pwc-archive/papers-with-abstracts huggingface dataset:
Deep Industrial Espionage
Authors: Samuel Albanie, James Thewlis, Sebastien Ehrhardt, Joao Henriques
Date: 2019-04-01 00:00:00

The theory of deep learning is now considered largely solved, and is well
understood by researchers and influencers alike. To maintain our relevance, we
therefore seek to apply our skills to under-explored, lucrative applications of
this technology. To this end, we propose and Deep Industrial Espionage, an
efficient end-to-end framework for industrial information propagation and
productisation. Specifically, given a single image of a product or service, we
aim to reverse-engineer, rebrand and distribute a copycat of the product at a
profitable price-point to consumers in an emerging market---all within in a
single forward pass of a Neural Network. Differently from prior work in machine
perception which has been restricted to classifying, detecting and reasoning
about object instances, our method offers tangible business value in a wide
range of corporate settings. Our approach draws heavily on a promising recent
arxiv paper until its original authors' names can no longer be read (we use
felt tip pen). We then rephrase the anonymised paper, add the word "novel" to
the title, and submit it a prestigious, closed-access espionage journal who
assure us that someday, we will be entitled to some fraction of their
extortionate readership fees.
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction
Authors: Florian Knoll, Kerstin Hammernik, Chi Zhang, Steen Moeller, Thomas Pock, Daniel K. Sodickson, Mehmet Akcakaya
Date: 2019-04-01 00:00:00
Tasks: BIG-bench Machine Learning, compressed sensing, Deep Learning, Image Reconstruction, MRI Reconstruction

Following the success of deep learning in a wide range of applications,
neural network-based machine learning techniques have received interest as a
means of accelerating magnetic resonance imaging (MRI). A number of ideas
inspired by deep learning techniques from computer vision and image processing
have been successfully applied to non-linear image reconstruction in the spirit
of compressed sensing for both low dose computed tomography and accelerated
MRI. The additional integration of multi-coil information to recover missing
k-space lines in the MRI reconstruction process, is still studied less
frequently, even though it is the de-facto standard for currently used
accelerated MR acquisitions. This manuscript provides an overview of the recent
machine learning approaches that have been proposed specifically for improving
parallel imaging. A general background introduction to parallel MRI is given
that is structured around the classical view of image space and k-space based
methods. Both linear and non-linear methods are covered, followed by a
discussion of recent efforts to further improve parallel imaging using machine
learning, and specifically using artificial neural networks. Image-domain based
techniques that introduce improved regularizers are covered as well as k-space
based methods, where the focus is on better interpolation strategies using
neural networks. Issues and open problems are discussed as well as recent
efforts for producing open datasets and benchmarks for the community.
Creativity Inspired Zero-Shot Learning
Authors: Mohamed Elhoseiny, Mohamed Elfeki
Date: 2019-04-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Attribute, Transfer Learning, Zero-Shot Learning

Zero-shot learning (ZSL) aims at understanding unseen categories with no training examples from class-level descriptions. To improve the discriminative power of zero-shot learning, we model the visual learning process of unseen categories with inspiration from the psychology of human creativity for producing novel art. We relate ZSL to human creativity by observing that zero-shot learning is about recognizing the unseen and creativity is about creating a likable unseen. We introduce a learning signal inspired by creativity literature that explores the unseen space with hallucinated class-descriptions and encourages careful deviation of their visual feature generations from seen classes while allowing knowledge transfer from seen to unseen classes. Empirically, we show consistent improvement over the state of the art of several percents on the largest available benchmarks on the challenging task or generalized ZSL from a noisy text that we focus on, using the CUB and NABirds datasets. We also show the advantage of our approach on Attribute-based ZSL on three additional datasets (AwA2, aPY, and SUN). Code is available.
Fingerprints: Fixed Length Representation via Deep Networks and Domain Knowledge
Authors: Joshua J. Engelsma, Kai Cao, Anil K. Jain
Date: 2019-04-01 00:00:00

We learn a discriminative fixed length feature representation of fingerprints
which stands in contrast to commonly used unordered, variable length sets of
minutiae points. To arrive at this fixed length representation, we embed
fingerprint domain knowledge into a multitask deep convolutional neural network
architecture. Empirical results, on two public-domain fingerprint databases
(NIST SD4 and FVC 2004 DB1) show that compared to minutiae representations,
extracted by two state-of-the-art commercial matchers (Verifinger v6.3 and
Innovatrics v2.0.3), our fixed-length representations provide (i) higher search
accuracy: Rank-1 accuracy of 97.9% vs. 97.3% on NIST SD4 against a gallery size
of 2000 and (ii) significantly faster, large scale search: 682,594 matches per
second vs. 22 matches per second for commercial matchers on an i5 3.3 GHz
processor with 8 GB of RAM.
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity
Authors: Yunsheng Bai, Hao Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Ting Chen, Yizhou Sun, Wei Wang
Date: 2019-04-01 00:00:00
Tasks: General Classification, Graph Classification, Graph Embedding, Graph Similarity, Representation Learning

We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity. Our approach, UGRAPHEMB, is a general framework that provides a novel means to performing graph-level embedding in a completely unsupervised and inductive manner. The learned neural network can be considered as a function that receives any graph as input, either seen or unseen in the training set, and transforms it into an embedding. A novel graph-level embedding generation mechanism called Multi-Scale Node Attention (MSNA), is proposed. Experiments on five real graph datasets show that UGRAPHEMB achieves competitive accuracy in the tasks of graph classification, similarity ranking, and graph visualization.
Infant-Prints: Fingerprints for Reducing Infant Mortality
Authors: Joshua J. Engelsma, Debayan Deb, Anil K. Jain, Prem S. Sudhish, Anjoo Bhatnager
Date: 2019-04-01 00:00:00
Tasks: TAR

In developing countries around the world, a multitude of infants continue to
suffer and die from vaccine-preventable diseases, and malnutrition. Lamentably,
the lack of any official identification documentation makes it exceedingly
difficult to prevent these infant deaths. To solve this global crisis, we
propose Infant-Prints which is comprised of (i) a custom, compact, low-cost (85
USD), high-resolution (1,900 ppi) fingerprint reader, (ii) a high-resolution
fingerprint matcher, and (iii) a mobile application for search and verification
for the infant fingerprint. Using Infant-Prints, we have collected a
longitudinal database of infant fingerprints and demonstrate its ability to
perform accurate and reliable recognition of infants enrolled at the ages 0-3
months, in time for effective delivery of critical vaccinations and nutritional
supplements (TAR=90% @ FAR = 0.1% for infants older than 8 weeks).
DeepCloud. The Application of a Data-driven, Generative Model in Design
Authors: Ardavan Bidgoli, Pedro Veloso
Date: 2019-04-01 00:00:00

Generative systems have a significant potential to synthesize innovative
design alternatives. Still, most of the common systems that have been adopted
in design require the designer to explicitly define the specifications of the
procedures and in some cases the design space. In contrast, a generative system
could potentially learn both aspects through processing a database of existing
solutions without the supervision of the designer. To explore this possibility,
we review recent advancements of generative models in machine learning and
current applications of learning techniques in design. Then, we describe the
development of a data-driven generative system titled DeepCloud. It combines an
autoencoder architecture for point clouds with a web-based interface and analog
input devices to provide an intuitive experience for data-driven generation of
design alternatives. We delineate the implementation of two prototypes of
DeepCloud, their contributions, and potentials for generative design.
Learning Matchable Image Transformations for Long-term Metric Visual Localization
Authors: Lee Clement, Mona Gridseth, Justin Tomasi, Jonathan Kelly
Date: 2019-04-01 00:00:00
Tasks: Color Constancy, Visual Localization

Long-term metric self-localization is an essential capability of autonomous mobile robots, but remains challenging for vision-based systems due to appearance changes caused by lighting, weather, or seasonal variations. While experience-based mapping has proven to be an effective technique for bridging the `appearance gap,' the number of experiences required for reliable metric localization over days or months can be very large, and methods for reducing the necessary number of experiences are needed for this approach to scale. Taking inspiration from color constancy theory, we learn a nonlinear RGB-to-grayscale mapping that explicitly maximizes the number of inlier feature matches for images captured under different lighting and weather conditions, and use it as a pre-processing step in a conventional single-experience localization pipeline to improve its robustness to appearance change. We train this mapping by approximating the target non-differentiable localization pipeline with a deep neural network, and find that incorporating a learned low-dimensional context feature can further improve cross-appearance feature matching. Using synthetic and real-world datasets, we demonstrate substantial improvements in localization performance across day-night cycles, enabling continuous metric localization over a 30-hour period using a single mapping experience, and allowing experience-based localization to scale to long deployments with dramatically reduced data requirements.
Multimodal Sparse Classifier for Adolescent Brain Age Prediction
Authors: Peyman Hosseinzadeh Kassani, Alexej Gossmann, Yu-Ping Wang
Date: 2019-04-01 00:00:00
Tasks: Functional Connectivity, Prediction

The study of healthy brain development helps to better understand the brain
transformation and brain connectivity patterns which happen during childhood to
adulthood. This study presents a sparse machine learning solution across
whole-brain functional connectivity (FC) measures of three sets of data,
derived from resting state functional magnetic resonance imaging (rs-fMRI) and
task fMRI data, including a working memory n-back task (nb-fMRI) and an emotion
identification task (em-fMRI). These multi-modal image data are collected on a
sample of adolescents from the Philadelphia Neurodevelopmental Cohort (PNC) for
the prediction of brain ages. Due to extremely large variable-to-instance ratio
of PNC data, a high dimensional matrix with several irrelevant and highly
correlated features is generated and hence a pattern learning approach is
necessary to extract significant features. We propose a sparse learner based on
the residual errors along the estimation of an inverse problem for the extreme
learning machine (ELM) neural network. The purpose of the approach is to
overcome the overlearning problem through pruning of several redundant features
and their corresponding output weights. The proposed multimodal sparse ELM
classifier based on residual errors (RES-ELM) is highly competitive in terms of
the classification accuracy compared to its counterparts such as conventional
ELM, and sparse Bayesian learning ELM.
Efficient and Safe Exploration in Deterministic Markov Decision Processes with Unknown Transition Models
Authors: Erdem Biyik, Jonathan Margoliash, Shahrouz Ryan Alimo, Dorsa Sadigh
Date: 2019-04-01 00:00:00
Tasks: Safe Exploration

We propose a safe exploration algorithm for deterministic Markov Decision
Processes with unknown transition models. Our algorithm guarantees safety by
leveraging Lipschitz-continuity to ensure that no unsafe states are visited
during exploration. Unlike many other existing techniques, the provided safety
guarantee is deterministic. Our algorithm is optimized to reduce the number of
actions needed for exploring the safe space. We demonstrate the performance of
our algorithm in comparison with baseline methods in simulation on navigation
tasks.
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning
Authors: Ahmed Salem, Apratim Bhattacharya, Michael Backes, Mario Fritz, Yang Zhang
Date: 2019-04-01 00:00:00
Tasks: Decoder

Machine learning (ML) has progressed rapidly during the past decade and the major factor that drives such development is the unprecedented large-scale data. As data generation is a continuous process, this leads to ML model owners updating their models frequently with newly-collected data in an online learning scenario. In consequence, if an ML model is queried with the same set of data samples at two different points in time, it will provide different results. In this paper, we investigate whether the change in the output of a black-box ML model before and after being updated can leak information of the dataset used to perform the update, namely the updating set. This constitutes a new attack surface against black-box ML models and such information leakage may compromise the intellectual property and data privacy of the ML model owner. We propose four attacks following an encoder-decoder formulation, which allows inferring diverse information of the updating set. Our new attacks are facilitated by state-of-the-art deep learning techniques. In particular, we propose a hybrid generative model (CBM-GAN) that is based on generative adversarial networks (GANs) but includes a reconstructive loss that allows reconstructing accurate samples. Our experiments show that the proposed attacks achieve strong performance.
Optimal Obfuscation Mechanisms via Machine Learning
Authors: Marco Romanelli, Konstantinos Chatzikokolakis, Catuscia Palamidessi
Date: 2019-04-01 00:00:00
Tasks: BIG-bench Machine Learning

We consider the problem of obfuscating sensitive information while preserving utility, and we propose a machine learning approach inspired by the generative adversarial networks paradigm. The idea is to set up two nets: the generator, that tries to produce an optimal obfuscation mechanism to protect the data, and the classifier, that tries to de-obfuscate the data. By letting the two nets compete against each other, the mechanism improves its degree of protection, until an equilibrium is reached. We apply our method to the case of location privacy, and we perform experiments on synthetic data and on real data from the Gowalla dataset. We evaluate the privacy of the mechanism not only by its capacity to defeat the classifier, but also in terms of the Bayes error, which represents the strongest possible adversary. We compare the privacy-utility tradeoff of our method to that of the planar Laplace mechanism used in geo-indistinguishability, showing favorable results. Like the Laplace mechanism, our system can be deployed at the user end for protecting his location.
Responsible team players wanted: an analysis of soft skill requirements in job advertisements
Authors: Federica Calanca, Luiza Sayfullina, Lara Minkus, Claudia Wagner, Eric Malmi
Date: 2018-10-13 00:00:00
Tasks: Sociology

During the past decades the importance of soft skills for labour market
outcomes has grown substantially. This carries implications for labour market
inequality, since previous research shows that soft skills are not valued
equally across race and gender. This work explores the role of soft skills in
job advertisements by drawing on methods from computational science as well as
on theoretical and empirical insights from economics, sociology and psychology.
We present a semi-automatic approach based on crowdsourcing and text mining for
extracting a list of soft skills. We find that soft skills are a crucial
component of job ads, especially of low-paid jobs and jobs in female-dominated
professions. Our work shows that soft skills can serve as partial predictors of
the gender composition in job categories and that not all soft skills receive
equal wage returns at the labour market. Especially "female" skills are
frequently associated with wage penalties. Our results expand the growing
literature on the association of soft skills on wage inequality and highlight
their importance for occupational gender segregation at labour markets.
Bayesian Optimization for Policy Search via Online-Offline Experimentation
Authors: Benjamin Letham, Eytan Bakshy
Date: 2019-04-01 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning

Online field experiments are the gold-standard way of evaluating changes to
real-world interactive machine learning systems. Yet our ability to explore
complex, multi-dimensional policy spaces - such as those found in
recommendation and ranking problems - is often constrained by the limited
number of experiments that can be run simultaneously. To alleviate these
constraints, we augment online experiments with an offline simulator and apply
multi-task Bayesian optimization to tune live machine learning systems. We
describe practical issues that arise in these types of applications, including
biases that arise from using a simulator and assumptions for the multi-task
kernel. We measure empirical learning curves which show substantial gains from
including data from biased offline experiments, and show how these learning
curves are consistent with theoretical results for multi-task Gaussian process
generalization. We find that improved kernel inference is a significant driver
of multi-task generalization. Finally, we show several examples of Bayesian
optimization efficiently tuning a live machine learning system by combining
offline and online experiments.
Dynamically Optimal Treatment Allocation
Authors: Karun Adusumilli, Friedrich Geiecke, Claudio Schilter
Date: 2019-04-01 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Dynamic decisions are pivotal to economic policy making. We show how existing evidence from randomized control trials can be utilized to guide personalized decisions in challenging dynamic environments with budget and capacity constraints. Recent advances in reinforcement learning now enable the solution of many complex, real-world problems for the first time. We allow for restricted classes of policy functions and prove that their regret decays at rate n^(-0.5), the same as in the static case. Applying our methods to job training, we find that by exploiting the problem's dynamic structure, we achieve significantly higher welfare compared to static approaches.
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
Authors: Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli
Date: 2019-04-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Language Modeling, Language Modelling, Text Generation, Translation

fairseq is an open-source sequence modeling toolkit that allows researchers
and developers to train custom models for translation, summarization, language
modeling, and other text generation tasks. The toolkit is based on PyTorch and
supports distributed training across multiple GPUs and machines. We also
support fast mixed-precision training and inference on modern GPUs. A demo
video can be found at https://www.youtube.com/watch?v=OtgDdWtHvto
Multitask Soft Option Learning
Authors: Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N. Siddharth, Wendelin Böhmer, Shimon Whiteson
Date: 2019-04-01 00:00:00
Tasks: Transfer Learning

We present Multitask Soft Option Learning(MSOL), a hierarchical multitask framework based on Planning as Inference. MSOL extends the concept of options, using separate variational posteriors for each task, regularized by a shared prior. This ''soft'' version of options avoids several instabilities during training in a multitask setting, and provides a natural way to learn both intra-option policies and their terminations. Furthermore, it allows fine-tuning of options for new tasks without forgetting their learned policies, leading to faster training without reducing the expressiveness of the hierarchical policy. We demonstrate empirically that MSOL significantly outperforms both hierarchical and flat transfer-learning baselines.
Learning to Stop in Structured Prediction for Neural Machine Translation
Authors: Mingbo Ma, Renjie Zheng, Liang Huang
Date: 2019-04-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Machine Translation, Structured Prediction, Translation

Beam search optimization resolves many issues in neural machine translation. However, this method lacks principled stopping criteria and does not learn how to stop during training, and the model naturally prefers the longer hypotheses during the testing time in practice since they use the raw score instead of the probability-based score. We propose a novel ranking method which enables an optimal beam search stopping criteria. We further introduce a structured prediction loss function which penalizes suboptimal finished candidates produced by beam search during training. Experiments of neural machine translation on both synthetic data and real languages (German-to-English and Chinese-to-English) demonstrate our proposed methods lead to better length and BLEU score.
Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey
Authors: Basheer Qolomany, Ala Al-Fuqaha, Ajay Gupta, Driss Benhaddou, Safaa Alwajidi, Junaid Qadir, Alvis C. Fong
Date: 2019-04-01 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Survey

Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves into people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart buildings includes making the residents' experience as easy and comfortable as possible. The massive streaming data generated and captured by smart building appliances and devices contains valuable information that needs to be mined to facilitate timely actions and better decision making. Machine learning and big data analytics will undoubtedly play a critical role to enable the delivery of such smart services. In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics. This survey also reviews the current trends and challenges faced in the development of smart building services.