pwc-archive/papers-with-abstracts huggingface dataset:
VC-Dimension Based Generalization Bounds for Relational Learning
Authors: Ondrej Kuzelka, Yuyi Wang, Steven Schockaert
Date: 2018-04-17 00:00:00
Tasks: Generalization Bounds, Relational Reasoning

In many applications of relational learning, the available data can be seen
as a sample from a larger relational structure (e.g. we may be given a small
fragment from some social network). In this paper we are particularly concerned
with scenarios in which we can assume that (i) the domain elements appearing in
the given sample have been uniformly sampled without replacement from the
(unknown) full domain and (ii) the sample is complete for these domain elements
(i.e. it is the full substructure induced by these elements). Within this
setting, we study bounds on the error of sufficient statistics of relational
models that are estimated on the available data. As our main result, we prove a
bound based on a variant of the Vapnik-Chervonenkis dimension which is suitable
for relational data.
Memetic Algorithms Beat Evolutionary Algorithms on the Class of Hurdle Problems
Authors: Phan Trung Hai Nguyen, Dirk Sudholt
Date: 2018-04-17 00:00:00
Tasks: Evolutionary Algorithms

Memetic algorithms are popular hybrid search heuristics that integrate local
search into the search process of an evolutionary algorithm in order to combine
the advantages of rapid exploitation and global optimisation. However, these
algorithms are not well understood and the field is lacking a solid theoretical
foundation that explains when and why memetic algorithms are effective.
  We provide a rigorous runtime analysis of a simple memetic algorithm, the
$(1+1)$ MA, on the Hurdle problem class, a landscape class of tuneable
difficulty that shows a "big valley structure", a characteristic feature of
many hard problems from combinatorial optimisation. The only parameter of this
class is the hurdle width w, which describes the length of fitness valleys that
have to be overcome. We show that the $(1+1)$ EA requires $\Theta(n^w)$
expected function evaluations to find the optimum, whereas the $(1+1)$ MA with
best-improvement and first-improvement local search can find the optimum in
$\Theta(n^2+n^3/w^2)$ and $\Theta(n^3/w^2)$ function evaluations, respectively.
Surprisingly, while increasing the hurdle width makes the problem harder for
evolutionary algorithms, the problem becomes easier for memetic algorithms. We
discuss how these findings can explain and illustrate the success of memetic
algorithms for problems with big valley structures.
Learning to Navigate in Cities Without a Map
Authors: Piotr Mirowski, Matthew Koichi Grimes, Mateusz Malinowski, Karl Moritz Hermann, Keith Anderson, Denis Teplyashin, Karen Simonyan, Koray Kavukcuoglu, Andrew Zisserman, Raia Hadsell
Date: 2018-03-31 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Autonomous Navigation, Deep Reinforcement Learning, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Navigating through unstructured environments is a basic capability of
intelligent creatures, and thus is of fundamental interest in the study and
development of artificial intelligence. Long-range navigation is a complex
cognitive task that relies on developing an internal representation of space,
grounded by recognisable landmarks and robust visual processing, that can
simultaneously support continuous self-localisation ("I am here") and a
representation of the goal ("I am going there"). Building upon recent research
that applies deep reinforcement learning to maze navigation problems, we
present an end-to-end deep reinforcement learning approach that can be applied
on a city scale. Recognising that successful navigation relies on integration
of general policies with locale-specific knowledge, we propose a dual pathway
architecture that allows locale-specific features to be encapsulated, while
still enabling transfer to multiple cities. We present an interactive
navigation environment that uses Google StreetView for its photographic content
and worldwide coverage, and demonstrate that our learning method allows agents
to learn to navigate multiple cities and to traverse to target destinations
that may be kilometres away. The project webpage http://streetlearn.cc contains
a video summarising our research and showing the trained agent in diverse city
environments and on the transfer task, the form to request the StreetLearn
dataset and links to further resources. The StreetLearn environment code is
available at https://github.com/deepmind/streetlearn
Image biomarker standardisation initiative
Authors: Alex Zwanenburg, Stefan Leger, Martin Vallières, Steffen Löck
Date: 2016-12-21 00:00:00

The image biomarker standardisation initiative (IBSI) is an independent international collaboration which works towards standardising the extraction of image biomarkers from acquired imaging for the purpose of high-throughput quantitative image analysis (radiomics). Lack of reproducibility and validation of high-throughput quantitative image analysis studies is considered to be a major challenge for the field. Part of this challenge lies in the scantiness of consensus-based guidelines and definitions for the process of translating acquired imaging into high-throughput image biomarkers. The IBSI therefore seeks to provide image biomarker nomenclature and definitions, benchmark data sets, and benchmark values to verify image processing and image biomarker calculations, as well as reporting guidelines, for high-throughput image analysis.
Exact Sampling from Determinantal Point Processes
Authors: Philipp Hennig, Roman Garnett
Date: 2016-09-22 00:00:00
Tasks: Active Learning, Bayesian Optimization, BIG-bench Machine Learning, Point Processes, Reinforcement Learning

Determinantal point processes (DPPs) are an important concept in random
matrix theory and combinatorics. They have also recently attracted interest in
the study of numerical methods for machine learning, as they offer an elegant
"missing link" between independent Monte Carlo sampling and deterministic
evaluation on regular grids, applicable to a general set of spaces. This is
helpful whenever an algorithm explores to reduce uncertainty, such as in active
learning, Bayesian optimization, reinforcement learning, and marginalization in
graphical models. To draw samples from a DPP in practice, existing literature
focuses on approximate schemes of low cost, or comparably inefficient exact
algorithms like rejection sampling. We point out that, for many settings of
relevance to machine learning, it is also possible to draw exact samples from
DPPs on continuous domains. We start from an intuitive example on the real
line, which is then generalized to multivariate real vector spaces. We also
compare to previously studied approximations, showing that exact sampling,
despite higher cost, can be preferable where precision is needed.
SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets
Authors: Venkatesh Duppada, Royal Jain, Sushant Hiray
Date: 2018-04-17 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Domain Adaptation, General Classification, Ordinal Classification, Position, regression

The paper describes the best performing system for the SemEval-2018 Affect in
Tweets (English) sub-tasks. The system focuses on the ordinal classification
and regression sub-tasks for valence and emotion. For ordinal classification
valence is classified into 7 different classes ranging from -3 to 3 whereas
emotion is classified into 4 different classes 0 to 3 separately for each
emotion namely anger, fear, joy and sadness. The regression sub-tasks estimate
the intensity of valence and each emotion. The system performs domain
adaptation of 4 different models and creates an ensemble to give the final
prediction. The proposed system achieved 1st position out of 75 teams which
participated in the fore-mentioned sub-tasks. We outperform the baseline model
by margins ranging from 49.2% to 76.4%, thus, pushing the state-of-the-art
significantly.
Automatic Assessment of Artistic Quality of Photos
Authors: Ashish Verma, Kranthi Koukuntla, Rohit Varma, Snehasis Mukherjee
Date: 2018-04-17 00:00:00

This paper proposes a technique to assess the aesthetic quality of
photographs. The goal of the study is to predict whether a given photograph is
captured by professional photographers, or by common people, based on a
measurement of artistic quality of the photograph. We propose a
Multi-Layer-Perceptron based system to analyze some low, mid and high level
image features and find their effectiveness to measure artistic quality of the
image and produce a measurement of the artistic quality of the image on a scale
of 10. We validate the proposed system on a large dataset, containing images
downloaded from the internet. The dataset contains some images captured by
professional photographers and the rest of the images captured by common
people. The proposed measurement of artistic quality of images provides higher
value of photo quality for the images captured by professional photographers,
compared to the values provided for the other images.
The TUM VI Benchmark for Evaluating Visual-Inertial Odometry
Authors: David Schubert, Thore Goll, Nikolaus Demmel, Vladyslav Usenko, Jörg Stückler, Daniel Cremers
Date: 2018-04-17 00:00:00
Tasks: Visual Odometry

Visual odometry and SLAM methods have a large variety of applications in domains such as augmented reality or robotics. Complementing vision sensors with inertial measurements tremendously improves tracking accuracy and robustness, and thus has spawned large interest in the development of visual-inertial (VI) odometry approaches. In this paper, we propose the TUM VI benchmark, a novel dataset with a diverse set of sequences in different scenes for evaluating VI odometry. It provides camera images with 1024x1024 resolution at 20 Hz, high dynamic range and photometric calibration. An IMU measures accelerations and angular velocities on 3 axes at 200 Hz, while the cameras and IMU sensors are time-synchronized in hardware. For trajectory evaluation, we also provide accurate pose ground truth from a motion capture system at high frequency (120 Hz) at the start and end of the sequences which we accurately aligned with the camera and IMU measurements. The full dataset with raw and calibrated data is publicly available. We also evaluate state-of-the-art VI odometry approaches on our dataset.
A Support Tensor Train Machine
Authors: Cong Chen, Kim Batselier, Ching-Yun Ko, Ngai Wong
Date: 2018-04-17 00:00:00
Tasks: BIG-bench Machine Learning

There has been growing interest in extending traditional vector-based machine
learning techniques to their tensor forms. An example is the support tensor
machine (STM) that utilizes a rank-one tensor to capture the data structure,
thereby alleviating the overfitting and curse of dimensionality problems in the
conventional support vector machine (SVM). However, the expressive power of a
rank-one tensor is restrictive for many real-world data. To overcome this
limitation, we introduce a support tensor train machine (STTM) by replacing the
rank-one tensor in an STM with a tensor train. Experiments validate and confirm
the superiority of an STTM over the SVM and STM.
Human Motion Capture Using a Drone
Authors: Xiaowei Zhou, Sikang Liu, Georgios Pavlakos, Vijay Kumar, Kostas Daniilidis
Date: 2018-04-17 00:00:00

Current motion capture (MoCap) systems generally require markers and multiple
calibrated cameras, which can be used only in constrained environments. In this
work we introduce a drone-based system for 3D human MoCap. The system only
needs an autonomously flying drone with an on-board RGB camera and is usable in
various indoor and outdoor environments. A reconstruction algorithm is
developed to recover full-body motion from the video recorded by the drone. We
argue that, besides the capability of tracking a moving subject, a flying drone
also provides fast varying viewpoints, which is beneficial for motion
reconstruction. We evaluate the accuracy of the proposed system using our new
DroCap dataset and also demonstrate its applicability for MoCap in the wild
using a consumer drone.
Feature Propagation on Graph: A New Perspective to Graph Representation Learning
Authors: Biao Xiang, Ziqi Liu, Jun Zhou, Xiaolong Li
Date: 2018-04-17 00:00:00
Tasks: Graph Embedding, Graph Representation Learning, Representation Learning

We study feature propagation on graph, an inference process involved in graph
representation learning tasks. It's to spread the features over the whole graph
to the $t$-th orders, thus to expand the end's features. The process has been
successfully adopted in graph embedding or graph neural networks, however few
works studied the convergence of feature propagation. Without convergence
guarantees, it may lead to unexpected numerical overflows and task failures. In
this paper, we first define the concept of feature propagation on graph
formally, and then study its convergence conditions to equilibrium states. We
further link feature propagation to several established approaches such as
node2vec and structure2vec. In the end of this paper, we extend existing
approaches from represent nodes to edges (edge2vec) and demonstrate its
applications on fraud transaction detection in real world scenario. Experiments
show that it is quite competitive.
Development of a N-type GM-PHD Filter for Multiple Target, Multiple Type Visual Tracking
Authors: Nathanael L. Baisa, Andrew Wallace
Date: 2017-05-31 00:00:00
Tasks: Visual Tracking, Vocal Bursts Type Prediction

We propose a new framework that extends the standard Probability Hypothesis
Density (PHD) filter for multiple targets having $N\geq2$ different types based
on Random Finite Set theory, taking into account not only background clutter,
but also confusions among detections of different target types, which are in
general different in character from background clutter. Under Gaussianity and
linearity assumptions, our framework extends the existing Gaussian mixture (GM)
implementation of the standard PHD filter to create a N-type GM-PHD filter. The
methodology is applied to real video sequences by integrating object detectors'
information into this filter for two scenarios. For both cases, Munkres's
variant of the Hungarian assignment algorithm is used to associate tracked
target identities between frames. This approach is evaluated and compared to
both raw detection and independent GM-PHD filters using the Optimal Sub-pattern
Assignment metric and discrimination rate. This shows the improved performance
of our strategy on real video sequences.
Synthesizing realistic neural population activity patterns using Generative Adversarial Networks
Authors: Manuel Molano-Mazon, Arno Onken, Eugenio Piasini, Stefano Panzeri
Date: 2018-03-01 00:00:00
Proceeding: ICLR 2018 1

The ability to synthesize realistic patterns of neural activity is crucial
for studying neural information processing. Here we used the Generative
Adversarial Networks (GANs) framework to simulate the concerted activity of a
population of neurons. We adapted the Wasserstein-GAN variant to facilitate the
generation of unconstrained neural population activity patterns while still
benefiting from parameter sharing in the temporal domain. We demonstrate that
our proposed GAN, which we termed Spike-GAN, generates spike trains that match
accurately the first- and second-order statistics of datasets of tens of
neurons and also approximates well their higher-order statistics. We applied
Spike-GAN to a real dataset recorded from salamander retina and showed that it
performs as well as state-of-the-art approaches based on the maximum entropy
and the dichotomized Gaussian frameworks. Importantly, Spike-GAN does not
require to specify a priori the statistics to be matched by the model, and so
constitutes a more flexible method than these alternative approaches. Finally,
we show how to exploit a trained Spike-GAN to construct 'importance maps' to
detect the most relevant statistical structures present in a spike train.
Spike-GAN provides a powerful, easy-to-use technique for generating realistic
spiking neural activity and for describing the most relevant features of the
large-scale neural population recordings studied in modern systems
neuroscience.
A Multilinear Tongue Model Derived from Speech Related MRI Data of the Human Vocal Tract
Authors: Alexander Hewer, Stefanie Wuhrer, Ingmar Steiner, Korin Richmond
Date: 2016-12-15 00:00:00
Tasks: Denoising, Image Denoising, Image Segmentation, Semantic Segmentation

We present a multilinear statistical model of the human tongue that captures
anatomical and tongue pose related shape variations separately. The model is
derived from 3D magnetic resonance imaging data of 11 speakers sustaining
speech related vocal tract configurations. The extraction is performed by using
a minimally supervised method that uses as basis an image segmentation approach
and a template fitting technique. Furthermore, it uses image denoising to deal
with possibly corrupt data, palate surface information reconstruction to handle
palatal tongue contacts, and a bootstrap strategy to refine the obtained
shapes. Our evaluation concludes that limiting the degrees of freedom for the
anatomical and speech related variations to 5 and 4, respectively, produces a
model that can reliably register unknown data while avoiding overfitting
effects. Furthermore, we show that it can be used to generate a plausible
tongue animation by tracking sparse motion capture data.
Parametric Models for Mutual Kernel Matrix Completion
Authors: Rachelle Rivero, Tsuyoshi Kato
Date: 2018-04-17 00:00:00
Tasks: Matrix Completion

Recent studies utilize multiple kernel learning to deal with incomplete-data
problem. In this study, we introduce new methods that do not only complete
multiple incomplete kernel matrices simultaneously, but also allow control of
the flexibility of the model by parameterizing the model matrix. By imposing
restrictions on the model covariance, overfitting of the data is avoided. A
limitation of kernel matrix estimations done via optimization of an objective
function is that the positive definiteness of the result is not guaranteed. In
view of this limitation, our proposed methods employ the LogDet divergence,
which ensures the positive definiteness of the resulting inferred kernel
matrix. We empirically show that our proposed restricted covariance models,
employed with LogDet divergence, yield significant improvements in the
generalization performance of previous completion methods.
Sparse Unsupervised Capsules Generalize Better
Authors: David Rawlinson, Abdelrahman Ahmed, Gideon Kowadlo
Date: 2018-04-17 00:00:00
Tasks: General Classification

We show that unsupervised training of latent capsule layers using only the
reconstruction loss, without masking to select the correct output class, causes
a loss of equivariances and other desirable capsule qualities. This implies
that supervised capsules networks can't be very deep. Unsupervised sparsening
of latent capsule layer activity both restores these qualities and appears to
generalize better than supervised masking, while potentially enabling deeper
capsules networks. We train a sparse, unsupervised capsules network of similar
geometry to Sabour et al (2017) on MNIST, and then test classification accuracy
on affNIST using an SVM layer. Accuracy is improved from benchmark 79% to 90%.
Automatic Construction of Parallel Portfolios via Explicit Instance Grouping
Authors: Shengcai Liu, Ke Tang, Xin Yao
Date: 2018-04-17 00:00:00

Simultaneously utilizing several complementary solvers is a simple yet
effective strategy for solving computationally hard problems. However, manually
building such solver portfolios typically requires considerable domain
knowledge and plenty of human effort. As an alternative, automatic construction
of parallel portfolios (ACPP) aims at automatically building effective parallel
portfolios based on a given problem instance set and a given rich design space.
One promising way to solve the ACPP problem is to explicitly group the
instances into different subsets and promote a component solver to handle each
of them.This paper investigates solving ACPP from this perspective, and
especially studies how to obtain a good instance grouping.The experimental
results showed that the parallel portfolios constructed by the proposed method
could achieve consistently superior performances to the ones constructed by the
state-of-the-art ACPP methods,and could even rival sophisticated hand-designed
parallel solvers.
Rafiki: Machine Learning as an Analytics Service System
Authors: Wei Wang, Sheng Wang, Jinyang Gao, Meihui Zhang, Gang Chen, Teck Khim Ng, Beng Chin Ooi
Date: 2018-04-17 00:00:00
Proceeding: PVLDB (The Proceedings of the VLDB Endowment) 2018 10
Tasks: AutoML, BIG-bench Machine Learning, Hyperparameter Optimization, image-classification, Image Classification, Sentiment Analysis

Big data analytics is gaining massive momentum in the last few years.
Applying machine learning models to big data has become an implicit requirement
or an expectation for most analysis tasks, especially on high-stakes
applications.Typical applications include sentiment analysis against reviews
for analyzing on-line products, image classification in food logging
applications for monitoring user's daily intake and stock movement prediction.
Extending traditional database systems to support the above analysis is
intriguing but challenging. First, it is almost impossible to implement all
machine learning models in the database engines. Second, expertise knowledge is
required to optimize the training and inference procedures in terms of
efficiency and effectiveness, which imposes heavy burden on the system users.
In this paper, we develop and present a system, called Rafiki, to provide the
training and inference service of machine learning models, and facilitate
complex analytics on top of cloud platforms. Rafiki provides distributed
hyper-parameter tuning for the training service, and online ensemble modeling
for the inference service which trades off between latency and accuracy.
Experimental results confirm the efficiency, effectiveness, scalability and
usability of Rafiki.
Encoding Longer-term Contextual Multi-modal Information in a Predictive Coding Model
Authors: Junpei Zhong, Tetsuya OGATA, Angelo Cangelosi
Date: 2018-04-17 00:00:00

Studies suggest that within the hierarchical architecture, the topological
higher level possibly represents a conscious category of the current sensory
events with slower changing activities. They attempt to predict the activities
on the lower level by relaying the predicted information. On the other hand,
the incoming sensory information corrects such prediction of the events on the
higher level by the novel or surprising signal. We propose a predictive
hierarchical artificial neural network model that examines this hypothesis on
neurorobotic platforms, based on the AFA-PredNet model. In this neural network
model, there are different temporal scales of predictions exist on different
levels of the hierarchical predictive coding, which are defined in the temporal
parameters in the neurons. Also, both the fast and the slow-changing neural
activities are modulated by the active motor activities. A neurorobotic
experiment based on the architecture was also conducted based on the data
collected from the VRep simulator.