pwc-archive/papers-with-abstracts huggingface dataset:
Graph Laplacian for Image Anomaly Detection
Authors: Francesco Verdoja, Marco Grangetto
Date: 2018-02-27 00:00:00
Tasks: Anomaly Detection

Reed-Xiaoli detector (RXD) is recognized as the benchmark algorithm for image anomaly detection; however, it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a high-dimensional covariance matrix, and the inability to effectively include spatial awareness in its evaluation. In this work, a novel graph-based solution to the image anomaly detection problem is proposed; leveraging the graph Fourier transform, we are able to overcome some of RXD's limitations while reducing computational cost at the same time. Tests over both hyperspectral and medical images, using both synthetic and real anomalies, prove the proposed technique is able to obtain significant gains over performance by other algorithms in the state of the art.
Adversarial Active Learning for Deep Networks: a Margin Based Approach
Authors: Melanie Ducoffe, Frederic Precioso
Date: 2018-02-27 00:00:00
Tasks: Active Learning

We propose a new active learning strategy designed for deep neural networks.
The goal is to minimize the number of data annotation queried from an oracle
during training. Previous active learning strategies scalable for deep networks
were mostly based on uncertain sample selection. In this work, we focus on
examples lying close to the decision boundary. Based on theoretical works on
margin theory for active learning, we know that such examples may help to
considerably decrease the number of annotations. While measuring the exact
distance to the decision boundaries is intractable, we propose to rely on
adversarial examples. We do not consider anymore them as a threat instead we
exploit the information they provide on the distribution of the input space in
order to approximate the distance to decision boundaries. We demonstrate
empirically that adversarial active queries yield faster convergence of CNNs
trained on MNIST, the Shoe-Bag and the Quick-Draw datasets.
Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition
Authors: Chaolong Li, Zhen Cui, Wenming Zheng, Chunyan Xu, Jian Yang
Date: 2018-02-27 00:00:00
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Variations of human body skeletons may be considered as dynamic graphs, which
are generic data representation for numerous real-world applications. In this
paper, we propose a spatio-temporal graph convolution (STGC) approach for
assembling the successes of local convolutional filtering and sequence learning
ability of autoregressive moving average. To encode dynamic graphs, the
constructed multi-scale local graph convolution filters, consisting of matrices
of local receptive fields and signal mappings, are recursively performed on
structured graph data of temporal and spatial domain. The proposed model is
generic and principled as it can be generalized into other dynamic models. We
theoretically prove the stability of STGC and provide an upper-bound of the
signal transformation to be learnt. Further, the proposed recursive model can
be stacked into a multi-layer architecture. To evaluate our model, we conduct
extensive experiments on four benchmark skeleton-based action datasets,
including the large-scale challenging NTU RGB+D. The experimental results
demonstrate the effectiveness of our proposed model and the improvement over
the state-of-the-art.
Coarse to fine non-rigid registration: a chain of scale-specific neural networks for multimodal image alignment with application to remote sensing
Authors: Armand Zampieri, Guillaume Charpiat, Yuliya Tarabalka
Date: 2018-02-27 00:00:00

We tackle here the problem of multimodal image non-rigid registration, which
is of prime importance in remote sensing and medical imaging. The difficulties
encountered by classical registration approaches include feature design and
slow optimization by gradient descent. By analyzing these methods, we note the
significance of the notion of scale. We design easy-to-train,
fully-convolutional neural networks able to learn scale-specific features. Once
chained appropriately, they perform global registration in linear time, getting
rid of gradient descent schemes by predicting directly the deformation.We show
their performance in terms of quality and speed through various tasks of remote
sensing multimodal image alignment. In particular, we are able to register
correctly cadastral maps of buildings as well as road polylines onto RGB
images, and outperform current keypoint matching methods.
A Bayesian optimization approach to find Nash equilibria
Authors: Victor Picheny, Mickael Binois, Abderrahmane Habbal
Date: 2016-11-08 00:00:00
Tasks: Bayesian Optimization

Game theory finds nowadays a broad range of applications in engineering and
machine learning. However, in a derivative-free, expensive black-box context,
very few algorithmic solutions are available to find game equilibria. Here, we
propose a novel Gaussian-process based approach for solving games in this
context. We follow a classical Bayesian optimization framework, with sequential
sampling decisions based on acquisition functions. Two strategies are proposed,
based either on the probability of achieving equilibrium or on the Stepwise
Uncertainty Reduction paradigm. Practical and numerical aspects are discussed
in order to enhance the scalability and reduce computation time. Our approach
is evaluated on several synthetic game problems with varying number of players
and decision space dimensions. We show that equilibria can be found reliably
for a fraction of the cost (in terms of black-box evaluations) compared to
classical, derivative-based algorithms. The method is available in the R
package GPGame available on CRAN at https://cran.r-project.org/package=GPGame.
Human-in-the-Loop Synthesis for Partially Observable Markov Decision Processes
Authors: Steven Carr, Nils Jansen, Ralf Wimmer, Jie Fu, Ufuk Topcu
Date: 2018-02-27 00:00:00

We study planning problems where autonomous agents operate inside
environments that are subject to uncertainties and not fully observable.
Partially observable Markov decision processes (POMDPs) are a natural formal
model to capture such problems. Because of the potentially huge or even
infinite belief space in POMDPs, synthesis with safety guarantees is, in
general, computationally intractable. We propose an approach that aims to
circumvent this difficulty: in scenarios that can be partially or fully
simulated in a virtual environment, we actively integrate a human user to
control an agent. While the user repeatedly tries to safely guide the agent in
the simulation, we collect data from the human input. Via behavior cloning, we
translate the data into a strategy for the POMDP. The strategy resolves all
nondeterminism and non-observability of the POMDP, resulting in a discrete-time
Markov chain (MC). The efficient verification of this MC gives quantitative
insights into the quality of the inferred human strategy by proving or
disproving given system specifications. For the case that the quality of the
strategy is not sufficient, we propose a refinement method using
counterexamples presented to the human. Experiments show that by including
humans into the POMDP verification loop we improve the state of the art by
orders of magnitude in terms of scalability.
DiGrad: Multi-Task Reinforcement Learning with Shared Actions
Authors: Parijat Dewangan, S Phaniteja, K. Madhava Krishna, Abhishek Sarkar, Balaraman Ravindran
Date: 2018-02-27 00:00:00
Tasks: Multi-Task Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Most reinforcement learning algorithms are inefficient for learning multiple
tasks in complex robotic systems, where different tasks share a set of actions.
In such environments a compound policy may be learnt with shared neural network
parameters, which performs multiple tasks concurrently. However such compound
policy may get biased towards a task or the gradients from different tasks
negate each other, making the learning unstable and sometimes less data
efficient. In this paper, we propose a new approach for simultaneous training
of multiple tasks sharing a set of common actions in continuous action spaces,
which we call as DiGrad (Differential Policy Gradient). The proposed framework
is based on differential policy gradients and can accommodate multi-task
learning in a single actor-critic network. We also propose a simple heuristic
in the differential policy gradient update to further improve the learning. The
proposed architecture was tested on 8 link planar manipulator and 27 degrees of
freedom(DoF) Humanoid for learning multi-goal reachability tasks for 3 and 2
end effectors respectively. We show that our approach supports efficient
multi-task learning in complex robotic systems, outperforming related methods
in continuous action spaces.
Tone Biased MMR Text Summarization
Authors: Mayank Chaudhari, Aakash Nelson Mattukoyya
Date: 2018-02-26 00:00:00
Tasks: Text Summarization

Text summarization is an interesting area for researchers to develop new
techniques to provide human like summaries for vast amounts of information.
Summarization techniques tend to focus on providing accurate representation of
content, and often the tone of the content is ignored. Tone of the content sets
a baseline for how a reader perceives the content. As such being able to
generate summary with tone that is appropriate for the reader is important. In
our work we implement Maximal Marginal Relevance [MMR] based multi-document
text summarization and propose a naive model to change tone of the
summarization by setting a bias to specific set of words and restricting other
words in the summarization output. This bias towards a specified set of words
produces a summary whose tone is same as tone of specified words.
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
Authors: Aleksandar Bojchevski, Stephan Günnemann
Date: 2017-07-12 00:00:00
Proceeding: ICLR 2018 1
Tasks: Inductive Learning, Link Prediction, Network Embedding, Node Classification

Methods that learn representations of nodes in a graph play a critical role
in network analysis since they enable many downstream learning tasks. We
propose Graph2Gauss - an approach that can efficiently learn versatile node
embeddings on large scale (attributed) graphs that show strong performance on
tasks such as link prediction and node classification. Unlike most approaches
that represent nodes as point vectors in a low-dimensional continuous space, we
embed each node as a Gaussian distribution, allowing us to capture uncertainty
about the representation. Furthermore, we propose an unsupervised method that
handles inductive learning scenarios and is applicable to different types of
graphs: plain/attributed, directed/undirected. By leveraging both the network
structure and the associated node attributes, we are able to generalize to
unseen nodes without additional training. To learn the embeddings we adopt a
personalized ranking formulation w.r.t. the node distances that exploits the
natural ordering of the nodes imposed by the network structure. Experiments on
real world networks demonstrate the high performance of our approach,
outperforming state-of-the-art network embedding methods on several different
tasks. Additionally, we demonstrate the benefits of modeling uncertainty - by
analyzing it we can estimate neighborhood diversity and detect the intrinsic
latent dimensionality of a graph.
Autonomous Quadrotor Landing using Deep Reinforcement Learning
Authors: Riccardo Polvara, Massimiliano Patacchiola, Sanjay Sharma, Jian Wan, Andrew Manning, Robert Sutton, Angelo Cangelosi
Date: 2017-09-11 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Landing an unmanned aerial vehicle (UAV) on a ground marker is an open
problem despite the effort of the research community. Previous attempts mostly
focused on the analysis of hand-crafted geometric features and the use of
external sensors in order to allow the vehicle to approach the land-pad. In
this article, we propose a method based on deep reinforcement learning that
only requires low-resolution images taken from a down-looking camera in order
to identify the position of the marker and land the UAV on it. The proposed
approach is based on a hierarchy of Deep Q-Networks (DQNs) used as high-level
control policy for the navigation toward the marker. We implemented different
technical solutions, such as the combination of vanilla and double DQNs, and a
partitioned buffer replay. Using domain randomization we trained the vehicle on
uniform textures and we tested it on a large variety of simulated and
real-world environments. The overall performance is comparable with a
state-of-the-art algorithm and human pilots.
Matching Convolutional Neural Networks without Priors about Data
Authors: Carlos Eduardo Rosar Kos Lassance, Jean-Charles Vialatte, Vincent Gripon
Date: 2018-02-27 00:00:00
Tasks: Data Augmentation

We propose an extension of Convolutional Neural Networks (CNNs) to
graph-structured data, including strided convolutions and data augmentation on
graphs.
  Our method matches the accuracy of state-of-the-art CNNs when applied on
images, without any prior about their 2D regular structure.
  On fMRI data, we obtain a significant gain in accuracy compared with existing
graph-based alternatives.
Full-info Training for Deep Speaker Feature Learning
Authors: Lantian Li, Zhiyuan Tang, Dong Wang, Thomas Fang Zheng
Date: 2017-10-31 00:00:00
Tasks: Speaker Verification

In recent studies, it has shown that speaker patterns can be learned from
very short speech segments (e.g., 0.3 seconds) by a carefully designed
convolutional & time-delay deep neural network (CT-DNN) model. By enforcing the
model to discriminate the speakers in the training data, frame-level speaker
features can be derived from the last hidden layer. In spite of its good
performance, a potential problem of the present model is that it involves a
parametric classifier, i.e., the last affine layer, which may consume some
discriminative knowledge, thus leading to `information leak' for the feature
learning. This paper presents a full-info training approach that discards the
parametric classifier and enforces all the discriminative knowledge learned by
the feature net. Our experiments on the Fisher database demonstrate that this
new training scheme can produce more coherent features, leading to consistent
and notable performance improvement on the speaker verification task.
Identification of LTV Dynamical Models with Smooth or Discontinuous Time Evolution by means of Convex Optimization
Authors: Fredrik Bagge Carlson, Anders Robertsson, Rolf Johansson
Date: 2018-02-27 00:00:00
Tasks: Friction, Reinforcement Learning, State Space Models

We establish a connection between trend filtering and system identification
which results in a family of new identification methods for linear,
time-varying (LTV) dynamical models based on convex optimization. We
demonstrate how the design of the cost function promotes a model with either a
continuous change in dynamics over time, or causes discontinuous changes in
model coefficients occurring at a finite (sparse) set of time instances. We
further discuss the introduction of priors on the model parameters for
situations where excitation is insufficient for identification. The
identification problems are cast as convex optimization problems and are
applicable to, e.g., ARX models and state-space models with time-varying
parameters. We illustrate usage of the methods in simulations of jump-linear
systems, a nonlinear robot arm with non-smooth friction and stiff contacts as
well as in model-based, trajectory centric reinforcement learning on a smooth
nonlinear system.
Bioinformatics and Medicine in the Era of Deep Learning
Authors: Davide Bacciu, Paulo J. G. Lisboa, José D. Martín, Ruxandra Stoean, Alfredo Vellido
Date: 2018-02-27 00:00:00
Tasks: Deep Learning

Many of the current scientific advances in the life sciences have their
origin in the intensive use of data for knowledge discovery. In no area this is
so clear as in bioinformatics, led by technological breakthroughs in data
acquisition technologies. It has been argued that bioinformatics could quickly
become the field of research generating the largest data repositories, beating
other data-intensive areas such as high-energy physics or astroinformatics.
Over the last decade, deep learning has become a disruptive advance in machine
learning, giving new live to the long-standing connectionist paradigm in
artificial intelligence. Deep learning methods are ideally suited to
large-scale data and, therefore, they should be ideally suited to knowledge
discovery in bioinformatics and biomedicine at large. In this brief paper, we
review key aspects of the application of deep learning in bioinformatics and
medicine, drawing from the themes covered by the contributions to an ESANN 2018
special session devoted to this topic.
Lenient Multi-Agent Deep Reinforcement Learning
Authors: Gregory Palmer, Karl Tuyls, Daan Bloembergen, Rahul Savani
Date: 2017-07-14 00:00:00
Tasks: Deep Reinforcement Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Much of the success of single agent deep reinforcement learning (DRL) in
recent years can be attributed to the use of experience replay memories (ERM),
which allow Deep Q-Networks (DQNs) to be trained efficiently through sampling
stored state transitions. However, care is required when using ERMs for
multi-agent deep reinforcement learning (MA-DRL), as stored transitions can
become outdated because agents update their policies in parallel [11]. In this
work we apply leniency [23] to MA-DRL. Lenient agents map state-action pairs to
decaying temperature values that control the amount of leniency applied towards
negative policy updates that are sampled from the ERM. This introduces optimism
in the value-function update, and has been shown to facilitate cooperation in
tabular fully-cooperative multi-agent reinforcement learning problems. We
evaluate our Lenient-DQN (LDQN) empirically against the related Hysteretic-DQN
(HDQN) algorithm [22] as well as a modified version we call scheduled-HDQN,
that uses average reward learning near terminal states. Evaluations take place
in extended variations of the Coordinated Multi-Agent Object Transportation
Problem (CMOTP) [8] which include fully-cooperative sub-tasks and stochastic
rewards. We find that LDQN agents are more likely to converge to the optimal
policy in a stochastic reward CMOTP compared to standard and scheduled-HDQN
agents.
Time-sensitive Customer Churn Prediction based on PU Learning
Authors: Li Wang, Chaochao Chen, Jun Zhou, Xiaolong Li
Date: 2018-02-27 00:00:00

With the fast development of Internet companies throughout the world,
customer churn has become a serious concern. To better help the companies
retain their customers, it is important to build a customer churn prediction
model to identify the customers who are most likely to churn ahead of time. In
this paper, we propose a Time-sensitive Customer Churn Prediction (TCCP)
framework based on Positive and Unlabeled (PU) learning technique.
Specifically, we obtain the recent data by shortening the observation period,
and start to train model as long as enough positive samples are collected,
ignoring the absence of the negative examples. We conduct thoroughly
experiments on real industry data from Alipay.com. The experimental results
demonstrate that TCCP outperforms the rule-based models and the traditional
supervised learning models.
Gaussian meta-embeddings for efficient scoring of a heavy-tailed PLDA model
Authors: Niko Brummer, Anna Silnova, Lukas Burget, Themos Stafylakis
Date: 2018-02-27 00:00:00
Tasks: Speaker Recognition

Embeddings in machine learning are low-dimensional representations of complex
input patterns, with the property that simple geometric operations like
Euclidean distances and dot products can be used for classification and
comparison tasks. The proposed meta-embeddings are special embeddings that live
in more general inner product spaces. They are designed to propagate
uncertainty to the final output in speaker recognition and similar
applications. The familiar Gaussian PLDA model (GPLDA) can be re-formulated as
an extractor for Gaussian meta-embeddings (GMEs), such that likelihood ratio
scores are given by Hilbert space inner products between Gaussian likelihood
functions. GMEs extracted by the GPLDA model have fixed precisions and do not
propagate uncertainty. We show that a generalization to heavy-tailed PLDA gives
GMEs with variable precisions, which do propagate uncertainty. Experiments on
NIST SRE 2010 and 2016 show that the proposed method applied to i-vectors
without length normalization is up to 20% more accurate than GPLDA applied to
length-normalized ivectors.
L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks
Authors: Shuang Wu, Guoqi Li, Lei Deng, Liu Liu, Yuan Xie, Luping Shi
Date: 2018-02-27 00:00:00
Tasks: Computational Efficiency, Quantization

Batch Normalization (BN) has been proven to be quite effective at
accelerating and improving the training of deep neural networks (DNNs).
However, BN brings additional computation, consumes more memory and generally
slows down the training process by a large margin, which aggravates the
training effort. Furthermore, the nonlinear square and root operations in BN
also impede the low bit-width quantization techniques, which draws much
attention in deep learning hardware community. In this work, we propose an
L1-norm BN (L1BN) with only linear operations in both the forward and the
backward propagations during training. L1BN is shown to be approximately
equivalent to the original L2-norm BN (L2BN) by multiplying a scaling factor.
Experiments on various convolutional neural networks (CNNs) and generative
adversarial networks (GANs) reveal that L1BN maintains almost the same
accuracies and convergence rates compared to L2BN but with higher computational
efficiency. On FPGA platform, the proposed signum and absolute operations in
L1BN can achieve 1.5$\times$ speedup and save 50\% power consumption, compared
with the original costly square and root operations, respectively. This
hardware-friendly normalization method not only surpasses L2BN in speed, but
also simplify the hardware design of ASIC accelerators with higher energy
efficiency. Last but not the least, L1BN promises a fully quantized training of
DNNs, which is crucial to future adaptive terminal devices.
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising
Authors: Junqi Jin, Chengru Song, Han Li, Kun Gai, Jun Wang, Wei-Nan Zhang
Date: 2018-02-27 00:00:00
Tasks: Clustering, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Real-time advertising allows advertisers to bid for each impression for a
visiting user. To optimize specific goals such as maximizing revenue and return
on investment (ROI) led by ad placements, advertisers not only need to estimate
the relevance between the ads and user's interests, but most importantly
require a strategic response with respect to other advertisers bidding in the
market. In this paper, we formulate bidding optimization with multi-agent
reinforcement learning. To deal with a large number of advertisers, we propose
a clustering method and assign each cluster with a strategic bidding agent. A
practical Distributed Coordinated Multi-Agent Bidding (DCMAB) has been proposed
and implemented to balance the tradeoff between the competition and cooperation
among advertisers. The empirical study on our industry-scaled real-world data
has demonstrated the effectiveness of our methods. Our results show
cluster-based bidding would largely outperform single-agent and bandit
approaches, and the coordinated bidding achieves better overall objectives than
purely self-interested bidding agents.