pwc-archive/papers-with-abstracts huggingface dataset:
Class Imbalance Techniques for High Energy Physics
Authors: Christopher W. Murphy
Date: 2019-05-01 00:00:00
Tasks: Vocal Bursts Intensity Prediction

A common problem in a high energy physics experiment is extracting a signal from a much larger background. Posed as a classification task, there is said to be an imbalance in the number of samples belonging to the signal class versus the number of samples from the background class. In this work we provide a brief overview of class imbalance techniques in a high energy physics setting. Two case studies are presented: (1) the measurement of the longitudinal polarization fraction in same-sign $WW$ scattering, and (2) the decay of the Higgs boson to charm-quark pairs.
Bean Split Ratio for Dry Bean Canning Quality and Variety Analysis
Authors: Yunfei Long, Amber Bassett, Karen Cichy, Addie Thompson, Daniel Morris
Date: 2019-05-01 00:00:00

Splits on canned beans appear in the process of preparation and canning.
Researchers are studying how they are influenced by cooking environment and
genotype. However, there is no existing method to automatically quantify or to
characterize the severity of splits. To solve this, we propose two measures:
the Bean Split Ratio (BSR) that quantifies the overall severity of splits, and
the Bean Split Histogram (BSH) that characterizes the size distribution of
splits. We create a pixel-wise segmentation method to automatically estimate
these measures from images. We also present a bean dataset of recombinant
inbred lines of two genotypes, use the BSR and BSH to assess canning quality,
and explore heritability of these properties.
LS-SVR as a Bayesian RBF network
Authors: Diego P. P. Mesquita, Luis A. Freitas, João P. P. Gomes, César L. C. Mattos
Date: 2019-05-01 00:00:00
Tasks: regression

We show theoretical similarities between the Least Squares Support Vector Regression (LS-SVR) model with a Radial Basis Functions (RBF) kernel and maximum a posteriori (MAP) inference on Bayesian RBF networks with a specific Gaussian prior on the regression weights. Although previous works have pointed out similar expressions between those learning approaches, we explicit and formally state the existing correspondences. We empirically demonstrate our result by performing computational experiments with standard regression benchmarks. Our findings open a range of possibilities to improve LS-SVR by borrowing strength from well-established developments in Bayesian methodology.
High-Performance Support Vector Machines and Its Applications
Authors: Taiping He, Tao Wang, Ralph Abbey, Joshua Griffin
Date: 2019-05-01 00:00:00
Tasks: Classification, General Classification, Vocal Bursts Intensity Prediction

The support vector machines (SVM) algorithm is a popular classification
technique in data mining and machine learning. In this paper, we propose a
distributed SVM algorithm and demonstrate its use in a number of applications.
The algorithm is named high-performance support vector machines (HPSVM). The
major contribution of HPSVM is two-fold. First, HPSVM provides a new way to
distribute computations to the machines in the cloud without shuffling the
data. Second, HPSVM minimizes the inter-machine communications in order to
maximize the performance. We apply HPSVM to some real-world classification
problems and compare it with the state-of-the-art SVM technique implemented in
R on several public data sets. HPSVM achieves similar or better results.
Interpretable multiclass classification by MDL-based rule lists
Authors: Hugo M. Proença, Matthijs van Leeuwen
Date: 2019-05-01 00:00:00
Tasks: Classification, General Classification, Model Selection

Interpretable classifiers have recently witnessed an increase in attention from the data mining community because they are inherently easier to understand and explain than their more complex counterparts. Examples of interpretable classification models include decision trees, rule sets, and rule lists. Learning such models often involves optimizing hyperparameters, which typically requires substantial amounts of data and may result in relatively large models. In this paper, we consider the problem of learning compact yet accurate probabilistic rule lists for multiclass classification. Specifically, we propose a novel formalization based on probabilistic rule lists and the minimum description length (MDL) principle. This results in virtually parameter-free model selection that naturally allows to trade-off model complexity with goodness of fit, by which overfitting and the need for hyperparameter tuning are effectively avoided. Finally, we introduce the Classy algorithm, which greedily finds rule lists according to the proposed criterion. We empirically demonstrate that Classy selects small probabilistic rule lists that outperform state-of-the-art classifiers when it comes to the combination of predictive performance and interpretability. We show that Classy is insensitive to its only parameter, i.e., the candidate set, and that compression on the training set correlates with classification performance, validating our MDL-based selection criterion.
Multi-level Encoder-Decoder Architectures for Image Restoration
Authors: Indra Deep Mastan, Shanmuganathan Raman
Date: 2019-05-01 00:00:00
Tasks: Decoder, Denoising, Image Reconstruction, Image Restoration, Super-Resolution

Many real-world solutions for image restoration are learning-free and based on handcrafted image priors such as self-similarity. Recently, deep-learning methods that use training data have achieved state-of-the-art results in various image restoration tasks (e.g., super-resolution and inpainting). Ulyanov et al. bridge the gap between these two families of methods (CVPR 18). They have shown that learning-free methods perform close to the state-of-the-art learning-based methods (approximately 1 PSNR). Their approach benefits from the encoder-decoder network. In this paper, we propose a framework based on the multi-level extensions of the encoder-decoder network, to investigate interesting aspects of the relationship between image restoration and network construction independent of learning. Our framework allows various network structures by modifying the following network components: skip links, cascading of the network input into intermediate layers, a composition of the encoder-decoder subnetworks, and network depth. These handcrafted network structures illustrate how the construction of untrained networks influence the following image restoration tasks: denoising, super-resolution, and inpainting. We also demonstrate image reconstruction using flash and no-flash image pairs. We provide performance comparisons with the state-of-the-art methods for all the restoration tasks above.
Towards computer vision powered color-nutrient assessment of pureed food
Authors: Kaylen J. Pfisterer, Robert Amelard, Braeden Syrnyk, Alexander Wong
Date: 2019-05-01 00:00:00
Tasks: Nutrition

With one in four individuals afflicted with malnutrition, computer vision may
provide a way of introducing a new level of automation in the nutrition field
to reliably monitor food and nutrient intake. In this study, we present a novel
approach to modeling the link between color and vitamin A content using
transmittance imaging of a pureed foods dilution series in a computer vision
powered nutrient sensing system via a fine-tuned deep autoencoder network,
which in this case was trained to predict the relative concentration of sweet
potato purees. Experimental results show the deep autoencoder network can
achieve an accuracy of 80% across beginner (6 month) and intermediate (8 month)
commercially prepared pureed sweet potato samples. Prediction errors may be
explained by fundamental differences in optical properties which are further
discussed.
3DFaceGAN: Adversarial Nets for 3D Face Representation, Generation, and Translation
Authors: Stylianos Moschoglou, Stylianos Ploumpis, Mihalis Nicolaou, Athanasios Papaioannou, Stefanos Zafeiriou
Date: 2019-05-01 00:00:00
Tasks: 3D Shape Representation, Image Generation, Imputation, Super-Resolution, Translation

Over the past few years, Generative Adversarial Networks (GANs) have garnered increased interest among researchers in Computer Vision, with applications including, but not limited to, image generation, translation, imputation, and super-resolution. Nevertheless, no GAN-based method has been proposed in the literature that can successfully represent, generate or translate 3D facial shapes (meshes). This can be primarily attributed to two facts, namely that (a) publicly available 3D face databases are scarce as well as limited in terms of sample size and variability (e.g., few subjects, little diversity in race and gender), and (b) mesh convolutions for deep networks present several challenges that are not entirely tackled in the literature, leading to operator approximations and model instability, often failing to preserve high-frequency components of the distribution. As a result, linear methods such as Principal Component Analysis (PCA) have been mainly utilized towards 3D shape analysis, despite being unable to capture non-linearities and high frequency details of the 3D face - such as eyelid and lip variations. In this work, we present 3DFaceGAN, the first GAN tailored towards modeling the distribution of 3D facial surfaces, while retaining the high frequency details of 3D face shapes. We conduct an extensive series of both qualitative and quantitative experiments, where the merits of 3DFaceGAN are clearly demonstrated against other, state-of-the-art methods in tasks such as 3D shape representation, generation, and translation.
Introducing Graph Smoothness Loss for Training Deep Learning Architectures
Authors: Myriam Bontonou, Carlos Lassance, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega
Date: 2019-05-01 00:00:00
Tasks: Deep Learning, General Classification

We introduce a novel loss function for training deep learning architectures
to perform classification. It consists in minimizing the smoothness of label
signals on similarity graphs built at the output of the architecture.
Equivalently, it can be seen as maximizing the distances between the network
function images of training inputs from distinct classes. As such, only
distances between pairs of examples in distinct classes are taken into account
in the process, and the training does not prevent inputs from the same class to
be mapped to distant locations in the output domain. We show that this loss
leads to similar performance in classification as architectures trained using
the classical cross-entropy, while offering interesting degrees of freedom and
properties. We also demonstrate the interest of the proposed loss to increase
robustness of trained architectures to deviations of the inputs.
AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations
Authors: Xiao Zhang, Rui Zhao, Yu Qiao, Xiaogang Wang, Hongsheng Li
Date: 2019-05-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Face Recognition, Face Verification

The cosine-based softmax losses and their variants achieve great success in deep learning based face recognition. However, hyperparameter settings in these losses have significant influences on the optimization path as well as the final recognition performance. Manually tuning those hyperparameters heavily relies on user experience and requires many training tricks. In this paper, we investigate in depth the effects of two important hyperparameters of cosine-based softmax losses, the scale parameter and angular margin parameter, by analyzing how they modulate the predicted classification probability. Based on these analysis, we propose a novel cosine-based softmax loss, AdaCos, which is hyperparameter-free and leverages an adaptive scale parameter to automatically strengthen the training supervisions during the training process. We apply the proposed AdaCos loss to large-scale face verification and identification datasets, including LFW, MegaFace, and IJB-C 1:1 Verification. Our results show that training deep neural networks with the AdaCos loss is stable and able to achieve high face recognition accuracy. Our method outperforms state-of-the-art softmax losses on all the three datasets.
Learn to synthesize and synthesize to learn
Authors: Behzad Bozorgtabar, Mohammad Saeed Rad, Hazim Kemal Ekenel, Jean-Philippe Thiran
Date: 2019-05-01 00:00:00
Tasks: Attribute, Data Augmentation, Facial Expression Recognition, Facial Expression Recognition (FER), Image Generation, Image-to-Image Translation, Translation

Attribute guided face image synthesis aims to manipulate attributes on a face
image. Most existing methods for image-to-image translation can either perform
a fixed translation between any two image domains using a single attribute or
require training data with the attributes of interest for each subject.
Therefore, these methods could only train one specific model for each pair of
image domains, which limits their ability in dealing with more than two
domains. Another disadvantage of these methods is that they often suffer from
the common problem of mode collapse that degrades the quality of the generated
images. To overcome these shortcomings, we propose attribute guided face image
generation method using a single model, which is capable to synthesize multiple
photo-realistic face images conditioned on the attributes of interest. In
addition, we adopt the proposed model to increase the realism of the simulated
face images while preserving the face characteristics. Compared to existing
models, synthetic face images generated by our method present a good
photorealistic quality on several face datasets. Finally, we demonstrate that
generated facial images can be used for synthetic data augmentation, and
improve the performance of the classifier used for facial expression
recognition.
ASER: A Large-scale Eventuality Knowledge Graph
Authors: Hongming Zhang, Xin Liu, Haojie Pan, Yangqiu Song, Cane Wing-Ki Leung
Date: 2019-05-01 00:00:00
Tasks: Knowledge Graphs, World Knowledge

Understanding human's language requires complex world knowledge. However, existing large-scale knowledge graphs mainly focus on knowledge about entities while ignoring knowledge about activities, states, or events, which are used to describe how entities or things act in the real world. To fill this gap, we develop ASER (activities, states, events, and their relations), a large-scale eventuality knowledge graph extracted from more than 11-billion-token unstructured textual data. ASER contains 15 relation types belonging to five categories, 194-million unique eventualities, and 64-million unique edges among them. Both intrinsic and extrinsic evaluations demonstrate the quality and effectiveness of ASER.
Automatic Dataset Augmentation Using Virtual Human Simulation
Authors: Marcelo C. Ghilardi, Leandro Dihl, Estevão Testa, Pedro Braga, João P. Pianta, Isabel H. Manssour, Soraia R. Musse
Date: 2019-05-01 00:00:00
Tasks: BIG-bench Machine Learning, Event Detection, Pedestrian Detection

Virtual Human Simulation has been widely used for different purposes, such as
comfort or accessibility analysis. In this paper, we investigate the
possibility of using this type of technique to extend the training datasets of
pedestrians to be used with machine learning techniques. Our main goal is to
verify if Computer Graphics (CG) images of virtual humans with a simplistic
rendering can be efficient in order to augment datasets used for training
machine learning methods. In fact, from a machine learning point of view, there
is a need to collect and label large datasets for ground truth, which sometimes
demands manual annotation. In addition, find out images and videos with real
people and also provide ground truth of people detection and counting is not
trivial. If CG images, which can have a ground truth automatically generated,
can also be used as training in machine learning techniques for pedestrian
detection and counting, it can certainly facilitate and optimize the whole
process of event detection. In particular, we propose to parametrize virtual
humans using a data-driven approach. Results demonstrated that using the
extended datasets with CG images outperforms the results when compared to only
real images sequences.
Surface Type Classification for Autonomous Robot Indoor Navigation
Authors: Francesco Lomio, Erjon Skenderi, Damoon Mohamadi, Jussi Collin, Reza Ghabcheloo, Heikki Huttunen
Date: 2019-05-01 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Time Series, Time Series Analysis, Time Series Classification, Vocal Bursts Type Prediction

In this work we describe the preparation of a time series dataset of inertial
measurements for determining the surface type under a wheeled robot. The data
consists of over 7600 labeled time series samples, with the corresponding
surface type annotation. This data was used in two public competitions with
over 1500 participant in total. Additionally, we describe the performance of
state-of-art deep learning models for time series classification, as well as
propose a baseline model based on an ensemble of machine learning methods. The
baseline achieves an accuracy of over 68% with our nine-category dataset.
A Self-Organizing Network with Varying Density Structure for Characterizing Sensorimotor Transformations in Robotic Systems
Authors: Omar Zahra, David Navarro-Alarcon
Date: 2019-05-01 00:00:00

In this work, we present the development of a neuro-inspired approach for
characterizing sensorimotor relations in robotic systems. The proposed method
has self-organizing and associative properties that enable it to autonomously
obtain these relations without any prior knowledge of either the motor (e.g.
mechanical structure) or perceptual (e.g. sensor calibration) models.
Self-organizing topographic properties are used to build both sensory and motor
maps, then the associative properties rule the stability and accuracy of the
emerging connections between these maps. Compared to previous works, our method
introduces a new varying density self-organizing map (VDSOM) that controls the
concentration of nodes in regions with large transformation errors without
affecting much the computational time. A distortion metric is measured to
achieve a self-tuning sensorimotor model that adapts to changes in either motor
or sensory models. The obtained sensorimotor maps prove to have less error than
conventional self-organizing methods and potential for further development.
Context-Dependent Semantic Parsing over Temporally Structured Data
Authors: Charles Chen, Razvan Bunescu
Date: 2019-05-01 00:00:00
Tasks: Decoder, Semantic Parsing, Time Series, Time Series Analysis

We describe a new semantic parsing setting that allows users to query the
system using both natural language questions and actions within a graphical
user interface. Multiple time series belonging to an entity of interest are
stored in a database and the user interacts with the system to obtain a better
understanding of the entity's state and behavior, entailing sequences of
actions and questions whose answers may depend on previous factual or
navigational interactions. We design an LSTM-based encoder-decoder architecture
that models context dependency through copying mechanisms and multiple levels
of attention over inputs and previous outputs. When trained to predict tokens
using supervised learning, the proposed architecture substantially outperforms
standard sequence generation baselines. Training the architecture using policy
gradient leads to further improvements in performance, reaching a
sequence-level accuracy of 88.7% on artificial data and 74.8% on real data.
Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving
Authors: Sascha Rosbach, Vinit James, Simon Großjohann, Silviu Homoceanu, Stefan Roth
Date: 2019-05-01 00:00:00
Tasks: Motion Planning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Behavior and motion planning play an important role in automated driving. Traditionally, behavior planners instruct local motion planners with predefined behaviors. Due to the high scene complexity in urban environments, unpredictable situations may occur in which behavior planners fail to match predefined behavior templates. Recently, general-purpose planners have been introduced, combining behavior and local motion planning. These general-purpose planners allow behavior-aware motion planning given a single reward function. However, two challenges arise: First, this function has to map a complex feature space into rewards. Second, the reward function has to be manually tuned by an expert. Manually tuning this reward function becomes a tedious task. In this paper, we propose an approach that relies on human driving demonstrations to automatically tune reward functions. This study offers important insights into the driving style optimization of general-purpose planners with maximum entropy inverse reinforcement learning. We evaluate our approach based on the expected value difference between learned and demonstrated policies. Furthermore, we compare the similarity of human driven trajectories with optimal policies of our planner under learned and expert-tuned reward functions. Our experiments show that we are able to learn reward functions exceeding the level of manual expert tuning without prior domain knowledge.
Declarative Question Answering over Knowledge Bases containing Natural Language Text with Answer Set Programming
Authors: Arindam Mitra, Peter Clark, Oyvind Tafjord, Chitta Baral
Date: 2019-05-01 00:00:00
Tasks: Logical Reasoning, Natural Language Inference, Question Answering

While in recent years machine learning (ML) based approaches have been the
popular approach in developing end-to-end question answering systems, such
systems often struggle when additional knowledge is needed to correctly answer
the questions. Proposed alternatives involve translating the question and the
natural language text to a logical representation and then use logical
reasoning. However, this alternative falters when the size of the text gets
bigger. To address this we propose an approach that does logical reasoning over
premises written in natural language text. The proposed method uses recent
features of Answer Set Programming (ASP) to call external NLP modules (which
may be based on ML) which perform simple textual entailment. To test our
approach we develop a corpus based on the life cycle questions and showed that
Our system achieves up to $18\%$ performance gain when compared to standard MCQ
solvers.
Nested Variational Autoencoder for Topic Modeling on Microtexts with Word Vectors
Authors: Trung Trinh, Tho Quan, Trung Mai
Date: 2019-05-01 00:00:00
Tasks: Topic Models, Word Embeddings

Most of the information on the Internet is represented in the form of microtexts, which are short text snippets such as news headlines or tweets. These sources of information are abundant, and mining these data could uncover meaningful insights. Topic modeling is one of the popular methods to extract knowledge from a collection of documents; however, conventional topic models such as latent Dirichlet allocation (LDA) are unable to perform well on short documents, mostly due to the scarcity of word co-occurrence statistics embedded in the data. The objective of our research is to create a topic model that can achieve great performances on microtexts while requiring a small runtime for scalability to large datasets. To solve the lack of information of microtexts, we allow our method to take advantage of word embeddings for additional knowledge of relationships between words. For speed and scalability, we apply autoencoding variational Bayes, an algorithm that can perform efficient black-box inference in probabilistic models. The result of our work is a novel topic model called the nested variational autoencoder, which is a distribution that takes into account word vectors and is parameterized by a neural network architecture. For optimization, the model is trained to approximate the posterior distribution of the original LDA model. Experiments show the improvements of our model on microtexts as well as its runtime advantage.