pwc-archive/papers-with-abstracts huggingface dataset:
Shallow Water Bathymetry Mapping from UAV Imagery based on Machine Learning
Authors: Panagiotis Agrafiotis, Dimitrios Skarlatos, Andreas Georgopoulos, Konstantinos Karantzalos
Date: 2019-02-27 00:00:00
Tasks: BIG-bench Machine Learning

The determination of accurate bathymetric information is a key element for near offshore activities, hydrological studies such as coastal engineering applications, sedimentary processes, hydrographic surveying as well as archaeological mapping and biological research. UAV imagery processed with Structure from Motion (SfM) and Multi View Stereo (MVS) techniques can provide a low-cost alternative to established shallow seabed mapping techniques offering as well the important visual information. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this paper, in order to overcome the water refraction errors, we employ machine learning tools that are able to learn the systematic underestimation of the estimated depths. In the proposed approach, based on known depth observations from bathymetric LiDAR surveys, an SVR model was developed able to estimate more accurately the real depths of point clouds derived from SfM-MVS procedures. Experimental results over two test sites along with the performed quantitative validation indicated the high potential of the developed approach.
Private Center Points and Learning of Halfspaces
Authors: Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer
Date: 2019-02-27 00:00:00

We present a private learner for halfspaces over an arbitrary finite domain
$X\subset \mathbb{R}^d$ with sample complexity $mathrm{poly}(d,2^{\log^*|X|})$.
The building block for this learner is a differentially private algorithm for
locating an approximate center point of $m>\mathrm{poly}(d,2^{\log^*|X|})$
points -- a high dimensional generalization of the median function. Our
construction establishes a relationship between these two problems that is
reminiscent of the relation between the median and learning one-dimensional
thresholds [Bun et al.\ FOCS '15]. This relationship suggests that the problem
of privately locating a center point may have further applications in the
design of differentially private algorithms.
  We also provide a lower bound on the sample complexity for privately finding
a point in the convex hull. For approximate differential privacy, we show a
lower bound of $m=\Omega(d+\log^*|X|)$, whereas for pure differential privacy
$m=\Omega(d\log|X|)$.
Degenerate Feedback Loops in Recommender Systems
Authors: Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli
Date: 2019-02-27 00:00:00
Tasks: Recommendation Systems

Machine learning is used extensively in recommender systems deployed in
products. The decisions made by these systems can influence user beliefs and
preferences which in turn affect the feedback the learning system receives -
thus creating a feedback loop. This phenomenon can give rise to the so-called
"echo chambers" or "filter bubbles" that have user and societal implications.
In this paper, we provide a novel theoretical analysis that examines both the
role of user dynamics and the behavior of recommender systems, disentangling
the echo chamber from the filter bubble effect. In addition, we offer practical
solutions to slow down system degeneracy. Our study contributes toward
understanding and developing solutions to commonly cited issues in the complex
temporal scenario, an area that is still largely unexplored.
High probability generalization bounds for uniformly stable algorithms with nearly optimal rate
Authors: Vitaly Feldman, Jan Vondrak
Date: 2019-02-27 00:00:00
Tasks: Generalization Bounds, Vocal Bursts Intensity Prediction

Algorithmic stability is a classical approach to understanding and analysis of the generalization error of learning algorithms. A notable weakness of most stability-based generalization bounds is that they hold only in expectation. Generalization with high probability has been established in a landmark paper of Bousquet and Elisseeff (2002) albeit at the expense of an additional $\sqrt{n}$ factor in the bound. Specifically, their bound on the estimation error of any $\gamma$-uniformly stable learning algorithm on $n$ samples and range in $[0,1]$ is $O(\gamma \sqrt{n \log(1/\delta)} + \sqrt{\log(1/\delta)/n})$ with probability $\geq 1-\delta$. The $\sqrt{n}$ overhead makes the bound vacuous in the common settings where $\gamma \geq 1/\sqrt{n}$. A stronger bound was recently proved by the authors (Feldman and Vondrak, 2018) that reduces the overhead to at most $O(n^{1/4})$. Still, both of these results give optimal generalization bounds only when $\gamma = O(1/n)$. We prove a nearly tight bound of $O(\gamma \log(n)\log(n/\delta) + \sqrt{\log(1/\delta)/n})$ on the estimation error of any $\gamma$-uniformly stable algorithm. It implies that for algorithms that are uniformly stable with $\gamma = O(1/\sqrt{n})$, estimation error is essentially the same as the sampling error. Our result leads to the first high-probability generalization bounds for multi-pass stochastic gradient descent and regularized ERM for stochastic convex problems with nearly optimal rate --- resolving open problems in prior work. Our proof technique is new and we introduce several analysis tools that might find additional applications.
A Wasserstein distance approach for concentration of empirical risk estimates
Authors: Prashanth L. A., Sanjay P. Bhat
Date: 2019-02-27 00:00:00
Proceeding: NeurIPS 2019 12

This paper presents a unified approach based on Wasserstein distance to derive concentration bounds for empirical estimates for two broad classes of risk measures defined in the paper. The classes of risk measures introduced include as special cases well known risk measures from the finance literature such as conditional value at risk (CVaR), optimized certainty equivalent risk, spectral risk measures, utility-based shortfall risk, cumulative prospect theory (CPT) value, rank dependent expected utility and distorted risk measures. Two estimation schemes are considered, one for each class of risk measures. One estimation scheme involves applying the risk measure to the empirical distribution function formed from a collection of i.i.d. samples of the random variable (r.v.), while the second scheme involves applying the same procedure to a truncated sample. The bounds provided apply to three popular classes of distributions, namely sub-Gaussian, sub-exponential and heavy-tailed distributions. The bounds are derived by first relating the estimation error to the Wasserstein distance between the true and empirical distributions, and then using recent concentration bounds for the latter. Previous concentration bounds are available only for specific risk measures such as CVaR and CPT-value. The bounds derived in this paper are shown to either match or improve upon previous bounds in cases where they are available. The usefulness of the bounds is illustrated through an algorithm and the corresponding regret bound for a stochastic bandit problem involving a general risk measure from each of the two classes introduced in the paper.
Neural Imaging Pipelines - the Scourge or Hope of Forensics?
Authors: Pawel Korus, Nasir Memon
Date: 2019-02-27 00:00:00
Tasks: Image Manipulation, Image Manipulation Detection

Forensic analysis of digital photographs relies on intrinsic statistical
traces introduced at the time of their acquisition or subsequent editing. Such
traces are often removed by post-processing (e.g., down-sampling and
re-compression applied upon distribution in the Web) which inhibits reliable
provenance analysis. Increasing adoption of computational methods within
digital cameras further complicates the process and renders explicit
mathematical modeling infeasible. While this trend challenges forensic analysis
even in near-acquisition conditions, it also creates new opportunities. This
paper explores end-to-end optimization of the entire image acquisition and
distribution workflow to facilitate reliable forensic analysis at the end of
the distribution channel, where state-of-the-art forensic techniques fail. We
demonstrate that a neural network can be trained to replace the entire photo
development pipeline, and jointly optimized for high-fidelity photo rendering
and reliable provenance analysis. Such optimized neural imaging pipeline
allowed us to increase image manipulation detection accuracy from approx. 45%
to over 90%. The network learns to introduce carefully crafted artifacts, akin
to digital watermarks, which facilitate subsequent manipulation detection.
Analysis of performance trade-offs indicates that most of the gains can be
obtained with only minor distortion. The findings encourage further research
towards building more reliable imaging pipelines with explicit
provenance-guaranteeing properties.
Adaptive Gaussian Copula ABC
Authors: Yanzhi Chen, Michael U. Gutmann
Date: 2019-02-27 00:00:00
Tasks: Bayesian Inference

Approximate Bayesian computation (ABC) is a set of techniques for Bayesian
inference when the likelihood is intractable but sampling from the model is
possible. This work presents a simple yet effective ABC algorithm based on the
combination of two classical ABC approaches --- regression ABC and sequential
ABC. The key idea is that rather than learning the posterior directly, we first
target another auxiliary distribution that can be learned accurately by
existing methods, through which we then subsequently learn the desired
posterior with the help of a Gaussian copula. During this process, the
complexity of the model changes adaptively according to the data at hand.
Experiments on a synthetic dataset as well as three real-world inference tasks
demonstrates that the proposed method is fast, accurate, and easy to use.
On the generalization of GAN image forensics
Authors: Xinsheng Xuan, Bo Peng, Wei Wang, Jing Dong
Date: 2019-02-27 00:00:00
Tasks: GAN image forensics, Image Forensics

Recently the GAN generated face images are more and more realistic with high-quality, even hard for human eyes to detect. On the other hand, the forensics community keeps on developing methods to detect these generated fake images and try to guarantee the credibility of visual contents. Although researchers have developed some methods to detect generated images, few of them explore the important problem of generalization ability of forensics model. As new types of GANs are emerging fast, the generalization ability of forensics models to detect new types of GAN images is absolutely an essential research topic. In this paper, we explore this problem and propose to use preprocessed images to train a forensic CNN model. By applying similar image level preprocessing to both real and fake training images, the forensics model is forced to learn more intrinsic features to classify the generated and real face images. Our experimental results also prove the effectiveness of the proposed method.
A Deep-learning Approach for Prognosis of Age-Related Macular Degeneration Disease using SD-OCT Imaging Biomarkers
Authors: Imon Banerjee, Luis de Sisternes, Joelle Hallak, Theodore Leng, Aaron Osborne, Mary Durbin, Daniel Rubin
Date: 2019-02-27 00:00:00
Tasks: Prognosis

We propose a hybrid sequential deep learning model to predict the risk of AMD
progression in non-exudative AMD eyes at multiple timepoints, starting from
short-term progression (3-months) up to long-term progression (21-months).
Proposed model combines radiomics and deep learning to handle challenges
related to imperfect ratio of OCT scan dimension and training cohort size. We
considered a retrospective clinical trial dataset that includes 671 fellow eyes
with 13,954 dry AMD observations for training and validating the machine
learning models on a 10-fold cross validation setting. The proposed RNN model
achieved high accuracy (0.96 AUCROC) for the prediction of both short term and
long-term AMD progression, and outperformed the traditional random forest model
trained. High accuracy achieved by the RNN establishes the ability to identify
AMD patients at risk of progressing to advanced AMD at an early stage which
could have a high clinical impact as it allows for optimal clinical follow-up,
with more frequent screening and potential earlier treatment for those patients
at high risk.
Disentangled Deep Autoencoding Regularization for Robust Image Classification
Authors: Zhenyu Duan, Martin Renqiang Min, Li Erran Li, Mingbo Cai, Yi Xu, Bingbing Ni
Date: 2019-02-27 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Object Recognition

In spite of achieving revolutionary successes in machine learning, deep
convolutional neural networks have been recently found to be vulnerable to
adversarial attacks and difficult to generalize to novel test images with
reasonably large geometric transformations. Inspired by a recent neuroscience
discovery revealing that primate brain employs disentangled shape and
appearance representations for object recognition, we propose a general
disentangled deep autoencoding regularization framework that can be easily
applied to any deep embedding based classification model for improving the
robustness of deep neural networks. Our framework effectively learns
disentangled appearance code and geometric code for robust image
classification, which is the first disentangling based method defending against
adversarial attacks and complementary to standard defense methods. Extensive
experiments on several benchmark datasets show that, our proposed
regularization framework leveraging disentangled embedding significantly
outperforms traditional unregularized convolutional neural networks for image
classification on robustness against adversarial attacks and generalization to
novel test data.
FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning
Authors: Paul N. Whatmough, Chuteng Zhou, Patrick Hansen, Shreyas Kolala Venkataramanaiah, Jae-sun Seo, Matthew Mattina
Date: 2019-02-27 00:00:00
Tasks: General Classification, image-classification, Image Classification, Transfer Learning

The computational demands of computer vision tasks based on state-of-the-art
Convolutional Neural Network (CNN) image classification far exceed the energy
budgets of mobile devices. This paper proposes FixyNN, which consists of a
fixed-weight feature extractor that generates ubiquitous CNN features, and a
conventional programmable CNN accelerator which processes a dataset-specific
CNN. Image classification models for FixyNN are trained end-to-end via transfer
learning, with the common feature extractor representing the transfered part,
and the programmable part being learnt on the target dataset. Experimental
results demonstrate FixyNN hardware can achieve very high energy efficiencies
up to 26.6 TOPS/W ($4.81 \times$ better than iso-area programmable
accelerator). Over a suite of six datasets we trained models via transfer
learning with an accuracy loss of $<1\%$ resulting in up to 11.2 TOPS/W -
nearly $2 \times$ more efficient than a conventional programmable CNN
accelerator of the same area.
Target-based Hyperspectral Demixing via Generalized Robust PCA
Authors: Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
Date: 2019-02-26 00:00:00

Localizing targets of interest in a given hyperspectral (HS) image has
applications ranging from remote sensing to surveillance. This task of target
detection leverages the fact that each material/object possesses its own
characteristic spectral response, depending upon its composition. As
$\textit{signatures}$ of different materials are often correlated, matched
filtering based approaches may not be appropriate in this case. In this work,
we present a technique to localize targets of interest based on their spectral
signatures. We also present the corresponding recovery guarantees, leveraging
our recent theoretical results. To this end, we model a HS image as a
superposition of a low-rank component and a dictionary sparse component,
wherein the dictionary consists of the $\textit{a priori}$ known characteristic
spectral responses of the target we wish to localize. Finally, we analyze the
performance of the proposed approach via experimental validation on real HS
data for a classification task, and compare it with related techniques.
Integrated analysis of the urban water-electricity demand nexus in the Midwestern United States
Authors: Renee Obringer, Rohini Kumar, Roshanak Nateghi
Date: 2019-02-26 00:00:00
Tasks: Learning Theory

Considering the interdependencies between water and electricity use is
critical for ensuring conservation measures are successful in lowering the net
water and electricity use in a city. This water-electricity demand nexus will
become even more important as cities continue to grow, causing water and
electricity utilities additional stress, especially given the likely impacts of
future global climatic and socioeconomic changes. Here, we propose a modeling
framework based in statistical learning theory for predicting the
climate-sensitive portion of the coupled water-electricity demand nexus. The
predictive models were built and tested on six Midwestern cities. The results
showed that water use was better predicted than electricity use, indicating
that water use is slightly more sensitive to climate than electricity use.
Additionally, the results demonstrated the importance of the variability in the
El Nino/Southern Oscillation index, which explained the majority of the
covariance in the water-electricity nexus. Our modeling results suggest that
stronger El Ninos lead to an overall increase in water and electricity use in
these cities. The integrated modeling framework presented here can be used to
characterize the climate-related sensitivity of the water-electricity demand
nexus, accounting for the coupled water and electricity use rather than
modeling them separately, as independent variables.
High Flux Passive Imaging with Single-Photon Sensors
Authors: Atul Ingle, Andreas Velten, Mohit Gupta
Date: 2019-02-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: Vocal Bursts Intensity Prediction

Single-photon avalanche diodes (SPADs) are an emerging technology with a
unique capability of capturing individual photons with high timing precision.
SPADs are being used in several active imaging systems (e.g., fluorescence
lifetime microscopy and LiDAR), albeit mostly limited to low photon flux
settings. We propose passive free-running SPAD (PF-SPAD) imaging, an imaging
modality that uses SPADs for capturing 2D intensity images with unprecedented
dynamic range under ambient lighting, without any active light source. Our key
observation is that the precise inter-photon timing measured by a SPAD can be
used for estimating scene brightness under ambient lighting conditions, even
for very bright scenes. We develop a theoretical model for PF-SPAD imaging, and
derive a scene brightness estimator based on the average time of darkness
between successive photons detected by a PF-SPAD pixel. Our key insight is that
due to the stochastic nature of photon arrivals, this estimator does not suffer
from a hard saturation limit. Coupled with high sensitivity at low flux, this
enables a PF-SPAD pixel to measure a wide range of scene brightness, from very
low to very high, thereby achieving extreme dynamic range. We demonstrate an
improvement of over 2 orders of magnitude over conventional sensors by imaging
scenes spanning a dynamic range of 1,000,000:1.
Learning Dynamical Systems from Partial Observations
Authors: Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot, Julien Brajard, Patrick Gallinari
Date: 2019-02-26 00:00:00

We consider the problem of forecasting complex, nonlinear space-time
processes when observations provide only partial information of on the system's
state. We propose a natural data-driven framework, where the system's dynamics
are modelled by an unknown time-varying differential equation, and the
evolution term is estimated from the data, using a neural network. Any future
state can then be computed by placing the associated differential equation in
an ODE solver. We first evaluate our approach on shallow water and Euler
simulations. We find that our method not only demonstrates high quality
long-term forecasts, but also learns to produce hidden states closely
resembling the true states of the system, without direct supervision on the
latter. Additional experiments conducted on challenging, state of the art ocean
simulations further validate our findings, while exhibiting notable
improvements over classical baselines.
Unsupervised Segmentation Algorithms' Implementation in ITK for Tissue Classification via Human Head MRI Scans
Authors: Shadman Sakib, Md. Abu Bakr Siddique
Date: 2019-02-26 00:00:00
Tasks: Classification, General Classification, Image Registration

Tissue classification is one of the significant tasks in the field of biomedical image analysis. Magnetic Resonance Imaging (MRI) is of great importance in tissue classification especially in the areas of brain tissue classification which is able to recognize anatomical areas of interest such as surgical planning, monitoring therapy, clinical drug trials, image registration, stereotactic neurosurgery, radiotherapy etc. The task of this paper is to implement different unsupervised classification algorithms in ITK and perform tissue classification (white matter, gray matter, cerebrospinal fluid (CSF) and background of the human brain). For this purpose, 5 grayscale head MRI scans are provided. In order of classifying brain tissues, three algorithms are used. These are: Otsu thresholding, Bayesian classification and Bayesian classification with Gaussian smoothing. The obtained classification results are analyzed in the results and discussion section.
Imaging and Classification Techniques for Seagrass Mapping and Monitoring: A Comprehensive Survey
Authors: Md Moniruzzaman, S. M. Shamsul Islam, Paul Lavery, Mohammed Bennamoun, C. Peng Lam
Date: 2019-02-26 00:00:00
Tasks: General Classification

Monitoring underwater habitats is a vital part of observing the condition of
the environment. The detection and mapping of underwater vegetation, especially
seagrass has drawn the attention of the research community as early as the
nineteen eighties. Initially, this monitoring relied on in situ observation by
experts. Later, advances in remote-sensing technology, satellite-monitoring
techniques and, digital photo- and video-based techniques opened a window to
quicker, cheaper, and, potentially, more accurate seagrass-monitoring methods.
So far, for seagrass detection and mapping, digital images from airborne
cameras, spectral images from satellites, acoustic image data using underwater
sonar technology, and digital underwater photo and video images have been used
to map the seagrass meadows or monitor their condition. In this article, we
have reviewed the recent approaches to seagrass detection and mapping to
understand the gaps of the present approaches and determine further research
scope to monitor the ocean health more easily. We have identified four classes
of approach to seagrass mapping and assessment: still image-, video data-,
acoustic image-, and spectral image data-based techniques. We have critically
analysed the surveyed approaches and found the research gaps including the need
for quick, cheap and effective imaging techniques robust to depth, turbidity,
location and weather conditions, fully automated seagrass detectors that can
work in real-time, accurate techniques for estimating the seagrass density, and
the availability of high computation facilities for processing large scale
data. For addressing these gaps, future research should focus on developing
cheaper image and video data collection techniques, deep learning based
automatic annotation and classification, and real-time percentage-cover
calculation.