pwc-archive/papers-with-abstracts huggingface dataset:
Paradigm shift in electron-based crystallography via machine learning
Authors: Kevin Kaufmann, Chaoyi Zhu, Alexander S. Rosengarten, Daniel Maryanovsky, Tyler J. Harrington, Eduardo Marin, Kenneth S. Vecchio
Date: 2019-02-10 00:00:00
Tasks: BIG-bench Machine Learning

Accurately determining the crystallographic structure of a material, organic
or inorganic, is a critical primary step in material development and analysis.
The most common practices involve analysis of diffraction patterns produced in
laboratory XRD, TEM, and synchrotron X-ray sources. However, these techniques
are slow, require careful sample preparation, can be difficult to access, and
are prone to human error during analysis. This paper presents a newly developed
methodology that represents a paradigm change in electron diffraction-based
structure analysis techniques, with the potential to revolutionize multiple
crystallography-related fields. A machine learning-based approach for rapid and
autonomous identification of the crystal structure of metals and alloys,
ceramics, and geological specimens, without any prior knowledge of the sample,
is presented and demonstrated utilizing the electron backscatter diffraction
(EBSD) technique. Electron backscatter diffraction patterns are collected from
materials with well-known crystal structures, then a deep neural network model
is constructed for classification to a specific Bravais lattice or point group.
The applicability of this approach is evaluated on diffraction patterns from
samples unknown to the computer without any human input or data filtering. This
is in comparison to traditional Hough transform EBSD, which requires that you
have already determined the phases present in your sample. The internal
operations of the neural network are elucidated through visualizing the
symmetry features learned by the convolutional neural network. It is determined
that the model looks for the same features a crystallographer would use, even
though it is not explicitly programmed to do so. This study opens the door to
fully automated, high-throughput determination of crystal structures via
several electron-based diffraction techniques.
Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion
Authors: Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C. Alexander, Nathan Silberman
Date: 2019-02-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: image-classification, Image Classification

The predictive performance of supervised learning algorithms depends on the quality of labels. In a typical label collection process, multiple annotators provide subjective noisy estimates of the "truth" under the influence of their varying skill-levels and biases. Blindly treating these noisy labels as the ground truth limits the accuracy of learning algorithms in the presence of strong disagreement. This problem is critical for applications in domains such as medical imaging where both the annotation cost and inter-observer variability are high. In this work, we present a method for simultaneously learning the individual annotator model and the underlying true label distribution, using only noisy observations. Each annotator is modeled by a confusion matrix that is jointly estimated along with the classifier predictions. We propose to add a regularization term to the loss function that encourages convergence to the true annotator confusion matrix. We provide a theoretical argument as to how the regularization is essential to our approach both for the case of single annotator and multiple annotators. Despite the simplicity of the idea, experiments on image classification tasks with both simulated and real labels show that our method either outperforms or performs on par with the state-of-the-art methods and is capable of estimating the skills of annotators even with a single label available per image.
Differential Similarity in Higher Dimensional Spaces: Theory and Applications
Authors: L. Thorne McCarty
Date: 2019-02-10 00:00:00
Tasks: Clustering

This paper presents an extension and an elaboration of the theory of differential similarity, which was originally proposed in arXiv:1401.2411 [cs.LG]. The goal is to develop an algorithm for clustering and coding that combines a geometric model with a probabilistic model in a principled way. For simplicity, the geometric model in the earlier paper was restricted to the three-dimensional case. The present paper removes this restriction, and considers the full $n$-dimensional case. Although the mathematical model is the same, the strategies for computing solutions in the $n$-dimensional case are different, and one of the main purposes of this paper is to develop and analyze these strategies. Another main purpose is to devise techniques for estimating the parameters of the model from sample data, again in $n$ dimensions. We evaluate the solution strategies and the estimation techniques by applying them to two familiar real-world examples: the classical MNIST dataset and the CIFAR-10 dataset.
Neural embeddings for metaphor detection in a corpus of Greek texts
Authors: Eirini Florou, Konstantinos Perifanos, Dionysis Goutsos
Date: 2019-02-10 00:00:00

One of the major challenges that NLP faces is metaphor detection, especially
by automatic means, a task that becomes even more difficult for languages
lacking in linguistic resources and tools. Our purpose is the automatic
differentiation between literal and metaphorical meaning in authentic
non-annotated phrases from the Corpus of Greek Texts by means of computational
methods of machine learning. For this purpose the theoretical background of
distributional semantics is discussed and employed. Distributional Semantics
Theory develops concepts and methods for the quantification and classification
of semantic similarities displayed by linguistic elements in large amounts of
linguistic data according to their distributional properties. In accordance
with this model, the approach followed in the thesis takes into account the
linguistic context for the computation of the distributional representation of
phrases in geometrical space, as well as for their comparison with the
distributional representations of other phrases, whose function in speech is
already "known" with the objective to reach conclusions about their literal or
metaphorical function in the specific linguistic context. This procedure aims
at dealing with the lack of linguistic resources for the Greek language, as the
almost impossible up to now semantic comparison between "phrases", takes the
form of an arithmetical comparison of their distributional representations in
geometrical space.
Word embeddings for idiolect identification
Authors: Konstantinos Perifanos, Eirini Florou, Dionysis Goutsos
Date: 2019-02-10 00:00:00
Tasks: Authorship Attribution, Word Embeddings

The term idiolect refers to the unique and distinctive use of language of an
individual and it is the theoretical foundation of Authorship Attribution. In
this paper we are focusing on learning distributed representations (embeddings)
of social media users that reflect their writing style. These representations
can be considered as stylistic fingerprints of the authors. We are exploring
the performance of the two main flavours of distributed representations, namely
embeddings produced by Neural Probabilistic Language models (such as word2vec)
and matrix factorization (such as GloVe).
A Bandit Framework for Optimal Selection of Reinforcement Learning Agents
Authors: Andreas Merentitis, Kashif Rasul, Roland Vollgraf, Abdul-Saboor Sheikh, Urs Bergmann
Date: 2019-02-10 00:00:00
Tasks: Deep Reinforcement Learning, Inductive Bias, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep Reinforcement Learning has been shown to be very successful in complex
games, e.g. Atari or Go. These games have clearly defined rules, and hence
allow simulation. In many practical applications, however, interactions with
the environment are costly and a good simulator of the environment is not
available. Further, as environments differ by application, the optimal
inductive bias (architecture, hyperparameters, etc.) of a reinforcement agent
depends on the application. In this work, we propose a multi-arm bandit
framework that selects from a set of different reinforcement learning agents to
choose the one with the best inductive bias. To alleviate the problem of sparse
rewards, the reinforcement learning agents are augmented with surrogate
rewards. This helps the bandit framework to select the best agents early, since
these rewards are smoother and less sparse than the environment reward. The
bandit has the double objective of maximizing the reward while the agents are
learning and selecting the best agent after a finite number of learning steps.
Our experimental results on standard environments show that the proposed
framework is able to consistently select the optimal agent after a finite
number of steps, while collecting more cumulative reward compared to selecting
a sub-optimal architecture or uniformly alternating between different agents.
Iterative Least Trimmed Squares for Mixed Linear Regression
Authors: Yanyao Shen, Sujay Sanghavi
Date: 2019-02-10 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: regression

Given a linear regression setting, Iterative Least Trimmed Squares (ILTS) involves alternating between (a) selecting the subset of samples with lowest current loss, and (b) re-fitting the linear model only on that subset. Both steps are very fast and simple. In this paper we analyze ILTS in the setting of mixed linear regression with corruptions (MLR-C). We first establish deterministic conditions (on the features etc.) under which the ILTS iterate converges linearly to the closest mixture component. We also provide a global algorithm that uses ILTS as a subroutine, to fully solve mixed linear regressions with corruptions. We then evaluate it for the widely studied setting of isotropic Gaussian features, and establish that we match or better existing results in terms of sample complexity. Finally, we provide an ODE analysis for a gradient-descent variant of ILTS that has optimal time complexity. Our results provide initial theoretical evidence that iteratively fitting to the best subset of samples -- a potentially widely applicable idea -- can provably provide state of the art performance in bad training data settings.
Context-Aware Visual Compatibility Prediction
Authors: Guillem Cucurull, Perouz Taslakian, David Vazquez
Date: 2019-02-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Graph Neural Network, Metric Learning, Prediction, Recommendation Systems, Slot Filling

How do we determine whether two or more clothing items are compatible or
visually appealing? Part of the answer lies in understanding of visual
aesthetics, and is biased by personal preferences shaped by social attitudes,
time, and place. In this work we propose a method that predicts compatibility
between two items based on their visual features, as well as their context. We
define context as the products that are known to be compatible with each of
these item. Our model is in contrast to other metric learning approaches that
rely on pairwise comparisons between item features alone. We address the
compatibility prediction problem using a graph neural network that learns to
generate product embeddings conditioned on their context. We present results
for two prediction tasks (fill in the blank and outfit compatibility) tested on
two fashion datasets Polyvore and Fashion-Gen, and on a subset of the Amazon
dataset; we achieve state of the art results when using context information and
show how test performance improves as more context is used.
(q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs
Authors: Anton Mallasto, Jes Frellsen, Wouter Boomsma, Aasa Feragen
Date: 2019-02-10 00:00:00

Generative Adversial Networks (GANs) have made a major impact in computer
vision and machine learning as generative models. Wasserstein GANs (WGANs)
brought Optimal Transport (OT) theory into GANs, by minimizing the
$1$-Wasserstein distance between model and data distributions as their
objective function. Since then, WGANs have gained considerable interest due to
their stability and theoretical framework. We contribute to the WGAN literature
by introducing the family of $(q,p)$-Wasserstein GANs, which allow the use of
more general $p$-Wasserstein metrics for $p\geq 1$ in the GAN learning
procedure. While the method is able to incorporate any cost function as the
ground metric, we focus on studying the $l^q$ metrics for $q\geq 1$. This is a
notable generalization as in the WGAN literature the OT distances are commonly
based on the $l^2$ ground metric. We demonstrate the effect of different
$p$-Wasserstein distances in two toy examples. Furthermore, we show that the
ground metric does make a difference, by comparing different $(q,p)$ pairs on
the MNIST and CIFAR-10 datasets. Our experiments demonstrate that changing the
ground metric and $p$ can notably improve on the common $(q,p) = (2,1)$ case.
Machine Learning With Feature Selection Using Principal Component Analysis for Malware Detection: A Case Study
Authors: Jason Zhang
Date: 2019-02-10 00:00:00
Tasks: BIG-bench Machine Learning, Dimensionality Reduction, feature selection, Malware Detection

Cyber security threats have been growing significantly in both volume and
sophistication over the past decade. This poses great challenges to malware
detection without considerable automation. In this paper, we have proposed a
novel approach by extending our recently suggested artificial neural network
(ANN) based model with feature selection using the principal component analysis
(PCA) technique for malware detection. The effectiveness of the approach has
been successfully demonstrated with the application in PDF malware detection. A
varying number of principal components is examined in the comparative study.
Our evaluation shows that the model with PCA can significantly reduce feature
redundancy and learning time with minimum impact on data information loss, as
confirmed by both training and testing results based on around 105,000
real-world PDF documents. Of the evaluated models using PCA, the model with 32
principal feature components exhibits very similar training accuracy to the
model using the 48 original features, resulting in around 33% dimensionality
reduction and 22% less learning time. The testing results further confirm the
effectiveness and show that the model is able to achieve 93.17% true positive
rate (TPR) while maintaining the same low false positive rate (FPR) of 0.08% as
the case when no feature selection is applied, which significantly outperforms
all evaluated seven well known commercial antivirus (AV) scanners of which the
best scanner only has a TPR of 84.53%.
An Algorithm for Approximating Continuous Functions on Compact Subsets with a Neural Network with one Hidden Layer
Authors: Elliott Zaresky-Williams
Date: 2019-02-10 00:00:00

George Cybenko's landmark 1989 paper showed that there exists a feedforward
neural network, with exactly one hidden layer (and a finite number of neurons),
that can arbitrarily approximate a given continuous function $f$ on the unit
hypercube. The paper did not address how to find the weight/parameters of such
a network, or if finding them would be computationally feasible. This paper
outlines an algorithm for a neural network with exactly one hidden layer to
reconstruct any continuous scalar or vector valued continuous function.
Kurdyka-Łojasiewicz exponent via inf-projection
Authors: Peiran Yu, Guoyin Li, Ting Kei Pong
Date: 2019-02-10 00:00:00

Kurdyka-Lojasiewicz (KL) exponent plays an important role in estimating the convergence rate of many contemporary first-order methods. In particular, a KL exponent of $\frac12$ for a suitable potential function is related to local linear convergence. Nevertheless, KL exponent is in general extremely hard to estimate. In this paper, we show under mild assumptions that KL exponent is preserved via inf-projection. Inf-projection is a fundamental operation that is ubiquitous when reformulating optimization problems via the lift-and-project approach. By studying its operation on KL exponent, we show that the KL exponent is $\frac12$ for several important convex optimization models, including some semidefinite-programming-representable functions and some functions that involve $C^2$-cone reducible structures, under conditions such as strict complementarity. Our results are applicable to concrete optimization models such as group fused Lasso and overlapping group Lasso. In addition, for nonconvex models, we show that the KL exponent of many difference-of-convex functions can be derived from that of their natural majorant functions, and the KL exponent of the Bregman envelope of a function is the same as that of the function itself. Finally, we estimate the KL exponent of the sum of the least squares function and the indicator function of the set of matrices of rank at most $k$.
Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition
Authors: Sze-Teng Liong, Y. S. Gan, John See, Huai-Qian Khor, Yen-Chang Huang
Date: 2019-02-10 00:00:00
Tasks: Micro Expression Recognition, Micro-Expression Recognition, Optical Flow Estimation

In the recent year, state-of-the-art for facial micro-expression recognition have been significantly advanced by deep neural networks. The robustness of deep learning has yielded promising performance beyond that of traditional handcrafted approaches. Most works in literature emphasized on increasing the depth of networks and employing highly complex objective functions to learn more features. In this paper, we design a Shallow Triple Stream Three-dimensional CNN (STSTNet) that is computationally light whilst capable of extracting discriminative high level features and details of micro-expressions. The network learns from three optical flow features (i.e., optical strain, horizontal and vertical optical flow fields) computed based on the onset and apex frames of each video. Our experimental results demonstrate the effectiveness of the proposed STSTNet, which obtained an unweighted average recall rate of 0.7605 and unweighted F1-score of 0.7353 on the composite database consisting of 442 samples from the SMIC, CASME II and SAMM databases.
Diverse Exploration via Conjugate Policies for Policy Gradient Methods
Authors: Andrew Cohen, Xingye Qiao, Lei Yu, Elliot Way, Xiangrong Tong
Date: 2019-02-10 00:00:00
Tasks: Policy Gradient Methods

We address the challenge of effective exploration while maintaining good
performance in policy gradient methods. As a solution, we propose diverse
exploration (DE) via conjugate policies. DE learns and deploys a set of
conjugate policies which can be conveniently generated as a byproduct of
conjugate gradient descent. We provide both theoretical and empirical results
showing the effectiveness of DE at achieving exploration, improving policy
performance, and the advantage of DE over exploration by random policy
perturbations.
A Decoupled 3D Facial Shape Model by Adversarial Training
Authors: Victoria Fernandez Abrevaya, Adnane Boukhayma, Stefanie Wuhrer, Edmond Boyer
Date: 2019-02-10 00:00:00
Proceeding: ICCV 2019 10
Tasks: Diversity

Data-driven generative 3D face models are used to compactly encode facial shape data into meaningful parametric representations. A desirable property of these models is their ability to effectively decouple natural sources of variation, in particular identity and expression. While factorized representations have been proposed for that purpose, they are still limited in the variability they can capture and may present modeling artifacts when applied to tasks such as expression transfer. In this work, we explore a new direction with Generative Adversarial Networks and show that they contribute to better face modeling performances, especially in decoupling natural factors, while also achieving more diverse samples. To train the model we introduce a novel architecture that combines a 3D generator with a 2D discriminator that leverages conventional CNNs, where the two components are bridged by a geometry mapping layer. We further present a training scheme, based on auxiliary classifiers, to explicitly disentangle identity and expression attributes. Through quantitative and qualitative results on standard face datasets, we illustrate the benefits of our model and demonstrate that it outperforms competing state of the art methods in terms of decoupling and diversity.
Towards Automatic Lesion Classification in the Upper Aerodigestive Tract Using OCT and Deep Transfer Learning Methods
Authors: Nils Gessert, Matthias Schlüter, Sarah Latus, Veronika Volgger, Christian Betz, Alexander Schlaefer
Date: 2019-02-10 00:00:00
Tasks: Decision Making, General Classification, Lesion Classification, Transfer Learning

Early detection of cancer is crucial for treatment and overall patient
survival. In the upper aerodigestive tract (UADT) the gold standard for
identification of malignant tissue is an invasive biopsy. Recently,
non-invasive imaging techniques such as confocal laser microscopy and optical
coherence tomography (OCT) have been used for tissue assessment. In particular,
in a recent study experts classified lesions in the UADT with respect to their
invasiveness using OCT images only. As the results were promising, automatic
classification of lesions might be feasible which could assist experts in their
decision making. Therefore, we address the problem of automatic lesion
classification from OCT images. This task is very challenging as the available
dataset is extremely small and the data quality is limited. However, as similar
issues are typical in many clinical scenarios we study to what extent deep
learning approaches can still be trained and used for decision support.
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"
Authors: Erich Schubert, Arthur Zimek
Date: 2019-02-10 00:00:00
Tasks: Benchmarking, Clustering, Dimensionality Reduction, Information Retrieval, Outlier Detection, Retrieval, Text Clustering, Unsupervised Anomaly Detection

This paper documents the release of the ELKI data mining framework, version
0.7.5.
  ELKI is an open source (AGPLv3) data mining software written in Java. The
focus of ELKI is research in algorithms, with an emphasis on unsupervised
methods in cluster analysis and outlier detection. In order to achieve high
performance and scalability, ELKI offers data index structures such as the
R*-tree that can provide major performance gains. ELKI is designed to be easy
to extend for researchers and students in this domain, and welcomes
contributions of additional methods. ELKI aims at providing a large collection
of highly parameterizable algorithms, in order to allow easy and fair
evaluation and benchmarking of algorithms.
  We will first outline the motivation for this release, the plans for the
future, and then give a brief overview over the new functionality in this
version. We also include an appendix presenting an overview on the overall
implemented functionality.