pwc-archive/papers-with-abstracts huggingface dataset:
Skopus: Mining top-k sequential patterns under leverage
Authors: Francois Petitjean, Tao Li, Nikolaj Tatti, Geoffrey I. Webb
Date: 2015-06-26 00:00:00

This paper presents a framework for exact discovery of the top-k sequential
patterns under Leverage. It combines (1) a novel definition of the expected
support for a sequential pattern - a concept on which most interestingness
measures directly rely - with (2) SkOPUS: a new branch-and-bound algorithm for
the exact discovery of top-k sequential patterns under a given measure of
interest. Our interestingness measure employs the partition approach. A pattern
is interesting to the extent that it is more frequent than can be explained by
assuming independence between any of the pairs of patterns from which it can be
composed. The larger the support compared to the expectation under
independence, the more interesting is the pattern. We build on these two
elements to exactly extract the k sequential patterns with highest leverage,
consistent with our definition of expected support. We conduct experiments on
both synthetic data with known patterns and real-world datasets; both
experiments confirm the consistency and relevance of our approach with regard
to the state of the art. This article was published in Data Mining and
Knowledge Discovery and is accessible at
http://dx.doi.org/10.1007/s10618-016-0467-9.
Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks
Authors: Salman Ul Hassan Dar, Mahmut Yurt, Levent Karacan, Aykut Erdem, Erkut Erdem, Tolga Çukur
Date: 2018-02-05 00:00:00
Tasks: Anatomy, Diagnostic, Image Generation

Acquiring images of the same anatomy with multiple different contrasts
increases the diversity of diagnostic information available in an MR exam. Yet,
scan time limitations may prohibit acquisition of certain contrasts, and images
for some contrast may be corrupted by noise and artifacts. In such cases, the
ability to synthesize unacquired or corrupted contrasts from remaining
contrasts can improve diagnostic utility. For multi-contrast synthesis, current
methods learn a nonlinear intensity transformation between the source and
target images, either via nonlinear regression or deterministic neural
networks. These methods can in turn suffer from loss of high-spatial-frequency
information in synthesized images. Here we propose a new approach for
multi-contrast MRI synthesis based on conditional generative adversarial
networks. The proposed approach preserves high-frequency details via an
adversarial loss; and it offers enhanced synthesis performance via a pixel-wise
loss for registered multi-contrast images and a cycle-consistency loss for
unregistered images. Information from neighboring cross-sections are utilized
to further improved synthesis quality. Demonstrations on T1- and T2-weighted
images from healthy subjects and patients clearly indicate the superior
performance of the proposed approach compared to previous state-of-the-art
methods. Our synthesis approach can help improve quality and versatility of
multi-contrast MRI exams without the need for prolonged examinations.
Efficient Video Object Segmentation via Network Modulation
Authors: Linjie Yang, Yanran Wang, Xuehan Xiong, Jianchao Yang, Aggelos K. Katsaggelos
Date: 2018-02-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Instance Segmentation, Video Object Segmentation, Video Semantic Segmentation, Visual Object Tracking

Video object segmentation targets at segmenting a specific object throughout
a video sequence, given only an annotated first frame. Recent deep learning
based approaches find it effective by fine-tuning a general-purpose
segmentation model on the annotated frame using hundreds of iterations of
gradient descent. Despite the high accuracy these methods achieve, the
fine-tuning process is inefficient and fail to meet the requirements of real
world applications. We propose a novel approach that uses a single forward pass
to adapt the segmentation model to the appearance of a specific object.
Specifically, a second meta neural network named modulator is learned to
manipulate the intermediate layers of the segmentation network given limited
visual and spatial information of the target object. The experiments show that
our approach is 70times faster than fine-tuning approaches while achieving
similar accuracy.
Deceptive Games
Authors: Damien Anderson, Matthew Stephenson, Julian Togelius, Christian Salge, John Levine, Jochen Renz
Date: 2018-01-31 00:00:00

Deceptive games are games where the reward structure or other aspects of the
game are designed to lead the agent away from a globally optimal policy. While
many games are already deceptive to some extent, we designed a series of games
in the Video Game Description Language (VGDL) implementing specific types of
deception, classified by the cognitive biases they exploit. VGDL games can be
run in the General Video Game Artificial Intelligence (GVGAI) Framework, making
it possible to test a variety of existing AI agents that have been submitted to
the GVGAI Competition on these deceptive games. Our results show that all
tested agents are vulnerable to several kinds of deception, but that different
agents have different weaknesses. This suggests that we can use deception to
understand the capabilities of a game-playing algorithm, and game-playing
algorithms to characterize the deception displayed by a game.
Heuristic Feature Selection for Clickbait Detection
Authors: Matti Wiegmann, Michael Völske, Benno Stein, Matthias Hagen, Martin Potthast
Date: 2018-02-04 00:00:00
Tasks: Clickbait Detection, Deep Learning, Feature Engineering, feature selection, General Classification

We study feature selection as a means to optimize the baseline clickbait
detector employed at the Clickbait Challenge 2017. The challenge's task is to
score the "clickbaitiness" of a given Twitter tweet on a scale from 0 (no
clickbait) to 1 (strong clickbait). Unlike most other approaches submitted to
the challenge, the baseline approach is based on manual feature engineering and
does not compete out of the box with many of the deep learning-based
approaches. We show that scaling up feature selection efforts to heuristically
identify better-performing feature subsets catapults the performance of the
baseline classifier to second rank overall, beating 12 other competing
approaches and improving over the baseline performance by 20%. This
demonstrates that traditional classification approaches can still keep up with
deep learning on this task.
Personalized Machine Learning for Robot Perception of Affect and Engagement in Autism Therapy
Authors: Ognjen Rudovic, Jaeryoung Lee, Miles Dai, Bjorn Schuller, Rosalind Picard
Date: 2018-02-04 00:00:00
Tasks: BIG-bench Machine Learning

Robots have great potential to facilitate future therapies for children on
the autism spectrum. However, existing robots lack the ability to automatically
perceive and respond to human affect, which is necessary for establishing and
maintaining engaging interactions. Moreover, their inference challenge is made
harder by the fact that many individuals with autism have atypical and
unusually diverse styles of expressing their affective-cognitive states. To
tackle the heterogeneity in behavioral cues of children with autism, we use the
latest advances in deep learning to formulate a personalized machine learning
(ML) framework for automatic perception of the childrens affective states and
engagement during robot-assisted autism therapy. The key to our approach is a
novel shift from the traditional ML paradigm - instead of using
'one-size-fits-all' ML models, our personalized ML framework is optimized for
each child by leveraging relevant contextual information (demographics and
behavioral assessment scores) and individual characteristics of each child. We
designed and evaluated this framework using a dataset of multi-modal audio,
video and autonomic physiology data of 35 children with autism (age 3-13) and
from 2 cultures (Asia and Europe), participating in a 25-minute child-robot
interaction (~500k datapoints). Our experiments confirm the feasibility of the
robot perception of affect and engagement, showing clear improvements due to
the model personalization. The proposed approach has potential to improve
existing therapies for autism by offering more efficient monitoring and
summarization of the therapy progress.
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents
Authors: Joel Z. Leibo, Cyprien de Masson d'Autume, Daniel Zoran, David Amos, Charles Beattie, Keith Anderson, Antonio García Castañeda, Manuel Sanchez, Simon Green, Audrunas Gruslys, Shane Legg, Demis Hassabis, Matthew M. Botvinick
Date: 2018-01-24 00:00:00
Tasks: Change Detection, Deep Reinforcement Learning, Multiple Object Tracking, Object Tracking, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Psychlab is a simulated psychology laboratory inside the first-person 3D game
world of DeepMind Lab (Beattie et al. 2016). Psychlab enables implementations
of classical laboratory psychological experiments so that they work with both
human and artificial agents. Psychlab has a simple and flexible API that
enables users to easily create their own tasks. As examples, we are releasing
Psychlab implementations of several classical experimental paradigms including
visual search, change detection, random dot motion discrimination, and multiple
object tracking. We also contribute a study of the visual psychophysics of a
specific state-of-the-art deep reinforcement learning agent: UNREAL (Jaderberg
et al. 2016). This study leads to the surprising conclusion that UNREAL learns
more quickly about larger target stimuli than it does about smaller stimuli. In
turn, this insight motivates a specific improvement in the form of a simple
model of foveal vision that turns out to significantly boost UNREAL's
performance, both on Psychlab tasks, and on standard DeepMind Lab tasks. By
open-sourcing Psychlab we hope to facilitate a range of future such studies
that simultaneously advance deep reinforcement learning and improve its links
with cognitive science.
A Scheme-Driven Approach to Learning Programs from Input/Output Equations
Authors: Jochen Burghardt
Date: 2018-02-04 00:00:00

We describe an approach to learn, in a term-rewriting setting, function
definitions from input/output equations. By confining ourselves to structurally
recursive definitions we obtain a fairly fast learning algorithm that often
yields definitions close to intuitive expectations. We provide a Prolog
prototype implementation of our approach, and indicate open issues of further
investigation.
Graph Attention Networks
Authors: Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio
Date: 2017-10-30 00:00:00
Proceeding: ICLR 2018 1
Tasks: Document Classification, Graph Attention, Graph Classification, Graph Embedding, Graph Regression, Heterogeneous Node Classification, Link Prediction, Molecular Property Prediction, MUlTI-LABEL-ClASSIFICATION, Node Classification, Node Classification on Non-Homophilic (Heterophilic) Graphs, Node Property Prediction, Question Answering, Skeleton Based Action Recognition

We present graph attention networks (GATs), novel neural network
architectures that operate on graph-structured data, leveraging masked
self-attentional layers to address the shortcomings of prior methods based on
graph convolutions or their approximations. By stacking layers in which nodes
are able to attend over their neighborhoods' features, we enable (implicitly)
specifying different weights to different nodes in a neighborhood, without
requiring any kind of costly matrix operation (such as inversion) or depending
on knowing the graph structure upfront. In this way, we address several key
challenges of spectral-based graph neural networks simultaneously, and make our
model readily applicable to inductive as well as transductive problems. Our GAT
models have achieved or matched state-of-the-art results across four
established transductive and inductive graph benchmarks: the Cora, Citeseer and
Pubmed citation network datasets, as well as a protein-protein interaction
dataset (wherein test graphs remain unseen during training).
Testing to distinguish measures on metric spaces
Authors: Andrew J. Blumberg, Prithwish Bhaumik, Stephen G. Walker
Date: 2018-02-04 00:00:00

We study the problem of distinguishing between two distributions on a metric
space; i.e., given metric measure spaces $({\mathbb X}, d, \mu_1)$ and
$({\mathbb X}, d, \mu_2)$, we are interested in the problem of determining from
finite data whether or not $\mu_1$ is $\mu_2$. The key is to use pairwise
distances between observations and, employing a reconstruction theorem of
Gromov, we can perform such a test using a two sample Kolmogorov--Smirnov test.
A real analysis using phylogenetic trees and flu data is presented.
Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting
Authors: Hanzi Wang, Guobao Xiao, Yan Yan, David Suter
Date: 2018-02-04 00:00:00

In this paper, we propose a simple and effective {geometric} model fitting
method to fit and segment multi-structure data even in the presence of severe
outliers. We cast the task of geometric model fitting as a representative
mode-seeking problem on hypergraphs. Specifically, a hypergraph is firstly
constructed, where the vertices represent model hypotheses and the hyperedges
denote data points. The hypergraph involves higher-order similarities (instead
of pairwise similarities used on a simple graph), and it can characterize
complex relationships between model hypotheses and data points. {In addition,
we develop a hypergraph reduction technique to remove "insignificant" vertices
while retaining as many "significant" vertices as possible in the hypergraph}.
Based on the {simplified hypergraph, we then propose a novel mode-seeking
algorithm to search for representative modes within reasonable time. Finally,
the} proposed mode-seeking algorithm detects modes according to two key
elements, i.e., the weighting scores of vertices and the similarity analysis
between vertices. Overall, the proposed fitting method is able to efficiently
and effectively estimate the number and the parameters of model instances in
the data simultaneously. Experimental results demonstrate that the proposed
method achieves significant superiority over {several} state-of-the-art model
fitting methods on both synthetic data and real images.
Object Sorting Using a Global Texture-Shape 3D Feature Descriptor
Authors: Zhun Fan, Zhongxing Li, Benzhang Qiu, Wenji Li, Jianye Hu, Alex Noel Josephraj, Heping Chen
Date: 2018-02-04 00:00:00
Tasks: Object, Object Detection, Object Recognition

Object recognition and grasping plays a key role in robotic systems, especially for the autonomous robots to implement object sorting tasks in a warehouse. In this paper, we present a global texture-shape 3D feature descriptor which can be utilized in a system of object recognition and grasping, and can perform object sorting tasks well. Our proposed descriptor stems from the clustered viewpoint feature histogram (CVFH), which relies on the geometrical information of the whole 3D object surface only, and can not perform well in recognizing the objects with similar geometrical information. Therefore, we extend the CVFH descriptor with texture and color information to generate a new global 3D feature descriptor. The proposed descriptor is evaluated in tasks of recognizing and classifying 3D objects by applying multi-class support vector machines (SVM) in both public 3D image dataset and real scenes. The results of evaluation show that the proposed descriptor achieves a significant better performance for object recognition compared with the original CVFH. Then, the proposed descriptor is applied in our object recognition and grasping system, showing that the proposed descriptor helps the system implement the object recognition, object grasping and object sorting tasks well.
End2You -- The Imperial Toolkit for Multimodal Profiling by End-to-End Learning
Authors: Panagiotis Tzirakis, Stefanos Zafeiriou, Bjorn W. Schuller
Date: 2018-02-04 00:00:00
Tasks: Self-Learning

We introduce End2You -- the Imperial College London toolkit for multimodal
profiling by end-to-end deep learning. End2You is an open-source toolkit
implemented in Python and is based on Tensorflow. It provides capabilities to
train and evaluate models in an end-to-end manner, i.e., using raw input. It
supports input from raw audio, visual, physiological or other types of
information or combination of those, and the output can be of an arbitrary
representation, for either classification or regression tasks. To our
knowledge, this is the first toolkit that provides generic end-to-end learning
for profiling capabilities in either unimodal or multimodal cases. To test our
toolkit, we utilise the RECOLA database as was used in the AVEC 2016 challenge.
Experimental results indicate that End2You can provide comparable results to
state-of-the-art methods despite no need of expert-alike feature
representations, but self-learning these from the data "end to end".
A Bridge Between Hyperparameter Optimization and Learning-to-learn
Authors: Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil
Date: 2017-12-18 00:00:00
Tasks: Few-Shot Learning, Hyperparameter Optimization, Meta-Learning

We consider a class of a nested optimization problems involving inner and outer objectives. We observe that by taking into explicit account the optimization dynamics for the inner objective it is possible to derive a general framework that unifies gradient-based hyperparameter optimization and meta-learning (or learning-to-learn). Depending on the specific setting, the variables of the outer objective take either the meaning of hyperparameters in a supervised learning problem or parameters of a meta-learner. We show that some recently proposed methods in the latter setting can be instantiated in our framework and tackled with the same gradient-based algorithms. Finally, we discuss possible design patterns for learning-to-learn and present encouraging preliminary experiments for few-shot learning.
Museum Exhibit Identification Challenge for Domain Adaptation and Beyond
Authors: Piotr Koniusz, Yusuf Tas, Hongguang Zhang, Mehrtash Harandi, Fatih Porikli, Rui Zhang
Date: 2018-02-04 00:00:00
Tasks: Domain Adaptation, Few-Shot Learning

In this paper, we approach an open problem of artwork identification and
propose a new dataset dubbed Open Museum Identification Challenge (Open MIC).
It contains photos of exhibits captured in 10 distinct exhibition spaces of
several museums which showcase paintings, timepieces, sculptures, glassware,
relics, science exhibits, natural history pieces, ceramics, pottery, tools and
indigenous crafts. The goal of Open MIC is to stimulate research in domain
adaptation, egocentric recognition and few-shot learning by providing a testbed
complementary to the famous Office dataset which reaches 90% accuracy. To form
our dataset, we captured a number of images per art piece with a mobile phone
and wearable cameras to form the source and target data splits, respectively.
To achieve robust baselines, we build on a recent approach that aligns
per-class scatter matrices of the source and target CNN streams [15]. Moreover,
we exploit the positive definite nature of such representations by using
end-to-end Bregman divergences and the Riemannian metric. We present baselines
such as training/evaluation per exhibition and training/evaluation on the
combined set covering 866 exhibit identities. As each exhibition poses distinct
challenges e.g., quality of lighting, motion blur, occlusions, clutter,
viewpoint and scale variations, rotations, glares, transparency, non-planarity,
clipping, we break down results w.r.t. these factors.
Optimal Stochastic Delivery Planning in Full-Truckload and Less-Than-Truckload Delivery
Authors: Suttinee Sawadsitang, Rakpong Kaewpuang, Siwei Jiang, Dusit Niyato, Ping Wang
Date: 2018-02-04 00:00:00

With an increasing demand from emerging logistics businesses, Vehicle Routing
Problem with Private fleet and common Carrier (VRPPC) has been introduced to
manage package delivery services from a supplier to customers. However, almost
all of existing studies focus on the deterministic problem that assumes all
parameters are known perfectly at the time when the planning and routing
decisions are made. In reality, some parameters are random and unknown.
Therefore, in this paper, we consider VRPPC with hard time windows and random
demand, called Optimal Delivery Planning (ODP). The proposed ODP aims to
minimize the total package delivery cost while meeting the customer time window
constraints. We use stochastic integer programming to formulate the
optimization problem incorporating the customer demand uncertainty. Moreover,
we evaluate the performance of the ODP using test data from benchmark dataset
and from actual Singapore road map.
Script Identification in Natural Scene Image and Video Frame using Attention based Convolutional-LSTM Network
Authors: Ankan Kumar Bhunia, Aishik Konwer, Ayan Kumar Bhunia, Abir Bhowmick, Partha P. Roy, Umapada Pal
Date: 2018-01-01 00:00:00

Script identification plays a significant role in analysing documents and
videos. In this paper, we focus on the problem of script identification in
scene text images and video scripts. Because of low image quality, complex
background and similar layout of characters shared by some scripts like Greek,
Latin, etc., text recognition in those cases become challenging. In this paper,
we propose a novel method that involves extraction of local and global features
using CNN-LSTM framework and weighting them dynamically for script
identification. First, we convert the images into patches and feed them into a
CNN-LSTM framework. Attention-based patch weights are calculated applying
softmax layer after LSTM. Next, we do patch-wise multiplication of these
weights with corresponding CNN to yield local features. Global features are
also extracted from last cell state of LSTM. We employ a fusion technique which
dynamically weights the local and global features for an individual patch.
Experiments have been done in four public script identification datasets:
SIW-13, CVSI2015, ICDAR-17 and MLe2e. The proposed framework achieves superior
results in comparison to conventional methods.
Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All
Authors: Minh C. Phan, Aixin Sun, Yi Tay, Jialong Han, Chenliang Li
Date: 2018-02-04 00:00:00
Tasks: All, Decision Making, Entity Disambiguation

Collective entity disambiguation aims to jointly resolve multiple mentions by
linking them to their associated entities in a knowledge base. Previous works
are primarily based on the underlying assumption that entities within the same
document are highly related. However, the extend to which these mentioned
entities are actually connected in reality is rarely studied and therefore
raises interesting research questions. For the first time, we show that the
semantic relationships between the mentioned entities are in fact less dense
than expected. This could be attributed to several reasons such as noise, data
sparsity and knowledge base incompleteness. As a remedy, we introduce MINTREE,
a new tree-based objective for the entity disambiguation problem. The key
intuition behind MINTREE is the concept of coherence relaxation which utilizes
the weight of a minimum spanning tree to measure the coherence between
entities. Based on this new objective, we design a novel entity disambiguation
algorithms which we call Pair-Linking. Instead of considering all the given
mentions, Pair-Linking iteratively selects a pair with the highest confidence
at each step for decision making. Via extensive experiments, we show that our
approach is not only more accurate but also surprisingly faster than many
state-of-the-art collective linking algorithms.