pwc-archive/papers-with-abstracts huggingface dataset:
Matrix Cofactorization for Joint Representation Learning and Supervised Classification -- Application to Hyperspectral Image Analysis
Authors: Adrien Lagrange, Mathieu Fauvel, Stéphane May, José Bioucas-Dias, Nicolas Dobigeon
Date: 2019-02-07 00:00:00
Tasks: Classification, Clustering, General Classification, Hyperspectral image analysis, Representation Learning

Supervised classification and representation learning are two widely used classes of methods to analyze multivariate images. Although complementary, these methods have been scarcely considered jointly in a hierarchical modeling. In this paper, a method coupling these two approaches is designed using a matrix cofactorization formulation. Each task is modeled as a factorization matrix problem and a term relating both coding matrices is then introduced to drive an appropriate coupling. The link can be interpreted as a clustering operation over a low-dimensional representation vectors. The attribution vectors of the clustering are then used as features vectors for the classification task, i.e., the coding vectors of the corresponding factorization problem. A proximal gradient descent algorithm, ensuring convergence to a critical point of the objective function, is then derived to solve the resulting non-convex non-smooth optimization problem. An evaluation of the proposed method is finally conducted both on synthetic and real data in the specific context of hyperspectral image interpretation, unifying two standard analysis techniques, namely unmixing and classification.
A Manually-Curated Dataset of Fixes to Vulnerabilities of Open-Source Software
Authors: Serena E. Ponta, Henrik Plate, Antonino Sabetta, Michele Bezzi, Cédric Dangremont
Date: 2019-02-07 00:00:00

Advancing our understanding of software vulnerabilities, automating their
identification, the analysis of their impact, and ultimately their mitigation
is necessary to enable the development of software that is more secure. While
operating a vulnerability assessment tool that we developed and that is
currently used by hundreds of development units at SAP, we manually collected
and curated a dataset of vulnerabilities of open-source software and the
commits fixing them. The data was obtained both from the National Vulnerability
Database (NVD) and from project-specific Web resources that we monitor on a
continuous basis. From that data, we extracted a dataset that maps 624 publicly
disclosed vulnerabilities affecting 205 distinct open-source Java projects,
used in SAP products or internal tools, onto the 1282 commits that fix them.
Out of 624 vulnerabilities, 29 do not have a CVE identifier at all and 46,
which do have a CVE identifier assigned by a numbering authority, are not
available in the NVD yet. The dataset is released under an open-source license,
together with supporting scripts that allow researchers to automatically
retrieve the actual content of the commits from the corresponding repositories
and to augment the attributes available for each instance. Also, these scripts
allow to complement the dataset with additional instances that are not security
fixes (which is useful, for example, in machine learning applications). Our
dataset has been successfully used to train classifiers that could
automatically identify security-relevant commits in code repositories. The
release of this dataset and the supporting code as open-source will allow
future research to be based on data of industrial relevance; also, it
represents a concrete step towards making the maintenance of this dataset a
shared effort involving open-source communities, academia, and the industry.
Beholder-GAN: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level
Authors: Nir Diamant, Dean Zadok, Chaim Baskin, Eli Schwartz, Alex M. Bronstein
Date: 2019-02-07 00:00:00

Beauty is in the eye of the beholder. This maxim, emphasizing the
subjectivity of the perception of beauty, has enjoyed a wide consensus since
ancient times. In the digitalera, data-driven methods have been shown to be
able to predict human-assigned beauty scores for facial images. In this work,
we augment this ability and train a generative model that generates faces
conditioned on a requested beauty score. In addition, we show how this trained
generator can be used to beautify an input face image. By doing so, we achieve
an unsupervised beautification model, in the sense that it relies on no ground
truth target images.
Self-Adjusting Mutation Rates with Provably Optimal Success Rules
Authors: Benjamin Doerr, Carola Doerr, Johannes Lengler
Date: 2019-02-07 00:00:00
Tasks: Evolutionary Algorithms

The one-fifth success rule is one of the best-known and most widely accepted techniques to control the parameters of evolutionary algorithms. While it is often applied in the literal sense, a common interpretation sees the one-fifth success rule as a family of success-based updated rules that are determined by an update strength $F$ and a success rate. We analyze in this work how the performance of the (1+1) Evolutionary Algorithm on LeadingOnes depends on these two hyper-parameters. Our main result shows that the best performance is obtained for small update strengths $F=1+o(1)$ and success rate $1/e$. We also prove that the running time obtained by this parameter setting is, apart from lower order terms, the same that is achieved with the best fitness-dependent mutation rate. We show similar results for the resampling variant of the (1+1) Evolutionary Algorithm, which enforces to flip at least one bit per iteration.
Unsupervised Data Uncertainty Learning in Visual Retrieval Systems
Authors: Ahmed Taha, Yi-Ting Chen, Teruhisa Misu, Abhinav Shrivastava, Larry Davis
Date: 2019-02-07 00:00:00
Tasks: Retrieval, Triplet, Video Retrieval

We introduce an unsupervised formulation to estimate heteroscedastic
uncertainty in retrieval systems. We propose an extension to triplet loss that
models data uncertainty for each input. Besides improving performance, our
formulation models local noise in the embedding space. It quantifies input
uncertainty and thus enhances interpretability of the system. This helps
identify noisy observations in query and search databases. Evaluation on both
image and video retrieval applications highlight the utility of our approach.
We highlight our efficiency in modeling local noise using two real-world
datasets: Clothing1M and Honda Driving datasets. Qualitative results illustrate
our ability in identifying confusing scenarios in various domains. Uncertainty
learning also enables data cleaning by detecting noisy training labels.
The few-get-richer: a surprising consequence of popularity-based rankings
Authors: Fabrizio Germano, Vicenç Gómez, Gaël Le Mens
Date: 2019-02-07 00:00:00
Tasks: Misinformation, Recommendation Systems

Ranking algorithms play a crucial role in online platforms ranging from search engines to recommender systems. In this paper, we identify a surprising consequence of popularity-based rankings: the fewer the items reporting a given signal, the higher the share of the overall traffic they collectively attract. This few-get-richer effect emerges in settings where there are few distinct classes of items (e.g., left-leaning news sources versus right-leaning news sources), and items are ranked based on their popularity. We demonstrate analytically that the few-get-richer effect emerges when people tend to click on top-ranked items and have heterogeneous preferences for the classes of items. Using simulations, we analyze how the strength of the effect changes with assumptions about the setting and human behavior. We also test our predictions experimentally in an online experiment with human participants. Our findings have important implications to understand the spread of misinformation.
The Actor-Advisor: Policy Gradient With Off-Policy Advice
Authors: Hélène Plisnier, Denis Steckelmacher, Diederik M. Roijers, Ann Nowé
Date: 2019-02-07 00:00:00
Tasks: Transfer Learning

Actor-critic algorithms learn an explicit policy (actor), and an accompanying
value function (critic). The actor performs actions in the environment, while
the critic evaluates the actor's current policy. However, despite their
stability and promising convergence properties, current actor-critic algorithms
do not outperform critic-only ones in practice. We believe that the fact that
the critic learns Q^pi, instead of the optimal Q-function Q*, prevents
state-of-the-art robust and sample-efficient off-policy learning algorithms
from being used. In this paper, we propose an elegant solution, the
Actor-Advisor architecture, in which a Policy Gradient actor learns from
unbiased Monte-Carlo returns, while being shaped (or advised) by the Softmax
policy arising from an off-policy critic. The critic can be learned
independently from the actor, using any state-of-the-art algorithm. Being
advised by a high-quality critic, the actor quickly and robustly learns the
task, while its use of the Monte-Carlo return helps overcome any bias the
critic may have. In addition to a new Actor-Critic formulation, the
Actor-Advisor, a method that allows an external advisory policy to shape a
Policy Gradient actor, can be applied to many other domains. By varying the
source of advice, we demonstrate the wide applicability of the Actor-Advisor to
three other important subfields of RL: safe RL with backup policies, efficient
leverage of domain knowledge, and transfer learning in RL. Our experimental
results demonstrate the benefits of the Actor-Advisor compared to
state-of-the-art actor-critic methods, illustrate its applicability to the
three other application scenarios listed above, and show that many important
challenges of RL can now be solved using a single elegant solution.
Random Matrix Improved Covariance Estimation for a Large Class of Metrics
Authors: Malik Tiomoko, Florent Bouchard, Guillaume Ginholac, Romain Couillet
Date: 2019-02-07 00:00:00
Tasks: BIG-bench Machine Learning

Relying on recent advances in statistical estimation of covariance distances
based on random matrix theory, this article proposes an improved covariance and
precision matrix estimation for a wide family of metrics. The method is shown
to largely outperform the sample covariance matrix estimate and to compete with
state-of-the-art methods, while at the same time being computationally simpler.
Applications to linear and quadratic discriminant analyses also demonstrate
significant gains, therefore suggesting practical interest to statistical
machine learning.
Online Clustering by Penalized Weighted GMM
Authors: Shlomo Bugdary, Shay Maymon
Date: 2019-02-07 00:00:00
Tasks: Clustering, Online Clustering

With the dawn of the Big Data era, data sets are growing rapidly. Data is
streaming from everywhere - from cameras, mobile phones, cars, and other
electronic devices. Clustering streaming data is a very challenging problem.
Unlike the traditional clustering algorithms where the dataset can be stored
and scanned multiple times, clustering streaming data has to satisfy
constraints such as limit memory size, real-time response, unknown data
statistics and an unknown number of clusters. In this paper, we present a novel
online clustering algorithm which can be used to cluster streaming data without
knowing the number of clusters a priori. Results on both synthetic and real
datasets show that the proposed algorithm produces partitions which are close
to what you could get if you clustered the whole data at one time.
Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks
Authors: Panos Parpas, Corey Muir
Date: 2019-02-07 00:00:00
Tasks: Distributed Optimization

The links between optimal control of dynamical systems and neural networks
have proved beneficial both from a theoretical and from a practical point of
view. Several researchers have exploited these links to investigate the
stability of different neural network architectures and develop memory
efficient training algorithms. We also adopt the dynamical systems view of
neural networks, but our aim is different from earlier works. We exploit the
links between dynamical systems, optimal control, and neural networks to
develop a novel distributed optimization algorithm. The proposed algorithm
addresses the most significant obstacle for distributed algorithms for neural
network optimization: the network weights cannot be updated until the forward
propagation of the data, and backward propagation of the gradients are
complete. Using the dynamical systems point of view, we interpret the layers of
a (residual) neural network as the discretized dynamics of a dynamical system
and exploit the relationship between the co-states (adjoints) of the optimal
control problem and backpropagation. We then develop a parallel-in-time method
that updates the parameters of the network without waiting for the forward or
back propagation algorithms to complete in full. We establish the convergence
of the proposed algorithm. Preliminary numerical results suggest that the
algorithm is competitive and more efficient than the state-of-the-art.
Ensemble Prediction of Time to Event Outcomes with Competing Risks: A Case Study of Surgical Complications in Crohn's Disease
Authors: Michael C Sachs, Andrea Discacciati, Åsa Everhov, Ola Olén, Erin E Gabriel
Date: 2019-02-07 00:00:00
Tasks: BIG-bench Machine Learning

We develop a novel algorithm to predict the occurrence of major abdominal
surgery within 5 years following Crohn's disease diagnosis using a panel of 29
baseline covariates from the Swedish population registers. We model
pseudo-observations based on the Aalen-Johansen estimator of the cause-specific
cumulative incidence with an ensemble of modern machine learning approaches.
Pseudo-observation pre-processing easily extends all existing or new machine
learning procedures to right-censored event history data. We propose
pseudo-observation based estimators for the area under the time varying ROC
curve, for optimizing the ensemble, and the predictiveness curve, for
evaluating and summarizing predictive performance.
DoPAMINE: Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling
Authors: Sunghwan Joo, Sungmin Cha, Taesup Moon
Date: 2019-02-07 00:00:00
Tasks: Denoising

We propose DoPAMINE, a new neural network based multiplicative noise
despeckling algorithm. Our algorithm is inspired by Neural AIDE (N-AIDE), which
is a recently proposed neural adaptive image denoiser. While the original
N-AIDE was designed for the additive noise case, we show that the same
framework, i.e., adaptively learning a network for pixel-wise affine denoisers
by minimizing an unbiased estimate of MSE, can be applied to the multiplicative
noise case as well. Moreover, we derive a double-sided masked CNN architecture
which can control the variance of the activation values in each layer and
converge fast to high denoising performance during supervised training. In the
experimental results, we show our DoPAMINE possesses high adaptivity via
fine-tuning the network parameters based on the given noisy image and achieves
significantly better despeckling results compared to SAR-DRN, a
state-of-the-art CNN-based algorithm.
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
Authors: Tiago Ramalho, Marta Garnelo
Date: 2019-02-07 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decoder, Few-Shot Learning, General Classification, Meta-Learning

The ability to generalize quickly from few observations is crucial for
intelligent systems. In this paper we introduce APL, an algorithm that
approximates probability distributions by remembering the most surprising
observations it has encountered. These past observations are recalled from an
external memory module and processed by a decoder network that can combine
information from different memory slots to generalize beyond direct recall. We
show this algorithm can perform as well as state of the art baselines on
few-shot classification benchmarks with a smaller memory footprint. In
addition, its memory compression allows it to scale to thousands of unknown
labels. Finally, we introduce a meta-learning reasoning task which is more
challenging than direct classification. In this setting, APL is able to
generalize with fewer than one example per class via deductive reasoning.
Agent-Based Adaptive Level Generation for Dynamic Difficulty Adjustment in Angry Birds
Authors: Matthew Stephenson, Jochen Renz
Date: 2019-02-07 00:00:00

This paper presents an adaptive level generation algorithm for the
physics-based puzzle game Angry Birds. The proposed algorithm is based on a
pre-existing level generator for this game, but where the difficulty of the
generated levels can be adjusted based on the player's performance. This allows
for the creation of personalised levels tailored specifically to the player's
own abilities. The effectiveness of our proposed method is evaluated using
several agents with differing strategies and AI techniques. By using these
agents as models / representations of real human player's characteristics, we
can optimise level properties efficiently over a large number of generations.
As a secondary investigation, we also demonstrate that by combining the
performance of several agents together it is possible to generate levels that
are especially challenging for certain players but not others.
Model Selection for Simulator-based Statistical Models: A Kernel Approach
Authors: Takafumi Kajihara, Motonobu Kanagawa, Yuuki Nakaguchi, Kanishka Khandelwal, Kenji Fukumiziu
Date: 2019-02-07 00:00:00
Tasks: Epidemiology, Model Selection

We propose a novel approach to model selection for simulator-based
statistical models. The proposed approach defines a mixture of candidate
models, and then iteratively updates the weight coefficients for those models
as well as the parameters in each model simultaneously; this is done by
recursively applying Bayes' rule, using the recently proposed kernel recursive
ABC algorithm. The practical advantage of the method is that it can be used
even when a modeler lacks appropriate prior knowledge about the parameters in
each model. We demonstrate the effectiveness of the proposed approach with a
number of experiments, including model selection for dynamical systems in
ecology and epidemiology.
Advances on CNN-based super-resolution of Sentinel-2 images
Authors: Massimiliano Gargiulo
Date: 2019-02-07 00:00:00
Tasks: Super-Resolution

Thanks to their temporal-spatial coverage and free access, Sentinel-2 images
are very interesting for the community. However, a relatively coarse spatial
resolution, compared to that of state-of-the-art commercial products, motivates
the study of super-resolution techniques to mitigate such a limitation.
Specifically, thirtheen bands are sensed simultaneously but at different
spatial resolutions: 10, 20, and 60 meters depending on the spectral location.
Here, building upon our previous convolutional neural network (CNN) based
method, we propose an improved CNN solution to super-resolve the 20-m
resolution bands benefiting spatial details conveyed by the accompanying 10-m
spectral bands.
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso
Authors: Quentin Bertrand, Mathurin Massias, Alexandre Gramfort, Joseph Salmon
Date: 2019-02-07 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: regression

Sparsity promoting norms are frequently used in high dimensional regression. A limitation of such Lasso-type estimators is that the optimal regularization parameter depends on the unknown noise level. Estimators such as the concomitant Lasso address this dependence by jointly estimating the noise level and the regression coefficients. Additionally, in many applications, the data is obtained by averaging multiple measurements: this reduces the noise variance, but it dramatically reduces sample sizes and prevents refined noise modeling. In this work, we propose a concomitant estimator that can cope with complex noise structure by using non-averaged measurements. The resulting optimization problem is convex and amenable, thanks to smoothing theory, to state-of-the-art optimization techniques that leverage the sparsity of the solutions. Practical benefits are demonstrated on toy datasets, realistic simulated data and real neuroimaging data.
Towards Autoencoding Variational Inference for Aspect-based Opinion Summary
Authors: Tai Hoang, Huy Le, Tho Quan
Date: 2019-02-07 00:00:00
Tasks: General Classification, Sentiment Analysis, Sentiment Classification, Topic Models, Variational Inference

Aspect-based Opinion Summary (AOS), consisting of aspect discovery and sentiment classification steps, has recently been emerging as one of the most crucial data mining tasks in e-commerce systems. Along this direction, the LDA-based model is considered as a notably suitable approach, since this model offers both topic modeling and sentiment classification. However, unlike traditional topic modeling, in the context of aspect discovery it is often required some initial seed words, whose prior knowledge is not easy to be incorporated into LDA models. Moreover, LDA approaches rely on sampling methods, which need to load the whole corpus into memory, making them hardly scalable. In this research, we study an alternative approach for AOS problem, based on Autoencoding Variational Inference (AVI). Firstly, we introduce the Autoencoding Variational Inference for Aspect Discovery (AVIAD) model, which extends the previous work of Autoencoding Variational Inference for Topic Models (AVITM) to embed prior knowledge of seed words. This work includes enhancement of the previous AVI architecture and also modification of the loss function. Ultimately, we present the Autoencoding Variational Inference for Joint Sentiment/Topic (AVIJST) model. In this model, we substantially extend the AVI model to support the JST model, which performs topic modeling for corresponding sentiment. The experimental results show that our proposed models enjoy higher topic coherent, faster convergence time and better accuracy on sentiment classification, as compared to their LDA-based counterparts.
Spatial Mixture Models with Learnable Deep Priors for Perceptual Grouping
Authors: Jinyang Yuan, Bin Li, xiangyang xue
Date: 2019-02-07 00:00:00
Tasks: Object

Humans perceive the seemingly chaotic world in a structured and compositional
way with the prerequisite of being able to segregate conceptual entities from
the complex visual scenes. The mechanism of grouping basic visual elements of
scenes into conceptual entities is termed as perceptual grouping. In this work,
we propose a new type of spatial mixture models with learnable priors for
perceptual grouping. Different from existing methods, the proposed method
disentangles the attributes of an object into ``shape'' and ``appearance''
which are modeled separately by the mixture weights and the mixture components.
More specifically, each object in the visual scene is fully characterized by
one latent representation, which is in turn transformed into parameters of the
mixture weight and the mixture component by two neural networks. The mixture
weights focus on modeling spatial dependencies (i.e., shape) and the mixture
components deal with intra-object variations (i.e., appearance). In addition,
the background is separately modeled as a special component complementary to
the foreground objects. Our extensive empirical tests on two perceptual
grouping datasets demonstrate that the proposed method outperforms the
state-of-the-art methods under most experimental configurations. The learned
conceptual entities are generalizable to novel visual scenes and insensitive to
the diversity of objects. Code is available at
https://github.com/jinyangyuan/learnable-deep-priors.