pwc-archive/papers-with-abstracts huggingface dataset:
Report on the SIGIR 2019 Workshop on eCommerce (ECOM19)
Authors: Jon Degenhardt, Surya Kallumadi, Utkarsh Porwal, Andrew Trotman
Date: 2019-12-27 00:00:00
Tasks: Information Retrieval, Retrieval

The SIGIR 2019 Workshop on eCommerce (ECOM19), was a full day workshop that took place on Thursday, July 25, 2019 in Paris, France. The purpose of the workshop was to serve as a platform for publication and discussion of Information Retrieval and NLP research and their applications in the domain of eCommerce. The workshop program was designed to bring together practitioners and researchers from academia and industry to discuss the challenges and approaches to product search and recommendation in the eCommerce domain. A second goal was to run a data challenge on real-world eCommerce data. The workshop drew contributions from both industry as well as academia, in total the workshop received 38 submissions, and accepted 24 (63%). There were two keynotes by invited speakers, a poster session where all the accepted submissions were presented, a panel discussion, and three short talks by invited speakers.
Statistical Agnostic Mapping: a Framework in Neuroimaging based on Concentration Inequalities
Authors: J M Gorriz, SiPBA Group, CAM neuroscience
Date: 2019-12-27 00:00:00
Tasks: Two-sample testing

In the 70s a novel branch of statistics emerged focusing its effort in selecting a function in the pattern recognition problem, which fulfils a definite relationship between the quality of the approximation and its complexity. These data-driven approaches are mainly devoted to problems of estimating dependencies with limited sample sizes and comprise all the empirical out-of sample generalization approaches, e.g. cross validation (CV) approaches. Although the latter are \emph{not designed for testing competing hypothesis or comparing different models} in neuroimaging, there are a number of theoretical developments within this theory which could be employed to derive a Statistical Agnostic (non-parametric) Mapping (SAM) at voxel or multi-voxel level. Moreover, SAMs could relieve i) the problem of instability in limited sample sizes when estimating the actual risk via the CV approaches, e.g. large error bars, and provide ii) an alternative way of Family-wise-error (FWE) corrected p-value maps in inferential statistics for hypothesis testing. In this sense, we propose a novel framework in neuroimaging based on concentration inequalities, which results in (i) a rigorous development for model validation with a small sample/dimension ratio, and (ii) a less-conservative procedure than FWE p-value correction, to determine the brain significance maps from the inferences made using small upper bounds of the actual risk.
Combining Deep Learning and Verification for Precise Object Instance Detection
Authors: Siddharth Ancha, Junyu Nan, David Held
Date: 2019-12-27 00:00:00
Tasks: Deep Learning, Object

Deep learning object detectors often return false positives with very high confidence. Although they optimize generic detection performance, such as mean average precision (mAP), they are not designed for reliability. For a reliable detection system, if a high confidence detection is made, we would want high certainty that the object has indeed been detected. To achieve this, we have developed a set of verification tests which a proposed detection must pass to be accepted. We develop a theoretical framework which proves that, under certain assumptions, our verification tests will not accept any false positives. Based on an approximation to this framework, we present a practical detection system that can verify, with high precision, whether each detection of a machine-learning based object detector is correct. We show that these tests can improve the overall accuracy of a base detector and that accepted examples are highly likely to be correct. This allows the detector to operate in a high precision regime and can thus be used for robotic perception systems as a reliable instance detection method. Code is available at https://github.com/siddancha/FlowVerify.
Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems
Authors: Yuwen Yang, Feifei Gao, Zhimeng Zhong, Bo Ai, Ahmed Alkhateeb
Date: 2019-12-27 00:00:00
Tasks: Meta-Learning, Prediction, Transfer Learning

Artificial intelligence (AI) based downlink channel state information (CSI) prediction for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems has attracted growing attention recently. However, existing works focus on the downlink CSI prediction for the users under a given environment and is hard to adapt to users in new environment especially when labeled data is limited. To address this issue, we formulate the downlink channel prediction as a deep transfer learning (DTL) problem, where each learning task aims to predict the downlink CSI from the uplink CSI for one single environment. Specifically, we develop the direct-transfer algorithm based on the fully-connected neural network architecture, where the network is trained on the data from all previous environments in the manner of classical deep learning and is then fine-tuned for new environments. To further improve the transfer efficiency, we propose the meta-learning algorithm that trains the network by alternating inner-task and across-task updates and then adapts to a new environment with a small number of labeled data. Simulation results show that the direct-transfer algorithm achieves better performance than the deep learning algorithm, which implies that the transfer learning benefits the downlink channel prediction in new environments. Moreover, the meta-learning algorithm significantly outperforms the direct-transfer algorithm in terms of both prediction accuracy and stability, which validates its effectiveness and superiority.
Predicting Attributes of Nodes Using Network Structure
Authors: Sarwan Ali, Muhammad Haroon Shakeel, Imdadullah Khan, Safiullah Faizullah, Muhammad Asad Khan
Date: 2019-12-27 00:00:00
Tasks: Attribute, Recommendation Systems

In many graphs such as social networks, nodes have associated attributes representing their behavior. Predicting node attributes in such graphs is an important problem with applications in many domains like recommendation systems, privacy preservation, and targeted advertisement. Attributes values can be predicted by analyzing patterns and correlations among attributes and employing classification/regression algorithms. However, these approaches do not utilize readily available network topology information. In this regard, interconnections between different attributes of nodes can be exploited to improve the prediction accuracy. In this paper, we propose an approach to represent a node by a feature map with respect to an attribute $a_i$ (which is used as input for machine learning algorithms) using all attributes of neighbors to predict attributes values for $a_i$. We perform extensive experimentation on ten real-world datasets and show that the proposed feature map significantly improves the prediction accuracy as compared to baseline approaches on these datasets.
Emergence of Network Motifs in Deep Neural Networks
Authors: Matteo Zambra, Alberto Testolin, Amos Maritan
Date: 2019-12-27 00:00:00

Network science can offer fundamental insights into the structural and functional properties of complex systems. For example, it is widely known that neuronal circuits tend to organize into basic functional topological modules, called "network motifs". In this article we show that network science tools can be successfully applied also to the study of artificial neural networks operating according to self-organizing (learning) principles. In particular, we study the emergence of network motifs in multi-layer perceptrons, whose initial connectivity is defined as a stack of fully-connected, bipartite graphs. Our simulations show that the final network topology is primarily shaped by learning dynamics, but can be strongly biased by choosing appropriate weight initialization schemes. Overall, our results suggest that non-trivial initialization strategies can make learning more effective by promoting the development of useful network motifs, which are often surprisingly consistent with those observed in general transduction networks.
Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation
Authors: Hao Tang, Dan Xu, Yan Yan, Philip H. S. Torr, Nicu Sebe
Date: 2019-12-27 00:00:00
Proceeding: CVPR 2020 6
Tasks: Image Generation, Scene Generation

In this paper, we address the task of semantic-guided scene generation. One open challenge in scene generation is the difficulty of the generation of small objects and detailed local texture, which has been widely observed in global image-level generation methods. To tackle this issue, in this work we consider learning the scene generation in a local context, and correspondingly design a local class-specific generative network with semantic maps as a guidance, which separately constructs and learns sub-generators concentrating on the generation of different classes, and is able to provide more scene details. To learn more discriminative class-specific feature representations for the local generation, a novel classification module is also proposed. To combine the advantage of both the global image-level and the local class-specific generation, a joint generation network is designed with an attention fusion module and a dual-discriminator structure embedded. Extensive experiments on two scene image generation tasks show superior generation performance of the proposed model. The state-of-the-art results are established by large margins on both tasks and on challenging public benchmarks. The source code and trained models are available at https://github.com/Ha0Tang/LGGAN.
Job Prediction: From Deep Neural Network Models to Applications
Authors: Tin Van Huynh, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen, Anh Gia-Tuan Nguyen
Date: 2019-12-27 00:00:00
Tasks: Job classification, Job Prediction, Word Embeddings

Determining the job is suitable for a student or a person looking for work based on their job's descriptions such as knowledge and skills that are difficult, as well as how employers must find ways to choose the candidates that match the job they require. In this paper, we focus on studying the job prediction using different deep neural network models including TextCNN, Bi-GRU-LSTM-CNN, and Bi-GRU-CNN with various pre-trained word embeddings on the IT Job dataset. In addition, we also proposed a simple and effective ensemble model combining different deep neural network models. The experimental results illustrated that our proposed ensemble model achieved the highest result with an F1 score of 72.71%. Moreover, we analyze these experimental results to have insights about this problem to find better solutions in the future.
Nonlinear Markov Clustering by Minimum Curvilinear Sparse Similarity
Authors: C. Duran, A. Acevedo, S. Ciucci, A. Muscoloni, CV. Cannistraci
Date: 2019-12-27 00:00:00
Tasks: Clustering

The development of algorithms for unsupervised pattern recognition by nonlinear clustering is a notable problem in data science. Markov clustering (MCL) is a renowned algorithm that simulates stochastic flows on a network of sample similarities to detect the structural organization of clusters in the data, but it has never been generalized to deal with data nonlinearity. Minimum Curvilinearity (MC) is a principle that approximates nonlinear sample distances in the high-dimensional feature space by curvilinear distances, which are computed as transversal paths over their minimum spanning tree, and then stored in a kernel. Here we propose MC-MCL, which is the first nonlinear kernel extension of MCL and exploits Minimum Curvilinearity to enhance the performance of MCL in real and synthetic data with underlying nonlinear patterns. MC-MCL is compared with baseline clustering methods, including DBSCAN, K-means and affinity propagation. We find that Minimum Curvilinearity provides a valuable framework to estimate nonlinear distances also when its kernel is applied in combination with MCL. Indeed, MC-MCL overcomes classical MCL and even baseline clustering algorithms in different nonlinear datasets.
Learning Neural Activations
Authors: Fayyaz ul Amir Afsar Minhas, Amina Asif
Date: 2019-12-27 00:00:00

An artificial neuron is modelled as a weighted summation followed by an activation function which determines its output. A wide variety of activation functions such as rectified linear units (ReLU), leaky-ReLU, Swish, MISH, etc. have been explored in the literature. In this short paper, we explore what happens when the activation function of each neuron in an artificial neural network is learned natively from data alone. This is achieved by modelling the activation function of each neuron as a small neural network whose weights are shared by all neurons in the original network. We list our primary findings in the conclusions section. The code for our analysis is available at: https://github.com/amina01/Learning-Neural-Activations.
The Chi-Square Test of Distance Correlation
Authors: Cencheng Shen, Sambit Panda, Joshua T. Vogelstein
Date: 2019-12-27 00:00:00
Tasks: valid

Distance correlation has gained much recent attention in the data science community: the sample statistic is straightforward to compute and asymptotically equals zero if and only if independence, making it an ideal choice to discover any type of dependency structure given sufficient sample size. One major bottleneck is the testing process: because the null distribution of distance correlation depends on the underlying random variables and metric choice, it typically requires a permutation test to estimate the null and compute the p-value, which is very costly for large amount of data. To overcome the difficulty, in this paper we propose a chi-square test for distance correlation. Method-wise, the chi-square test is non-parametric, extremely fast, and applicable to bias-corrected distance correlation using any strong negative type metric or characteristic kernel. The test exhibits a similar testing power as the standard permutation test, and can be utilized for K-sample and partial testing. Theory-wise, we show that the underlying chi-square distribution well approximates and dominates the limiting null distribution in upper tail, prove the chi-square test can be valid and universally consistent for testing independence, and establish a testing power inequality with respect to the permutation test.
Comparative Analysis of Predictive Methods for Early Assessment of Compliance with Continuous Positive Airway Pressure Therapy
Authors: Xavier Rafael-Palou, Cecilia Turino, Alexander Steblin, Manuel Sánchez-de-la-Torre, Ferran Barbé, Eloisa Vargiu
Date: 2019-12-27 00:00:00

Patients suffering from obstructive sleep apnea are mainly treated with continuous positive airway pressure (CPAP). Good compliance with this therapy is broadly accepted as more than 4h of CPAP average use nightly. Although it is a highly effective treatment, compliance with this therapy is problematic to achieve with serious consequences for the patients' health. Previous works already reported factors significantly related to compliance with the therapy. However, further research is still required to support clinicians to early anticipate patients' therapy compliance. This work intends to take a further step in this direction by building compliance classifiers with CPAP therapy at three different moments of the patient follow-up (i.e. before the therapy starts and at months 1 and 3 after the baseline). Results of the clinical trial confirmed that month 3 was the time-point with the most accurate classifier reaching an f1-score of 87% and 84% in cross-validation and test. At month 1, performances were almost as high as in month 3 with 82% and 84% of f1-score. At baseline, where no information about patients' CPAP use was given yet, the best classifier achieved 73% and 76% of f1-score in cross-validation and test set respectively. Subsequent analyses carried out with the best classifiers of each time point revealed that certain baseline factors (i.e. headaches, psychological symptoms, arterial hypertension and EuroQol visual analogue scale) were closely related to the prediction of compliance independently of the time-point. In addition, among the variables taken only during the follow-up of the patients, Epworth and the average nighttime hours were the most important to predict compliance with CPAP.
Split Learning for collaborative deep learning in healthcare
Authors: Maarten G. Poirot, Praneeth Vepakomma, Ken Chang, Jayashree Kalpathy-Cramer, Rajiv Gupta, Ramesh Raskar
Date: 2019-12-27 00:00:00
Tasks: Binary Classification, Deep Learning, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Shortage of labeled data has been holding the surge of deep learning in healthcare back, as sample sizes are often small, patient information cannot be shared openly, and multi-center collaborative studies are a burden to set up. Distributed machine learning methods promise to mitigate these problems. We argue for a split learning based approach and apply this distributed learning method for the first time in the medical field to compare performance against (1) centrally hosted and (2) non collaborative configurations for a range of participants. Two medical deep learning tasks are used to compare split learning to conventional single and multi center approaches: a binary classification problem of a data set of 9000 fundus photos, and multi-label classification problem of a data set of 156,535 chest X-rays. The several distributed learning setups are compared for a range of 1-50 distributed participants. Performance of the split learning configuration remained constant for any number of clients compared to a single center study, showing a marked difference compared to the non collaborative configuration after 2 clients (p < 0.001) for both sets. Our results affirm the benefits of collaborative training of deep neural networks in health care. Our work proves the significant benefit of distributed learning in healthcare, and paves the way for future real-world implementations.
A 3D-Deep-Learning-based Augmented Reality Calibration Method for Robotic Environments using Depth Sensor Data
Authors: Linh Kästner, Vlad Catalin Frasineanu, Jens Lambrecht
Date: 2019-12-27 00:00:00

Augmented Reality and mobile robots are gaining much attention within industries due to the high potential to make processes cost and time efficient. To facilitate augmented reality, a calibration between the Augmented Reality device and the environment is necessary. This is a challenge when dealing with mobile robots due to the mobility of all entities making the environment dynamic. On this account, we propose a novel approach to calibrate the Augmented Reality device using 3D depth sensor data. We use the depth camera of a cutting edge Augmented Reality Device - the Microsoft Hololens for deep learning based calibration. Therefore, we modified a neural network based on the recently published VoteNet architecture which works directly on the point cloud input observed by the Hololens. We achieve satisfying results and eliminate external tools like markers, thus enabling a more intuitive and flexible work flow for Augmented Reality integration. The results are adaptable to work with all depth cameras and are promising for further research. Furthermore, we introduce an open source 3D point cloud labeling tool, which is to our knowledge the first open source tool for labeling raw point cloud data.
Quaternion Equivariant Capsule Networks for 3D Point Clouds
Authors: Yongheng Zhao, Tolga Birdal, Jan Eric Lenssen, Emanuele Menegatti, Leonidas Guibas, Federico Tombari
Date: 2019-12-27 00:00:00
Proceeding: ECCV 2020 8
Tasks: Pose Estimation

We present a 3D capsule module for processing point clouds that is equivariant to 3D rotations and translations, as well as invariant to permutations of the input points. The operator receives a sparse set of local reference frames, computed from an input point cloud and establishes end-to-end transformation equivariance through a novel dynamic routing procedure on quaternions. Further, we theoretically connect dynamic routing between capsules to the well-known Weiszfeld algorithm, a scheme for solving \emph{iterative re-weighted least squares} (IRLS) problems with provable convergence properties. It is shown that such group dynamic routing can be interpreted as robust IRLS rotation averaging on capsule votes, where information is routed based on the final inlier scores. Based on our operator, we build a capsule network that disentangles geometry from pose, paving the way for more informative descriptors and a structured latent space. Our architecture allows joint object classification and orientation estimation without explicit supervision of rotations. We validate our algorithm empirically on common benchmark datasets.
One Point, One Object: Simultaneous 3D Object Segmentation and 6-DOF Pose Estimation
Authors: Hongsen Liu
Date: 2019-12-27 00:00:00
Tasks: Object, Pose Estimation, Semantic Segmentation

We propose a single-shot method for simultaneous 3D object segmentation and 6-DOF pose estimation in pure 3D point clouds scenes based on a consensus that \emph{one point only belongs to one object}, i.e., each point has the potential power to predict the 6-DOF pose of its corresponding object. Unlike the recently proposed methods of the similar task, which rely on 2D detectors to predict the projection of 3D corners of the 3D bounding boxes and the 6-DOF pose must be estimated by a PnP like spatial transformation method, ours is concise enough not to require additional spatial transformation between different dimensions. Due to the lack of training data for many objects, the recently proposed 2D detection methods try to generate training data by using rendering engine and achieve good results. However, rendering in 3D space along with 6-DOF is relatively difficult. Therefore, we propose an augmented reality technology to generate the training data in semi-virtual reality 3D space. The key component of our method is a multi-task CNN architecture that can simultaneously predicts the 3D object segmentation and 6-DOF pose estimation in pure 3D point clouds. For experimental evaluation, we generate expanded training data for two state-of-the-arts 3D object datasets \cite{PLCHF}\cite{TLINEMOD} by using Augmented Reality technology (AR). We evaluate our proposed method on the two datasets. The results show that our method can be well generalized into multiple scenarios and provide performance comparable to or better than the state-of-the-arts.