pwc-archive/papers-with-abstracts huggingface dataset:
Learning Invariances using the Marginal Likelihood
Authors: Mark van der Wilk, Matthias Bauer, ST John, James Hensman
Date: 2018-08-16 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Data Augmentation, Gaussian Processes, Translation, Variational Inference

Generalising well in supervised learning tasks relies on correctly
extrapolating the training data to a large region of the input space. One way
to achieve this is to constrain the predictions to be invariant to
transformations on the input that are known to be irrelevant (e.g.
translation). Commonly, this is done through data augmentation, where the
training set is enlarged by applying hand-crafted transformations to the
inputs. We argue that invariances should instead be incorporated in the model
structure, and learned using the marginal likelihood, which correctly rewards
the reduced complexity of invariant models. We demonstrate this for Gaussian
process models, due to the ease with which their marginal likelihood can be
estimated. Our main contribution is a variational inference scheme for Gaussian
processes containing invariances described by a sampling procedure. We learn
the sampling procedure by back-propagating through it to maximise the marginal
likelihood.
Emotion Recognition in Speech using Cross-Modal Transfer in the Wild
Authors: Samuel Albanie, Arsha Nagrani, Andrea Vedaldi, Andrew Zisserman
Date: 2018-08-16 00:00:00
Tasks: Emotion Recognition, Facial Emotion Recognition, Facial Expression Recognition (FER), Speech Emotion Recognition

Obtaining large, human labelled speech datasets to train models for emotion
recognition is a notoriously challenging task, hindered by annotation cost and
label ambiguity. In this work, we consider the task of learning embeddings for
speech classification without access to any form of labelled audio. We base our
approach on a simple hypothesis: that the emotional content of speech
correlates with the facial expression of the speaker. By exploiting this
relationship, we show that annotations of expression can be transferred from
the visual domain (faces) to the speech domain (voices) through cross-modal
distillation. We make the following contributions: (i) we develop a strong
teacher network for facial emotion recognition that achieves the state of the
art on a standard benchmark; (ii) we use the teacher to train a student, tabula
rasa, to learn representations (embeddings) for speech emotion recognition
without access to labelled audio data; and (iii) we show that the speech
emotion embedding can be used for speech emotion recognition on external
benchmark datasets. Code, models and data are available.
Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach
Authors: Filipe Rodrigues, Ioulia Markou, Francisco Pereira
Date: 2018-08-16 00:00:00
Tasks: Demand Forecasting, Time Series, Time Series Analysis, Time Series Forecasting, Word Embeddings

Accurate time-series forecasting is vital for numerous areas of application
such as transportation, energy, finance, economics, etc. However, while modern
techniques are able to explore large sets of temporal data to build forecasting
models, they typically neglect valuable information that is often available
under the form of unstructured text. Although this data is in a radically
different format, it often contains contextual explanations for many of the
patterns that are observed in the temporal data. In this paper, we propose two
deep learning architectures that leverage word embeddings, convolutional layers
and attention mechanisms for combining text information with time-series data.
We apply these approaches for the problem of taxi demand forecasting in event
areas. Using publicly available taxi data from New York, we empirically show
that by fusing these two complementary cross-modal sources of information, the
proposed models are able to significantly reduce the error in the forecasts.
Deep Learning for Energy Markets
Authors: Michael Polson, Vadim Sokolov
Date: 2018-08-16 00:00:00
Tasks: Deep Learning, Time Series, Time Series Analysis

Deep Learning is applied to energy markets to predict extreme loads observed
in energy grids. Forecasting energy loads and prices is challenging due to
sharp peaks and troughs that arise due to supply and demand fluctuations from
intraday system constraints. We propose deep spatio-temporal models and extreme
value theory (EVT) to capture theses effects and in particular the tail
behavior of load spikes. Deep LSTM architectures with ReLU and $\tanh$
activation functions can model trends and temporal dependencies while EVT
captures highly volatile load spikes above a pre-specified threshold. To
illustrate our methodology, we use hourly price and demand data from 4719 nodes
of the PJM interconnection, and we construct a deep predictor. We show that
DL-EVT outperforms traditional Fourier time series methods, both in-and
out-of-sample, by capturing the observed nonlinearities in prices. Finally, we
conclude with directions for future research.
Experiential Robot Learning with Accelerated Neuroevolution
Authors: Ahmed Aly, Joanne B. Dugan
Date: 2018-08-16 00:00:00
Tasks: Navigate

Derivative-based optimization techniques such as Stochastic Gradient Descent
has been wildly successful in training deep neural networks. However, it has
constraints such as end-to-end network differentiability. As an alternative, we
present the Accelerated Neuroevolution algorithm. The new algorithm is aimed
towards physical robotic learning tasks following the Experiential Robot
Learning method. We test our algorithm first on a simulated task of playing the
game Flappy Bird, then on a physical NAO robot in a static Object Centering
task. The agents successfully navigate the given tasks, in a relatively low
number of generations. Based on our results, we propose to use the algorithm in
more complex tasks.
Network Decoupling: From Regular to Depthwise Separable Convolutions
Authors: Jianbo Guo, Yuxi Li, Weiyao Lin, Yurong Chen, Jianguo Li
Date: 2018-08-16 00:00:00
Tasks: object-detection, Object Detection

Depthwise separable convolution has shown great efficiency in network design,
but requires time-consuming training procedure with full training-set
available. This paper first analyzes the mathematical relationship between
regular convolutions and depthwise separable convolutions, and proves that the
former one could be approximated with the latter one in closed form. We show
depthwise separable convolutions are principal components of regular
convolutions. And then we propose network decoupling (ND), a training-free
method to accelerate convolutional neural networks (CNNs) by transferring
pre-trained CNN models into the MobileNet-like depthwise separable convolution
structure, with a promising speedup yet negligible accuracy loss. We further
verify through experiments that the proposed method is orthogonal to other
training-free methods like channel decomposition, spatial decomposition, etc.
Combining the proposed method with them will bring even larger CNN speedup. For
instance, ND itself achieves about 2X speedup for the widely used VGG16, and
combined with other methods, it reaches 3.7X speedup with graceful accuracy
degradation. We demonstrate that ND is widely applicable to classification
networks like ResNet, and object detection network like SSD300.
An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification
Authors: Boyu Lu, Jun-Cheng Chen, Carlos D. Castillo, Rama Chellappa
Date: 2018-08-16 00:00:00
Tasks: Face Recognition, Face Verification

Covariates are factors that have a debilitating influence on face
verification performance. In this paper, we comprehensively study two covariate
related problems for unconstrained face verification: first, how covariates
affect the performance of deep neural networks on the large-scale unconstrained
face verification problem; second, how to utilize covariates to improve
verification performance. To study the first problem, we implement five
state-of-the-art deep convolutional networks (DCNNs) for face verification and
evaluate them on three challenging covariates datasets. In total, seven
covariates are considered: pose (yaw and roll), age, facial hair, gender,
indoor/outdoor, occlusion (nose and mouth visibility, eyes visibility, and
forehead visibility), and skin tone. These covariates cover both intrinsic
subject-specific characteristics and extrinsic factors of faces. Some of the
results confirm and extend the findings of previous studies, others are new
findings that were rarely mentioned previously or did not show consistent
trends. For the second problem, we demonstrate that with the assistance of
gender information, the quality of a pre-curated noisy large-scale face dataset
for face recognition can be further improved. After retraining the face
recognition model using the curated data, performance improvement is observed
at low False Acceptance Rates (FARs) (FAR=$10^{-5}$, $10^{-6}$, $10^{-7}$).
Paraphrase Thought: Sentence Embedding Module Imitating Human Language Recognition
Authors: Myeongjun Jang, Pilsung Kang
Date: 2018-08-16 00:00:00
Tasks: Document Classification, General Classification, Machine Translation, Paraphrase Identification, Sentence, Sentence Classification, Sentence Embedding, Sentence-Embedding, Sentiment Analysis, STS, STS Benchmark

Sentence embedding is an important research topic in natural language
processing. It is essential to generate a good embedding vector that fully
reflects the semantic meaning of a sentence in order to achieve an enhanced
performance for various natural language processing tasks, such as machine
translation and document classification. Thus far, various sentence embedding
models have been proposed, and their feasibility has been demonstrated through
good performances on tasks following embedding, such as sentiment analysis and
sentence classification. However, because the performances of sentence
classification and sentiment analysis can be enhanced by using a simple
sentence representation method, it is not sufficient to claim that these models
fully reflect the meanings of sentences based on good performances for such
tasks. In this paper, inspired by human language recognition, we propose the
following concept of semantic coherence, which should be satisfied for a good
sentence embedding method: similar sentences should be located close to each
other in the embedding space. Then, we propose the Paraphrase-Thought
(P-thought) model to pursue semantic coherence as much as possible.
Experimental results on two paraphrase identification datasets (MS COCO and STS
benchmark) show that the P-thought models outperform the benchmarked sentence
embedding methods.
Robust training of recurrent neural networks to handle missing data for disease progression modeling
Authors: Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin, Lauge Sørensen
Date: 2018-08-16 00:00:00
Tasks: image-classification, Image Classification, Imputation, Medical Image Classification, Missing Values

Disease progression modeling (DPM) using longitudinal data is a challenging
task in machine learning for healthcare that can provide clinicians with better
tools for diagnosis and monitoring of disease. Existing DPM algorithms neglect
temporal dependencies among measurements and make parametric assumptions about
biomarker trajectories. In addition, they do not model multiple biomarkers
jointly and need to align subjects' trajectories. In this paper, recurrent
neural networks (RNNs) are utilized to address these issues. However, in many
cases, longitudinal cohorts contain incomplete data, which hinders the
application of standard RNNs and requires a pre-processing step such as
imputation of the missing values. We, therefore, propose a generalized training
rule for the most widely used RNN architecture, long short-term memory (LSTM)
networks, that can handle missing values in both target and predictor
variables. This algorithm is applied for modeling the progression of
Alzheimer's disease (AD) using magnetic resonance imaging (MRI) biomarkers. The
results show that the proposed LSTM algorithm achieves a lower mean absolute
error for prediction of measurements across all considered MRI biomarkers
compared to using standard LSTM networks with data imputation or using a
regression-based DPM method. Moreover, applying linear discriminant analysis to
the biomarkers' values predicted by the proposed algorithm results in a larger
area under the receiver operating characteristic curve (AUC) for clinical
diagnosis of AD compared to the same alternatives, and the AUC is comparable to
state-of-the-art AUCs from a recent cross-sectional medical image
classification challenge. This paper shows that built-in handling of missing
values in LSTM network training paves the way for application of RNNs in
disease progression modeling.
Measuring the Temporal Behavior of Real-World Person Re-Identification
Authors: Meng Zheng, Srikrishna Karanam, Richard J. Radke
Date: 2018-08-16 00:00:00
Tasks: Person Re-Identification

Designing real-world person re-identification (re-id) systems requires
attention to operational aspects not typically considered in academic research.
Typically, the probe image or image sequence is matched to a gallery set with a
fixed candidate list. On the other hand, in real-world applications of re-id,
we would search for a person of interest in a gallery set that is continuously
populated by new candidates over time. A key question of interest for the
operator of such a system is: how long is a correct match to a probe likely to
remain in a rank-k shortlist of candidates? In this paper, we propose to
distill this information into what we call a Rank Persistence Curve (RPC),
which unlike a conventional cumulative match characteristic (CMC) curve helps
directly compare the temporal performance of different re-id algorithms. To
carefully illustrate the concept, we collected a new multi-shot person re-id
dataset called RPIfield. The RPIfield dataset is constructed using a network of
12 cameras with 112 explicitly time-stamped actor paths among about 4000
distractors. We then evaluate the temporal performance of different re-id
algorithms using the proposed RPCs using single and pairwise camera videos from
RPIfield, and discuss considerations for future research.
Occlusion Resistant Object Rotation Regression from Point Cloud Segments
Authors: Ge Gao, Mikko Lauri, Jianwei Zhang, Simone Frintrop
Date: 2018-08-16 00:00:00
Tasks: Object, regression

Rotation estimation of known rigid objects is important for robotic
applications such as dexterous manipulation. Most existing methods for rotation
estimation use intermediate representations such as templates, global or local
feature descriptors, or object coordinates, which require multiple steps in
order to infer the object pose. We propose to directly regress a pose vector
from raw point cloud segments using a convolutional neural network.
Experimental results show that our method can potentially achieve competitive
performance compared to a state-of-the-art method, while also showing more
robustness against occlusion. Our method does not require any post processing
such as refinement with the iterative closest point algorithm.
Metric Learning for Novelty and Anomaly Detection
Authors: Marc Masana, Idoia Ruiz, Joan Serrat, Joost Van de Weijer, Antonio M. Lopez
Date: 2018-08-16 00:00:00
Tasks: Anomaly Detection, Metric Learning, Novelty Detection, Out-of-Distribution Detection, Traffic Sign Recognition

When neural networks process images which do not resemble the distribution
seen during training, so called out-of-distribution images, they often make
wrong predictions, and do so too confidently. The capability to detect
out-of-distribution images is therefore crucial for many real-world
applications. We divide out-of-distribution detection between novelty detection
---images of classes which are not in the training set but are related to
those---, and anomaly detection ---images with classes which are unrelated to
the training set. By related we mean they contain the same type of objects,
like digits in MNIST and SVHN. Most existing work has focused on anomaly
detection, and has addressed this problem considering networks trained with the
cross-entropy loss. Differently from them, we propose to use metric learning
which does not have the drawback of the softmax layer (inherent to
cross-entropy methods), which forces the network to divide its prediction power
over the learned classes. We perform extensive experiments and evaluate both
novelty and anomaly detection, even in a relevant application such as traffic
sign recognition, obtaining comparable or better results than previous works.
Transfer Learning and Organic Computing for Autonomous Vehicles
Authors: Christofer Fellicious
Date: 2018-08-16 00:00:00
Tasks: Autonomous Vehicles, Transfer Learning

Autonomous Vehicles(AV) are one of the brightest promises of the future which
would help cut down fatalities and improve travel time while working in
harmony. Autonomous vehicles will face with challenging situations and
experiences not seen before. These experiences should be converted to knowledge
and help the vehicle prepare better in the future. Online Transfer Learning
will help transferring prior knowledge to a new task and also keep the
knowledge updated as the task evolves. This paper presents the different
methods of transfer learning, online transfer learning and organic computing
that could be adapted to the domain of autonomous vehicles.
Linguistic data mining with complex networks: a stylometric-oriented approach
Authors: Tomasz Stanisz, Jarosław Kwapień, Stanisław Drożdż
Date: 2018-08-16 00:00:00
Tasks: Authorship Attribution, Clustering

By representing a text by a set of words and their co-occurrences, one
obtains a word-adjacency network being a reduced representation of a given
language sample. In this paper, the possibility of using network representation
to extract information about individual language styles of literary texts is
studied. By determining selected quantitative characteristics of the networks
and applying machine learning algorithms, it is possible to distinguish between
texts of different authors. Within the studied set of texts, English and
Polish, a properly rescaled weighted clustering coefficients and weighted
degrees of only a few nodes in the word-adjacency networks are sufficient to
obtain the authorship attribution accuracy over 90%. A correspondence between
the text authorship and the word-adjacency network structure can therefore be
found. The network representation allows to distinguish individual language
styles by comparing the way the authors use particular words and punctuation
marks. The presented approach can be viewed as a generalization of the
authorship attribution methods based on simple lexical features.
  Additionally, other network parameters are studied, both local and global
ones, for both the unweighted and weighted networks. Their potential to capture
the writing style diversity is discussed; some differences between languages
are observed.
Sememe Prediction: Learning Semantic Knowledge from Unstructured Textual Wiki Descriptions
Authors: Wei Li, Xuancheng Ren, Damai Dai, Yunfang Wu, Houfeng Wang, Xu sun
Date: 2018-08-16 00:00:00

Huge numbers of new words emerge every day, leading to a great need for
representing them with semantic meaning that is understandable to NLP systems.
Sememes are defined as the minimum semantic units of human languages, the
combination of which can represent the meaning of a word. Manual construction
of sememe based knowledge bases is time-consuming and labor-intensive.
Fortunately, communities are devoted to composing the descriptions of words in
the wiki websites. In this paper, we explore to automatically predict lexical
sememes based on the descriptions of the words in the wiki websites. We view
this problem as a weakly ordered multi-label task and propose a Label
Distributed seq2seq model (LD-seq2seq) with a novel soft loss function to solve
the problem. In the experiments, we take a real-world sememe knowledge base
HowNet and the corresponding descriptions of the words in Baidu Wiki for
training and evaluation. The results show that our LD-seq2seq model not only
beats all the baselines significantly on the test set, but also outperforms
amateur human annotators in a random subset of the test set.
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning
Authors: Fei Wen, Lei Chu, Peilin Liu, Robert C. Qiu
Date: 2018-08-16 00:00:00
Tasks: BIG-bench Machine Learning, Compressive Sensing, Matrix Completion, Variable Selection

In the past decade, sparse and low-rank recovery have drawn much attention in many areas such as signal/image processing, statistics, bioinformatics and machine learning. To achieve sparsity and/or low-rankness inducing, the $\ell_1$ norm and nuclear norm are of the most popular regularization penalties due to their convexity. While the $\ell_1$ and nuclear norm are convenient as the related convex optimization problems are usually tractable, it has been shown in many applications that a nonconvex penalty can yield significantly better performance. In recent, nonconvex regularization based sparse and low-rank recovery is of considerable interest and it in fact is a main driver of the recent progress in nonconvex and nonsmooth optimization. This paper gives an overview of this topic in various fields in signal processing, statistics and machine learning, including compressive sensing (CS), sparse regression and variable selection, sparse signals separation, sparse principal component analysis (PCA), large covariance and inverse covariance matrices estimation, matrix completion, and robust PCA. We present recent developments of nonconvex regularization based sparse and low-rank recovery in these fields, addressing the issues of penalty selection, applications and the convergence of nonconvex algorithms. Code is available at https://github.com/FWen/ncreg.git.
Landmark Weighting for 3DMM Shape Fitting
Authors: Yu Yanga, Xiao-Jun Wu, Josef Kittler
Date: 2018-08-16 00:00:00
Tasks: 3D Face Reconstruction, Face Reconstruction

Human face is a 3D object with shape and surface texture. 3D Morphable Model
(3DMM) is a powerful tool for reconstructing the 3D face from a single 2D face
image. In the shape fitting process, 3DMM estimates the correspondence between
2D and 3D landmarks. Most traditional 3DMM fitting methods fail to reconstruct
an accurate model because face shape fitting is a difficult non-linear
optimization problem. In this paper we show that landmark weighting is
instrumental to improve the accuracy of shape reconstruction and propose a
novel 3D Morphable Model Fitting method. Different from previous works that
treat all landmarks equally, we take into consideration the estimated errors
for each pair of 2D and 3D corresponding landmarks. The landmark points are
weighted in the optimization cost function based on these errors. Obviously,
these landmarks have different semantics because they locate on different
facial components. In the context of the solution of fitting is approximated,
there are deviations in landmarks matching. However, these landmarks with
different semantics have different effects on reconstructing 3D faces. Thus, it
is necessary to consider each landmark individually. To our knowledge, we are
the first to analyze each feature point for 3D face reconstruction by 3DMM. The
weight is adaptive with the estimation residuals of landmarks. Experimental
results show that the proposed method significantly reduces the reconstruction
error and improves the authenticity of the 3D model expression.