pwc-archive/papers-with-abstracts huggingface dataset:
ROBUST ESTIMATION VIA GENERATIVE ADVERSARIAL NETWORKS
Authors: Chao GAO, jiyi LIU, Yuan YAO, Weizhi Zhu
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

Robust estimation under Huber's $\epsilon$-contamination model has become an important topic in statistics and theoretical computer science. Rate-optimal procedures such as Tukey's median and other estimators based on statistical depth functions are impractical because of their computational intractability. In this paper, we establish an intriguing connection between f-GANs and various depth functions through the lens of f-Learning. Similar to the derivation of f-GAN, we show that these depth functions that lead to rate-optimal robust estimators can all be viewed as variational lower bounds of the total variation distance in the framework of f-Learning. This connection opens the door of computing robust estimators using tools developed for training GANs. In particular, we show that a JS-GAN that uses a neural network discriminator with at least one hidden layer is able to achieve the minimax rate of robust mean estimation under Huber's $\epsilon$-contamination model. Interestingly, the hidden layers of the neural net structure in the discriminator class are shown to be necessary for robust estimation.
Human Activity Prediction in Smart Home Environments with LSTM Neural Networks
Authors: Niek Tax
Date: 2018-06-28 00:00:00
Proceeding: 14th International Conference on Intelligent Environments (IE) 2018 6
Tasks: Activity Prediction, Activity Recognition, Data Compression, Home Activity Monitoring, Human Activity Recognition, Prediction, Recognizing And Localizing Human Actions

In this paper, we investigate the performance of several sequence prediction techniques on the prediction of future events of human behavior in a smart home, as well as the timestamps of those next events. Prediction techniques in smart home environments have several use cases, such as the real-time identification of abnormal behavior, identifying coachable moments for e-coaching, and a plethora of applications in the area of home automation. We give an overview of several sequence prediction techniques, including techniques that originate from the areas of data mining, process mining, and data compression, and we evaluate the predictive accuracy of those techniques on a collection of publicly available real-life datasets from the smart home environments domain. This contrast our work with existing work on prediction in smart homes, which often evaluate their techniques on a single smart home instead of a larger collection of logs. We found that LSTM neural networks outperform the other prediction methods on the task of predicting the next activity as well as on the task of predicting the timestamp of the next event. However, surprisingly, we found that it is very dependent on the dataset which technique works best for the task of predicting a window of multiple next activities.
Privacy of Existence of Secrets: Introducing Steganographic DCOPs and Revisiting DCOP Frameworks
Authors: Viorel D. Silaghi, Marius C. Silaghi, René Mandiau
Date: 2019-02-15 00:00:00

Here we identify a type of privacy concern in Distributed Constraint
Optimization (DCOPs) not previously addressed in literature, despite its
importance and impact on the application field: the privacy of existence of
secrets. Science only starts where metrics and assumptions are clearly defined.
The area of Distributed Constraint Optimization has emerged at the intersection
of the multi-agent system community and constraint programming. For the
multi-agent community, the constraint optimization problems are an elegant way
to express many of the problems occurring in trading and distributed robotics.
For the theoretical constraint programming community the DCOPs are a natural
extension of their main object of study, the constraint satisfaction problem.
As such, the understanding of the DCOP framework has been refined with the
needs of the two communities, but sometimes without spelling the new
assumptions formally and therefore making it difficult to compare techniques.
Here we give a direction to the efforts for structuring concepts in this area.
Bayesian Image Classification with Deep Convolutional Gaussian Processes
Authors: Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman
Date: 2019-02-15 00:00:00
Tasks: Classification, Decision Making, Gaussian Processes, General Classification, image-classification, Image Classification, Model Selection, Translation

In decision-making systems, it is important to have classifiers that have calibrated uncertainties, with an optimisation objective that can be used for automated model selection and training. Gaussian processes (GPs) provide uncertainty estimates and a marginal likelihood objective, but their weak inductive biases lead to inferior accuracy. This has limited their applicability in certain tasks (e.g. image classification). We propose a translation-insensitive convolutional kernel, which relaxes the translation invariance constraint imposed by previous convolutional GPs. We show how we can use the marginal likelihood to learn the degree of insensitivity. We also reformulate GP image-to-image convolutional mappings as multi-output GPs, leading to deep convolutional GPs. We show experimentally that our new kernel improves performance in both single-layer and deep models. We also demonstrate that our fully Bayesian approach improves on dropout-based Bayesian deep learning methods in terms of uncertainty and marginal likelihood estimates.
Street Scene: A new dataset and evaluation protocol for video anomaly detection
Authors: Bharathkumar Ramachandra, Michael Jones
Date: 2019-02-15 00:00:00
Tasks: Anomaly Detection, Video Anomaly Detection

Progress in video anomaly detection research is currently slowed by small datasets that lack a wide variety of activities as well as flawed evaluation criteria. This paper aims to help move this research effort forward by introducing a large and varied new dataset called Street Scene, as well as two new evaluation criteria that provide a better estimate of how an algorithm will perform in practice. In addition to the new dataset and evaluation criteria, we present two variations of a novel baseline video anomaly detection algorithm and show they are much more accurate on Street Scene than two state-of-the-art algorithms from the literature.
Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI)
Authors: Janek Gröhl, Thomas Kirchner, Tim Adler, Lena Maier-Hein
Date: 2019-02-15 00:00:00

One of the main applications of photoacoustic (PA) imaging is the recovery of
functional tissue properties, such as blood oxygenation (sO2). This is
typically achieved by linear spectral unmixing of relevant chromophores from
multispectral photoacoustic images. Despite the progress that has been made
towards quantitative PA imaging (qPAI), most sO2 estimation methods yield poor
results in realistic settings. In this work, we tackle the challenge by
employing learned spectral decoloring for quantitative photoacoustic imaging
(LSD-qPAI) to obtain quantitative estimates for blood oxygenation. LSD-qPAI
computes sO2 directly from pixel-wise initial pressure spectra Sp0, which are
vectors comprised of the initial pressure at the same spatial location over all
recorded wavelengths. Initial results suggest that LSD-qPAI is able to obtain
accurate sO2 estimates directly from multispectral photoacoustic measurements
in silico and plausible estimates in vivo.
Deeply Supervised Multimodal Attentional Translation Embeddings for Visual Relationship Detection
Authors: Nikolaos Gkanatsios, Vassilis Pitsikalis, Petros Koutras, Athanasia Zlatintsi, Petros Maragos
Date: 2019-02-15 00:00:00
Tasks: Relationship Detection, Scene Understanding, Translation, Visual Relationship Detection

Detecting visual relationships, i.e. <Subject, Predicate, Object> triplets,
is a challenging Scene Understanding task approached in the past via linguistic
priors or spatial information in a single feature branch. We introduce a new
deeply supervised two-branch architecture, the Multimodal Attentional
Translation Embeddings, where the visual features of each branch are driven by
a multimodal attentional mechanism that exploits spatio-linguistic similarities
in a low-dimensional space. We present a variety of experiments comparing
against all related approaches in the literature, as well as by re-implementing
and fine-tuning several of them. Results on the commonly employed VRD dataset
[1] show that the proposed method clearly outperforms all others, while we also
justify our claims both quantitatively and qualitatively.
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric
Authors: Nathan Kallus, Angela Zhou
Date: 2019-02-15 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Binary Classification, Fairness, General Classification

Where machine-learned predictive risk scores inform high-stakes decisions, such as bail and sentencing in criminal justice, fairness has been a serious concern. Recent work has characterized the disparate impact that such risk scores can have when used for a binary classification task. This may not account, however, for the more diverse downstream uses of risk scores and their non-binary nature. To better account for this, in this paper, we investigate the fairness of predictive risk scores from the point of view of a bipartite ranking task, where one seeks to rank positive examples higher than negative ones. We introduce the xAUC disparity as a metric to assess the disparate impact of risk scores and define it as the difference in the probabilities of ranking a random positive example from one protected group above a negative one from another group and vice versa. We provide a decomposition of bipartite ranking loss into components that involve the discrepancy and components that involve pure predictive ability within each group. We use xAUC analysis to audit predictive risk scores for recidivism prediction, income prediction, and cardiac arrest prediction, where it describes disparities that are not evident from simply comparing within-group predictive performance.
Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network
Authors: Rui Cao, Qian Zhang, Jiasong Zhu, Qing Li, Qingquan Li, Bozhi Liu, Guoping Qiu
Date: 2019-02-15 00:00:00
Tasks: Image Retrieval, Metric Learning, Retrieval, Triplet

With the rapid growing of remotely sensed imagery data, there is a high
demand for effective and efficient image retrieval tools to manage and exploit
such data. In this letter, we present a novel content-based remote sensing
image retrieval method based on Triplet deep metric learning convolutional
neural network (CNN). By constructing a Triplet network with metric learning
objective function, we extract the representative features of the images in a
semantic space in which images from the same class are close to each other
while those from different classes are far apart. In such a semantic space,
simple metric measures such as Euclidean distance can be used directly to
compare the similarity of images and effectively retrieve images of the same
class. We also investigate a supervised and an unsupervised learning methods
for reducing the dimensionality of the learned semantic features. We present
comprehensive experimental results on two publicly available remote sensing
image retrieval datasets and show that our method significantly outperforms
state-of-the-art.
Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank
Authors: Qiao Zheng, Hervé Delingette, Kenneth Fung, Steffen E. Petersen, Nicholas Ayache
Date: 2019-02-15 00:00:00
Tasks: Clustering, Dimensionality Reduction, feature selection, General Classification

We perform unsupervised analysis of image-derived shape and motion features
extracted from 3822 cardiac 4D MRIs of the UK Biobank. First, with a feature
extraction method previously published based on deep learning models, we
extract from each case 9 feature values characterizing both the cardiac shape
and motion. Second, a feature selection is performed to remove highly
correlated feature pairs. Third, clustering is carried out using a Gaussian
mixture model on the selected features. After analysis, we identify two small
clusters which probably correspond to two pathological categories. Further
confirmation using a trained classification model and dimensionality reduction
tools is carried out to support this discovery. Moreover, we examine the
differences between the other large clusters and compare our measures with the
ground-truth.
Supervised Deep Neural Networks (DNNs) for Pricing/Calibration of Vanilla/Exotic Options Under Various Different Processes
Authors: Ali Hirsa, Tugce Karatas, Amir Oskoui
Date: 2019-02-15 00:00:00
Tasks: parameter estimation

We apply supervised deep neural networks (DNNs) for pricing and calibration of both vanilla and exotic options under both diffusion and pure jump processes with and without stochastic volatility. We train our neural network models under different number of layers, neurons per layer, and various different activation functions in order to find which combinations work better empirically. For training, we consider various different loss functions and optimization routines. We demonstrate that deep neural networks exponentially expedite option pricing compared to commonly used option pricing methods which consequently make calibration and parameter estimation super fast.
Staying Alive - CPR Quality Parameters from Wrist-worn Inertial Sensor Data with Evolutionary Fitted Sinusoidal Models
Authors: Christian Lins, Andreas Klausen, Sandra Hellmers, Andreas Hein, Sebastian Fudickar
Date: 2018-08-31 00:00:00
Tasks: Position

In this paper, a robust sinusoidal model fitting method based on the
Differential Evolution (DE) algorithm for determining cardiopulmonary
resuscitation (CPR) quality-parameters - naming chest compression frequency and
depth - as measured by an inertial sensor placed at the wrist is presented.
Once included into a smartphone or smartwatch app, the proposed algorithm will
enable bystanders to improve CPR (as part of a continuous closed-loop
support-system). By evaluating the precision of the model with data recorded by
a Laerdal Resusci Anne mannequin as reference standard, a low variance for
compression frequency of $\pm 2.0$ cpm has been found for the sensor placed at
the wrist, making this previously unconsidered position a suitable alternative
to the typical placement in the hand for CPR-training smartphone apps.
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations
Authors: Dmitry Kobak, George Linderman, Stefan Steinerberger, Yuval Kluger, Philipp Berens
Date: 2019-02-15 00:00:00

T-distributed stochastic neighbour embedding (t-SNE) is a widely used data
visualisation technique. It differs from its predecessor SNE by the
low-dimensional similarity kernel: the Gaussian kernel was replaced by the
heavy-tailed Cauchy kernel, solving the "crowding problem" of SNE. Here, we
develop an efficient implementation of t-SNE for a $t$-distribution kernel with
an arbitrary degree of freedom $\nu$, with $\nu\to\infty$ corresponding to SNE
and $\nu=1$ corresponding to the standard t-SNE. Using theoretical analysis and
toy examples, we show that $\nu<1$ can further reduce the crowding problem and
reveal finer cluster structure that is invisible in standard t-SNE. We further
demonstrate the striking effect of heavier-tailed kernels on large real-life
data sets such as MNIST, single-cell RNA-sequencing data, and the HathiTrust
library. We use domain knowledge to confirm that the revealed clusters are
meaningful. Overall, we argue that modifying the tail heaviness of the t-SNE
kernel can yield additional insight into the cluster structure of the data.
Robust Reinforcement Learning in POMDPs with Incomplete and Noisy Observations
Authors: Yuhui Wang, Hao He, Xiaoyang Tan
Date: 2019-02-15 00:00:00
Tasks: continuous-control, Continuous Control, Imputation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In real-world scenarios, the observation data for reinforcement learning with
continuous control is commonly noisy and part of it may be dynamically missing
over time, which violates the assumption of many current methods developed for
this. We addressed the issue within the framework of partially observable
Markov Decision Process (POMDP) using a model-based method, in which the
transition model is estimated from the incomplete and noisy observations using
a newly proposed surrogate loss function with local approximation, while the
policy and value function is learned with the help of belief imputation. For
the latter purpose, a generative model is constructed and is seamlessly
incorporated into the belief updating procedure of POMDP, which enables robust
execution even under a significant incompleteness and noise. The effectiveness
of the proposed method is verified on a collection of benchmark tasks, showing
that our approach outperforms several compared methods under various
challenging scenarios.
Fast Task-Aware Architecture Inference
Authors: Efi Kokiopoulou, Anja Hauth, Luciano Sbaiz, Andrea Gesmundo, Gabor Bartok, Jesse Berent
Date: 2019-02-15 00:00:00
Tasks: Computational Efficiency, Neural Architecture Search

Neural architecture search has been shown to hold great promise towards the
automation of deep learning. However in spite of its potential, neural
architecture search remains quite costly. To this point, we propose a novel
gradient-based framework for efficient architecture search by sharing
information across several tasks. We start by training many model architectures
on several related (training) tasks. When a new unseen task is presented, the
framework performs architecture inference in order to quickly identify a good
candidate architecture, before any model is trained on the new task. At the
core of our framework lies a deep value network that can predict the
performance of input architectures on a task by utilizing task meta-features
and the previous model training experiments performed on related tasks. We
adopt a continuous parametrization of the model architecture which allows for
efficient gradient-based optimization. Given a new task, an effective
architecture is quickly identified by maximizing the estimated performance with
respect to the model architecture parameters with simple gradient ascent. It is
key to point out that our goal is to achieve reasonable performance at the
lowest cost. We provide experimental results showing the effectiveness of the
framework despite its high computational efficiency.
Deep Reinforcement Learning Based High-level Driving Behavior Decision-making Model in Heterogeneous Traffic
Authors: Zhengwei Bai, Baigen Cai, Wei Shangguan, Linguo Chai
Date: 2019-02-15 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

High-level driving behavior decision-making is an open-challenging problem
for connected vehicle technology, especially in heterogeneous traffic
scenarios. In this paper, a deep reinforcement learning based high-level
driving behavior decision-making approach is proposed for connected vehicle in
heterogeneous traffic situations. The model is composed of three main parts: a
data preprocessor that maps hybrid data into a data format called hyper-grid
matrix, a two-stream deep neural network that extracts the hidden features, and
a deep reinforcement learning network that learns the optimal policy. Moreover,
a simulation environment, which includes different heterogeneous traffic
scenarios, is built to train and test the proposed method. The results
demonstrate that the model has the capability to learn the optimal high-level
driving policy such as driving fast through heterogeneous traffic without
unnecessary lane changes. Furthermore, two separate models are used to compare
with the proposed model, and the performances are analyzed in detail.
Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement
Authors: Zi-Yi Dou, Zhaopeng Tu, Xing Wang, Long-Yue Wang, Shuming Shi, Tong Zhang
Date: 2019-02-15 00:00:00
Tasks: Machine Translation, Translation

With the promising progress of deep neural networks, layer aggregation has
been used to fuse information across layers in various fields, such as computer
vision and machine translation. However, most of the previous methods combine
layers in a static fashion in that their aggregation strategy is independent of
specific hidden states. Inspired by recent progress on capsule networks, in
this paper we propose to use routing-by-agreement strategies to aggregate
layers dynamically. Specifically, the algorithm learns the probability of a
part (individual layer representations) assigned to a whole (aggregated
representations) in an iterative way and combines parts accordingly. We
implement our algorithm on top of the state-of-the-art neural machine
translation model TRANSFORMER and conduct experiments on the widely-used WMT14
English-German and WMT17 Chinese-English translation datasets. Experimental
results across language pairs show that the proposed approach consistently
outperforms the strong baseline model and a representative static aggregation
model.
Context-Aware Self-Attention Networks
Authors: Baosong Yang, Jian Li, Derek Wong, Lidia S. Chao, Xing Wang, Zhaopeng Tu
Date: 2019-02-15 00:00:00
Tasks: Translation

Self-attention model have shown its flexibility in parallel computation and
the effectiveness on modeling both long- and short-term dependencies. However,
it calculates the dependencies between representations without considering the
contextual information, which have proven useful for modeling dependencies
among neural representations in various natural language tasks. In this work,
we focus on improving self-attention networks through capturing the richness of
context. To maintain the simplicity and flexibility of the self-attention
networks, we propose to contextualize the transformations of the query and key
layers, which are used to calculates the relevance between elements.
Specifically, we leverage the internal representations that embed both global
and deep contexts, thus avoid relying on external resources. Experimental
results on WMT14 English-German and WMT17 Chinese-English translation tasks
demonstrate the effectiveness and universality of the proposed methods.
Furthermore, we conducted extensive analyses to quantity how the context
vectors participate in the self-attention model.
Asymptotically exact data augmentation: models, properties and algorithms
Authors: Maxime Vono, Nicolas Dobigeon, Pierre Chainais
Date: 2019-02-15 00:00:00
Tasks: Data Augmentation

Data augmentation, by the introduction of auxiliary variables, has become an ubiquitous technique to improve convergence properties, simplify the implementation or reduce the computational time of inference methods such as Markov chain Monte Carlo ones. Nonetheless, introducing appropriate auxiliary variables while preserving the initial target probability distribution and offering a computationally efficient inference cannot be conducted in a systematic way. To deal with such issues, this paper studies a unified framework, coined asymptotically exact data augmentation (AXDA), which encompasses both well-established and more recent approximate augmented models. In a broader perspective, this paper shows that AXDA models can benefit from interesting statistical properties and yield efficient inference algorithms. In non-asymptotic settings, the quality of the proposed approximation is assessed with several theoretical results. The latter are illustrated on standard statistical problems. Supplementary materials including computer code for this paper are available online.