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DAOC: Stable Clustering of Large NetworksSep 19 2019Clustering is a crucial component of many data mining systems involving the analysis and exploration of various data. Data diversity calls for clustering algorithms to be accurate while providing stable (i.e., deterministic and robust) results on arbitrary ... More
Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier RejectionSep 18 2019Semidefinite Programming (SDP) and Sums-of-Squares (SOS) relaxations have led to certifiably optimal non-minimal solvers for several robotics and computer vision problems. However, most non-minimal solvers rely on least squares formulations, and, as a ... More
Distributed Machine Learning on Mobile Devices: A SurveySep 18 2019In recent years, mobile devices have gained increasingly development with stronger computation capability and larger storage. Some of the computation-intensive machine learning and deep learning tasks can now be run on mobile devices. To take advantage ... More
Towards Ethical Machines Via Logic ProgrammingSep 18 2019Autonomous intelligent agents are playing increasingly important roles in our lives. They contain information about us and start to perform tasks on our behalves. Chatbots are an example of such agents that need to engage in a complex conversations with ... More
A Rule-Based System for Explainable Donor-Patient Matching in Liver TransplantationSep 18 2019In this paper we present web-liver, a rule-based system for decision support in the medical domain, focusing on its application in a liver transplantation unit for implementing policies for donor-patient matching. The rule-based system is built on top ... More
Epistemic Logic Programs: A Different World ViewSep 18 2019Epistemic Logic Programs (ELPs), an extension of Answer Set Programming (ASP) with epistemic operators, have received renewed attention from the research community in recent years. Classically, evaluating an ELP yields a set of world views, with each ... More
Strategic Formation and Reliability of Supply Chain NetworksSep 17 2019Supply chains are the backbone of the global economy. Disruptions to them can be costly. Centrally managed supply chains invest in ensuring their resilience. Decentralized supply chains, however, must rely upon the self-interest of their individual components ... More
Ensemble Learning based Convexification of Power Flow with Application in OPFSep 12 2019This paper proposes an ensemble learning based approach for convexifying AC power flow equations, which differs from the existing relaxation-based convexification techniques. The proposed approach is based on the quadratic power flow equations in rectangular ... More
High order transition elements: The xNy-element concept -- Part I: StaticsSep 11 2019Advanced transition elements are of utmost importance in many applications of the finite element method (FEM) where a local mesh refinement is required. Considering problems that exhibit singularities in the solution, an adaptive hp-refinement procedure ... More
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for ElectroencephalographySep 11 2019Electroencephalography (EEG) classification techniques have been widely studied for human behavior and emotion recognition tasks. But it is still a challenging issue since the data may vary from subject to subject, may change over time for the same subject, ... More
Optimality of the Subgradient Algorithm in the Stochastic SettingSep 10 2019Recently Jaouad Mourtada and St\' ephane Ga\"iffas showed the anytime hedge algorithm has pseudo-regret $O(\log (d) / \Delta)$ if the cost vectors are generated by an i.i.d sequence in the cube $[0,1]^d$. Here $d$ is the dimension and $\Delta$ the suboptimality ... More
A Bayesian Approach to Direct and Inverse Abstract Argumentation ProblemsSep 10 2019This paper studies a fundamental mechanism of how to detect a conflict between arguments given sentiments regarding acceptability of the arguments. We introduce a concept of the inverse problem of the abstract argumentation to tackle the problem. Given ... More
A Flexible Framework for Anomaly Detection via Dimensionality ReductionSep 09 2019Anomaly detection is challenging, especially for large datasets in high dimensions. Here we explore a general anomaly detection framework based on dimensionality reduction and unsupervised clustering. We release DRAMA, a general python package that implements ... More
Sampling Conditionally on a Rare Event via Generalized SplittingSep 08 2019We propose and analyze a generalized splitting method to sample approximately from a distribution conditional on the occurrence of a rare event. This has important applications in a variety of contexts in operations research, engineering, and computational ... More
Blind Geometric Distortion Correction on Images Through Deep LearningSep 08 2019We propose the first general framework to automatically correct different types of geometric distortion in a single input image. Our proposed method employs convolutional neural networks (CNNs) trained by using a large synthetic distortion dataset to ... More
Calibrating Wayfinding Decisions in Pedestrian Simulation Models: The Entropy MapSep 06 2019This paper presents entropy maps, an approach to describing and visualising uncertainty among alternative potential movement intentions in pedestrian simulation models. In particular, entropy maps show the instantaneous level of randomness in decisions ... More
Generalized Integrated Gradients: A practical method for explaining diverse ensemblesSep 04 2019We introduce Generalized Integrated Gradients (GIG), a formal extension of the Integrated Gradients (IG) (Sundararajan et al., 2017) method for attributing credit to the input variables of a predictive model. GIG improves IG by explaining a broader variety ... More
Generalized Integrated Gradients: A practical method for explaining diverse ensemblesSep 04 2019Sep 06 2019We introduce Generalized Integrated Gradients (GIG), a formal extension of the Integrated Gradients (IG) (Sundararajan et al., 2017) method for attributing credit to the input variables of a predictive model. GIG improves IG by explaining a broader variety ... More
qiBullet, a Bullet-based simulator for the Pepper and NAO robotsSep 02 2019Sep 04 2019The Pepper and NAO robots are widely used for in-store advertizing and education, but also as robotic platforms for research purposes. Their presence in the academic field is expressed through various publications, multiple collaborative projects, and ... More
Software-Defined Network-Based Vehicular Networks: A Position Paper on Their Modeling and ImplementationAug 31 2019There is a strong devotion in the automotive industry to be part of a wider progression towards the Fifth Generation (5G) era. In-vehicle integration costs between cellular and vehicle-to-vehicle networks using Dedicated Short Range Communication could ... More
A single-layer RNN can approximate stacked and bidirectional RNNs, and topologies in betweenAug 30 2019To enhance the expressiveness and representational capacity of recurrent neural networks (RNN), a large body of work has emerged exploring stacked architectures with additional topological modifications like shortcut connections or bidirectionality. However, ... More
Scalable Probabilistic Matrix Factorization with Graph-Based PriorsAug 25 2019In matrix factorization, available graph side-information may not be well suited for the matrix completion problem, having edges that disagree with the latent-feature relations learnt from the incomplete data matrix. We show that removing these contested ... More
Scalable Probabilistic Matrix Factorization with Graph-Based PriorsAug 25 2019Sep 11 2019In matrix factorization, available graph side-information may not be well suited for the matrix completion problem, having edges that disagree with the latent-feature relations learnt from the incomplete data matrix. We show that removing these $\textit{contested}$ ... More
A Method for Estimating the Proximity of Vector Representation Groups in Multidimensional Space. On the Example of the Paraphrase TaskAug 25 2019The following paper presents a method of comparing two sets of vectors. The method can be applied in all tasks, where it is necessary to measure the closeness of two objects presented as sets of vectors. It may be applicable when we compare the meanings ... More
A Method for Estimating the Proximity of Vector Representation Groups in Multidimensional Space. On the Example of the Paraphrase TaskAug 25 2019Aug 29 2019The following paper presents a method of comparing two sets of vectors. The method can be applied in all tasks, where it is necessary to measure the closeness of two objects presented as sets of vectors. It may be applicable when we compare the meanings ... More
Efficient Cross-Validation of Echo State NetworksAug 22 2019Echo State Networks (ESNs) are known for their fast and precise one-shot learning of time series. But they often need good hyper-parameter tuning for best performance. For this good validation is key, but usually, a single validation split is used. In ... More
Interactive Duplicate Search in Software DocumentationAug 22 2019Various software features such as classes, methods, requirements, and tests often have similar functionality. This can lead to emergence of duplicates in their descriptive documentation. Uncontrolled duplicates created via copy/paste hinder the process ... More
Towards a Structural Framework for Explicit Domain Knowledge in Visual AnalyticsAug 21 2019Clinicians and other analysts working with healthcare data are in need for better support to cope with large and complex data. While an increasing number of visual analytics environments integrates explicit domain knowledge as a means to deliver a precise ... More
Modeling Major Transitions in Evolution with the Game of LifeAug 19 2019Maynard Smith and Szathm\'ary's book, The Major Transitions in Evolution, describes eight major events in the evolution of life on Earth and identifies a common theme that unites these events. In each event, smaller entities came together to form larger ... More
The Natural Selection of Words: Finding the Features of FitnessAug 19 2019We introduce a dataset for studying the evolution of words, constructed from WordNet and the Google Books Ngram Corpus. The dataset tracks the evolution of 4,000 synonym sets (synsets), containing 9,000 English words, from 1800 AD to 2000 AD. We present ... More
Mitigating Multi-Stage Cascading Failure by Reinforcement LearningAug 19 2019This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL) method. Firstly, the principles of RL are introduced. Then, the Multi-Stage Cascading Failure (MSCF) problem is presented and its challenges are investigated. ... More
Multivariate Spatial Data Visualization: A SurveyAug 18 2019Multivariate spatial data plays an important role in computational science and engineering simulations. The potential features and hidden relationships in multivariate data can assist scientists to gain an in-depth understanding of a scientific process, ... More
Parametric Majorization for Data-Driven Energy Minimization MethodsAug 17 2019Energy minimization methods are a classical tool in a multitude of computer vision applications. While they are interpretable and well-studied, their regularity assumptions are difficult to design by hand. Deep learning techniques on the other hand are ... More
Swarm Intelligence for Morphogenetic EngineeringAug 13 2019We argue that embryological morphogenesis provides a model of how massive swarms of microscopic agents can be coordinated to assemble complex, multiscale hierarchical structures. This is accomplished by understanding natural morphogenetic processes in ... More
Convolutional Humanoid Animation via DeformationAug 12 2019In this paper we present a new deep learning-driven approach to image-based synthesis of animations involving humanoid characters. Unlike previous deep approaches to image-based animation our method makes no assumptions on the type of motion to be animated ... More
EdgeNet: Semantic Scene Completion from RGB-D imagesAug 08 2019Semantic scene completion is the task of predicting a complete 3D representation of volumetric occupancy with corresponding semantic labels for a scene from a single point of view. Previous works on Semantic Scene Completion from RGB-D data used either ... More
On cylindrical regression in three-dimensional Euclidean spaceAug 06 2019The three-dimensional cylindrical regression problem is a problem of finding a cylinder best fitting a group of points in three-dimensional Euclidean space. The words best fitting are usually understood in the sense of the minimum root mean square deflection ... More
Semiparametric Wavelet-based JPEG IV Estimator for endogenously truncated dataAug 06 2019A new and an enriched JPEG algorithm is provided for identifying redundancies in a sequence of irregular noisy data points which also accommodates a reference-free criterion function. Our main contribution is by formulating analytically (instead of approximating) ... More
A comparative study of general fuzzy min-max neural networks for pattern classification problemsJul 31 2019General fuzzy min-max (GFMM) neural network is a generalization of fuzzy neural networks formed by hyperbox fuzzy sets for classification and clustering problems. Two principle algorithms are deployed to train this type of neural network, i.e., incremental ... More
Phase Transition Unbiased Estimation in High Dimensional SettingsJul 25 2019An important challenge in statistical analysis concerns the control of the finite sample bias of estimators. For example, the maximum likelihood estimator has a bias that can result in a significant inferential loss. This problem is typically magnified ... More
Phase Transition Unbiased Estimation in High Dimensional SettingsJul 25 2019Aug 23 2019An important challenge in statistical analysis concerns the control of the finite sample bias of estimators. For example, the maximum likelihood estimator has a bias that can result in a significant inferential loss. This problem is typically magnified ... More
Training future teachers in natural sciences and mathematics by means of computer simulation: a social constructivist approachJul 23 2019The monograph defines the conditions of training of future teachers in natural sciences and mathematics by means of computer simulation, developed a structural-functional model of training, selected socio-constructivist forms of organization, methods ... More
Tracking Holistic Object RepresentationsJul 21 2019Recent advances in visual tracking are based on siamese feature extractors and template matching. For this category of trackers, latest research focuses on better feature embeddings and similarity measures. In this work, we focus on building holistic ... More
Tracking Holistic Object RepresentationsJul 21 2019Aug 06 2019Recent advances in visual tracking are based on siamese feature extractors and template matching. For this category of trackers, latest research focuses on better feature embeddings and similarity measures. In this work, we focus on building holistic ... More
Yield Uncertainty and Strategic Formation of Supply Chain NetworksJul 20 2019How does supply uncertainty affect the structure of supply chain networks? To answer this question we consider a setting where retailers and suppliers must establish a costly relationship with each other prior to engaging in trade. Suppliers, with uncertain ... More
Delegative Reinforcement Learning: learning to avoid traps with a little helpJul 19 2019Most known regret bounds for reinforcement learning are either episodic or assume an environment without traps. We derive a regret bound without making either assumption, by allowing the algorithm to occasionally delegate an action to an external advisor. ... More
A feasibility study of deep neural networks for the recognition of banknotes regarding central bank requirementsJul 18 2019This paper contains a feasibility study of deep neural networks for the classification of Euro banknotes with respect to requirements of central banks on the ATM and high speed sorting industry. Instead of concentrating on the accuracy for a large number ... More
Band-structure and electronic transport calculations in cylindrical wires : the issue of bound states in transfer-matrix calculationsJul 16 2019The transfer-matrix methodology is used to solve linear systems of differential equations, such as those that arise when solving Schr\"odinger's equation, in situations where the solutions of interest are in the continuous part of the energy spectrum. ... More
Deep learning-based color holographic microscopyJul 15 2019We report a framework based on a generative adversarial network (GAN) that performs high-fidelity color image reconstruction using a single hologram of a sample that is illuminated simultaneously by light at three different wavelengths. The trained network ... More
Quick, Stat!: A Statistical Analysis of the Quick, Draw! DatasetJul 15 2019The Quick, Draw! Dataset is a Google dataset with a collection of 50 million drawings, divided in 345 categories, collected from the users of the game Quick, Draw!. In contrast with most of the existing image datasets, in the Quick, Draw! Dataset, drawings ... More
Learning better generative models for dexterous, single-view grasping of novel objectsJul 13 2019This paper concerns the problem of how to learn to grasp dexterously, so as to be able to then grasp novel objects seen only from a single view-point. Recently, progress has been made in data-efficient learning of generative grasp models which transfer ... More
On linear regression in three-dimensional Euclidean spaceJul 13 2019The three-dimensional linear regression problem is a problem of finding a spacial straight line best fitting a group of points in three-dimensional Euclidean space. This problem is considered in the present paper and a solution to it is given in a coordinate-free ... More
Gravitational-wave parameter estimation with gaps in LISA: a Bayesian data augmentation methodJul 10 2019By listening to gravity in the low frequency band, between 0.1 mHz and 1 Hz, the future space-based gravitational-wave observatory LISA will be able to detect tens of thousands of astrophysical sources from cosmic dawn to the present. The detection and ... More
Discontinuous Galerkin discretization for two-equation turbulence closure modelJul 10 2019Accurate representation of vertical turbulence is crucial for numerical ocean modelling, both in global and coastal applications. The state-of-the-art approach is to use two-equation turbulence closure models which introduces two dynamic equations to ... More
Applications of a Novel Knowledge Discovery and Data Mining Process Model for MetabolomicsJul 09 2019Jul 30 2019This work demonstrates the execution of a novel process model for knowledge discovery and data mining for metabolomics (MeKDDaM). It aims to illustrate MeKDDaM process model applicability using four different real-world applications and to highlight its ... More
Applications of a Novel Knowledge Discovery and Data Mining Process Model for MetabolomicsJul 09 2019This work demonstrates the execution of a novel process model for knowledge discovery and data mining for metabolomics (MeKDDaM). It aims to illustrate MeKDDaM process model applicability using four different real-world applications and to highlight its ... More
Computer-Aided Data Mining: Automating a Novel Knowledge Discovery and Data Mining Process Model for MetabolomicsJul 09 2019This work presents MeKDDaM-SAGA, computer-aided automation software for implementing a novel knowledge discovery and data mining process model that was designed for performing justifiable, traceable and reproducible metabolomics data analysis. The process ... More
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at ScaleJul 08 2019Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models. However, applications to science remain limited because of the impracticability of rewriting complex scientific ... More
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at ScaleJul 08 2019Aug 27 2019Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models. However, applications to science remain limited because of the impracticability of rewriting complex scientific ... More
Attentive Multi-Task Deep Reinforcement LearningJul 05 2019Sharing knowledge between tasks is vital for efficient learning in a multi-task setting. However, most research so far has focused on the easier case where knowledge transfer is not harmful, i.e., where knowledge from one task cannot negatively impact ... More
Koalja: from Data Plumbing to Smart Workspaces in the Extended CloudJul 03 2019Koalja describes a generalized data wiring or `pipeline' platform, built on top of Kubernetes, for plugin user code. Koalja makes the Kubernetes underlay transparent to users (for a `serverless' experience), and offers a breadboarding experience for development ... More
Emergence of multiplicity of time scales in the modeling of climate, matter, life, and economyJul 01 2019We address dfferences between characteristic times in climate change and show the universal emergence of multiple time scales in material sciences, biomedicine and economics.
Quantile Regression Deep Reinforcement LearningJun 27 2019Policy gradient based reinforcement learning algorithms coupled with neural networks have shown success in learning complex policies in the model free continuous action space control setting. However, explicitly parameterized policies are limited by the ... More
A Winograd-based Integrated Photonics Accelerator for Convolutional Neural NetworksJun 25 2019Neural Networks (NNs) have become the mainstream technology in the artificial intelligence (AI) renaissance over the past decade. Among different types of neural networks, convolutional neural networks (CNNs) have been widely adopted as they have achieved ... More
Reserve Pricing in Repeated Second-Price Auctions with Strategic BiddersJun 21 2019We study revenue optimization learning algorithms for repeated second-price auctions with reserve where a seller interacts with multiple strategic bidders each of which holds a fixed private valuation for a good and seeks to maximize his expected future ... More
Privacy Preserving QoE Modeling using Collaborative LearningJun 21 2019Machine Learning based Quality of Experience (QoE) models potentially suffer from over-fitting due to limitations including low data volume, and limited participant profiles. This prevents models from becoming generic. Consequently, these trained models ... More
Privacy Preserving QoE Modeling using Collaborative LearningJun 21 2019Jun 26 2019Machine Learning based Quality of Experience (QoE) models potentially suffer from over-fitting due to limitations including low data volume, and limited participant profiles. This prevents models from becoming generic. Consequently, these trained models ... More
Theory of the Frequency Principle for General Deep Neural NetworksJun 21 2019Along with fruitful applications of Deep Neural Networks (DNNs) to realistic problems, recently, some empirical studies of DNNs reported a universal phenomenon of Frequency Principle (F-Principle): a DNN tends to learn a target function from low to high ... More
Theory of the Frequency Principle for General Deep Neural NetworksJun 21 2019Jul 02 2019Along with fruitful applications of Deep Neural Networks (DNNs) to realistic problems, recently, some empirical studies of DNNs reported a universal phenomenon of Frequency Principle (F-Principle): a DNN tends to learn a target function from low to high ... More
A Layered Aggregate Engine for Analytics WorkloadsJun 20 2019This paper introduces LMFAO (Layered Multiple Functional Aggregate Optimization), an in-memory optimization and execution engine for batches of aggregates over the input database. The primary motivation for this work stems from the observation that for ... More
Low-dimensional Embodied Semantics for Music and LanguageJun 20 2019Embodied cognition states that semantics is encoded in the brain as firing patterns of neural circuits, which are learned according to the statistical structure of human multimodal experience. However, each human brain is idiosyncratically biased, according ... More
Self-organized inductive reasoning with NeMuSJun 16 2019Neural Multi-Space (NeMuS) is a weighted multi-space representation for a portion of first-order logic designed for use with machine learning and neural network methods. It was demonstrated that it can be used to perform reasoning based on regions forming ... More
LioNets: Local Interpretation of Neural Networks through Penultimate Layer DecodingJun 15 2019Technological breakthroughs on smart homes, self-driving cars, health care and robotic assistants, in addition to reinforced law regulations, have critically influenced academic research on explainable machine learning. A sufficient number of researchers ... More
LioNets: Local Interpretation of Neural Networks through Penultimate Layer DecodingJun 15 2019Jul 30 2019Technological breakthroughs on smart homes, self-driving cars, health care and robotic assistants, in addition to reinforced law regulations, have critically influenced academic research on explainable machine learning. A sufficient number of researchers ... More
Deep neural network for fringe pattern filtering and normalisationJun 14 2019We propose a new framework for processing Fringe Patterns (FP). Our novel approach builds upon the hypothesis that the denoising and normalisation of FPs can be learned by a deep neural network if enough pairs of corrupted and cleaned FPs are provided. ... More
A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable GamesJun 13 2019We consider differentiable games: multi-objective minimization problems, where the goal is to find a Nash equilibrium. The machine learning community has recently started using extrapolation-based variants of the gradient method. A prime example is the ... More
A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable GamesJun 13 2019Jun 24 2019We consider differentiable games: multi-objective minimization problems, where the goal is to find a Nash equilibrium. The machine learning community has recently started using extrapolation-based variants of the gradient method. A prime example is the ... More
N-body Approach to the Traveling Salesman Problem (TSP)Jun 13 2019In the Traveling Salesman Problem (TSP), a list of cities and the distances between them are given. The goal is to find the shortest possible route that visits each city exactly once and returns to the original city. The TSP has a wide range of applications ... More
Empowering swarm-based optimizers by multi-scale search to enhance Gradient Descent initialization performanceJun 13 2019Swarm-based optimizers like Particle Swarm Optimization or Imperialistic Competitive Algorithm that act under influences of cooperation or competition among groups, are unable to search in multiple volumes of locality or globality and do not have nested ... More
Meta-heuristic for non-homogeneous peak density spaces and implementation on 2 real-world parameter learning/tuning applicationsJun 13 2019Observer effect in physics (/psychology) regards bias in measurement (/perception) due to the interference of instrument (/knowledge). Based on these concepts, a new meta-heuristic algorithm is proposed for controlling memory usage per localities without ... More
Causal Inference in Higher Education: Building Better CurriculumsJun 11 2019Higher educational institutions constantly look for ways to meet students' needs and support them through graduation. Recent work in the field of learning analytics have developed methods for grade prediction and course recommendations. Although these ... More
LSTM Networks Can Perform Dynamic CountingJun 09 2019In this paper, we systematically assess the ability of standard recurrent networks to perform dynamic counting and to encode hierarchical representations. All the neural models in our experiments are designed to be small-sized networks both to prevent ... More
Kinetic Market Model: An Evolutionary AlgorithmJun 04 2019This research proposes the econophysics kinetic market model as an evolutionary algorithm's instance. The immediate results from this proposal is a new replacement rule for family competition genetic algorithms. It also represents a starting point to ... More
Adaptive Multimodal Music Learning via Interactive-haptic InstrumentJun 04 2019Haptic interfaces have untapped the sense of touch to assist multimodal music learning. We have recently seen various improvements of interface design on tactile feedback and force guidance aiming to make instrument learning more effective. However, most ... More
A Perspective on Objects and Systematic Generalization in Model-Based RLJun 03 2019In order to meet the diverse challenges in solving many real-world problems, an intelligent agent has to be able to dynamically construct a model of its environment. Objects facilitate the modular reuse of prior knowledge and the combinatorial construction ... More
Neural Network-based Object Classification by Known and Unknown Features (Based on Text Queries)Jun 03 2019The article presents a method that improves the quality of classification of objects described by a combination of known and unknown features. The method is based on modernized Informational Neurobayesian Approach with consideration of unknown features. ... More
Algorithmically generating new algebraic features of polynomial systems for machine learningJun 03 2019There are a variety of choices to be made in both computer algebra systems (CASs) and satisfiability modulo theory (SMT) solvers which can impact performance without affecting mathematical correctness. Such choices are candidates for machine learning ... More
Learning Patterns in Sample Distributions for Monte Carlo Variance ReductionJun 01 2019This paper investigates a novel a-posteriori variance reduction approach in Monte Carlo image synthesis. Unlike most established methods based on lateral filtering in the image space, our proposition is to produce the best possible estimate for each pixel ... More
Deep Learning Recommendation Model for Personalization and Recommendation SystemsMay 31 2019With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks. These networks differ significantly from other deep learning networks due to their need ... More
PowerSGD: Practical Low-Rank Gradient Compression for Distributed OptimizationMay 31 2019We study gradient compression methods to alleviate the communication bottleneck in data-parallel distributed optimization. Despite the significant attention received, current compression schemes either do not scale well or fail to achieve the target test ... More
Memory-efficient and fast implementation of local adaptive binarization methodsMay 30 2019Binarization is widely used as an image preprocessing step to separate object especially text from background before recognition. For noisy images with uneven illumination, threshold values should be computed pixel by pixel to obtain a good segmentation. ... More
Memory-efficient and fast implementation of local adaptive binarization methodsMay 30 2019Jul 01 2019Binarization is widely used as an image preprocessing step to separate object especially text from background before recognition. For noisy images with uneven illumination such as degraded documents, threshold values need to be computed pixel by pixel ... More
Memory-efficient and fast implementation of local adaptive binarization methodsMay 30 2019Jul 31 2019Binarization is widely used as an image preprocessing step to separate object especially text from background before recognition. For noisy images with uneven illumination such as degraded documents, threshold values need to be computed pixel by pixel ... More
On the Effectiveness of Low-rank Approximations for Collaborative Filtering compared to Neural NetworksMay 30 2019Even in times of deep learning, low-rank approximations by factorizing a matrix into user and item latent factors continue to be a method of choice for collaborative filtering tasks due to their great performance. While deep learning based approaches ... More
Clustering without Over-RepresentationMay 29 2019In this paper we consider clustering problems in which each point is endowed with a color. The goal is to cluster the points to minimize the classical clustering cost but with the additional constraint that no color is over-represented in any cluster. ... More
A Quaternion-based Certifiably Optimal Solution to the Wahba Problem with OutliersMay 29 2019Jul 23 2019The Wahba problem, also known as rotation search, seeks to find the best rotation to align two sets of vector observations given putative correspondences, and is a fundamental routine in many computer vision and robotics applications. This work proposes ... More
A Quaternion-based Certifiably Optimal Solution to the Wahba Problem with OutliersMay 29 2019Aug 16 2019The Wahba problem, also known as rotation search, seeks to find the best rotation to align two sets of vector observations given putative correspondences, and is a fundamental routine in many computer vision and robotics applications. This work proposes ... More
A Quaternion-based Certifiably Optimal Solution to the Wahba Problem with OutliersMay 29 2019The Wahba problem, also known as rotation search, seeks to find the best rotation to align two sets of vector observations given putative correspondences, and is a fundamental routine in many computer vision and robotics applications. This work proposes ... More
Are Disentangled Representations Helpful for Abstract Visual Reasoning?May 29 2019A disentangled representation encodes information about the salient factors of variation in the data independently. Although it is often argued that this representational format is useful in learning to solve many real-world up-stream tasks, there is ... More
Asymptotically Unambitious Artificial General IntelligenceMay 29 2019General intelligence, the ability to solve arbitrary solvable problems, is supposed by many to be artificially constructible. Narrow intelligence, the ability to solve a given particularly difficult problem, has seen impressive recent development. Notable ... More