pwc-archive/papers-with-abstracts huggingface dataset:
An Explicit Rate Bound for the Over-Relaxed ADMM
Authors: Guilherme França, José Bento
Date: 2015-12-07 00:00:00

The framework of Integral Quadratic Constraints of Lessard et al. (2014)
reduces the computation of upper bounds on the convergence rate of several
optimization algorithms to semi-definite programming (SDP). Followup work by
Nishihara et al. (2015) applies this technique to the entire family of
over-relaxed Alternating Direction Method of Multipliers (ADMM). Unfortunately,
they only provide an explicit error bound for sufficiently large values of some
of the parameters of the problem, leaving the computation for the general case
as a numerical optimization problem. In this paper we provide an exact
analytical solution to this SDP and obtain a general and explicit upper bound
on the convergence rate of the entire family of over-relaxed ADMM. Furthermore,
we demonstrate that it is not possible to extract from this SDP a general bound
better than ours. We end with a few numerical illustrations of our result and a
comparison between the convergence rate we obtain for the ADMM with known
convergence rates for the Gradient Descent.
Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments
Authors: Dan Barnes, Will Maddern, Geoffrey Pascoe, Ingmar Posner
Date: 2017-11-17 00:00:00
Tasks: Monocular Visual Odometry, Visual Odometry

We present a self-supervised approach to ignoring "distractors" in camera
images for the purposes of robustly estimating vehicle motion in cluttered
urban environments. We leverage offline multi-session mapping approaches to
automatically generate a per-pixel ephemerality mask and depth map for each
input image, which we use to train a deep convolutional network. At run-time we
use the predicted ephemerality and depth as an input to a monocular visual
odometry (VO) pipeline, using either sparse features or dense photometric
matching. Our approach yields metric-scale VO using only a single camera and
can recover the correct egomotion even when 90% of the image is obscured by
dynamic, independently moving objects. We evaluate our robust VO methods on
more than 400km of driving from the Oxford RobotCar Dataset and demonstrate
reduced odometry drift and significantly improved egomotion estimation in the
presence of large moving vehicles in urban traffic.
Towards Clinical Diagnosis: Automated Stroke Lesion Segmentation on Multimodal MR Image Using Convolutional Neural Network
Authors: Zhiyang Liu, Chen Cao, Shuxue Ding, Tong Han, Hong Wu, Sheng Liu
Date: 2018-03-05 00:00:00
Tasks: Diagnostic, Lesion Segmentation

The patient with ischemic stroke can benefit most from the earliest possible
definitive diagnosis. While the high quality medical resources are quite scarce
across the globe, an automated diagnostic tool is expected in analyzing the
magnetic resonance (MR) images to provide reference in clinical diagnosis. In
this paper, we propose a deep learning method to automatically segment ischemic
stroke lesions from multi-modal MR images. By using atrous convolution and
global convolution network, our proposed residual-structured fully
convolutional network (Res-FCN) is able to capture features from large
receptive fields. The network architecture is validated on a large dataset of
212 clinically acquired multi-modal MR images, which is shown to achieve a mean
dice coefficient of 0.645 with a mean number of false negative lesions of
1.515. The false negatives can reach a value that close to a common medical
image doctor, making it exceptive for a real clinical application.
On the regularization of Wasserstein GANs
Authors: Henning Petzka, Asja Fischer, Denis Lukovnicov
Date: 2017-09-26 00:00:00
Proceeding: ICLR 2018 1

Since their invention, generative adversarial networks (GANs) have become a
popular approach for learning to model a distribution of real (unlabeled) data.
Convergence problems during training are overcome by Wasserstein GANs which
minimize the distance between the model and the empirical distribution in terms
of a different metric, but thereby introduce a Lipschitz constraint into the
optimization problem. A simple way to enforce the Lipschitz constraint on the
class of functions, which can be modeled by the neural network, is weight
clipping. It was proposed that training can be improved by instead augmenting
the loss by a regularization term that penalizes the deviation of the gradient
of the critic (as a function of the network's input) from one. We present
theoretical arguments why using a weaker regularization term enforcing the
Lipschitz constraint is preferable. These arguments are supported by
experimental results on toy data sets.
A Genetic Programming Framework for 2D Platform AI
Authors: Swen E. Gaudl
Date: 2018-03-05 00:00:00

There currently exists a wide range of techniques to model and evolve
artificial players for games. Existing techniques range from black box neural
networks to entirely hand-designed solutions. In this paper, we demonstrate the
feasibility of a genetic programming framework using human controller input to
derive meaningful artificial players which can, later on, be optimised by hand.
The current state of the art in game character design relies heavily on human
designers to manually create and edit scripts and rules for game characters. To
address this manual editing bottleneck, current computational intelligence
techniques approach the issue with fully autonomous character generators,
replacing most of the design process using black box solutions such as neural
networks or the like. Our GP approach to this problem creates character
controllers which can be further authored and developed by a designer it also
offers designers to included their play style without the need to use a
programming language. This keeps the designer in the loop while reducing
repetitive manual labour. Our system also provides insights into how players
express themselves in games and into deriving appropriate models for
representing those insights. We present our framework, supporting findings and
open challenges.
Towards Automatic 3D Shape Instantiation for Deployed Stent Grafts: 2D Multiple-class and Class-imbalance Marker Segmentation with Equally-weighted Focal U-Net
Authors: Xiao-Yun Zhou Celia Riga, Su-Lin Lee, Guang-Zhong Yang
Date: 2017-11-04 00:00:00
Tasks: Segmentation

Robot-assisted Fenestrated Endovascular Aortic Repair (FEVAR) is currently
navigated by 2D fluoroscopy which is insufficiently informative. Previously, a
semi-automatic 3D shape instantiation method was developed to instantiate the
3D shape of a main, deployed, and fenestrated stent graft from a single
fluoroscopy projection in real-time, which could help 3D FEVAR navigation and
robotic path planning. This proposed semi-automatic method was based on the
Robust Perspective-5-Point (RP5P) method, graft gap interpolation and
semi-automatic multiple-class marker center determination. In this paper, an
automatic 3D shape instantiation could be achieved by automatic multiple-class
marker segmentation and hence automatic multiple-class marker center
determination. Firstly, the markers were designed into five different shapes.
Then, Equally-weighted Focal U-Net was proposed to segment the fluoroscopy
projections of customized markers into five classes and hence to determine the
marker centers. The proposed Equally-weighted Focal U-Net utilized U-Net as the
network architecture, equally-weighted loss function for initial marker
segmentation, and then equally-weighted focal loss function for improving the
initial marker segmentation. This proposed network outperformed traditional
Weighted U-Net on the class-imbalance segmentation in this paper with reducing
one hyper-parameter - the weight. An overall mean Intersection over Union
(mIoU) of 0.6943 was achieved on 78 testing images, where 81.01% markers were
segmented with a center position error <1.6mm. Comparable accuracy of 3D shape
instantiation was also achieved and stated. The data, trained models and
TensorFlow codes are available on-line.
Learnable pooling with Context Gating for video classification
Authors: Antoine Miech, Ivan Laptev, Josef Sivic
Date: 2017-06-21 00:00:00
Tasks: Classification, Clustering, General Classification, Video Classification, Video Understanding

Current methods for video analysis often extract frame-level features using
pre-trained convolutional neural networks (CNNs). Such features are then
aggregated over time e.g., by simple temporal averaging or more sophisticated
recurrent neural networks such as long short-term memory (LSTM) or gated
recurrent units (GRU). In this work we revise existing video representations
and study alternative methods for temporal aggregation. We first explore
clustering-based aggregation layers and propose a two-stream architecture
aggregating audio and visual features. We then introduce a learnable non-linear
unit, named Context Gating, aiming to model interdependencies among network
activations. Our experimental results show the advantage of both improvements
for the task of video classification. In particular, we evaluate our method on
the large-scale multi-modal Youtube-8M v2 dataset and outperform all other
methods in the Youtube 8M Large-Scale Video Understanding challenge.
Variance-Aware Regret Bounds for Undiscounted Reinforcement Learning in MDPs
Authors: Mohammad Sadegh Talebi, Odalric-Ambrym Maillard
Date: 2018-03-05 00:00:00
Tasks: LEMMA, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The problem of reinforcement learning in an unknown and discrete Markov
Decision Process (MDP) under the average-reward criterion is considered, when
the learner interacts with the system in a single stream of observations,
starting from an initial state without any reset. We revisit the minimax lower
bound for that problem by making appear the local variance of the bias function
in place of the diameter of the MDP. Furthermore, we provide a novel analysis
of the KL-UCRL algorithm establishing a high-probability regret bound scaling
as $\widetilde {\mathcal O}\Bigl({\textstyle \sqrt{S\sum_{s,a}{\bf
V}^\star_{s,a}T}}\Big)$ for this algorithm for ergodic MDPs, where $S$ denotes
the number of states and where ${\bf V}^\star_{s,a}$ is the variance of the
bias function with respect to the next-state distribution following action $a$
in state $s$. The resulting bound improves upon the best previously known
regret bound $\widetilde {\mathcal O}(DS\sqrt{AT})$ for that algorithm, where
$A$ and $D$ respectively denote the maximum number of actions (per state) and
the diameter of MDP. We finally compare the leading terms of the two bounds in
some benchmark MDPs indicating that the derived bound can provide an order of
magnitude improvement in some cases. Our analysis leverages novel variations of
the transportation lemma combined with Kullback-Leibler concentration
inequalities, that we believe to be of independent interest.
Tensorial and bipartite block models for link prediction in layered networks and temporal networks
Authors: Marc Tarres-Deulofeu, Antonia Godoy-Lorite, Roger Guimera, Marta Sales-Pardo
Date: 2018-03-05 00:00:00
Tasks: Link Prediction

Many real-world complex systems are well represented as multilayer networks;
predicting interactions in those systems is one of the most pressing problems
in predictive network science. To address this challenge, we introduce two
stochastic block models for multilayer and temporal networks; one of them uses
nodes as its fundamental unit, whereas the other focuses on links. We also
develop scalable algorithms for inferring the parameters of these models.
Because our models describe all layers simultaneously, our approach takes full
advantage of the information contained in the whole network when making
predictions about any particular layer. We illustrate the potential of our
approach by analyzing two empirical datasets---a temporal network of email
communications, and a network of drug interactions for treating different
cancer types. We find that modeling all layers simultaneously does result, in
general, in more accurate link prediction. However, the most predictive model
depends on the dataset under consideration; whereas the node-based model is
more appropriate for predicting drug interactions, the link-based model is more
appropriate for predicting email communication.
I Know What You See: Power Side-Channel Attack on Convolutional Neural Network Accelerators
Authors: Lingxiao Wei, Bo Luo, Yu Li, Yannan Liu, Qiang Xu
Date: 2018-03-05 00:00:00
Tasks: Deep Learning, Medical Image Analysis

Deep learning has become the de-facto computational paradigm for various kinds of perception problems, including many privacy-sensitive applications such as online medical image analysis. No doubt to say, the data privacy of these deep learning systems is a serious concern. Different from previous research focusing on exploiting privacy leakage from deep learning models, in this paper, we present the first attack on the implementation of deep learning models. To be specific, we perform the attack on an FPGA-based convolutional neural network accelerator and we manage to recover the input image from the collected power traces without knowing the detailed parameters in the neural network. For the MNIST dataset, our power side-channel attack is able to achieve up to 89% recognition accuracy.
Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle
Authors: Marco Scutari
Date: 2017-08-02 00:00:00
Tasks: Bayesian Inference, Model Selection

A classic approach for learning Bayesian networks from data is to identify a
maximum a posteriori (MAP) network structure. In the case of discrete Bayesian
networks, MAP networks are selected by maximising one of several possible
Bayesian Dirichlet (BD) scores; the most famous is the Bayesian Dirichlet
equivalent uniform (BDeu) score from Heckerman et al (1995). The key properties
of BDeu arise from its uniform prior over the parameters of each local
distribution in the network, which makes structure learning computationally
efficient; it does not require the elicitation of prior knowledge from experts;
and it satisfies score equivalence.
  In this paper we will review the derivation and the properties of BD scores,
and of BDeu in particular, and we will link them to the corresponding entropy
estimates to study them from an information theoretic perspective. To this end,
we will work in the context of the foundational work of Giffin and Caticha
(2007), who showed that Bayesian inference can be framed as a particular case
of the maximum relative entropy principle. We will use this connection to show
that BDeu should not be used for structure learning from sparse data, since it
violates the maximum relative entropy principle; and that it is also
problematic from a more classic Bayesian model selection perspective, because
it produces Bayes factors that are sensitive to the value of its only
hyperparameter. Using a large simulation study, we found in our previous work
(Scutari, 2016) that the Bayesian Dirichlet sparse (BDs) score seems to provide
better accuracy in structure learning; in this paper we further show that BDs
does not suffer from the issues above, and we recommend to use it for sparse
data instead of BDeu. Finally, will show that these issues are in fact
different aspects of the same problem and a consequence of the distributional
assumptions of the prior.
2^B3^C: 2 Box 3 Crop of Facial Image for Gender Classification with Convolutional Networks
Authors: Vandit Gajjar
Date: 2018-03-05 00:00:00
Tasks: Classification, Gender Classification, General Classification, GPU, image-classification, Image Classification

In this paper, we tackle the classification of gender in facial images with
deep learning. Our convolutional neural networks (CNN) use the VGG-16
architecture [1] and are pretrained on ImageNet for image classification. Our
proposed method (2^B3^C) first detects the face in the facial image, increases
the margin of a detected face by 50%, cropping the face with two boxes three
crop schemes (Left, Middle, and Right crop) and extracts the CNN predictions on
the cropped schemes. The CNNs of our method is fine-tuned on the Adience and
LFW with gender annotations. We show the effectiveness of our method by
achieving 90.8% classification on Adience and achieving competitive 95.3%
classification accuracy on LFW dataset. In addition, to check the true ability
of our method, our gender classification system has a frame rate of 7-10 fps
(frames per seconds) on a GPU considering real-time scenarios.
Spectral reflectance estimation from one RGB image using self-interreflections in a concave object
Authors: Rada Deeb, Damien Muselet, Mathieu Hebert, Alain Tremeau
Date: 2018-03-05 00:00:00

Light interreflections occurring in a concave object generate a color
gradient which is characteristic of the object's spectral reflectance. In this
paper, we use this property in order to estimate the spectral reflectance of
matte, uniformly colored, V-shaped surfaces from a single RGB image taken under
directional lighting. First, simulations show that using one image of the
concave object is equivalent to, and can even outperform, the state of the art
approaches based on three images taken under three lightings with different
colors. Experiments on real images of folded papers were performed under
unmeasured direct sunlight. The results show that our interreflection-based
approach outperforms existing approaches even when the latter are improved by a
calibration step. The mathematical solution for the interreflection equation
and the effect of surface parameters on the performance of the method are also
discussed in this paper.
N-body Networks: a Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentials
Authors: Risi Kondor
Date: 2018-03-05 00:00:00

We describe N-body networks, a neural network architecture for learning the
behavior and properties of complex many body physical systems. Our specific
application is to learn atomic potential energy surfaces for use in molecular
dynamics simulations. Our architecture is novel in that (a) it is based on a
hierarchical decomposition of the many body system into subsytems, (b) the
activations of the network correspond to the internal state of each subsystem,
(c) the "neurons" in the network are constructed explicitly so as to guarantee
that each of the activations is covariant to rotations, (d) the neurons operate
entirely in Fourier space, and the nonlinearities are realized by tensor
products followed by Clebsch-Gordan decompositions. As part of the description
of our network, we give a characterization of what way the weights of the
network may interact with the activations so as to ensure that the covariance
property is maintained.
Calculated attributes of synonym sets
Authors: Andrew Krizhanovsky, Alexander Kirillov
Date: 2018-03-05 00:00:00

The goal of formalization, proposed in this paper, is to bring together, as
near as possible, the theoretic linguistic problem of synonym conception and
the computer linguistic methods based generally on empirical intuitive
unjustified factors. Using the word vector representation we have proposed the
geometric approach to mathematical modeling of synonym set (synset). The word
embedding is based on the neural networks (Skip-gram, CBOW), developed and
realized as word2vec program by T. Mikolov. The standard cosine similarity is
used as the distance between word-vectors. Several geometric characteristics of
the synset words are introduced: the interior of synset, the synset word rank
and centrality. These notions are intended to select the most significant
synset words, i.e. the words which senses are the nearest to the sense of a
synset. Some experiments with proposed notions, based on RusVectores resources,
are represented. A brief description of this work can be viewed in slides
https://goo.gl/K82Fei
Predicting Out-of-View Feature Points for Model-Based Camera Pose Estimation
Authors: Oliver Moolan-Feroze, Andrew Calway
Date: 2018-03-05 00:00:00
Tasks: Camera Pose Estimation, Object, Pose Estimation

In this work we present a novel framework that uses deep learning to predict
object feature points that are out-of-view in the input image. This system was
developed with the application of model-based tracking in mind, particularly in
the case of autonomous inspection robots, where only partial views of the
object are available. Out-of-view prediction is enabled by applying scaling to
the feature point labels during network training. This is combined with a
recurrent neural network architecture designed to provide the final prediction
layers with rich feature information from across the spatial extent of the
input image. To show the versatility of these out-of-view predictions, we
describe how to integrate them in both a particle filter tracker and an
optimisation based tracker. To evaluate our work we compared our framework with
one that predicts only points inside the image. We show that as the amount of
the object in view decreases, being able to predict outside the image bounds
adds robustness to the final pose estimation.
Asymptotic Equivalence of Fixed-size and Varying-size Determinantal Point Processes
Authors: Simon Barthelmé, Pierre-Olivier Amblard, Nicolas Tremblay
Date: 2018-03-05 00:00:00
Tasks: Point Processes

Determinantal Point Processes (DPPs) are popular models for point processes
with repulsion. They appear in numerous contexts, from physics to graph theory,
and display appealing theoretical properties. On the more practical side of
things, since DPPs tend to select sets of points that are some distance apart
(repulsion), they have been advocated as a way of producing random subsets with
high diversity. DPPs come in two variants: fixed-size and varying-size. A
sample from a varying-size DPP is a subset of random cardinality, while in
fixed-size "$k$-DPPs" the cardinality is fixed. The latter makes more sense in
many applications, but unfortunately their computational properties are less
attractive, since, among other things, inclusion probabilities are harder to
compute. In this work we show that as the size of the ground set grows,
$k$-DPPs and DPPs become equivalent, meaning that their inclusion probabilities
converge. As a by-product, we obtain saddlepoint formulas for inclusion
probabilities in $k$-DPPs. These turn out to be extremely accurate, and suffer
less from numerical difficulties than exact methods do. Our results also
suggest that $k$-DPPs and DPPs also have equivalent maximum likelihood
estimators. Finally, we obtain results on asymptotic approximations of
elementary symmetric polynomials which may be of independent interest.
A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression
Authors: Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman
Date: 2018-03-05 00:00:00
Tasks: BIG-bench Machine Learning, regression, Zero-Shot Learning

Many machine learning problems can be formulated as predicting labels for a
pair of objects. Problems of that kind are often referred to as pairwise
learning, dyadic prediction or network inference problems. During the last
decade kernel methods have played a dominant role in pairwise learning. They
still obtain a state-of-the-art predictive performance, but a theoretical
analysis of their behavior has been underexplored in the machine learning
literature.
  In this work we review and unify existing kernel-based algorithms that are
commonly used in different pairwise learning settings, ranging from matrix
filtering to zero-shot learning. To this end, we focus on closed-form efficient
instantiations of Kronecker kernel ridge regression. We show that independent
task kernel ridge regression, two-step kernel ridge regression and a linear
matrix filter arise naturally as a special case of Kronecker kernel ridge
regression, implying that all these methods implicitly minimize a squared loss.
In addition, we analyze universality, consistency and spectral filtering
properties. Our theoretical results provide valuable insights in assessing the
advantages and limitations of existing pairwise learning methods.