pwc-archive/papers-with-abstracts huggingface dataset:
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data
Authors: Filipe Rodrigues, Francisco C. Pereira
Date: 2018-12-20 00:00:00
Tasks: Gaussian Processes, Imputation

Accurately modeling traffic speeds is a fundamental part of efficient
intelligent transportation systems. Nowadays, with the widespread deployment of
GPS-enabled devices, it has become possible to crowdsource the collection of
speed information to road users (e.g. through mobile applications or dedicated
in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced
speed data also brings very important challenges, such as the highly variable
measurement noise in the data due to a variety of driving behaviors and sample
sizes. When not properly accounted for, this noise can severely compromise any
application that relies on accurate traffic data. In this article, we propose
the use of heteroscedastic Gaussian processes (HGP) to model the time-varying
uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a
HGP conditioned on sample size and traffic regime (SRC-HGP), which makes use of
sample size information (probe vehicles per minute) as well as previous
observed speeds, in order to more accurately model the uncertainty in observed
speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we
empirically show that the proposed heteroscedastic models produce significantly
better predictive distributions when compared to current state-of-the-art
methods for both speed imputation and short-term forecasting tasks.
A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms
Authors: Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh
Date: 2018-12-20 00:00:00

Reconstructing continuous signals from a small number of discrete samples is
a fundamental problem across science and engineering. In practice, we are often
interested in signals with 'simple' Fourier structure, such as bandlimited,
multiband, and Fourier sparse signals. More broadly, any prior knowledge about
a signal's Fourier power spectrum can constrain its complexity. Intuitively,
signals with more highly constrained Fourier structure require fewer samples to
reconstruct.
  We formalize this intuition by showing that, roughly, a continuous signal
from a given class can be approximately reconstructed using a number of samples
proportional to the *statistical dimension* of the allowed power spectrum of
that class. Further, in nearly all settings, this natural measure tightly
characterizes the sample complexity of signal reconstruction.
  Surprisingly, we also show that, up to logarithmic factors, a universal
non-uniform sampling strategy can achieve this optimal complexity for *any
class of signals*. We present a simple and efficient algorithm for recovering a
signal from the samples taken. For bandlimited and sparse signals, our method
matches the state-of-the-art. At the same time, it gives the first
computationally and sample efficient solution to a broad range of problems,
including multiband signal reconstruction and kriging and Gaussian process
regression tasks in one dimension.
  Our work is based on a novel connection between randomized linear algebra and
signal reconstruction with constrained Fourier structure. We extend tools based
on statistical leverage score sampling and column-based matrix reconstruction
to the approximation of continuous linear operators that arise in signal
reconstruction. We believe that these extensions are of independent interest
and serve as a foundation for tackling a broad range of continuous time
problems using randomized methods.
RNNs Implicitly Implement Tensor Product Representations
Authors: R. Thomas McCoy, Tal Linzen, Ewan Dunbar, Paul Smolensky
Date: 2018-12-20 00:00:00
Tasks: Representation Learning, Sentence

Recurrent neural networks (RNNs) can learn continuous vector representations
of symbolic structures such as sequences and sentences; these representations
often exhibit linear regularities (analogies). Such regularities motivate our
hypothesis that RNNs that show such regularities implicitly compile symbolic
structures into tensor product representations (TPRs; Smolensky, 1990), which
additively combine tensor products of vectors representing roles (e.g.,
sequence positions) and vectors representing fillers (e.g., particular words).
To test this hypothesis, we introduce Tensor Product Decomposition Networks
(TPDNs), which use TPRs to approximate existing vector representations. We
demonstrate using synthetic data that TPDNs can successfully approximate linear
and tree-based RNN autoencoder representations, suggesting that these
representations exhibit interpretable compositional structure; we explore the
settings that lead RNNs to induce such structure-sensitive representations. By
contrast, further TPDN experiments show that the representations of four models
trained to encode naturally-occurring sentences can be largely approximated
with a bag of words, with only marginal improvements from more sophisticated
structures. We conclude that TPDNs provide a powerful method for interpreting
vector representations, and that standard RNNs can induce compositional
sequence representations that are remarkably well approximated by TPRs; at the
same time, existing training tasks for sentence representation learning may not
be sufficient for inducing robust structural representations.
DeepFakes: a New Threat to Face Recognition? Assessment and Detection
Authors: Pavel Korshunov, Sebastien Marcel
Date: 2018-12-20 00:00:00
Tasks: Constrained Lip-synchronization, Face Recognition, Face Swapping, Generative Adversarial Network

It is becoming increasingly easy to automatically replace a face of one
person in a video with the face of another person by using a pre-trained
generative adversarial network (GAN). Recent public scandals, e.g., the faces
of celebrities being swapped onto pornographic videos, call for automated ways
to detect these Deepfake videos. To help developing such methods, in this
paper, we present the first publicly available set of Deepfake videos generated
from videos of VidTIMIT database. We used open source software based on GANs to
create the Deepfakes, and we emphasize that training and blending parameters
can significantly impact the quality of the resulted videos. To demonstrate
this impact, we generated videos with low and high visual quality (320 videos
each) using differently tuned parameter sets. We showed that the state of the
art face recognition systems based on VGG and Facenet neural networks are
vulnerable to Deepfake videos, with 85.62% and 95.00% false acceptance rates
respectively, which means methods for detecting Deepfake videos are necessary.
By considering several baseline approaches, we found that audio-visual approach
based on lip-sync inconsistency detection was not able to distinguish Deepfake
videos. The best performing method, which is based on visual quality metrics
and is often used in presentation attack detection domain, resulted in 8.97%
equal error rate on high quality Deepfakes. Our experiments demonstrate that
GAN-generated Deepfake videos are challenging for both face recognition systems
and existing detection methods, and the further development of face swapping
technology will make it even more so.
Robust Estimation of Causal Effects via High-Dimensional Covariate Balancing Propensity Score
Authors: Yang Ning, Sida Peng, Kosuke Imai
Date: 2018-12-20 00:00:00
Tasks: valid

In this paper, we propose a robust method to estimate the average treatment
effects in observational studies when the number of potential confounders is
possibly much greater than the sample size. We first use a class of penalized
M-estimators for the propensity score and outcome models. We then calibrate the
initial estimate of the propensity score by balancing a carefully selected
subset of covariates that are predictive of the outcome. Finally, the estimated
propensity score is used to construct the inverse probability weighting
estimator. We prove that the proposed estimator, which has the sample
boundedness property, is root-n consistent, asymptotically normal, and
semiparametrically efficient when the propensity score model is correctly
specified and the outcome model is linear in covariates. More importantly, we
show that our estimator remains root-n consistent and asymptotically normal so
long as either the propensity score model or the outcome model is correctly
specified. We provide valid confidence intervals in both cases and further
extend these results to the case where the outcome model is a generalized
linear model. In simulation studies, we find that the proposed methodology
often estimates the average treatment effect more accurately than the existing
methods. We also present an empirical application, in which we estimate the
average causal effect of college attendance on adulthood political
participation. Open-source software is available for implementing the proposed
methodology.
A Method to Facilitate Cancer Detection and Type Classification from Gene Expression Data using a Deep Autoencoder and Neural Network
Authors: Xi Chen, Jin Xie, Qingcong Yuan
Date: 2018-12-20 00:00:00
Tasks: General Classification, Specificity

With the increased affordability and availability of whole-genome sequencing,
large-scale and high-throughput gene expression is widely used to characterize
diseases, including cancers. However, establishing specificity in cancer
diagnosis using gene expression data continues to pose challenges due to the
high dimensionality and complexity of the data. Here we present models of deep
learning (DL) and apply them to gene expression data for the diagnosis and
categorization of cancer. In this study, we have developed two DL models using
messenger ribonucleic acid (mRNA) datasets available from the Genomic Data
Commons repository. Our models achieved 98% accuracy in cancer detection, with
false negative and false positive rates below 1.7%. In our results, we
demonstrated that 18 out of 32 cancer-typing classifications achieved more than
90% accuracy. Due to the limitation of a small sample size (less than 50
observations), certain cancers could not achieve a higher accuracy in typing
classification, but still achieved high accuracy for the cancer detection task.
To validate our models, we compared them with traditional statistical models.
The main advantage of our models over traditional cancer detection is the
ability to use data from various cancer types to automatically form features to
enhance the detection and diagnosis of a specific cancer type.
nocaps: novel object captioning at scale
Authors: Harsh Agrawal, Karan Desai, YuFei Wang, Xinlei Chen, Rishabh Jain, Mark Johnson, Dhruv Batra, Devi Parikh, Stefan Lee, Peter Anderson
Date: 2018-12-20 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Captioning, Object, object-detection, Object Detection

Image captioning models have achieved impressive results on datasets containing limited visual concepts and large amounts of paired image-caption training data. However, if these models are to ever function in the wild, a much larger variety of visual concepts must be learned, ideally from less supervision. To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task. Dubbed 'nocaps', for novel object captioning at scale, our benchmark consists of 166,100 human-generated captions describing 15,100 images from the OpenImages validation and test sets. The associated training data consists of COCO image-caption pairs, plus OpenImages image-level labels and object bounding boxes. Since OpenImages contains many more classes than COCO, nearly 400 object classes seen in test images have no or very few associated training captions (hence, nocaps). We extend existing novel object captioning models to establish strong baselines for this benchmark and provide analysis to guide future work on this task.
Deep Theory of Functional Connections: A New Method for Estimating the Solutions of PDEs
Authors: Carl Leake
Date: 2018-12-20 00:00:00

This article presents a new methodology called deep Theory of Functional Connections (TFC) that estimates the solutions of partial differential equations (PDEs) by combining neural networks with TFC. TFC is used to transform PDEs with boundary conditions into unconstrained optimization problems by embedding the boundary conditions into a "constrained expression." In this work, a neural network is chosen as the free function, and used to solve the now unconstrained optimization problem. The loss function is taken as the square of the residual of the PDE. Then, the neural network is trained in an unsupervised manner to solve the unconstrained optimization problem. This methodology has two major differences when compared with popular methods used to estimate the solutions of PDEs. First, this methodology does not need to discretize the domain into a grid, rather, this methodology randomly samples points from the domain during the training phase. Second, after training, this methodology represents a closed form, analytical, differentiable approximation of the solution throughout the entire training domain. In contrast, other popular methods require interpolation if the estimated solution is desired at points that do not lie on the discretized grid. The deep TFC method for estimating the solution of PDEs is demonstrated on four problems with a variety of boundary conditions.
Automated detection of block falls in the north polar region of Mars
Authors: L. Fanara, K. Gwinner, E. Hauber, J. Oberst
Date: 2018-12-20 00:00:00
Tasks: Change Detection

We developed a change detection method for the identification of ice block
falls using NASA's HiRISE images of the north polar scarps on Mars. Our method
is based on a Support Vector Machine (SVM), trained using Histograms of
Oriented Gradients (HOG), and on blob detection. The SVM detects potential new
blocks between a set of images; the blob detection, then, confirms the
identification of a block inside the area indicated by the SVM and derives the
shape of the block. The results from the automatic analysis were compared with
block statistics from visual inspection. We tested our method in 6 areas
consisting of 1000x1000 pixels, where several hundreds of blocks were
identified. The results for the given test areas produced a true positive rate
of ~75% for blocks with sizes larger than 0.7 m (i.e., approx. 3 times the
available ground pixel size) and a false discovery rate of ~8.5%. Using blob
detection we also recover the size of each block within 3 pixels of their
actual size.
How Much Does Tokenization Affect Neural Machine Translation?
Authors: Miguel Domingo, Mercedes Garcıa-Martınez, Alexandre Helle, Francisco Casacuberta, Manuel Herranz
Date: 2018-12-20 00:00:00
Tasks: Machine Translation, NMT, Translation, Word Embeddings

Tokenization or segmentation is a wide concept that covers simple processes such as separating punctuation from words, or more sophisticated processes such as applying morphological knowledge. Neural Machine Translation (NMT) requires a limited-size vocabulary for computational cost and enough examples to estimate word embeddings. Separating punctuation and splitting tokens into words or subwords has proven to be helpful to reduce vocabulary and increase the number of examples of each word, improving the translation quality. Tokenization is more challenging when dealing with languages with no separator between words. In order to assess the impact of the tokenization in the quality of the final translation on NMT, we experimented on five tokenizers over ten language pairs. We reached the conclusion that the tokenization significantly affects the final translation quality and that the best tokenizer differs for different language pairs.
Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review
Authors: Juan J. Cerrolaza, Mirella Lopez-Picazo, Ludovic Humbert, Yoshinobu Sato, Daniel Rueckert, Miguel Angel Gonzalez Ballester, Marius George Linguraru
Date: 2018-12-20 00:00:00
Tasks: Anatomy, Medical Image Analysis

The medical image analysis field has traditionally been focused on the
development of organ-, and disease-specific methods. Recently, the interest in
the development of more 20 comprehensive computational anatomical models has
grown, leading to the creation of multi-organ models. Multi-organ approaches,
unlike traditional organ-specific strategies, incorporate inter-organ relations
into the model, thus leading to a more accurate representation of the complex
human anatomy. Inter-organ relations are not only spatial, but also functional
and physiological. Over the years, the strategies 25 proposed to efficiently
model multi-organ structures have evolved from the simple global modeling, to
more sophisticated approaches such as sequential, hierarchical, or machine
learning-based models. In this paper, we present a review of the state of the
art on multi-organ analysis and associated computation anatomy methodology. The
manuscript follows a methodology-based classification of the different
techniques 30 available for the analysis of multi-organs and multi-anatomical
structures, from techniques using point distribution models to the most recent
deep learning-based approaches. With more than 300 papers included in this
review, we reflect on the trends and challenges of the field of computational
anatomy, the particularities of each anatomical region, and the potential of
multi-organ analysis to increase the impact of 35 medical imaging applications
on the future of healthcare.
Adversarial Signal Denoising with Encoder-Decoder Networks
Authors: Leslie Casas, Attila Klimmek, Nassir Navab, Vasileios Belagiannis
Date: 2018-12-20 00:00:00
Tasks: Decoder, Denoising

The presence of noise is common in signal processing regardless the signal type. Deep neural networks have shown good performance in noise removal, especially on the image domain. In this work, we consider deep neural networks as a denoising tool where our focus is on one dimensional signals. We introduce an encoder-decoder architecture to denoise signals, represented by a sequence of measurements. Instead of relying only on the standard reconstruction error to train the encoder-decoder network, we treat the task of denoising as distribution alignment between the clean and noisy signals. Then, we propose an adversarial learning formulation where the goal is to align the clean and noisy signal latent representation given that both signals pass through the encoder. In our approach, the discriminator has the role of detecting whether the latent representation comes from clean or noisy signals. We evaluate on electrocardiogram and motion signal denoising; and show better performance than learning-based and non-learning approaches.
cuPC: CUDA-based Parallel PC Algorithm for Causal Structure Learning on GPU
Authors: Behrooz Zarebavani, Foad Jafarinejad, Matin Hashemi, Saber Salehkaleybar
Date: 2018-12-20 00:00:00
Tasks: CPU, GPU

The main goal in many fields in the empirical sciences is to discover causal relationships among a set of variables from observational data. PC algorithm is one of the promising solutions to learn underlying causal structure by performing a number of conditional independence tests. In this paper, we propose a novel GPU-based parallel algorithm, called cuPC, to execute an order-independent version of PC. The proposed solution has two variants, cuPC-E and cuPC-S, which parallelize PC in two different ways for multivariate normal distribution. Experimental results show the scalability of the proposed algorithms with respect to the number of variables, the number of samples, and different graph densities. For instance, in one of the most challenging datasets, the runtime is reduced from more than 11 hours to about 4 seconds. On average, cuPC-E and cuPC-S achieve 500 X and 1300 X speedup, respectively, compared to serial implementation on CPU. The source code of cuPC is available online [1].
The Query Complexity of a Permutation-Based Variant of Mastermind
Authors: Peyman Afshani, Manindra Agrawal, Benjamin Doerr, Carola Doerr, Kasper Green Larsen, Kurt Mehlhorn
Date: 2018-12-20 00:00:00

We study the query complexity of a permutation-based variant of the guessing
game Mastermind. In this variant, the secret is a pair $(z,\pi)$ which consists
of a binary string $z \in \{0,1\}^n$ and a permutation $\pi$ of $[n]$. The
secret must be unveiled by asking queries of the form $x \in \{0,1\}^n$. For
each such query, we are returned the score \[ f_{z,\pi}(x):= \max \{ i \in
[0..n]\mid \forall j \leq i: z_{\pi(j)} = x_{\pi(j)}\}\,;\] i.e., the score of
$x$ is the length of the longest common prefix of $x$ and $z$ with respect to
the order imposed by $\pi$. The goal is to minimize the number of queries
needed to identify $(z,\pi)$. This problem originates from the study of
black-box optimization heuristics, where it is known as the
\textsc{LeadingOnes} problem.
  In this work, we prove matching upper and lower bounds for the deterministic
and randomized query complexity of this game, which are $\Theta(n \log n)$ and
$\Theta(n \log \log n)$, respectively.
One-Class Feature Learning Using Intra-Class Splitting
Authors: Patrick Schlachter, Yiwen Liao, Bin Yang
Date: 2018-12-20 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, One-Class Classification

This paper proposes a novel generic one-class feature learning method based on intra-class splitting. In one-class classification, feature learning is challenging, because only samples of one class are available during training. Hence, state-of-the-art methods require reference multi-class datasets to pretrain feature extractors. In contrast, the proposed method realizes feature learning by splitting the given normal class into typical and atypical normal samples. By introducing closeness loss and dispersion loss, an intra-class joint training procedure between the two subsets after splitting enables the extraction of valuable features for one-class classification. Various experiments on three well-known image classification datasets demonstrate the effectiveness of our method which outperformed other baseline models in average.
Optimizing Quantum Error Correction Codes with Reinforcement Learning
Authors: Hendrik Poulsen Nautrup, Nicolas Delfosse, Vedran Dunjko, Hans J. Briegel, Nicolai Friis
Date: 2018-12-20 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Quantum error correction is widely thought to be the key to fault-tolerant quantum computation. However, determining the most suited encoding for unknown error channels or specific laboratory setups is highly challenging. Here, we present a reinforcement learning framework for optimizing and fault-tolerantly adapting quantum error correction codes. We consider a reinforcement learning agent tasked with modifying a family of surface code quantum memories until a desired logical error rate is reached. Using efficient simulations with about 70 data qubits with arbitrary connectivity, we demonstrate that such a reinforcement learning agent can determine near-optimal solutions, in terms of the number of data qubits, for various error models of interest. Moreover, we show that agents trained on one setting are able to successfully transfer their experience to different settings. This ability for transfer learning showcases the inherent strengths of reinforcement learning and the applicability of our approach for optimization from off-line simulations to on-line laboratory settings.