pwc-archive/papers-with-abstracts huggingface dataset:
Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework
Authors: Chun-Guang Li, Chong You, René Vidal
Date: 2016-10-17 00:00:00
Tasks: Clustering, Motion Segmentation, Segmentation

Subspace clustering refers to the problem of segmenting data drawn from a
union of subspaces. State-of-the-art approaches for solving this problem follow
a two-stage approach. In the first step, an affinity matrix is learned from the
data using sparse or low-rank minimization techniques. In the second step, the
segmentation is found by applying spectral clustering to this affinity. While
this approach has led to state-of-the-art results in many applications, it is
sub-optimal because it does not exploit the fact that the affinity and the
segmentation depend on each other. In this paper, we propose a joint
optimization framework --- Structured Sparse Subspace Clustering (S$^3$C) ---
for learning both the affinity and the segmentation. The proposed S$^3$C
framework is based on expressing each data point as a structured sparse linear
combination of all other data points, where the structure is induced by a norm
that depends on the unknown segmentation. Moreover, we extend the proposed
S$^3$C framework into Constrained Structured Sparse Subspace Clustering
(CS$^3$C) in which available partial side-information is incorporated into the
stage of learning the affinity. We show that both the structured sparse
representation and the segmentation can be found via a combination of an
alternating direction method of multipliers with spectral clustering.
Experiments on a synthetic data set, the Extended Yale B data set, the Hopkins
155 motion segmentation database, and three cancer data sets demonstrate the
effectiveness of our approach.
Ensemble of Neural Classifiers for Scoring Knowledge Base Triples
Authors: Ikuya Yamada, Motoki Sato, Hiroyuki Shindo
Date: 2017-03-15 00:00:00
Tasks: BIG-bench Machine Learning, Entity Retrieval, Retrieval

This paper describes our approach for the triple scoring task at the WSDM Cup
2017. The task required participants to assign a relevance score for each pair
of entities and their types in a knowledge base in order to enhance the ranking
results in entity retrieval tasks. We propose an approach wherein the outputs
of multiple neural network classifiers are combined using a supervised machine
learning model. The experimental results showed that our proposed method
achieved the best performance in one out of three measures (i.e., Kendall's
tau), and performed competitively in the other two measures (i.e., accuracy and
average score difference).
Detecting confounding in multivariate linear models via spectral analysis
Authors: Dominik Janzing, Bernhard Schoelkopf
Date: 2017-04-05 00:00:00

We study a model where one target variable Y is correlated with a vector
X:=(X_1,...,X_d) of predictor variables being potential causes of Y. We
describe a method that infers to what extent the statistical dependences
between X and Y are due to the influence of X on Y and to what extent due to a
hidden common cause (confounder) of X and Y. The method relies on concentration
of measure results for large dimensions d and an independence assumption
stating that, in the absence of confounding, the vector of regression
coefficients describing the influence of each X on Y typically has `generic
orientation' relative to the eigenspaces of the covariance matrix of X. For the
special case of a scalar confounder we show that confounding typically spoils
this generic orientation in a characteristic way that can be used to
quantitatively estimate the amount of confounding.
The UMCD Dataset
Authors: Danilo Avola, Gian Luca Foresti, Niki Martinel, Daniele Pannone, Claudio Piciarelli
Date: 2017-04-05 00:00:00
Tasks: Change Detection, General Classification

In recent years, the technological improvements of low-cost small-scale
Unmanned Aerial Vehicles (UAVs) are promoting an ever-increasing use of them in
different tasks. In particular, the use of small-scale UAVs is useful in all
these low-altitude tasks in which common UAVs cannot be adopted, such as
recurrent comprehensive view of wide environments, frequent monitoring of
military areas, real-time classification of static and moving entities (e.g.,
people, cars, etc.). These tasks can be supported by mosaicking and change
detection algorithms achieved at low-altitude. Currently, public datasets for
testing these algorithms are not available. This paper presents the UMCD
dataset, the first collection of geo-referenced video sequences acquired at
low-altitude for mosaicking and change detection purposes. Five reference
scenarios are also reported.
Linear Ensembles of Word Embedding Models
Authors: Avo Muromägi, Kairit Sirts, Sven Laur
Date: 2017-04-05 00:00:00
Proceeding: WS 2017 5
Tasks: regression, Word Embeddings

This paper explores linear methods for combining several word embedding
models into an ensemble. We construct the combined models using an iterative
method based on either ordinary least squares regression or the solution to the
orthogonal Procrustes problem.
  We evaluate the proposed approaches on Estonian---a morphologically complex
language, for which the available corpora for training word embeddings are
relatively small. We compare both combined models with each other and with the
input word embedding models using synonym and analogy tests. The results show
that while using the ordinary least squares regression performs poorly in our
experiments, using orthogonal Procrustes to combine several word embedding
models into an ensemble model leads to 7-10% relative improvements over the
mean result of the initial models in synonym tests and 19-47% in analogy tests.
Finite-Time Stabilization of Longitudinal Control for Autonomous Vehicles via a Model-Free Approach
Authors: Philip Polack, Brigitte d'Andréa-Novel, Michel Fliess, Arnaud de La Fortelle, Lghani Menhour
Date: 2017-04-05 00:00:00
Tasks: Autonomous Vehicles

This communication presents a longitudinal model-free control approach for
computing the wheel torque command to be applied on a vehicle. This setting
enables us to overcome the problem of unknown vehicle parameters for generating
a suitable control law. An important parameter in this control setting is made
time-varying for ensuring finite-time stability. Several convincing computer
simulations are displayed and discussed. Overshoots become therefore smaller.
The driving comfort is increased and the robustness to time-delays is improved.
On the construction of probabilistic Newton-type algorithms
Authors: Adrian G. Wills, Thomas B. Schön
Date: 2017-04-05 00:00:00
Tasks: Vocal Bursts Type Prediction

It has recently been shown that many of the existing quasi-Newton algorithms
can be formulated as learning algorithms, capable of learning local models of
the cost functions. Importantly, this understanding allows us to safely start
assembling probabilistic Newton-type algorithms, applicable in situations where
we only have access to noisy observations of the cost function and its
derivatives. This is where our interest lies.
  We make contributions to the use of the non-parametric and probabilistic
Gaussian process models in solving these stochastic optimisation problems.
Specifically, we present a new algorithm that unites these approximations
together with recent probabilistic line search routines to deliver a
probabilistic quasi-Newton approach.
  We also show that the probabilistic optimisation algorithms deliver promising
results on challenging nonlinear system identification problems where the very
nature of the problem is such that we can only access the cost function and its
derivative via noisy observations, since there are no closed-form expressions
available.
A deep learning classification scheme based on augmented-enhanced features to segment organs at risk on the optic region in brain cancer patients
Authors: Jose Dolz, Nicolas Reyns, Nacim Betrouni, Dris Kharroubi, Mathilde Quidet, Laurent Massoptier, Maximilien Vermandel
Date: 2017-03-30 00:00:00
Tasks: General Classification

Radiation therapy has emerged as one of the preferred techniques to treat
brain cancer patients. During treatment, a very high dose of radiation is
delivered to a very narrow area. Prescribed radiation therapy for brain cancer
requires precisely defining the target treatment area, as well as delineating
vital brain structures which must be spared from radiotoxicity. Nevertheless,
delineation task is usually still manually performed, which is inefficient and
operator-dependent. Several attempts of automatizing this process have
reported. however, marginal results when analyzing organs in the optic region.
In this work we present a deep learning classification scheme based on
augmented-enhanced features to automatically segment organs at risk (OARs) in
the optic region -optic nerves, optic chiasm, pituitary gland and pituitary
stalk-. Fifteen MR images with various types of brain tumors were
retrospectively collected to undergo manual and automatic segmentation. Mean
Dice Similarity coefficients around 0.80 were reported. Incorporation of
proposed features yielded to improvements on the segmentation. Compared with
support vector machines, our method achieved better performance with less
variation on the results, as well as a considerably reduction on the
classification time. Performance of the proposed approach was also evaluated
with respect to manual contours. In this case, results obtained from the
automatic contours mostly lie on the variability of the observers, showing no
significant differences with respect to them. These results suggest therefore
that the proposed system is more accurate than other presented approaches, up
to date, to segment these structures. The speed, reproducibility, and
robustness of the process make the proposed deep learning-based classification
system a valuable tool for assisting in the delineation task of small OARs in
brain cancer.
An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration
Authors: Hongzhou Lin, Julien Mairal, Zaid Harchaoui
Date: 2016-10-04 00:00:00

We propose an inexact variable-metric proximal point algorithm to accelerate
gradient-based optimization algorithms. The proposed scheme, called QNing can
be notably applied to incremental first-order methods such as the stochastic
variance-reduced gradient descent algorithm (SVRG) and other randomized
incremental optimization algorithms. QNing is also compatible with composite
objectives, meaning that it has the ability to provide exactly sparse solutions
when the objective involves a sparsity-inducing regularization. When combined
with limited-memory BFGS rules, QNing is particularly effective to solve
high-dimensional optimization problems, while enjoying a worst-case linear
convergence rate for strongly convex problems. We present experimental results
where QNing gives significant improvements over competing methods for training
machine learning methods on large samples and in high dimensions.
On the Relation between Color Image Denoising and Classification
Authors: Jiqing Wu, Radu Timofte, Zhiwu Huang, Luc van Gool
Date: 2017-04-05 00:00:00
Tasks: Classification, Color Image Denoising, Denoising, General Classification, Image Denoising, Relation

Large amount of image denoising literature focuses on single channel images
and often experimentally validates the proposed methods on tens of images at
most. In this paper, we investigate the interaction between denoising and
classification on large scale dataset. Inspired by classification models, we
propose a novel deep learning architecture for color (multichannel) image
denoising and report on thousands of images from ImageNet dataset as well as
commonly used imagery. We study the importance of (sufficient) training data,
how semantic class information can be traded for improved denoising results. As
a result, our method greatly improves PSNR performance by 0.34 - 0.51 dB on
average over state-of-the art methods on large scale dataset. We conclude that
it is beneficial to incorporate in classification models. On the other hand, we
also study how noise affect classification performance. In the end, we come to
a number of interesting conclusions, some being counter-intuitive.
Incremental Tube Construction for Human Action Detection
Authors: Harkirat Singh Behl, Michael Sapienza, Gurkirt Singh, Suman Saha, Fabio Cuzzolin, Philip H. S. Torr
Date: 2017-04-05 00:00:00
Tasks: Action Detection

Current state-of-the-art action detection systems are tailored for offline
batch-processing applications. However, for online applications like
human-robot interaction, current systems fall short, either because they only
detect one action per video, or because they assume that the entire video is
available ahead of time. In this work, we introduce a real-time and online
joint-labelling and association algorithm for action detection that can
incrementally construct space-time action tubes on the most challenging action
videos in which different action categories occur concurrently. In contrast to
previous methods, we solve the detection-window association and action
labelling problems jointly in a single pass. We demonstrate superior online
association accuracy and speed (2.2ms per frame) as compared to the current
state-of-the-art offline systems. We further demonstrate that the entire action
detection pipeline can easily be made to work effectively in real-time using
our action tube construction algorithm.
CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual Similarity
Authors: Jeremy Ferrero, Frederic Agnes, Laurent Besacier, Didier Schwab
Date: 2017-04-05 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Semantic Similarity, Semantic Textual Similarity, STS

We present our submitted systems for Semantic Textual Similarity (STS) Track
4 at SemEval-2017. Given a pair of Spanish-English sentences, each system must
estimate their semantic similarity by a score between 0 and 5. In our
submission, we use syntax-based, dictionary-based, context-based, and MT-based
methods. We also combine these methods in unsupervised and supervised way. Our
best run ranked 1st on track 4a with a correlation of 83.02% with human
annotations.
Not All Pixels Are Equal: Difficulty-aware Semantic Segmentation via Deep Layer Cascade
Authors: Xiaoxiao Li, Ziwei Liu, Ping Luo, Chen Change Loy, Xiaoou Tang
Date: 2017-04-05 00:00:00
Proceeding: CVPR 2017 7
Tasks: All, Semantic Segmentation

We propose a novel deep layer cascade (LC) method to improve the accuracy and
speed of semantic segmentation. Unlike the conventional model cascade (MC) that
is composed of multiple independent models, LC treats a single deep model as a
cascade of several sub-models. Earlier sub-models are trained to handle easy
and confident regions, and they progressively feed-forward harder regions to
the next sub-model for processing. Convolutions are only calculated on these
regions to reduce computations. The proposed method possesses several
advantages. First, LC classifies most of the easy regions in the shallow stage
and makes deeper stage focuses on a few hard regions. Such an adaptive and
'difficulty-aware' learning improves segmentation performance. Second, LC
accelerates both training and testing of deep network thanks to early decisions
in the shallow stage. Third, in comparison to MC, LC is an end-to-end trainable
framework, allowing joint learning of all sub-models. We evaluate our method on
PASCAL VOC and Cityscapes datasets, achieving state-of-the-art performance and
fast speed.
Automated Diagnosis of Epilepsy Employing Multifractal Detrended Fluctuation Analysis Based Features
Authors: S Pratiher, S. Chatterjee, R Bose
Date: 2017-04-05 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification, Time Series, Time Series Analysis

This contribution reports an application of MultiFractal Detrended
Fluctuation Analysis, MFDFA based novel feature extraction technique for
automated detection of epilepsy. In fractal geometry, Multifractal Detrended
Fluctuation Analysis MFDFA is a popular technique to examine the
self-similarity of a nonlinear, chaotic and noisy time series. In the present
research work, EEG signals representing healthy, interictal (seizure free) and
ictal activities (seizure) are acquired from an existing available database.
The acquired EEG signals of different states are at first analyzed using MFDFA.
To requisite the time series singularity quantification at local and global
scales, a novel set of fourteen different features. Suitable feature ranking
employing students t-test has been done to select the most statistically
significant features which are henceforth being used as inputs to a support
vector machines (SVM) classifier for the classification of different EEG
signals. Eight different classification problems have been presented in this
paper and it has been observed that the overall classification accuracy using
MFDFA based features are reasonably satisfactory for all classification
problems. The performance of the proposed method are also found to be quite
commensurable and in some cases even better when compared with the results
published in existing literature studied on the similar data set.
Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes
Authors: Cristian González García, Daniel Meana-Llorián, B. Cristina Pelayo G-Bustelo, Juan Manuel Cueva Lovelle, Néstor Garcia-Fernandez
Date: 2017-01-10 00:00:00

Could we use Computer Vision in the Internet of Things for using pictures as
sensors? This is the principal hypothesis that we want to resolve. Currently,
in order to create safety areas, cities, or homes, people use IP cameras.
Nevertheless, this system needs people who watch the camera images, watch the
recording after something occurred, or watch when the camera notifies them of
any movement. These are the disadvantages. Furthermore, there are many Smart
Cities and Smart Homes around the world. This is why we thought of using the
idea of the Internet of Things to add a way of automating the use of IP
cameras. In our case, we propose the analysis of pictures through Computer
Vision to detect people in the analysed pictures. With this analysis, we are
able to obtain if these pictures contain people and handle the pictures as if
they were sensors with two possible states. Notwithstanding, Computer Vision is
a very complicated field. This is why we needed a second hypothesis: Could we
work with Computer Vision in the Internet of Things with a good accuracy to
automate or semi-automate this kind of events? The demonstration of these
hypotheses required a testing over our Computer Vision module to check the
possibilities that we have to use this module in a possible real environment
with a good accuracy. Our proposal, as a possible solution, is the analysis of
entire sequence instead of isolated pictures for using pictures as sensors in
the Internet of Things.
Revisiting the problem of audio-based hit song prediction using convolutional neural networks
Authors: Li-Chia Yang, Szu-Yu Chou, Jen-Yu Liu, Yi-Hsuan Yang, Yi-An Chen
Date: 2017-04-05 00:00:00

Being able to predict whether a song can be a hit has impor- tant
applications in the music industry. Although it is true that the popularity of
a song can be greatly affected by exter- nal factors such as social and
commercial influences, to which degree audio features computed from musical
signals (whom we regard as internal factors) can predict song popularity is an
interesting research question on its own. Motivated by the recent success of
deep learning techniques, we attempt to ex- tend previous work on hit song
prediction by jointly learning the audio features and prediction models using
deep learning. Specifically, we experiment with a convolutional neural net-
work model that takes the primitive mel-spectrogram as the input for feature
learning, a more advanced JYnet model that uses an external song dataset for
supervised pre-training and auto-tagging, and the combination of these two
models. We also consider the inception model to characterize audio infor-
mation in different scales. Our experiments suggest that deep structures are
indeed more accurate than shallow structures in predicting the popularity of
either Chinese or Western Pop songs in Taiwan. We also use the tags predicted
by JYnet to gain insights into the result of different models.
Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders
Authors: Jesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Douglas Eck, Karen Simonyan, Mohammad Norouzi
Date: 2017-04-05 00:00:00
Proceeding: ICML 2017 8
Tasks: Audio Synthesis, Decoder

Generative models in vision have seen rapid progress due to algorithmic
improvements and the availability of high-quality image datasets. In this
paper, we offer contributions in both these areas to enable similar progress in
audio modeling. First, we detail a powerful new WaveNet-style autoencoder model
that conditions an autoregressive decoder on temporal codes learned from the
raw audio waveform. Second, we introduce NSynth, a large-scale and high-quality
dataset of musical notes that is an order of magnitude larger than comparable
public datasets. Using NSynth, we demonstrate improved qualitative and
quantitative performance of the WaveNet autoencoder over a well-tuned spectral
autoencoder baseline. Finally, we show that the model learns a manifold of
embeddings that allows for morphing between instruments, meaningfully
interpolating in timbre to create new types of sounds that are realistic and
expressive.
Self-Supervised Video Representation Learning With Odd-One-Out Networks
Authors: Basura Fernando, Hakan Bilen, Efstratios Gavves, Stephen Gould
Date: 2016-11-21 00:00:00
Proceeding: CVPR 2017 7
Tasks: Action Classification, Action Recognition, General Classification, Odd One Out, Representation Learning, Self-Supervised Action Recognition, Self-Supervised Learning, Temporal Action Localization

We propose a new self-supervised CNN pre-training technique based on a novel
auxiliary task called "odd-one-out learning". In this task, the machine is
asked to identify the unrelated or odd element from a set of otherwise related
elements. We apply this technique to self-supervised video representation
learning where we sample subsequences from videos and ask the network to learn
to predict the odd video subsequence. The odd video subsequence is sampled such
that it has wrong temporal order of frames while the even ones have the correct
temporal order. Therefore, to generate a odd-one-out question no manual
annotation is required. Our learning machine is implemented as multi-stream
convolutional neural network, which is learned end-to-end. Using odd-one-out
networks, we learn temporal representations for videos that generalizes to
other related tasks such as action recognition.
  On action classification, our method obtains 60.3\% on the UCF101 dataset
using only UCF101 data for training which is approximately 10% better than
current state-of-the-art self-supervised learning methods. Similarly, on HMDB51
dataset we outperform self-supervised state-of-the art methods by 12.7% on
action classification task.