pwc-archive/papers-with-abstracts huggingface dataset:
Contextual Bandits with Random Projection
Authors: Xiaotian Yu
Date: 2019-03-20 00:00:00
Tasks: Multi-Armed Bandits

Contextual bandits with linear payoffs, which are also known as linear
bandits, provide a powerful alternative for solving practical problems of
sequential decisions, e.g., online advertisements. In the era of big data,
contextual data usually tend to be high-dimensional, which leads to new
challenges for traditional linear bandits mostly designed for the setting of
low-dimensional contextual data. Due to the curse of dimensionality, there are
two challenges in most of the current bandit algorithms: the first is high
time-complexity; and the second is extreme large upper regret bounds with
high-dimensional data. In this paper, in order to attack the above two
challenges effectively, we develop an algorithm of Contextual Bandits via
RAndom Projection (\texttt{CBRAP}) in the setting of linear payoffs, which
works especially for high-dimensional contextual data. The proposed
\texttt{CBRAP} algorithm is time-efficient and flexible, because it enables
players to choose an arm in a low-dimensional space, and relaxes the sparsity
assumption of constant number of non-zero components in previous work. Besides,
we provide a linear upper regret bound for the proposed algorithm, which is
associated with reduced dimensions.
A Graph-structured Dataset for Wikipedia Research
Authors: Nicolas Aspert, Volodymyr Miz, Benjamin Ricaud, Pierre Vandergheynst
Date: 2019-03-20 00:00:00
Tasks: Articles

Wikipedia is a rich and invaluable source of information. Its central place
on the Web makes it a particularly interesting object of study for scientists.
Researchers from different domains used various complex datasets related to
Wikipedia to study language, social behavior, knowledge organization, and
network theory. While being a scientific treasure, the large size of the
dataset hinders pre-processing and may be a challenging obstacle for potential
new studies. This issue is particularly acute in scientific domains where
researchers may not be technically and data processing savvy. On one hand, the
size of Wikipedia dumps is large. It makes the parsing and extraction of
relevant information cumbersome. On the other hand, the API is straightforward
to use but restricted to a relatively small number of requests. The middle
ground is at the mesoscopic scale when researchers need a subset of Wikipedia
ranging from thousands to hundreds of thousands of pages but there exists no
efficient solution at this scale.
  In this work, we propose an efficient data structure to make requests and
access subnetworks of Wikipedia pages and categories. We provide convenient
tools for accessing and filtering viewership statistics or "pagecounts" of
Wikipedia web pages. The dataset organization leverages principles of graph
databases that allows rapid and intuitive access to subgraphs of Wikipedia
articles and categories. The dataset and deployment guidelines are available on
the LTS2 website \url{https://lts2.epfl.ch/Datasets/Wikipedia/}.
DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection
Authors: Zhanchao Huang, Jianlin Wang, Xuesong Fu, Tao Yu, Yongqi Guo, Rutong Wang
Date: 2019-03-20 00:00:00
Tasks: Object, object-detection, Object Detection

Although the YOLOv2 method is extremely fast on object detection, its detection accuracy is restricted due to the low performance of its backbone network and the underutilization of multi-scale region features. Therefore, a dense connection (DC) and spatial pyramid pooling (SPP) based YOLO (DC-SPP-YOLO) method for ameliorating the object detection accuracy of YOLOv2 is proposed in this paper. Specifically, the dense connection of convolution layers is employed in the backbone network of YOLOv2 to strengthen the feature extraction and alleviate the vanishing-gradient problem. Moreover, an improved spatial pyramid pooling is introduced to pool and concatenate the multi-scale region features, so that the network can learn the object features more comprehensively. The DC-SPP-YOLO model is established and trained based on a new loss function composed of MSE (mean square error) loss and cross-entropy loss. The experimental results indicated that the mAP (mean Average Precision) of DC-SPP-YOLO is higher than that of YOLOv2 on the PASCAL VOC datasets and the UA-DETRAC datasets. The effectiveness of DC-SPP-YOLO method proposed is demonstrated.
A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates
Authors: Heng Yang, Luca Carlone
Date: 2019-03-20 00:00:00
Tasks: Translation

We propose a robust approach for the registration of two sets of 3D points in the presence of a large amount of outliers. Our first contribution is to reformulate the registration problem using a Truncated Least Squares (TLS) cost that makes the estimation insensitive to a large fraction of spurious point-to-point correspondences. The second contribution is a general framework to decouple rotation, translation, and scale estimation, which allows solving in cascade for the three transformations. Since each subproblem (scale, rotation, and translation estimation) is still non-convex and combinatorial in nature, out third contribution is to show that (i) TLS scale and (component-wise) translation estimation can be solved exactly and in polynomial time via an adaptive voting scheme, (ii) TLS rotation estimation can be relaxed to a semidefinite program and the relaxation is tight in practice, even in the presence of an extreme amount of outliers. We validate the proposed algorithm, named TEASER (Truncated least squares Estimation And SEmidefinite Relaxation), in standard registration benchmarks showing that the algorithm outperforms RANSAC and robust local optimization techniques, and favorably compares with Branch-and-Bound methods, while being a polynomial-time algorithm. TEASER can tolerate up to 99% outliers and returns highly-accurate solutions.
Data Augmentation for Leaf Segmentation and Counting Tasks in Rosette Plants
Authors: Dmitry Kuznichov, Alon Zvirin, Yaron Honen, Ron Kimmel
Date: 2019-03-20 00:00:00
Tasks: Data Augmentation, Diversity, Plant Phenotyping, Segmentation, Semantic Segmentation

Deep learning techniques involving image processing and data analysis are
constantly evolving. Many domains adapt these techniques for object
segmentation, instantiation and classification. Recently, agricultural
industries adopted those techniques in order to bring automation to farmers
around the globe. One analysis procedure required for automatic visual
inspection in this domain is leaf count and segmentation. Collecting labeled
data from field crops and greenhouses is a complicated task due to the large
variety of crops, growth seasons, climate changes, phenotype diversity, and
more, especially when specific learning tasks require a large amount of labeled
data for training. Data augmentation for training deep neural networks is well
established, examples include data synthesis, using generative semi-synthetic
models, and applying various kinds of transformations. In this paper we propose
a method that preserves the geometric structure of the data objects, thus
keeping the physical appearance of the data-set as close as possible to imaged
plants in real agricultural scenes. The proposed method provides state of the
art results when applied to the standard benchmark in the field, namely, the
ongoing Leaf Segmentation Challenge hosted by Computer Vision Problems in Plant
Phenotyping.
Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices
Authors: Santosh S. Vempala, Andre Wibisono
Date: 2019-03-20 00:00:00
Proceeding: NeurIPS 2019 12

We study the Unadjusted Langevin Algorithm (ULA) for sampling from a probability distribution $\nu = e^{-f}$ on $\mathbb{R}^n$. We prove a convergence guarantee in Kullback-Leibler (KL) divergence assuming $\nu$ satisfies a log-Sobolev inequality and the Hessian of $f$ is bounded. Notably, we do not assume convexity or bounds on higher derivatives. We also prove convergence guarantees in R\'enyi divergence of order $q > 1$ assuming the limit of ULA satisfies either the log-Sobolev or Poincar\'e inequality. We also prove a bound on the bias of the limiting distribution of ULA assuming third-order smoothness of $f$, without requiring isoperimetry.
Traversing the noise of dynamic mini-batch sub-sampled loss functions: A visual guide
Authors: Dominic Kafka, Daniel Wilke
Date: 2019-03-20 00:00:00
Tasks: Stochastic Optimization

Mini-batch sub-sampling in neural network training is unavoidable, due to growing data demands, memory-limited computational resources such as graphical processing units (GPUs), and the dynamics of on-line learning. In this study we specifically distinguish between static mini-batch sub-sampled loss functions, where mini-batches are intermittently fixed during training, resulting in smooth but biased loss functions; and the dynamic sub-sampling equivalent, where new mini-batches are sampled at every loss evaluation, trading bias for variance in sampling induced discontinuities. These render automated optimization strategies such as minimization line searches ineffective, since critical points may not exist and function minimizers find spurious, discontinuity induced minima. This paper suggests recasting the optimization problem to find stochastic non-negative associated gradient projection points (SNN-GPPs). We demonstrate that the SNN-GPP optimality criterion is less susceptible to sub-sampling induced discontinuities than critical points or minimizers. We conduct a visual investigation, comparing local minimum and SNN-GPP optimality criteria in the loss functions of a simple neural network training problem for a variety of popular activation functions. Since SNN-GPPs better approximate the location of true optima, particularly when using smooth activation functions with high curvature characteristics, we postulate that line searches locating SNN-GPPs can contribute significantly to automating neural network training
OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations
Authors: Pramuditha Perera, Ramesh Nallapati, Bing Xiang
Date: 2019-03-20 00:00:00
Proceeding: CVPR 2019 6
Tasks: Anomaly Detection, Denoising, Novelty Detection

We present a novel model called OCGAN for the classical problem of one-class
novelty detection, where, given a set of examples from a particular class, the
goal is to determine if a query example is from the same class. Our solution is
based on learning latent representations of in-class examples using a denoising
auto-encoder network. The key contribution of our work is our proposal to
explicitly constrain the latent space to exclusively represent the given class.
In order to accomplish this goal, firstly, we force the latent space to have
bounded support by introducing a tanh activation in the encoder's output layer.
Secondly, using a discriminator in the latent space that is trained
adversarially, we ensure that encoded representations of in-class examples
resemble uniform random samples drawn from the same bounded space. Thirdly,
using a second adversarial discriminator in the input space, we ensure all
randomly drawn latent samples generate examples that look real. Finally, we
introduce a gradient-descent based sampling technique that explores points in
the latent space that generate potential out-of-class examples, which are fed
back to the network to further train it to generate in-class examples from
those points. The effectiveness of the proposed method is measured across four
publicly available datasets using two one-class novelty detection protocols
where we achieve state-of-the-art results.
Convolutional Sparse Coding for Compressed Sensing CT Reconstruction
Authors: Peng Bao, Wenjun Xia, Kang Yang, Weiyan Chen, Mianyi Chen, Yan Xi, Shanzhou Niu, Jiliu Zhou, He Zhang, Huaiqiang Sun, Zhangyang Wang, Yi Zhang
Date: 2019-03-20 00:00:00
Tasks: compressed sensing, Computed Tomography (CT), CT Reconstruction, Dictionary Learning, Image Reconstruction

Over the past few years, dictionary learning (DL)-based methods have been
successfully used in various image reconstruction problems. However,
traditional DL-based computed tomography (CT) reconstruction methods are
patch-based and ignore the consistency of pixels in overlapped patches. In
addition, the features learned by these methods always contain shifted versions
of the same features. In recent years, convolutional sparse coding (CSC) has
been developed to address these problems. In this paper, inspired by several
successful applications of CSC in the field of signal processing, we explore
the potential of CSC in sparse-view CT reconstruction. By directly working on
the whole image, without the necessity of dividing the image into overlapped
patches in DL-based methods, the proposed methods can maintain more details and
avoid artifacts caused by patch aggregation. With predetermined filters, an
alternating scheme is developed to optimize the objective function. Extensive
experiments with simulated and real CT data were performed to validate the
effectiveness of the proposed methods. Qualitative and quantitative results
demonstrate that the proposed methods achieve better performance than several
existing state-of-the-art methods.
Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression
Authors: Maurice Quach, Giuseppe Valenzise, Frederic Dufaux
Date: 2019-03-20 00:00:00
Tasks: Binary Classification, Mixed Reality, Quantization

Efficient point cloud compression is fundamental to enable the deployment of virtual and mixed reality applications, since the number of points to code can range in the order of millions. In this paper, we present a novel data-driven geometry compression method for static point clouds based on learned convolutional transforms and uniform quantization. We perform joint optimization of both rate and distortion using a trade-off parameter. In addition, we cast the decoding process as a binary classification of the point cloud occupancy map. Our method outperforms the MPEG reference solution in terms of rate-distortion on the Microsoft Voxelized Upper Bodies dataset with 51.5% BDBR savings on average. Moreover, while octree-based methods face exponential diminution of the number of points at low bitrates, our method still produces high resolution outputs even at low bitrates. Code and supplementary material are available at https://github.com/mauriceqch/pcc_geo_cnn .
Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning
Authors: Chris Beeler, Uladzimir Yahorau, Rory Coles, Kyle Mills, Stephen Whitelam, Isaac Tamblyn
Date: 2019-03-20 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Using a model heat engine, we show that neural network-based reinforcement learning can identify thermodynamic trajectories of maximal efficiency. We consider both gradient and gradient-free reinforcement learning. We use an evolutionary learning algorithm to evolve a population of neural networks, subject to a directive to maximize the efficiency of a trajectory composed of a set of elementary thermodynamic processes; the resulting networks learn to carry out the maximally-efficient Carnot, Stirling, or Otto cycles. When given an additional irreversible process, this evolutionary scheme learns a previously unknown thermodynamic cycle. Gradient-based reinforcement learning is able to learn the Stirling cycle, whereas an evolutionary approach achieves the optimal Carnot cycle. Our results show how the reinforcement learning strategies developed for game playing can be applied to solve physical problems conditioned upon path-extensive order parameters.
Segmentation-Based Deep-Learning Approach for Surface-Defect Detection
Authors: Domen Tabernik, Samo Šela, Jure Skvarč, Danijel Skočaj
Date: 2019-03-20 00:00:00
Tasks: Anomaly Detection, Deep Learning, Defect Detection

Automated surface-anomaly detection using machine learning has become an interesting and promising area of research, with a very high and direct impact on the application domain of visual inspection. Deep-learning methods have become the most suitable approaches for this task. They allow the inspection system to learn to detect the surface anomaly by simply showing it a number of exemplar images. This paper presents a segmentation-based deep-learning architecture that is designed for the detection and segmentation of surface anomalies and is demonstrated on a specific domain of surface-crack detection. The design of the architecture enables the model to be trained using a small number of samples, which is an important requirement for practical applications. The proposed model is compared with the related deep-learning methods, including the state-of-the-art commercial software, showing that the proposed approach outperforms the related methods on the specific domain of surface-crack detection. The large number of experiments also shed light on the required precision of the annotation, the number of required training samples and on the required computational cost. Experiments are performed on a newly created dataset based on a real-world quality control case and demonstrates that the proposed approach is able to learn on a small number of defected surfaces, using only approximately 25-30 defective training samples, instead of hundreds or thousands, which is usually the case in deep-learning applications. This makes the deep-learning method practical for use in industry where the number of available defective samples is limited. The dataset is also made publicly available to encourage the development and evaluation of new methods for surface-defect detection.
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set
Authors: Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, Xin Tong
Date: 2019-03-20 00:00:00
Tasks: 3D Face Reconstruction, Face Reconstruction, Weakly-supervised Learning

Recently, deep learning based 3D face reconstruction methods have shown promising results in both quality and efficiency.However, training deep neural networks typically requires a large volume of data, whereas face images with ground-truth 3D face shapes are scarce. In this paper, we propose a novel deep 3D face reconstruction approach that 1) leverages a robust, hybrid loss function for weakly-supervised learning which takes into account both low-level and perception-level information for supervision, and 2) performs multi-image face reconstruction by exploiting complementary information from different images for shape aggregation. Our method is fast, accurate, and robust to occlusion and large pose. We provide comprehensive experiments on three datasets, systematically comparing our method with fifteen recent methods and demonstrating its state-of-the-art performance.
Ontology of Card Sleights
Authors: Aaron Sterling
Date: 2019-03-20 00:00:00

We present a machine-readable movement writing for sleight-of-hand moves with
cards -- a "Labanotation of card magic." This scheme of movement writing
contains 440 categories of motion, and appears to taxonomize all card sleights
that have appeared in over 1500 publications. The movement writing is
axiomatized in $\mathcal{SROIQ}$(D) Description Logic, and collected formally
as an Ontology of Card Sleights, a computational ontology that extends the
Basic Formal Ontology and the Information Artifact Ontology. The Ontology of
Card Sleights is implemented in OWL DL, a Description Logic fragment of the Web
Ontology Language. While ontologies have historically been used to classify at
a less granular level, the algorithmic nature of card tricks allows us to
transcribe a performer's actions step by step. We conclude by discussing design
criteria we have used to ensure the ontology can be accessed and modified with
a simple click-and-drag interface. This may allow database searches and
performance transcriptions by users with card magic knowledge, but no ontology
background.
Topology-based Representative Datasets to Reduce Neural Network Training Resources
Authors: Rocio Gonzalez-Diaz, Miguel A. Gutiérrez-Naranjo, Eduardo Paluzo-Hidalgo
Date: 2019-03-20 00:00:00
Tasks: Computational Efficiency

One of the main drawbacks of the practical use of neural networks is the long time required in the training process. Such a training process consists of an iterative change of parameters trying to minimize a loss function. These changes are driven by a dataset, which can be seen as a set of labelled points in an n-dimensional space. In this paper, we explore the concept of are representative dataset which is a dataset smaller than the original one, satisfying a nearness condition independent of isometric transformations. Representativeness is measured using persistence diagrams (a computational topology tool) due to its computational efficiency. We prove that the accuracy of the learning process of a neural network on a representative dataset is "similar" to the accuracy on the original dataset when the neural network architecture is a perceptron and the loss function is the mean squared error. These theoretical results accompanied by experimentation open a door to reducing the size of the dataset to gain time in the training process of any neural network.
Preference rules for label ranking: Mining patterns in multi-target relations
Authors: Cláudio Rebelo de Sá, Paulo Azevedo, Carlos Soares, Alípio Mário Jorge, Arno Knobbe
Date: 2019-03-20 00:00:00
Tasks: Descriptive

In this paper we investigate two variants of association rules for preference
data, Label Ranking Association Rules and Pairwise Association Rules. Label
Ranking Association Rules (LRAR) are the equivalent of Class Association Rules
(CAR) for the Label Ranking task. In CAR, the consequent is a single class, to
which the example is expected to belong to. In LRAR, the consequent is a
ranking of the labels. The generation of LRAR requires special support and
confidence measures to assess the similarity of rankings. In this work, we
carry out a sensitivity analysis of these similarity-based measures. We want to
understand which datasets benefit more from such measures and which parameters
have more influence in the accuracy of the model. Furthermore, we propose an
alternative type of rules, the Pairwise Association Rules (PAR), which are
defined as association rules with a set of pairwise preferences in the
consequent. While PAR can be used both as descriptive and predictive models,
they are essentially descriptive models. Experimental results show the
potential of both approaches.