pwc-archive/papers-with-abstracts huggingface dataset:
Distributed Multivariate Regression Modeling For Selecting Biomarkers Under Data Protection Constraints
Authors: Daniela Zöller, Harald Binder
Date: 2018-03-01 00:00:00
Tasks: regression, Variable Selection

The discovery of clinical biomarkers requires large patient cohorts and is aided by a pooled data approach across institutions. In many countries, data protection constraints, especially in the clinical environment, forbid the exchange of individual-level data between different research institutes, impeding the conduct of a joint analyses. To circumvent this problem, only non-disclosive aggregated data is exchanged, which is often done manually and requires explicit permission before transfer, i.e., the number of data calls and the amount of data should be limited. This does not allow for more complex tasks such as variable selection, as only simple aggregated summary statistics are typically transferred. Other methods have been proposed that require more complex aggregated data or use input data perturbation, but these methods can either not deal with a high number of biomarkers or lose information. Here, we propose a multivariable regression approach for identifying biomarkers by automatic variable selection based on aggregated data in iterative calls, which can be implemented under data protection constraints. The approach can be used to jointly analyze data distributed across several locations. To minimize the amount of transferred data and the number of calls, we also provide a heuristic variant of the approach. When performing global data standardization, the proposed method yields the same results as pooled individual-level data analysis. In a simulation study, the information loss introduced by local standardization is seen to be minimal. In a typical scenario, the heuristic decreases the number of data calls from more than 10 to 3, rendering manual data releases feasible. To make our approach widely available for application, we provide an implementation of the heuristic version incorporated in the DataSHIELD framework.\
Poisson Image Denoising Using Best Linear Prediction: A Post-processing Framework
Authors: Milad Niknejad, Mario A. T. Figueiredo
Date: 2018-03-01 00:00:00
Tasks: Denoising, Image Denoising

In this paper, we address the problem of denoising images degraded by Poisson
noise. We propose a new patch-based approach based on best linear prediction to
estimate the underlying clean image. A simplified prediction formula is derived
for Poisson observations, which requires the covariance matrix of the
underlying clean patch. We use the assumption that similar patches in a
neighborhood share the same covariance matrix, and we use off-the-shelf Poisson
denoising methods in order to obtain an initial estimate of the covariance
matrices. Our method can be seen as a post-processing step for Poisson
denoising methods and the results show that it improves upon several Poisson
denoising methods by relevant margins.
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search
Authors: Masanori Suganuma, Mete Ozay, Takayuki Okatani
Date: 2018-03-01 00:00:00
Proceeding: ICML 2018 7
Tasks: Image Restoration

Researchers have applied deep neural networks to image restoration tasks, in
which they proposed various network architectures, loss functions, and training
methods. In particular, adversarial training, which is employed in recent
studies, seems to be a key ingredient to success. In this paper, we show that
simple convolutional autoencoders (CAEs) built upon only standard network
components, i.e., convolutional layers and skip connections, can outperform the
state-of-the-art methods which employ adversarial training and sophisticated
loss functions. The secret is to employ an evolutionary algorithm to
automatically search for good architectures. Training optimized CAEs by
minimizing the $\ell_2$ loss between reconstructed images and their ground
truths using the ADAM optimizer is all we need. Our experimental results show
that this approach achieves 27.8 dB peak signal to noise ratio (PSNR) on the
CelebA dataset and 40.4 dB on the SVHN dataset, compared to 22.8 dB and 33.0 dB
provided by the former state-of-the-art methods, respectively.
Fast semi-supervised discriminant analysis for binary classification of large data-sets
Authors: Joris Tavernier, Jaak Simm, Karl Meerbergen, Joerg Kurt Wegner, Hugo Ceulemans, Yves Moreau
Date: 2017-09-14 00:00:00
Tasks: Binary Classification, General Classification, subspace methods

High-dimensional data requires scalable algorithms. We propose and analyze
three scalable and related algorithms for semi-supervised discriminant analysis
(SDA). These methods are based on Krylov subspace methods which exploit the
data sparsity and the shift-invariance of Krylov subspaces. In addition, the
problem definition was improved by adding centralization to the semi-supervised
setting. The proposed methods are evaluated on a industry-scale data set from a
pharmaceutical company to predict compound activity on target proteins. The
results show that SDA achieves good predictive performance and our methods only
require a few seconds, significantly improving computation time on previous
state of the art.
Cross-lingual and Multilingual Speech Emotion Recognition on English and French
Authors: Michael Neumann, Ngoc Thang Vu
Date: 2018-03-01 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

Research on multilingual speech emotion recognition faces the problem that
most available speech corpora differ from each other in important ways, such as
annotation methods or interaction scenarios. These inconsistencies complicate
building a multilingual system. We present results for cross-lingual and
multilingual emotion recognition on English and French speech data with similar
characteristics in terms of interaction (human-human conversations). Further,
we explore the possibility of fine-tuning a pre-trained cross-lingual model
with only a small number of samples from the target language, which is of great
interest for low-resource languages. To gain more insights in what is learned
by the deployed convolutional neural network, we perform an analysis on the
attention mechanism inside the network.
Joint Training for Neural Machine Translation Models with Monolingual Data
Authors: Zhirui Zhang, Shujie Liu, Mu Li, Ming Zhou, Enhong Chen
Date: 2018-03-01 00:00:00
Tasks: Domain Adaptation, Machine Translation, NMT, Translation

Monolingual data have been demonstrated to be helpful in improving
translation quality of both statistical machine translation (SMT) systems and
neural machine translation (NMT) systems, especially in resource-poor or domain
adaptation tasks where parallel data are not rich enough. In this paper, we
propose a novel approach to better leveraging monolingual data for neural
machine translation by jointly learning source-to-target and target-to-source
NMT models for a language pair with a joint EM optimization method. The
training process starts with two initial NMT models pre-trained on parallel
data for each direction, and these two models are iteratively updated by
incrementally decreasing translation losses on training data. In each iteration
step, both NMT models are first used to translate monolingual data from one
language to the other, forming pseudo-training data of the other NMT model.
Then two new NMT models are learnt from parallel data together with the pseudo
training data. Both NMT models are expected to be improved and better
pseudo-training data can be generated in next step. Experiment results on
Chinese-English and English-German translation tasks show that our approach can
simultaneously improve translation quality of source-to-target and
target-to-source models, significantly outperforming strong baseline systems
which are enhanced with monolingual data for model training including
back-translation.
LaVAN: Localized and Visible Adversarial Noise
Authors: Danny Karmon, Daniel Zoran, Yoav Goldberg
Date: 2018-01-08 00:00:00
Proceeding: ICML 2018 7
Tasks: Object

Most works on adversarial examples for deep-learning based image classifiers
use noise that, while small, covers the entire image. We explore the case where
the noise is allowed to be visible but confined to a small, localized patch of
the image, without covering any of the main object(s) in the image. We show
that it is possible to generate localized adversarial noises that cover only 2%
of the pixels in the image, none of them over the main object, and that are
transferable across images and locations, and successfully fool a
state-of-the-art Inception v3 model with very high success rates.
A Deep Learning Approach for Multimodal Deception Detection
Authors: Gangeshwar Krishnamurthy, Navonil Majumder, Soujanya Poria, Erik Cambria
Date: 2018-03-01 00:00:00
Tasks: Deception Detection, Deep Learning

Automatic deception detection is an important task that has gained momentum
in computational linguistics due to its potential applications. In this paper,
we propose a simple yet tough to beat multi-modal neural model for deception
detection. By combining features from different modalities such as video,
audio, and text along with Micro-Expression features, we show that detecting
deception in real life videos can be more accurate. Experimental results on a
dataset of real-life deception videos show that our model outperforms existing
techniques for deception detection with an accuracy of 96.14% and ROC-AUC of
0.9799.
The K-Nearest Neighbour UCB algorithm for multi-armed bandits with covariates
Authors: Henry WJ Reeve, Joe Mellor, Gavin Brown
Date: 2018-03-01 00:00:00
Tasks: Multi-Armed Bandits

In this paper we propose and explore the k-Nearest Neighbour UCB algorithm
for multi-armed bandits with covariates. We focus on a setting where the
covariates are supported on a metric space of low intrinsic dimension, such as
a manifold embedded within a high dimensional ambient feature space. The
algorithm is conceptually simple and straightforward to implement. The
k-Nearest Neighbour UCB algorithm does not require prior knowledge of the
either the intrinsic dimension of the marginal distribution or the time
horizon. We prove a regret bound for the k-Nearest Neighbour UCB algorithm
which is minimax optimal up to logarithmic factors. In particular, the
algorithm automatically takes advantage of both low intrinsic dimensionality of
the marginal distribution over the covariates and low noise in the data,
expressed as a margin condition. In addition, focusing on the case of bounded
rewards, we give corresponding regret bounds for the k-Nearest Neighbour KL-UCB
algorithm, which is an analogue of the KL-UCB algorithm adapted to the setting
of multi-armed bandits with covariates. Finally, we present empirical results
which demonstrate the ability of both the k-Nearest Neighbour UCB and k-Nearest
Neighbour KL-UCB to take advantage of situations where the data is supported on
an unknown sub-manifold of a high-dimensional feature space.
Diversity and degrees of freedom in regression ensembles
Authors: Henry WJ Reeve, Gavin Brown
Date: 2018-03-01 00:00:00
Tasks: Diversity, regression

Ensemble methods are a cornerstone of modern machine learning. The
performance of an ensemble depends crucially upon the level of diversity
between its constituent learners. This paper establishes a connection between
diversity and degrees of freedom (i.e. the capacity of the model), showing that
diversity may be viewed as a form of inverse regularisation. This is achieved
by focusing on a previously published algorithm Negative Correlation Learning
(NCL), in which model diversity is explicitly encouraged through a diversity
penalty term in the loss function. We provide an exact formula for the
effective degrees of freedom in an NCL ensemble with fixed basis functions,
showing that it is a continuous, convex and monotonically increasing function
of the diversity parameter. We demonstrate a connection to Tikhonov
regularisation and show that, with an appropriately chosen diversity parameter,
an NCL ensemble can always outperform the unregularised ensemble in the
presence of noise. We demonstrate the practical utility of our approach by
deriving a method to efficiently tune the diversity parameter. Finally, we use
a Monte-Carlo estimator to extend the connection between diversity and degrees
of freedom to ensembles of deep neural networks.
Minimax rates for cost-sensitive learning on manifolds with approximate nearest neighbours
Authors: Henry WJ Reeve, Gavin Brown
Date: 2018-03-01 00:00:00
Tasks: General Classification

We study the approximate nearest neighbour method for cost-sensitive
classification on low-dimensional manifolds embedded within a high-dimensional
feature space. We determine the minimax learning rates for distributions on a
smooth manifold, in a cost-sensitive setting. This generalises a classic result
of Audibert and Tsybakov. Building upon recent work of Chaudhuri and Dasgupta
we prove that these minimax rates are attained by the approximate nearest
neighbour algorithm, where neighbours are computed in a randomly projected
low-dimensional space. In addition, we give a bound on the number of dimensions
required for the projection which depends solely upon the reach and dimension
of the manifold, combined with the regularity of the marginal.
Q-CP: Learning Action Values for Cooperative Planning
Authors: Francesco Riccio, Roberto Capobianco, Daniele Nardi
Date: 2018-03-01 00:00:00
Tasks: Model-based Reinforcement Learning, Q-Learning, Reinforcement Learning

Research on multi-robot systems has demonstrated promising results in
manifold applications and domains. Still, efficiently learning an effective
robot behaviors is very difficult, due to unstructured scenarios, high
uncertainties, and large state dimensionality (e.g. hyper-redundant and groups
of robot). To alleviate this problem, we present Q-CP a cooperative model-based
reinforcement learning algorithm, which exploits action values to both (1)
guide the exploration of the state space and (2) generate effective policies.
Specifically, we exploit Q-learning to attack the curse-of-dimensionality in
the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on
different stochastic cooperative (general-sum) games: (1) a simple cooperative
navigation problem among 3 robots, (2) a cooperation scenario between a pair of
KUKA YouBots performing hand-overs, and (3) a coordination task between two
mobile robots entering a door. The obtained results show the effectiveness of
Q-CP in the chosen applications, where action values drive the exploration and
reduce the computational demand of the planning process while achieving good
performance.
Differentially Private Federated Learning: A Client Level Perspective
Authors: Robin C. Geyer, Tassilo Klein, Moin Nabi
Date: 2017-12-20 00:00:00
Proceeding: ICLR 2019 5
Tasks: Federated Learning, Privacy Preserving

Federated learning is a recent advance in privacy protection. In this
context, a trusted curator aggregates parameters optimized in decentralized
fashion by multiple clients. The resulting model is then distributed back to
all clients, ultimately converging to a joint representative model without
explicitly having to share the data. However, the protocol is vulnerable to
differential attacks, which could originate from any party contributing during
federated optimization. In such an attack, a client's contribution during
training and information about their data set is revealed through analyzing the
distributed model. We tackle this problem and propose an algorithm for client
sided differential privacy preserving federated optimization. The aim is to
hide clients' contributions during training, balancing the trade-off between
privacy loss and model performance. Empirical studies suggest that given a
sufficiently large number of participating clients, our proposed procedure can
maintain client-level differential privacy at only a minor cost in model
performance.
Five-point Fundamental Matrix Estimation for Uncalibrated Cameras
Authors: Daniel Barath
Date: 2018-03-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Motion Estimation

We aim at estimating the fundamental matrix in two views from five
correspondences of rotation invariant features obtained by e.g.\ the SIFT
detector. The proposed minimal solver first estimates a homography from three
correspondences assuming that they are co-planar and exploiting their
rotational components. Then the fundamental matrix is obtained from the
homography and two additional point pairs in general position. The proposed
approach, combined with robust estimators like Graph-Cut RANSAC, is superior to
other state-of-the-art algorithms both in terms of accuracy and number of
iterations required. This is validated on synthesized data and $561$ real image
pairs. Moreover, the tests show that requiring three points on a plane is not
too restrictive in urban environment and locally optimized robust estimators
lead to accurate estimates even if the points are not entirely co-planar. As a
potential application, we show that using the proposed method makes two-view
multi-motion estimation more accurate.
Deep Reinforcement Learning for Sponsored Search Real-time Bidding
Authors: Jun Zhao, Guang Qiu, Ziyu Guan, Wei Zhao, Xiaofei He
Date: 2018-03-01 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Bidding optimization is one of the most critical problems in online
advertising. Sponsored search (SS) auction, due to the randomness of user query
behavior and platform nature, usually adopts keyword-level bidding strategies.
In contrast, the display advertising (DA), as a relatively simpler scenario for
auction, has taken advantage of real-time bidding (RTB) to boost the
performance for advertisers. In this paper, we consider the RTB problem in
sponsored search auction, named SS-RTB. SS-RTB has a much more complex dynamic
environment, due to stochastic user query behavior and more complex bidding
policies based on multiple keywords of an ad. Most previous methods for DA
cannot be applied. We propose a reinforcement learning (RL) solution for
handling the complex dynamic environment. Although some RL methods have been
proposed for online advertising, they all fail to address the "environment
changing" problem: the state transition probabilities vary between two days.
Motivated by the observation that auction sequences of two days share similar
transition patterns at a proper aggregation level, we formulate a robust MDP
model at hour-aggregation level of the auction data and propose a
control-by-model framework for SS-RTB. Rather than generating bid prices
directly, we decide a bidding model for impressions of each hour and perform
real-time bidding accordingly. We also extend the method to handle the
multi-agent problem. We deployed the SS-RTB system in the e-commerce search
auction platform of Alibaba. Empirical experiments of offline evaluation and
online A/B test demonstrate the effectiveness of our method.
Distance Measure Machines
Authors: Alain Rakotomamonjy, Abraham Traoré, Maxime Berar, Rémi Flamary, Nicolas Courty
Date: 2018-03-01 00:00:00

This paper presents a distance-based discriminative framework for learning
with probability distributions. Instead of using kernel mean embeddings or
generalized radial basis kernels, we introduce embeddings based on
dissimilarity of distributions to some reference distributions denoted as
templates. Our framework extends the theory of similarity of Balcan et al.
(2008) to the population distribution case and we show that, for some learning
problems, some dissimilarity on distribution achieves low-error linear decision
functions with high probability. Our key result is to prove that the theory
also holds for empirical distributions. Algorithmically, the proposed approach
consists in computing a mapping based on pairwise dissimilarity where learning
a linear decision function is amenable. Our experimental results show that the
Wasserstein distance embedding performs better than kernel mean embeddings and
computing Wasserstein distance is far more tractable than estimating pairwise
Kullback-Leibler divergence of empirical distributions.
New Ideas for Brain Modelling 3
Authors: Kieran Greer
Date: 2016-11-22 00:00:00
Tasks: Clustering

This paper considers a process for the creation and subsequent firing of
sequences of neuronal patterns, as might be found in the human brain. The scale
is one of larger patterns emerging from an ensemble mass, possibly through some
type of energy equation and a reduction procedure. The links between the
patterns can be formed naturally, as a residual effect of the pattern creation
itself. This paper follows-on closely from the earlier research, including two
earlier papers in the series and uses the ideas of entropy and cohesion. With a
small addition, it is possible to show how the inter-pattern links can be
determined. A compact Grid form of an earlier Counting Mechanism is also
demonstrated and may be a new clustering technique. It is possible to explain
how a very basic repeating structure can form the arbitrary patterns and
activation sequences between them, and a key question of how nodes synchronise
may even be answerable.
Monocular Depth Estimation using Multi-Scale Continuous CRFs as Sequential Deep Networks
Authors: Dan Xu, Elisa Ricci, Wanli Ouyang, Xiaogang Wang, Nicu Sebe
Date: 2018-03-01 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation

Depth cues have been proved very useful in various computer vision and
robotic tasks. This paper addresses the problem of monocular depth estimation
from a single still image. Inspired by the effectiveness of recent works on
multi-scale convolutional neural networks (CNN), we propose a deep model which
fuses complementary information derived from multiple CNN side outputs.
Different from previous methods using concatenation or weighted average
schemes, the integration is obtained by means of continuous Conditional Random
Fields (CRFs). In particular, we propose two different variations, one based on
a cascade of multiple CRFs, the other on a unified graphical model. By
designing a novel CNN implementation of mean-field updates for continuous CRFs,
we show that both proposed models can be regarded as sequential deep networks
and that training can be performed end-to-end. Through an extensive
experimental evaluation, we demonstrate the effectiveness of the proposed
approach and establish new state of the art results for the monocular depth
estimation task on three publicly available datasets, i.e. NYUD-V2, Make3D and
KITTI.
DRUNET: A Dilated-Residual U-Net Deep Learning Network to Digitally Stain Optic Nerve Head Tissues in Optical Coherence Tomography Images
Authors: Sripad Krishna Devalla, Prajwal K. Renukanand, Bharathwaj K. Sreedhar, Shamira Perera, Jean-Martial Mari, Khai Sing Chin, Tin A. Tun, Nicholas G. Strouthidis, Tin Aung, Alexandre H. Thiery, Michael J. A. Girard
Date: 2018-03-01 00:00:00
Tasks: Management

Given that the neural and connective tissues of the optic nerve head (ONH)
exhibit complex morphological changes with the development and progression of
glaucoma, their simultaneous isolation from optical coherence tomography (OCT)
images may be of great interest for the clinical diagnosis and management of
this pathology. A deep learning algorithm was designed and trained to digitally
stain (i.e. highlight) 6 ONH tissue layers by capturing both the local (tissue
texture) and contextual information (spatial arrangement of tissues). The
overall dice coefficient (mean of all tissues) was $0.91 \pm 0.05$ when
assessed against manual segmentations performed by an expert observer. We offer
here a robust segmentation framework that could be extended for the automated
parametric study of the ONH tissues.