pwc-archive/papers-with-abstracts huggingface dataset:
Secure SURF with Fully Homomorphic Encryption
Authors: Thomas Shortell, Ali Shokoufandeh
Date: 2017-07-19 00:00:00
Tasks: Cloud Computing

Cloud computing is an important part of today's world because offloading
computations is a method to reduce costs. In this paper, we investigate
computing the Speeded Up Robust Features (SURF) using Fully Homomorphic
Encryption (FHE). Performing SURF in FHE enables a method to offload the
computations while maintaining security and privacy of the original data. In
support of this research, we developed a framework to compute SURF via a
rational number based compatible with FHE. Although floating point (R) to
rational numbers (Q) conversion introduces error, our research provides tight
bounds on the magnitude of error in terms of parameters of FHE. We empirically
verified the proposed method against a set of images at different sizes and
showed that our framework accurately computes most of the SURF keypoints in
FHE.
Hybrid Conditional Planning using Answer Set Programming
Authors: Ibrahim Faruk Yalciner, Ahmed Nouman, Volkan Patoglu, Esra Erdem
Date: 2017-07-19 00:00:00

We introduce a parallel offline algorithm for computing hybrid conditional
plans, called HCP-ASP, oriented towards robotics applications. HCP-ASP relies
on modeling actuation actions and sensing actions in an expressive nonmonotonic
language of answer set programming (ASP), and computation of the branches of a
conditional plan in parallel using an ASP solver. In particular, thanks to
external atoms, continuous feasibility checks (like collision checks) are
embedded into formal representations of actuation actions and sensing actions
in ASP; and thus each branch of a hybrid conditional plan describes a feasible
execution of actions to reach their goals. Utilizing nonmonotonic constructs
and nondeterministic choices, partial knowledge about states and
nondeterministic effects of sensing actions can be explicitly formalized in
ASP; and thus each branch of a conditional plan can be computed by an ASP
solver without necessitating a conformant planner and an ordering of sensing
actions in advance. We apply our method in a service robotics domain and report
experimental evaluations. Furthermore, we present performance comparisons with
other compilation based conditional planners on standardized benchmark domains.
This paper is under consideration for acceptance in TPLP.
On-line Building Energy Optimization using Deep Reinforcement Learning
Authors: Elena Mocanu, Decebal Constantin Mocanu, Phuong H. Nguyen, Antonio Liotta, Michael E. Webber, Madeleine Gibescu, J. G. Slootweg
Date: 2017-07-18 00:00:00
Tasks: Deep Reinforcement Learning, energy management, Management, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

Unprecedented high volumes of data are becoming available with the growth of
the advanced metering infrastructure. These are expected to benefit planning
and operation of the future power system, and to help the customers transition
from a passive to an active role. In this paper, we explore for the first time
in the smart grid context the benefits of using Deep Reinforcement Learning, a
hybrid type of methods that combines Reinforcement Learning with Deep Learning,
to perform on-line optimization of schedules for building energy management
systems. The learning procedure was explored using two methods, Deep Q-learning
and Deep Policy Gradient, both of them being extended to perform multiple
actions simultaneously. The proposed approach was validated on the large-scale
Pecan Street Inc. database. This highly-dimensional database includes
information about photovoltaic power generation, electric vehicles as well as
buildings appliances. Moreover, these on-line energy scheduling strategies
could be used to provide real-time feedback to consumers to encourage more
efficient use of electricity.
On Adaptive Propensity Score Truncation in Causal Inference
Authors: Cheng Ju, Joshua Schwab, Mark J. Van Der Laan
Date: 2017-07-18 00:00:00
Tasks: Causal Inference

The positivity assumption, or the experimental treatment assignment (ETA)
assumption, is important for identifiability in causal inference. Even if the
positivity assumption holds, practical violations of this assumption may
jeopardize the finite sample performance of the causal estimator. One of the
consequences of practical violations of the positivity assumption is extreme
values in the estimated propensity score (PS). A common practice to address
this issue is truncating the PS estimate when constructing PS-based estimators.
In this study, we propose a novel adaptive truncation method,
Positivity-C-TMLE, based on the collaborative targeted maximum likelihood
estimation (C-TMLE) methodology. We demonstrate the outstanding performance of
our novel approach in a variety of simulations by comparing it with other
commonly studied estimators. Results show that by adaptively truncating the
estimated PS with a more targeted objective function, the Positivity-C-TMLE
estimator achieves the best performance for both point estimation and
confidence interval coverage among all estimators considered.
Logic Programming approaches for routing fault-free and maximally-parallel Wavelength Routed Optical Networks on Chip (Application paper)
Authors: Marco Gavanelli, Maddalena Nonato, Andrea Peano, Davide Bertozzi
Date: 2017-07-18 00:00:00

One promising trend in digital system integration consists of boosting
on-chip communication performance by means of silicon photonics, thus
materializing the so-called Optical Networks-on-Chip (ONoCs). Among them,
wavelength routing can be used to route a signal to destination by univocally
associating a routing path to the wavelength of the optical carrier. Such
wavelengths should be chosen so to minimize interferences among optical
channels and to avoid routing faults. As a result, physical parameter selection
of such networks requires the solution of complex constrained optimization
problems. In previous work, published in the proceedings of the International
Conference on Computer-Aided Design, we proposed and solved the problem of
computing the maximum parallelism obtainable in the communication between any
two endpoints while avoiding misrouting of optical signals. The underlying
technology, only quickly mentioned in that paper, is Answer Set Programming
(ASP). In this work, we detail the ASP approach we used to solve such problem.
  Another important design issue is to select the wavelengths of optical
carriers such that they are spread across the available spectrum, in order to
reduce the likelihood that, due to imperfections in the manufacturing process,
unintended routing faults arise. We show how to address such problem in
Constraint Logic Programming on Finite Domains (CLP(FD)).
  This paper is under consideration for possible publication on Theory and
Practice of Logic Programming.
Linear Time Complexity Deep Fourier Scattering Network and Extension to Nonlinear Invariants
Authors: Randall Balestriero, Herve Glotin
Date: 2017-07-18 00:00:00

In this paper we propose a scalable version of a state-of-the-art
deterministic time-invariant feature extraction approach based on consecutive
changes of basis and nonlinearities, namely, the scattering network. The first
focus of the paper is to extend the scattering network to allow the use of
higher order nonlinearities as well as extracting nonlinear and Fourier based
statistics leading to the required invariants of any inherently structured
input. In order to reach fast convolutions and to leverage the intrinsic
structure of wavelets, we derive our complete model in the Fourier domain. In
addition of providing fast computations, we are now able to exploit sparse
matrices due to extremely high sparsity well localized in the Fourier domain.
As a result, we are able to reach a true linear time complexity with inputs in
the Fourier domain allowing fast and energy efficient solutions to machine
learning tasks. Validation of the features and computational results will be
presented through the use of these invariant coefficients to perform
classification on audio recordings of bird songs captured in multiple different
soundscapes. In the end, the applicability of the presented solutions to deep
artificial neural networks is discussed.
Multiscale Residual Mixture of PCA: Dynamic Dictionaries for Optimal Basis Learning
Authors: Randall Balestriero
Date: 2017-07-18 00:00:00

In this paper we are interested in the problem of learning an over-complete
basis and a methodology such that the reconstruction or inverse problem does
not need optimization. We analyze the optimality of the presented approaches,
their link to popular already known techniques s.a. Artificial Neural
Networks,k-means or Oja's learning rule. Finally, we will see that one approach
to reach the optimal dictionary is a factorial and hierarchical approach. The
derived approach lead to a formulation of a Deep Oja Network. We present
results on different tasks and present the resulting very efficient learning
algorithm which brings a new vision on the training of deep nets. Finally, the
theoretical work shows that deep frameworks are one way to efficiently have
over-complete (combinatorially large) dictionary yet allowing easy
reconstruction. We thus present the Deep Residual Oja Network (DRON). We
demonstrate that a recursive deep approach working on the residuals allow
exponential decrease of the error w.r.t. the depth.
A deep learning approach to diabetic blood glucose prediction
Authors: H. N. Mhaskar, S. V. Pereverzyev, M. D. van der Walt
Date: 2017-07-18 00:00:00
Tasks: Deep Learning

We consider the question of 30-minute prediction of blood glucose levels
measured by continuous glucose monitoring devices, using clinical data. While
most studies of this nature deal with one patient at a time, we take a certain
percentage of patients in the data set as training data, and test on the
remainder of the patients; i.e., the machine need not re-calibrate on the new
patients in the data set. We demonstrate how deep learning can outperform
shallow networks in this example. One novelty is to demonstrate how a
parsimonious deep representation can be constructed using domain knowledge.
Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation
Authors: Arslan Chaudhry, Puneet K. Dokania, Philip H. S. Torr
Date: 2017-07-18 00:00:00
Tasks: Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

We propose an approach to discover class-specific pixels for the
weakly-supervised semantic segmentation task. We show that properly combining
saliency and attention maps allows us to obtain reliable cues capable of
significantly boosting the performance. First, we propose a simple yet powerful
hierarchical approach to discover the class-agnostic salient regions, obtained
using a salient object detector, which otherwise would be ignored. Second, we
use fully convolutional attention maps to reliably localize the class-specific
regions in a given image. We combine these two cues to discover class-specific
pixels which are then used as an approximate ground truth for training a CNN.
While solving the weakly supervised semantic segmentation task, we ensure that
the image-level classification task is also solved in order to enforce the CNN
to assign at least one pixel to each object present in the image.
Experimentally, on the PASCAL VOC12 val and test sets, we obtain the mIoU of
60.8% and 61.9%, achieving the performance gains of 5.1% and 5.2% compared to
the published state-of-the-art results. The code is made publicly available.
Large-scale Multiview 3D Hand Pose Dataset
Authors: Francisco Gomez-Donoso, Sergio Orts-Escolano, Miguel Cazorla
Date: 2017-07-12 00:00:00
Tasks: Deep Learning, Hand Pose Estimation, Pose Estimation

Accurate hand pose estimation at joint level has several uses on human-robot
interaction, user interfacing and virtual reality applications. Yet, it
currently is not a solved problem. The novel deep learning techniques could
make a great improvement on this matter but they need a huge amount of
annotated data. The hand pose datasets released so far present some issues that
make them impossible to use on deep learning methods such as the few number of
samples, high-level abstraction annotations or samples consisting in depth
maps. In this work, we introduce a multiview hand pose dataset in which we
provide color images of hands and different kind of annotations for each, i.e
the bounding box and the 2D and 3D location on the joints in the hand. Besides,
we introduce a simple yet accurate deep learning architecture for real-time
robust 2D hand pose estimation.
Crossing Nets: Combining GANs and VAEs with a Shared Latent Space for Hand Pose Estimation
Authors: Chengde Wan, Thomas Probst, Luc van Gool, Angela Yao
Date: 2017-02-11 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Hand Pose Estimation, CPU, Hand Pose Estimation, Pose Estimation

State-of-the-art methods for 3D hand pose estimation from depth images
require large amounts of annotated training data. We propose to model the
statistical relationships of 3D hand poses and corresponding depth images using
two deep generative models with a shared latent space. By design, our
architecture allows for learning from unlabeled image data in a semi-supervised
manner. Assuming a one-to-one mapping between a pose and a depth map, any given
point in the shared latent space can be projected into both a hand pose and a
corresponding depth map. Regressing the hand pose can then be done by learning
a discriminator to estimate the posterior of the latent pose given some depth
maps. To improve generalization and to better exploit unlabeled depth maps, we
jointly train a generator and a discriminator. At each iteration, the generator
is updated with the back-propagated gradient from the discriminator to
synthesize realistic depth maps of the articulated hand, while the
discriminator benefits from an augmented training set of synthesized and
unlabeled samples. The proposed discriminator network architecture is highly
efficient and runs at 90 FPS on the CPU with accuracies comparable or better
than state-of-art on 3 publicly available benchmarks.
A Novel Deep Learning Architecture for Testis Histology Image Classification
Authors: Chia-Yu Kao, Leonard McMillan
Date: 2017-07-18 00:00:00
Tasks: Classification, Deep Learning, General Classification, image-classification, Image Classification, Image Reconstruction

Unlike other histology analysis, classification of tubule status in testis
histology is very challenging due to their high similarity of texture and
shape. Traditional deep learning networks have difficulties to capture nuance
details among different tubule categories. In this paper, we propose a novel
deep learning architecture for feature learning, image classification, and
image reconstruction. It is based on stacked auto-encoders with an additional
layer, called a hyperlayer, which is created to capture features of an image at
different layers in the network. This addition effectively combines features at
different scales and thus provides a more complete profile for further
classification. Evaluation is performed on a set of 10,542 tubule image
patches. We demonstrate our approach with two experiments on two different
subsets of the dataset. The results show that the features learned from our
architecture achieve more than 98% accuracy and represent an improvement over
traditional deep network architectures.
Improving Gibbs Sampler Scan Quality with DoGS
Authors: Ioannis Mitliagkas, Lester Mackey
Date: 2017-07-18 00:00:00
Proceeding: ICML 2017 8
Tasks: Image Segmentation, Object Recognition, Semantic Segmentation, Variable Selection

The pairwise influence matrix of Dobrushin has long been used as an
analytical tool to bound the rate of convergence of Gibbs sampling. In this
work, we use Dobrushin influence as the basis of a practical tool to certify
and efficiently improve the quality of a discrete Gibbs sampler. Our
Dobrushin-optimized Gibbs samplers (DoGS) offer customized variable selection
orders for a given sampling budget and variable subset of interest, explicit
bounds on total variation distance to stationarity, and certifiable
improvements over the standard systematic and uniform random scan Gibbs
samplers. In our experiments with joint image segmentation and object
recognition, Markov chain Monte Carlo maximum likelihood estimation, and Ising
model inference, DoGS consistently deliver higher-quality inferences with
significantly smaller sampling budgets than standard Gibbs samplers.
Optimizing the Latent Space of Generative Networks
Authors: Piotr Bojanowski, Armand Joulin, David Lopez-Paz, Arthur Szlam
Date: 2017-07-18 00:00:00
Proceeding: ICML 2018 7

Generative Adversarial Networks (GANs) have achieved remarkable results in the task of generating realistic natural images. In most successful applications, GAN models share two common aspects: solving a challenging saddle point optimization problem, interpreted as an adversarial game between a generator and a discriminator functions; and parameterizing the generator and the discriminator as deep convolutional neural networks. The goal of this paper is to disentangle the contribution of these two factors to the success of GANs. In particular, we introduce Generative Latent Optimization (GLO), a framework to train deep convolutional generators using simple reconstruction losses. Throughout a variety of experiments, we show that GLO enjoys many of the desirable properties of GANs: synthesizing visually-appealing samples, interpolating meaningfully between samples, and performing linear arithmetic with noise vectors; all of this without the adversarial optimization scheme.
Estimation of Large Covariance and Precision Matrices from Temporally Dependent Observations
Authors: Hai Shu, Bin Nan
Date: 2014-12-16 00:00:00
Tasks: Functional Connectivity, Time Series, Time Series Analysis

We consider the estimation of large covariance and precision matrices from
high-dimensional sub-Gaussian or heavier-tailed observations with slowly
decaying temporal dependence. The temporal dependence is allowed to be
long-range so with longer memory than those considered in the current
literature. We show that several commonly used methods for independent
observations can be applied to the temporally dependent data. In particular,
the rates of convergence are obtained for the generalized thresholding
estimation of covariance and correlation matrices, and for the constrained
$\ell_1$ minimization and the $\ell_1$ penalized likelihood estimation of
precision matrix. Properties of sparsistency and sign-consistency are also
established. A gap-block cross-validation method is proposed for the tuning
parameter selection, which performs well in simulations. As a motivating
example, we study the brain functional connectivity using resting-state fMRI
time series data with long-range temporal dependence.
Transitioning between Convolutional and Fully Connected Layers in Neural Networks
Authors: Shazia Akbar, Mohammad Peikari, Sherine Salama, Sharon Nofech-Mozes, Anne Martel
Date: 2017-07-18 00:00:00
Tasks: General Classification

Digital pathology has advanced substantially over the last decade however
tumor localization continues to be a challenging problem due to highly complex
patterns and textures in the underlying tissue bed. The use of convolutional
neural networks (CNNs) to analyze such complex images has been well adopted in
digital pathology. However in recent years, the architecture of CNNs have
altered with the introduction of inception modules which have shown great
promise for classification tasks. In this paper, we propose a modified
"transition" module which learns global average pooling layers from filters of
varying sizes to encourage class-specific filters at multiple spatial
resolutions. We demonstrate the performance of the transition module in AlexNet
and ZFNet, for classifying breast tumors in two independent datasets of scanned
histology sections, of which the transition module was superior.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Authors: Chelsea Finn, Pieter Abbeel, Sergey Levine
Date: 2017-03-09 00:00:00
Proceeding: ICML 2017 8
Tasks: Category-Agnostic Pose Estimation, Few-Shot Image Classification, Few-Shot Learning, General Classification, image-classification, Image Classification, Meta-Learning, One-Shot Learning, regression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies.
Choosing Smartly: Adaptive Multimodal Fusion for Object Detection in Changing Environments
Authors: Oier Mees, Andreas Eitel, Wolfram Burgard
Date: 2017-07-18 00:00:00
Tasks: object-detection, Object Detection

Object detection is an essential task for autonomous robots operating in dynamic and changing environments. A robot should be able to detect objects in the presence of sensor noise that can be induced by changing lighting conditions for cameras and false depth readings for range sensors, especially RGB-D cameras. To tackle these challenges, we propose a novel adaptive fusion approach for object detection that learns weighting the predictions of different sensor modalities in an online manner. Our approach is based on a mixture of convolutional neural network (CNN) experts and incorporates multiple modalities including appearance, depth and motion. We test our method in extensive robot experiments, in which we detect people in a combined indoor and outdoor scenario from RGB-D data, and we demonstrate that our method can adapt to harsh lighting changes and severe camera motion blur. Furthermore, we present a new RGB-D dataset for people detection in mixed in- and outdoor environments, recorded with a mobile robot. Code, pretrained models and dataset are available at http://adaptivefusion.cs.uni-freiburg.de
Robust Bayesian Optimization with Student-t Likelihood
Authors: Ruben Martinez-Cantin, Michael McCourt, Kevin Tee
Date: 2017-07-18 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes

Bayesian optimization has recently attracted the attention of the automatic
machine learning community for its excellent results in hyperparameter tuning.
BO is characterized by the sample efficiency with which it can optimize
expensive black-box functions. The efficiency is achieved in a similar fashion
to the learning to learn methods: surrogate models (typically in the form of
Gaussian processes) learn the target function and perform intelligent sampling.
This surrogate model can be applied even in the presence of noise; however, as
with most regression methods, it is very sensitive to outlier data. This can
result in erroneous predictions and, in the case of BO, biased and inefficient
exploration. In this work, we present a GP model that is robust to outliers
which uses a Student-t likelihood to segregate outliers and robustly conduct
Bayesian optimization. We present numerical results evaluating the proposed
method in both artificial functions and real problems.