut Thorat, Marcel van Gerven, Marius Peelen
Date: 2019-03-25 00:00:00
Tasks: Object Recognition

Visual object recognition is not a trivial task, especially when the objects
are degraded or surrounded by clutter or presented briefly. External cues (such
as verbal cues or visual context) can boost recognition performance in such
conditions. In this work, we build an artificial neural network to model the
interaction between the object processing stream (OPS) and the cue. We study
the effects of varying neural and representational capacities of the OPS on the
performance boost provided by cue-driven feature-based feedback in the OPS. We
observe that the feedback provides performance boosts only if the
category-specific features about the objects cannot be fully represented in the
OPS. This representational limit is more dependent on task demands than neural
capacity. We also observe that the feedback scheme trained to maximise
recognition performance boost is not the same as tuning-based feedback, and
actually performs better than tuning-based feedback.
CODA: Counting Objects via Scale-aware Adversarial Density Adaption
Authors: Li Wang, Yongbo Li, xiangyang xue
Date: 2019-03-25 00:00:00
Tasks: Crowd Counting

Recent advances in crowd counting have achieved promising results with
increasingly complex convolutional neural network designs. However, due to the
unpredictable domain shift, generalizing trained model to unseen scenarios is
often suboptimal. Inspired by the observation that density maps of different
scenarios share similar local structures, we propose a novel adversarial
learning approach in this paper, i.e., CODA (\emph{Counting Objects via
scale-aware adversarial Density Adaption}). To deal with different object
scales and density distributions, we perform adversarial training with pyramid
patches of multi-scales from both source- and target-domain. Along with a
ranking constraint across levels of the pyramid input, consistent object counts
can be produced for different scales. Extensive experiments demonstrate that
our network produces much better results on unseen datasets compared with
existing counting adaption models. Notably, the performance of our CODA is
comparable with the state-of-the-art fully-supervised models that are trained
on the target dataset. Further analysis indicates that our density adaption
framework can effortlessly extend to scenarios with different objects.
\emph{The code is available at https://github.com/Willy0919/CODA.}
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems
Authors: Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen
Date: 2019-03-25 00:00:00
Tasks: Collaborative Filtering, Graph Attention, Recommendation Systems

Social recommendation leverages social information to solve data sparsity and
cold-start problems in traditional collaborative filtering methods. However,
most existing models assume that social effects from friend users are static
and under the forms of constant weights or fixed constraints. To relax this
strong assumption, in this paper, we propose dual graph attention networks to
collaboratively learn representations for two-fold social effects, where one is
modeled by a user-specific attention weight and the other is modeled by a
dynamic and context-aware attention weight. We also extend the social effects
in user domain to item domain, so that information from related items can be
leveraged to further alleviate the data sparsity problem. Furthermore,
considering that different social effects in two domains could interact with
each other and jointly influence user preferences for items, we propose a new
policy-based fusion strategy based on contextual multi-armed bandit to weigh
interactions of various social effects. Experiments on one benchmark dataset
and a commercial dataset verify the efficacy of the key components in our
model. The results show that our model achieves great improvement for
recommendation accuracy compared with other state-of-the-art social
recommendation methods.
Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation
Authors: Jaime Spencer, Richard Bowden, Simon Hadfield
Date: 2019-03-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Disparity Estimation, Sand, Semantic Segmentation

How do computers and intelligent agents view the world around them? Feature
extraction and representation constitutes one the basic building blocks towards
answering this question. Traditionally, this has been done with carefully
engineered hand-crafted techniques such as HOG, SIFT or ORB. However, there is
no ``one size fits all'' approach that satisfies all requirements. In recent
years, the rising popularity of deep learning has resulted in a myriad of
end-to-end solutions to many computer vision problems. These approaches, while
successful, tend to lack scalability and can't easily exploit information
learned by other systems. Instead, we propose SAND features, a dedicated deep
learning solution to feature extraction capable of providing hierarchical
context information. This is achieved by employing sparse relative labels
indicating relationships of similarity/dissimilarity between image locations.
The nature of these labels results in an almost infinite set of dissimilar
examples to choose from. We demonstrate how the selection of negative examples
during training can be used to modify the feature space and vary it's
properties. To demonstrate the generality of this approach, we apply the
proposed features to a multitude of tasks, each requiring different properties.
This includes disparity estimation, semantic segmentation, self-localisation
and SLAM. In all cases, we show how incorporating SAND features results in
better or comparable results to the baseline, whilst requiring little to no
additional training. Code can be found at:
https://github.com/jspenmar/SAND_features
Locomotion and gesture tracking in mice and small animals for neurosceince applications: A survey
Authors: Waseem Abbas, David Masip Rodo
Date: 2019-03-25 00:00:00

Neuroscience has traditionally relied on manually observing lab animals in
controlled environments. Researchers usually record animals behaving in free or
restrained manner and then annotate the data manually. The manual annotation is
not desirable for three reasons; one, it is time consuming, two, it is prone to
human errors and three, no two human annotators will 100\% agree on annotation,
so it is not reproducible. Consequently, automated annotation of such data has
gained traction because it is efficient and replicable. Usually, the automatic
annotation of neuroscience data relies on computer vision and machine leaning
techniques. In this article, we have covered most of the approaches taken by
researchers for locomotion and gesture tracking of lab animals. We have divided
these papers in categories based upon the hardware they use and the software
approach they take. We also have summarized their strengths and weaknesses.
A linear bound on the k-rendezvous time for primitive sets of NZ matrices
Authors: Costanza Catalano, Umer Azfar, Ludovic Charlier, Raphaël Jungers
Date: 2019-03-25 00:00:00

A set of nonnegative matrices is called primitive if there exists a product of these matrices that is entrywise positive. Motivated by recent results relating synchronizing automata and primitive sets, we study the length of the shortest product of a primitive set having a column or a row with k positive entries, called its k-rendezvous time (k-RT}), in the case of sets of matrices having no zero rows and no zero columns. We prove that the k-RT is at most linear w.r.t. the matrix size n for small k, while the problem is still open for synchronizing automata. We provide two upper bounds on the k-RT: the second is an improvement of the first one, although the latter can be written in closed form. We then report numerical results comparing our upper bounds on the k-RT with heuristic approximation methods.
Few-Shot Learning-Based Human Activity Recognition
Authors: Siwei Feng, Marco F. Duarte
Date: 2019-03-25 00:00:00
Tasks: Activity Recognition, Few-Shot Learning, Human Activity Recognition, Transfer Learning

Few-shot learning is a technique to learn a model with a very small amount of
labeled training data by transferring knowledge from relevant tasks. In this
paper, we propose a few-shot learning method for wearable sensor based human
activity recognition, a technique that seeks high-level human activity
knowledge from low-level sensor inputs. Due to the high costs to obtain human
generated activity data and the ubiquitous similarities between activity modes,
it can be more efficient to borrow information from existing activity
recognition models than to collect more data to train a new model from scratch
when only a few data are available for model training. The proposed few-shot
human activity recognition method leverages a deep learning model for feature
extraction and classification while knowledge transfer is performed in the
manner of model parameter transfer. In order to alleviate negative transfer, we
propose a metric to measure cross-domain class-wise relevance so that knowledge
of higher relevance is assigned larger weights during knowledge transfer.
Promising results in extensive experiments show the advantages of the proposed
approach.
ShopSign: a Diverse Scene Text Dataset of Chinese Shop Signs in Street Views
Authors: Chongsheng Zhang, Guowen Peng, Yuefeng Tao, Feifei Fu, Wei Jiang, George Almpanidis, Ke Chen
Date: 2019-03-25 00:00:00
Tasks: Scene Text Detection, Text Detection

In this paper, we introduce the ShopSign dataset, which is a newly developed
natural scene text dataset of Chinese shop signs in street views. Although a
few scene text datasets are already publicly available (e.g. ICDAR2015,
COCO-Text), there are few images in these datasets that contain Chinese
texts/characters. Hence, we collect and annotate the ShopSign dataset to
advance research in Chinese scene text detection and recognition.
  The new dataset has three distinctive characteristics: (1) large-scale: it
contains 25,362 Chinese shop sign images, with a total number of 196,010
text-lines. (2) diversity: the images in ShopSign were captured in different
scenes, from downtown to developing regions, using more than 50 different
mobile phones. (3) difficulty: the dataset is very sparse and imbalanced. It
also includes five categories of hard images (mirror, wooden, deformed, exposed
and obscure). To illustrate the challenges in ShopSign, we run baseline
experiments using state-of-the-art scene text detection methods (including
CTPN, TextBoxes++ and EAST), and cross-dataset validation to compare their
corresponding performance on the related datasets such as CTW, RCTW and ICPR
2018 MTWI challenge dataset.
  The sample images and detailed descriptions of our ShopSign dataset are
publicly available at: https://github.com/chongshengzhang/shopsign.
A Conceptual Bio-Inspired Framework for the Evolution of Artificial General Intelligence
Authors: Sidney Pontes-Filho, Stefano Nichele
Date: 2019-03-25 00:00:00
Tasks: Self-Learning, Self-Supervised Learning

In this work, a conceptual bio-inspired parallel and distributed learning framework for the emergence of general intelligence is proposed, where agents evolve through environmental rewards and learn throughout their lifetime without supervision, i.e., self-learning through embodiment. The chosen control mechanism for agents is a biologically plausible neuron model based on spiking neural networks. Network topologies become more complex through evolution, i.e., the topology is not fixed, while the synaptic weights of the networks cannot be inherited, i.e., newborn brains are not trained and have no innate knowledge of the environment. What is subject to the evolutionary process is the network topology, the type of neurons, and the type of learning. This process ensures that controllers that are passed through the generations have the intrinsic ability to learn and adapt during their lifetime in mutable environments. We envision that the described approach may lead to the emergence of the simplest form of artificial general intelligence.
On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning
Authors: Bharat Prakash, Mark Horton, {Nicholas R. Waytowich, William David Hairston, Tim Oates, Tinoosh Mohsenin
Date: 2019-03-25 00:00:00
Tasks: CPU, GPU, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In autonomous embedded systems, it is often vital to reduce the amount of
actions taken in the real world and energy required to learn a policy. Training
reinforcement learning agents from high dimensional image representations can
be very expensive and time consuming. Autoencoders are deep neural network used
to compress high dimensional data such as pixelated images into small latent
representations. This compression model is vital to efficiently learn policies,
especially when learning on embedded systems. We have implemented this model on
the NVIDIA Jetson TX2 embedded GPU, and evaluated the power consumption,
throughput, and energy consumption of the autoencoders for various CPU/GPU core
combinations, frequencies, and model parameters. Additionally, we have shown
the reconstructions generated by the autoencoder to analyze the quality of the
generated compressed representation and also the performance of the
reinforcement learning agent. Finally, we have presented an assessment of the
viability of training these models on embedded systems and their usefulness in
developing autonomous policies. Using autoencoders, we were able to achieve 4-5
$\times$ improved performance compared to a baseline RL agent with a
convolutional feature extractor, while using less than 2W of power.
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
Authors: Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil
Date: 2019-03-25 00:00:00

We study the problem of learning-to-learn: inferring a learning algorithm
that works well on tasks sampled from an unknown distribution. As class of
algorithms we consider Stochastic Gradient Descent on the true risk regularized
by the square euclidean distance to a bias vector. We present an average excess
risk bound for such a learning algorithm. This result quantifies the potential
benefit of using a bias vector with respect to the unbiased case. We then
address the problem of estimating the bias from a sequence of tasks. We propose
a meta-algorithm which incrementally updates the bias, as new tasks are
observed. The low space and time complexity of this approach makes it appealing
in practice. We provide guarantees on the learning ability of the
meta-algorithm. A key feature of our results is that, when the number of tasks
grows and their variance is relatively small, our learning-to-learn approach
has a significant advantage over learning each task in isolation by Stochastic
Gradient Descent without a bias term. We report on numerical experiments which
demonstrate the effectiveness of our approach.
Understanding Childhood Vulnerability in The City of Surrey
Authors: Cody Griffith, Varoon Mathur, Catherine Lin, Kevin Zhu
Date: 2019-03-25 00:00:00
Tasks: Decision Making

Understanding the community conditions that best support universal access and
improved childhood outcomes allows ultimately to improve decision-making in the
areas of planning and investment across the early stages of childhood
development. Here we describe two different data-driven approaches to
visualizing the lived experiences of children throughout the City of Surrey,
combining data derived from both public and private sources. In one approach,
we find specifically that the Early Development Instrument measuring childhood
vulnerabilities across varying domains can be used to cluster neighborhoods,
and that census variables can help explain similarities between neighborhoods
within these clusters. In our second approach, we use program registration data
from the City of Surrey's Community and Recreation Services Division. We also
find a critical age of entry and exit for each program related to early
childhood development and beyond, and find that certain neighborhoods and
recreational programs have larger retention rates than others. This report
details the journey of using data to tell the story of these neighborhoods, and
provides a lens to which community initiatives can be strategically crafted
through their use.
The LogBarrier adversarial attack: making effective use of decision boundary information
Authors: Chris Finlay, Aram-Alexandre Pooladian, Adam M. Oberman
Date: 2019-03-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: Adversarial Attack, image-classification, Image Classification

Adversarial attacks for image classification are small perturbations to
images that are designed to cause misclassification by a model. Adversarial
attacks formally correspond to an optimization problem: find a minimum norm
image perturbation, constrained to cause misclassification. A number of
effective attacks have been developed. However, to date, no gradient-based
attacks have used best practices from the optimization literature to solve this
constrained minimization problem. We design a new untargeted attack, based on
these best practices, using the established logarithmic barrier method. On
average, our attack distance is similar or better than all state-of-the-art
attacks on benchmark datasets (MNIST, CIFAR10, ImageNet-1K). In addition, our
method performs significantly better on the most challenging images, those
which normally require larger perturbations for misclassification. We employ
the LogBarrier attack on several adversarially defended models, and show that
it adversarially perturbs all images more efficiently than other attacks: the
distance needed to perturb all images is significantly smaller with the
LogBarrier attack than with other state-of-the-art attacks.