pwc-archive/papers-with-abstracts huggingface dataset:
A multi-layered energy consumption model for smart wireless acoustic sensor networks
Authors: Gert Dekkers, Fernando Rosas, Steven Lauwereins, Sreeraj Rajendran, Sofie Pollin, Bart Vanrumste, Toon van Waterschoot, Marian Verhelst, Peter Karsmakers
Date: 2018-12-17 00:00:00

Smart sensing is expected to become a pervasive technology in smart cities
and environments of the near future. These services are improving their
capabilities due to integrated devices shrinking in size while maintaining
their computational power, which can run diverse Machine Learning algorithms
and achieve high performance in various data-processing tasks. One attractive
sensor modality to be used for smart sensing are acoustic sensors, which can
convey highly informative data while keeping a moderate energy consumption.
Unfortunately, the energy budget of current wireless sensor networks is usually
not enough to support the requirements of standard microphones. Therefore,
energy efficiency needs to be increased at all layers --- sensing, signal
processing and communication --- in order to bring wireless smart acoustic
sensors into the market. To help to attain this goal, this paper introduces
WASN-EM: an energy consumption model for wireless acoustic sensors networks
(WASN), whose aim is to aid in the development of novel techniques to increase
the energy-efficient of smart wireless acoustic sensors. This model provides a
first step of exploration prior to custom design of a smart wireless acoustic
sensor, and also can be used to compare the energy consumption of different
protocols.
A Preliminary Study of Neural Network-based Approximation for HPC Applications
Authors: Wenqian Dong, Anzheng Guolu, Dong Li
Date: 2018-12-18 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning, as a tool to learn and model complicated (non)linear
relationships between input and output data sets, has shown preliminary success
in some HPC problems. Using machine learning, scientists are able to augment
existing simulations by improving accuracy and significantly reducing
latencies. Our ongoing research work is to create a general framework to apply
neural network-based models to HPC applications. In particular, we want to use
the neural network to approximate and replace code regions within the HPC
application to improve performance (i.e., reducing the execution time) of the
HPC application. In this paper, we present our preliminary study and results.
Using two applications (the Newton-Raphson method and the Lennard-Jones (LJ)
potential in LAMMP) for our case study, we achieve up to 2.7x and 2.46x
speedup, respectively.
Supervised Domain Enablement Attention for Personalized Domain Classification
Authors: Joo-Kyung Kim, Young-Bum Kim
Date: 2018-12-18 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Classification, domain classification, General Classification, Natural Language Understanding

In large-scale domain classification for natural language understanding,
leveraging each user's domain enablement information, which refers to the
preferred or authenticated domains by the user, with attention mechanism has
been shown to improve the overall domain classification performance. In this
paper, we propose a supervised enablement attention mechanism, which utilizes
sigmoid activation for the attention weighting so that the attention can be
computed with more expressive power without the weight sum constraint of
softmax attention. The attention weights are explicitly encouraged to be
similar to the corresponding elements of the ground-truth's one-hot vector by
supervised attention, and the attention information of the other enabled
domains is leveraged through self-distillation. By evaluating on the actual
utterances from a large-scale IPDA, we show that our approach significantly
improves domain classification performance.
Information-Directed Exploration for Deep Reinforcement Learning
Authors: Nikolay Nikolov, Johannes Kirschner, Felix Berkenkamp, Andreas Krause
Date: 2018-12-18 00:00:00
Proceeding: ICLR 2019 5
Tasks: Atari Games, Deep Reinforcement Learning, Distributional Reinforcement Learning, Efficient Exploration, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Thompson Sampling

Efficient exploration remains a major challenge for reinforcement learning.
One reason is that the variability of the returns often depends on the current
state and action, and is therefore heteroscedastic. Classical exploration
strategies such as upper confidence bound algorithms and Thompson sampling fail
to appropriately account for heteroscedasticity, even in the bandit setting.
Motivated by recent findings that address this issue in bandits, we propose to
use Information-Directed Sampling (IDS) for exploration in reinforcement
learning. As our main contribution, we build on recent advances in
distributional reinforcement learning and propose a novel, tractable
approximation of IDS for deep Q-learning. The resulting exploration strategy
explicitly accounts for both parametric uncertainty and heteroscedastic
observation noise. We evaluate our method on Atari games and demonstrate a
significant improvement over alternative approaches.
XOR_p A maximally intertwined p-classes problem used as a benchmark with built-in truth for neural networks gradient descent optimization
Authors: Danielle Thierry-Mieg, Jean Thierry-Mieg
Date: 2018-12-18 00:00:00
Tasks: General Classification

A natural p-classes generalization of the eXclusive OR problem, the
subtraction modulo p, where p is prime, is presented and solved using a single
fully connected hidden layer with p-neurons. Although the problem is very
simple, the landscape is intricate and challenging and represents an
interesting benchmark for gradient descent optimization algorithms. Testing 9
optimizers and 9 activation functions up to p = 191, the method converging most
often and the fastest to a perfect classification is the Adam optimizer
combined with the ELU activation function.
Consistent Robust Adversarial Prediction for General Multiclass Classification
Authors: Rizal Fathony, Kaiser Asif, Anqi Liu, Mohammad Ali Bashiri, Wei Xing, Sima Behpour, Xinhua Zhang, Brian D. Ziebart
Date: 2018-12-18 00:00:00
Tasks: Classification, General Classification, Prediction

We propose a robust adversarial prediction framework for general multiclass classification. Our method seeks predictive distributions that robustly optimize non-convex and non-continuous multiclass loss metrics against the worst-case conditional label distributions (the adversarial distributions) that (approximately) match the statistics of the training data. Although the optimized loss metrics are non-convex and non-continuous, the dual formulation of the framework is a convex optimization problem that can be recast as a risk minimization model with a prescribed convex surrogate loss we call the adversarial surrogate loss. We show that the adversarial surrogate losses fill an existing gap in surrogate loss construction for general multiclass classification problems, by simultaneously aligning better with the original multiclass loss, guaranteeing Fisher consistency, enabling a way to incorporate rich feature spaces via the kernel trick, and providing competitive performance in practice.
Learning Direct Optimization for Scene Understanding
Authors: Lukasz Romaszko, Christopher K. I. Williams, John Winn
Date: 2018-12-18 00:00:00
Tasks: Scene Understanding

We develop a Learning Direct Optimization (LiDO) method for the refinement of a latent variable model that describes input image x. Our goal is to explain a single image x with an interpretable 3D computer graphics model having scene graph latent variables z (such as object appearance, camera position). Given a current estimate of z we can render a prediction of the image g(z), which can be compared to the image x. The standard way to proceed is then to measure the error E(x, g(z)) between the two, and use an optimizer to minimize the error. However, it is unknown which error measure E would be most effective for simultaneously addressing issues such as misaligned objects, occlusions, textures, etc. In contrast, the LiDO approach trains a Prediction Network to predict an update directly to correct z, rather than minimizing the error with respect to z. Experiments show that our LiDO method converges rapidly as it does not need to perform a search on the error landscape, produces better solutions than error-based competitors, and is able to handle the mismatch between the data and the fitted scene model. We apply LiDO to a realistic synthetic dataset, and show that the method also transfers to work well with real images.
Entropy-Constrained Training of Deep Neural Networks
Authors: Simon Wiedemann, Arturo Marban, Klaus-Robert Müller, Wojciech Samek
Date: 2018-12-18 00:00:00
Tasks: Neural Network Compression

We propose a general framework for neural network compression that is
motivated by the Minimum Description Length (MDL) principle. For that we first
derive an expression for the entropy of a neural network, which measures its
complexity explicitly in terms of its bit-size. Then, we formalize the problem
of neural network compression as an entropy-constrained optimization objective.
This objective generalizes many of the compression techniques proposed in the
literature, in that pruning or reducing the cardinality of the weight elements
of the network can be seen special cases of entropy-minimization techniques.
Furthermore, we derive a continuous relaxation of the objective, which allows
us to minimize it using gradient based optimization techniques. Finally, we
show that we can reach state-of-the-art compression results on different
network architectures and data sets, e.g. achieving x71 compression gains on a
VGG-like architecture.
NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature
Authors: Franz J. Király, Bilal Mateen, Raphael Sonabend
Date: 2018-12-18 00:00:00

Objective: To determine the completeness of argumentative steps necessary to
conclude effectiveness of an algorithm in a sample of current ML/AI supervised
learning literature.
  Data Sources: Papers published in the Neural Information Processing Systems
(NeurIPS, n\'ee NIPS) journal where the official record showed a 2017 year of
publication.
  Eligibility Criteria: Studies reporting a (semi-)supervised model, or
pre-processing fused with (semi-)supervised models for tabular data.
  Study Appraisal: Three reviewers applied the assessment criteria to determine
argumentative completeness. The criteria were split into three groups,
including: experiments (e.g real and/or synthetic data), baselines (e.g
uninformed and/or state-of-art) and quantitative comparison (e.g. performance
quantifiers with confidence intervals and formal comparison of the algorithm
against baselines).
  Results: Of the 121 eligible manuscripts (from the sample of 679 abstracts),
99\% used real-world data and 29\% used synthetic data. 91\% of manuscripts did
not report an uninformed baseline and 55\% reported a state-of-art baseline.
32\% reported confidence intervals for performance but none provided references
or exposition for how these were calculated. 3\% reported formal comparisons.
  Limitations: The use of one journal as the primary information source may not
be representative of all ML/AI literature. However, the NeurIPS conference is
recognised to be amongst the top tier concerning ML/AI studies, so it is
reasonable to consider its corpus to be representative of high-quality
research.
  Conclusion: Using the 2017 sample of the NeurIPS supervised learning corpus
as an indicator for the quality and trustworthiness of current ML/AI research,
it appears that complete argumentative chains in demonstrations of algorithmic
effectiveness are rare.
Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis
Authors: Brendon Lutnick, Brandon Ginley, Darshana Govind, Sean D. McGarry, Peter S. LaViolette, Rabi Yacoub, Sanjay Jain, John E. Tomaszewski, Kuang-Yu Jen, Pinaki Sarder
Date: 2018-12-18 00:00:00
Tasks: BIG-bench Machine Learning, Medical Image Analysis, Segmentation, Translation

Neural networks promise to bring robust, quantitative analysis to medical
fields, but adoption is limited by the technicalities of training these
networks. To address this translation gap between medical researchers and
neural networks in the field of pathology, we have created an intuitive
interface which utilizes the commonly used whole slide image (WSI) viewer,
Aperio ImageScope (Leica Biosystems Imaging, Inc.), for the annotation and
display of neural network predictions on WSIs. Leveraging this, we propose the
use of a human-in-the-loop strategy to reduce the burden of WSI annotation. We
track network performance improvements as a function of iteration and quantify
the use of this pipeline for the segmentation of renal histologic findings on
WSIs. More specifically, we present network performance when applied to
segmentation of renal micro compartments, and demonstrate multi-class
segmentation in human and mouse renal tissue slides. Finally, to show the
adaptability of this technique to other medical imaging fields, we demonstrate
its ability to iteratively segment human prostate glands from radiology imaging
data.
Solving the Empirical Bayes Normal Means Problem with Correlated Noise
Authors: Lei Sun, Matthew Stephens
Date: 2018-12-18 00:00:00

The Normal Means problem plays a fundamental role in many areas of modern
high-dimensional statistics, both in theory and practice. And the Empirical
Bayes (EB) approach to solving this problem has been shown to be highly
effective, again both in theory and practice. However, almost all EB treatments
of the Normal Means problem assume that the observations are independent. In
practice correlations are ubiquitous in real-world applications, and these
correlations can grossly distort EB estimates. Here, exploiting theory from
Schwartzman (2010), we develop new EB methods for solving the Normal Means
problem that take account of unknown correlations among observations. We
provide practical software implementations of these methods, and illustrate
them in the context of large-scale multiple testing problems and False
Discovery Rate (FDR) control. In realistic numerical experiments our methods
compare favorably with other commonly-used multiple testing methods.
Efficient Autotuning of Hyperparameters in Approximate Nearest Neighbor Search
Authors: Elias Jääsaari, Ville Hyvönen, Teemu Roos
Date: 2018-12-18 00:00:00

Approximate nearest neighbor algorithms are used to speed up nearest neighbor
search in a wide array of applications. However, current indexing methods
feature several hyperparameters that need to be tuned to reach an acceptable
accuracy--speed trade-off. A grid search in the parameter space is often
impractically slow due to a time-consuming index-building procedure. Therefore,
we propose an algorithm for automatically tuning the hyperparameters of
indexing methods based on randomized space-partitioning trees. In particular,
we present results using randomized k-d trees, random projection trees and
randomized PCA trees. The tuning algorithm adds minimal overhead to the
index-building process but is able to find the optimal hyperparameters
accurately. We demonstrate that the algorithm is significantly faster than
existing approaches, and that the indexing methods used are competitive with
the state-of-the-art methods in query time while being faster to build.
A Factorial Mixture Prior for Compositional Deep Generative Models
Authors: Ulrich Paquet, Sumedh K. Ghaisas, Olivier Tieleman
Date: 2018-12-18 00:00:00
Tasks: Variational Inference

We assume that a high-dimensional datum, like an image, is a compositional
expression of a set of properties, with a complicated non-linear relationship
between the datum and its properties. This paper proposes a factorial mixture
prior for capturing latent properties, thereby adding structured
compositionality to deep generative models. The prior treats a latent vector as
belonging to Cartesian product of subspaces, each of which is quantized
separately with a Gaussian mixture model. Some mixture components can be set to
represent properties as observed random variables whenever labeled properties
are present. Through a combination of stochastic variational inference and
gradient descent, a method for learning how to infer discrete properties in an
unsupervised or semi-supervised way is outlined and empirically evaluated.
Multi-objective Evolutionary Federated Learning
Authors: Hangyu Zhu, Yaochu Jin
Date: 2018-12-18 00:00:00
Tasks: Federated Learning

Federated learning is an emerging technique used to prevent the leakage of private information. Unlike centralized learning that needs to collect data from users and store them collectively on a cloud server, federated learning makes it possible to learn a global model while the data are distributed on the users' devices. However, compared with the traditional centralized approach, the federated setting consumes considerable communication resources of the clients, which is indispensable for updating global models and prevents this technique from being widely used. In this paper, we aim to optimize the structure of the neural network models in federated learning using a multi-objective evolutionary algorithm to simultaneously minimize the communication costs and the global model test errors. A scalable method for encoding network connectivity is adapted to federated learning to enhance the efficiency in evolving deep neural networks. Experimental results on both multilayer perceptrons and convolutional neural networks indicate that the proposed optimization method is able to find optimized neural network models that can not only significantly reduce communication costs but also improve the learning performance of federated learning compared with the standard fully connected neural networks.
Learning a Probabilistic Model for Diffeomorphic Registration
Authors: Julian Krebs, Hervé Delingette, Boris Mailhé, Nicholas Ayache, Tommaso Mansi
Date: 2018-12-18 00:00:00
Tasks: Deformable Medical Image Registration, Diffeomorphic Medical Image Registration, Medical Image Registration, model, Variational Inference

We propose to learn a low-dimensional probabilistic deformation model from
data which can be used for registration and the analysis of deformations. The
latent variable model maps similar deformations close to each other in an
encoding space. It enables to compare deformations, generate normal or
pathological deformations for any new image or to transport deformations from
one image pair to any other image. Our unsupervised method is based on
variational inference. In particular, we use a conditional variational
autoencoder (CVAE) network and constrain transformations to be symmetric and
diffeomorphic by applying a differentiable exponentiation layer with a
symmetric loss function. We also present a formulation that includes spatial
regularization such as diffusion-based filters. Additionally, our framework
provides multi-scale velocity field estimations. We evaluated our method on 3-D
intra-subject registration using 334 cardiac cine-MRIs. On this dataset, our
method showed state-of-the-art performance with a mean DICE score of 81.2% and
a mean Hausdorff distance of 7.3mm using 32 latent dimensions compared to three
state-of-the-art methods while also demonstrating more regular deformation
fields. The average time per registration was 0.32s. Besides, we visualized the
learned latent space and show that the encoded deformations can be used to
transport deformations and to cluster diseases with a classification accuracy
of 83% after applying a linear projection.
Domain Adaptation for Reinforcement Learning on the Atari
Authors: Thomas Carr, Maria Chli, George Vogiatzis
Date: 2018-12-18 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, Domain Adaptation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Deep reinforcement learning agents have recently been successful across a
variety of discrete and continuous control tasks; however, they can be slow to
train and require a large number of interactions with the environment to learn
a suitable policy. This is borne out by the fact that a reinforcement learning
agent has no prior knowledge of the world, no pre-existing data to depend on
and so must devote considerable time to exploration. Transfer learning can
alleviate some of the problems by leveraging learning done on some source task
to help learning on some target task. Our work presents an algorithm for
initialising the hidden feature representation of the target task. We propose a
domain adaptation method to transfer state representations and demonstrate
transfer across domains, tasks and action spaces. We utilise adversarial domain
adaptation ideas combined with an adversarial autoencoder architecture. We
align our new policies' representation space with a pre-trained source policy,
taking target task data generated from a random policy. We demonstrate that
this initialisation step provides significant improvement when learning a new
reinforcement learning task, which highlights the wide applicability of
adversarial adaptation methods; even as the task and label/action space also
changes.
FDSNet: Finger dorsal image spoof detection network using light field camera
Authors: Avantika Singh, Gaurav Jaswal, Aditya Nigam
Date: 2018-12-18 00:00:00
Tasks: Finger Dorsal Image Spoof Detection, Transfer Learning

At present spoofing attacks via which biometric system is potentially
vulnerable against a fake biometric characteristic, introduces a great
challenge to recognition performance. Despite the availability of a broad range
of presentation attack detection (PAD) or liveness detection algorithms,
fingerprint sensors are vulnerable to spoofing via fake fingers. In such
situations, finger dorsal images can be thought of as an alternative which can
be captured without much user cooperation and are more appropriate for outdoor
security applications. In this paper, we present a first feasibility study of
spoofing attack scenarios on finger dorsal authentication system, which include
four types of presentation attacks such as printed paper, wrapped printed
paper, scan and mobile. This study also presents a CNN based spoofing attack
detection method which employ state-of-the-art deep learning techniques along
with transfer learning mechanism. We have collected 196 finger dorsal real
images from 33 subjects, captured with a Lytro camera and also created a set of
784 finger dorsal spoofing images. Extensive experimental results have been
performed that demonstrates the superiority of the proposed approach for
various spoofing attacks.
A Scalable Heuristic for Fastest-Path Computation on Very Large Road Maps
Authors: Renjie Chen, Craig Gotsman
Date: 2018-12-18 00:00:00

Fastest-path queries between two points in a very large road map is an increasingly important primitive in modern transportation and navigation systems, thus very efficient computation of these paths is critical for system performance and throughput. We present a method to compute an effective heuristic for the fastest path travel time between two points on a road map, which can be used to significantly accelerate the classical A* algorithm when computing fastest paths. Our method is based on two hierarchical sets of separators of the map represented by two binary trees. A preprocessing step computes a short vector of values per road junction based on the separator trees, which is then stored with the map and used to efficiently compute the heuristic at the online query stage. We demonstrate experimentally that this method scales well to any map size, providing a better quality heuristic, thus more efficient A* search, for fastest path queries between points at all distances - especially small and medium range - relative to other known heuristics.