Paper Group ANR 1552
Conditional Markov Chain Search for the Generalised Travelling Salesman Problem for Warehouse Order Picking. Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler. Bootstrapping Domain-Specific Content Discovery on the Web. Safe global optimization of expensive noisy black-box functions in the $δ$-Lipschitz framework …
Conditional Markov Chain Search for the Generalised Travelling Salesman Problem for Warehouse Order Picking
| Title | Conditional Markov Chain Search for the Generalised Travelling Salesman Problem for Warehouse Order Picking |
| Authors | Olegs Nalivajevs, Daniel Karapetyan |
| Abstract | The Generalised Travelling Salesman Problem (GTSP) is a well-known problem that, among other applications, arises in warehouse order picking, where each stock is distributed between several locations – a typical approach in large modern warehouses. However, the instances commonly used in the literature have a completely different structure, and the methods are designed with those instances in mind. In this paper, we give a new pseudo-random instance generator that reflects the warehouse order picking and publish new benchmark testbeds. We also use the Conditional Markov Chain Search framework to automatically generate new GTSP metaheuristics trained specifically for warehouse order picking. Finally, we report the computational results of our metaheuristics to enable further competition between solvers. |
| Tasks | |
| Published | 2019-07-19 |
| URL | https://arxiv.org/abs/1907.08647v2 |
| https://arxiv.org/pdf/1907.08647v2.pdf | |
| PWC | https://paperswithcode.com/paper/conditional-markov-chain-search-for-the-1 |
| Repo | |
| Framework | |
Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler
| Title | Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler |
| Authors | Duo Xu |
| Abstract | State space models (SSM) have been widely applied for the analysis and visualization of large sequential datasets. Sequential Monte Carlo (SMC) is a very popular particle-based method to sample latent states from intractable posteriors. However, SSM is significantly influenced by the choice of the proposal. Recently Hamiltonian Monte Carlo (HMC) sampling has shown success in many practical problems. In this paper, we propose an SMC augmented by HMC (HSMC) for inference and model learning of nonlinear SSM, which can exempt us from learning proposals and reduce the model complexity significantly. Based on the measure preserving property of HMC, the particles directly generated by transition function can approximate the posterior of latent states arbitrarily well. In order to better adapt to the local geometry of latent space, the HMC is conducted on Riemannian manifold defined by a positive definite metric. In addition, we show that the proposed HSMC method can improve SSMs realized by both Gaussian Processes (GP) and Neural Network (NN). |
| Tasks | Gaussian Processes |
| Published | 2019-01-03 |
| URL | http://arxiv.org/abs/1901.00862v1 |
| http://arxiv.org/pdf/1901.00862v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-nonlinear-state-space-models-with |
| Repo | |
| Framework | |
Bootstrapping Domain-Specific Content Discovery on the Web
| Title | Bootstrapping Domain-Specific Content Discovery on the Web |
| Authors | Kien Pham, Aécio Santos, Juliana Freire |
| Abstract | The ability to continuously discover domain-specific content from the Web is critical for many applications. While focused crawling strategies have been shown to be effective for discovery, configuring a focused crawler is difficult and time-consuming. Given a domain of interest $D$, subject-matter experts (SMEs) must search for relevant websites and collect a set of representative Web pages to serve as training examples for creating a classifier that recognizes pages in $D$, as well as a set of pages to seed the crawl. In this paper, we propose DISCO, an approach designed to bootstrap domain-specific search. Given a small set of websites, DISCO aims to discover a large collection of relevant websites. DISCO uses a ranking-based framework that mimics the way users search for information on the Web: it iteratively discovers new pages, distills, and ranks them. It also applies multiple discovery strategies, including keyword-based and related queries issued to search engines, backward and forward crawling. By systematically combining these strategies, DISCO is able to attain high harvest rates and coverage for a variety of domains. We perform extensive experiments in four social-good domains, using data gathered by SMEs in the respective domains, and show that our approach is effective and outperforms state-of-the-art methods. |
| Tasks | |
| Published | 2019-02-25 |
| URL | http://arxiv.org/abs/1902.09667v1 |
| http://arxiv.org/pdf/1902.09667v1.pdf | |
| PWC | https://paperswithcode.com/paper/bootstrapping-domain-specific-content |
| Repo | |
| Framework | |
Safe global optimization of expensive noisy black-box functions in the $δ$-Lipschitz framework
| Title | Safe global optimization of expensive noisy black-box functions in the $δ$-Lipschitz framework |
| Authors | Yaroslav D. Sergeyev, Antonio Candelieri, Dmitri E. Kvasov, Riccardo Perego |
| Abstract | In this paper, the problem of safe global maximization (it should not be confused with robust optimization) of expensive noisy black-box functions satisfying the Lipschitz condition is considered. The notion “safe” means that the objective function $f(x)$ during optimization should not violate a “safety” threshold, for instance, a certain a priori given value $h$ in a maximization problem. Thus, any new function evaluation must be performed at “safe points” only, namely, at points $y$ for which it is known that the objective function $f(y) > h$. The main difficulty here consists in the fact that the used optimization algorithm should ensure that the safety constraint will be satisfied at a point $y$ before evaluation of $f(y)$ will be executed. Thus, it is required both to determine the safe region $\Omega$ within the search domain~$D$ and to find the global maximum within $\Omega$. An additional difficulty consists in the fact that these problems should be solved in the presence of the noise. This paper starts with a theoretical study of the problem and it is shown that even though the objective function $f(x)$ satisfies the Lipschitz condition, traditional Lipschitz minorants and majorants cannot be used due to the presence of the noise. Then, a $\delta$-Lipschitz framework and two algorithms using it are proposed to solve the safe global maximization problem. The first method determines the safe area within the search domain and the second one executes the global maximization over the found safe region. For both methods a number of theoretical results related to their functioning and convergence is established. Finally, numerical experiments confirming the reliability of the proposed procedures are performed. |
| Tasks | |
| Published | 2019-08-15 |
| URL | https://arxiv.org/abs/1908.06010v1 |
| https://arxiv.org/pdf/1908.06010v1.pdf | |
| PWC | https://paperswithcode.com/paper/safe-global-optimization-of-expensive-noisy |
| Repo | |
| Framework | |
A Fully Bayesian Infinite Generative Model for Dynamic Texture Segmentation
| Title | A Fully Bayesian Infinite Generative Model for Dynamic Texture Segmentation |
| Authors | Sahar Yousefi, M. T. Manzuri Shalmani, Antoni B. Chan |
| Abstract | Generative dynamic texture models (GDTMs) are widely used for dynamic texture (DT) segmentation in the video sequences. GDTMs represent DTs as a set of linear dynamical systems (LDSs). A major limitation of these models concerns the automatic selection of a proper number of DTs. Dirichlet process mixture (DPM) models which have appeared recently as the cornerstone of the non-parametric Bayesian statistics, is an optimistic candidate toward resolving this issue. Under this motivation to resolve the aforementioned drawback, we propose a novel non-parametric fully Bayesian approach for DT segmentation, formulated on the basis of a joint DPM and GDTM construction. This interaction causes the algorithm to overcome the problem of automatic segmentation properly. We derive the Variational Bayesian Expectation-Maximization (VBEM) inference for the proposed model. Moreover, in the E-step of inference, we apply Rauch-Tung-Striebel smoother (RTSS) algorithm on Variational Bayesian LDSs. Ultimately, experiments on different video sequences are performed. Experiment results indicate that the proposed algorithm outperforms the previous methods in efficiency and accuracy noticeably. |
| Tasks | |
| Published | 2019-01-13 |
| URL | http://arxiv.org/abs/1901.03968v1 |
| http://arxiv.org/pdf/1901.03968v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-fully-bayesian-infinite-generative-model |
| Repo | |
| Framework | |
Solving Financial Regulatory Compliance Using Software Contracts
| Title | Solving Financial Regulatory Compliance Using Software Contracts |
| Authors | Newres Al Haider, Dilhan Thilakarathne, Joost Bosman |
| Abstract | Ensuring compliance with various laws and regulations is of utmost priority for financial institutions. Traditional methods in this area have been shown to be inefficient. Manual processing does not scale well. Automated efforts are hindered due to the lack of formalization of domain knowledge and problems of integrating such knowledge into software systems. In this work we propose an approach to tackle these issues by encoding them into software contracts using a Controlled Natural Language. In particular, we encode a portion of the Money Market Statistical Reporting (MMSR) regulations into contracts specified by the clojure.spec framework. We show how various features of a contract framework, in particular clojure.spec, can help to tackle issues that occur when dealing with compliance: validation with explanations and test data generation. We benchmark our proposed solution and show that this approach can effectively solve compliance issues in this particular use case. |
| Tasks | |
| Published | 2019-09-11 |
| URL | https://arxiv.org/abs/1909.08965v1 |
| https://arxiv.org/pdf/1909.08965v1.pdf | |
| PWC | https://paperswithcode.com/paper/solving-financial-regulatory-compliance-using |
| Repo | |
| Framework | |
Outlier-robust estimation of a sparse linear model using $\ell_1$-penalized Huber’s $M$-estimator
| Title | Outlier-robust estimation of a sparse linear model using $\ell_1$-penalized Huber’s $M$-estimator |
| Authors | Arnak S. Dalalyan, Philip Thompson |
| Abstract | We study the problem of estimating a $p$-dimensional $s$-sparse vector in a linear model with Gaussian design and additive noise. In the case where the labels are contaminated by at most $o$ adversarial outliers, we prove that the $\ell_1$-penalized Huber’s $M$-estimator based on $n$ samples attains the optimal rate of convergence $(s/n)^{1/2} + (o/n)$, up to a logarithmic factor. For more general design matrices, our results highlight the importance of two properties: the transfer principle and the incoherence property. These properties with suitable constants are shown to yield the optimal rates, up to log-factors, of robust estimation with adversarial contamination. |
| Tasks | |
| Published | 2019-04-12 |
| URL | https://arxiv.org/abs/1904.06288v3 |
| https://arxiv.org/pdf/1904.06288v3.pdf | |
| PWC | https://paperswithcode.com/paper/outlier-robust-estimation-of-a-sparse-linear |
| Repo | |
| Framework | |
Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders
| Title | Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders |
| Authors | Carl Yang, Jieyu Zhang, Haonan Wang, Sha Li, Myungwan Kim, Matt Walker, Yiou Xiao, Jiawei Han |
| Abstract | While node semantics have been extensively explored in social networks, little research attention has been paid to profile edge semantics, i.e., social relations. Ideal edge semantics should not only show that two users are connected, but also why they know each other and what they share in common. However, relations in social networks are often hard to profile, due to noisy multi-modal signals and limited user-generated ground-truth labels. In this work, we aim to develop a unified and principled framework that can profile user relations as edge semantics in social networks by integrating multi-modal signals in the presence of noisy and incomplete data. Our framework is also flexible towards limited or missing supervision. Specifically, we assume a latent distribution of multiple relations underlying each user link, and learn them with multi-modal graph edge variational autoencoders. We encode the network data with a graph convolutional network, and decode arbitrary signals with multiple reconstruction networks. Extensive experiments and case studies on two public DBLP author networks and two internal LinkedIn member networks demonstrate the superior effectiveness and efficiency of our proposed model. |
| Tasks | |
| Published | 2019-11-04 |
| URL | https://arxiv.org/abs/1911.05465v1 |
| https://arxiv.org/pdf/1911.05465v1.pdf | |
| PWC | https://paperswithcode.com/paper/relation-learning-on-social-networks-with |
| Repo | |
| Framework | |
Natural Language Interaction with Explainable AI Models
| Title | Natural Language Interaction with Explainable AI Models |
| Authors | Arjun R Akula, Sinisa Todorovic, Joyce Y Chai, Song-Chun Zhu |
| Abstract | This paper presents an explainable AI (XAI) system that provides explanations for its predictions. The system consists of two key components – namely, the prediction And-Or graph (AOG) model for recognizing and localizing concepts of interest in input data, and the XAI model for providing explanations to the user about the AOG’s predictions. In this work, we focus on the XAI model specified to interact with the user in natural language, whereas the AOG’s predictions are considered given and represented by the corresponding parse graphs (pg’s) of the AOG. Our XAI model takes pg’s as input and provides answers to the user’s questions using the following types of reasoning: direct evidence (e.g., detection scores), part-based inference (e.g., detected parts provide evidence for the concept asked), and other evidences from spatio-temporal context (e.g., constraints from the spatio-temporal surround). We identify several correlations between user’s questions and the XAI answers using Youtube Action dataset. |
| Tasks | |
| Published | 2019-03-13 |
| URL | https://arxiv.org/abs/1903.05720v2 |
| https://arxiv.org/pdf/1903.05720v2.pdf | |
| PWC | https://paperswithcode.com/paper/natural-language-interaction-with-explainable |
| Repo | |
| Framework | |
Representation Learning for Classical Planning from Partially Observed Traces
| Title | Representation Learning for Classical Planning from Partially Observed Traces |
| Authors | Zhanhao Xiao, Hai Wan, Hankui Hankz Zhuo, Jinxia Lin, Yanan Liu |
| Abstract | Specifying a complete domain model is time-consuming, which has been a bottleneck of AI planning technique application in many real-world scenarios. Most classical domain-model learning approaches output a domain model in the form of the declarative planning language, such as STRIPS or PDDL, and solve new planning instances by invoking an existing planner. However, planning in such a representation is sensitive to the accuracy of the learned domain model which probably cannot be used to solve real planning problems. In this paper, to represent domain models in a vectorization representation way, we propose a novel framework based on graph neural network (GNN) integrating model-free learning and model-based planning, called LP-GNN. By embedding propositions and actions in a graph, the latent relationship between them is explored to form a domain-specific heuristics. We evaluate our approach on five classical planning domains, comparing with the classical domain-model learner ARMS. The experimental results show that the domain models learned by our approach are much more effective on solving real planning problems. |
| Tasks | Representation Learning |
| Published | 2019-07-19 |
| URL | https://arxiv.org/abs/1907.08352v1 |
| https://arxiv.org/pdf/1907.08352v1.pdf | |
| PWC | https://paperswithcode.com/paper/representation-learning-for-classical |
| Repo | |
| Framework | |
ContactGrasp: Functional Multi-finger Grasp Synthesis from Contact
| Title | ContactGrasp: Functional Multi-finger Grasp Synthesis from Contact |
| Authors | Samarth Brahmbhatt, Ankur Handa, James Hays, Dieter Fox |
| Abstract | Grasping and manipulating objects is an important human skill. Since most objects are designed to be manipulated by human hands, anthropomorphic hands can enable richer human-robot interaction. Desirable grasps are not only stable, but also functional: they enable post-grasp actions with the object. However, functional grasp synthesis for high degree-of-freedom anthropomorphic hands from object shape alone is challenging because of the large optimization space. We present ContactGrasp, a framework for functional grasp synthesis from object shape and contact on the object surface. Contact can be manually specified or obtained through demonstrations. Our contact representation is object-centric and allows functional grasp synthesis even for hand models different than the one used for demonstration. Using a dataset of contact demonstrations from humans grasping diverse household objects, we synthesize functional grasps for three hand models and two functional intents. The project webpage is https://contactdb.cc.gatech.edu/contactgrasp.html. |
| Tasks | |
| Published | 2019-04-07 |
| URL | https://arxiv.org/abs/1904.03754v3 |
| https://arxiv.org/pdf/1904.03754v3.pdf | |
| PWC | https://paperswithcode.com/paper/contactgrasp-functional-multi-finger-grasp |
| Repo | |
| Framework | |
Decomposable Neural Paraphrase Generation
| Title | Decomposable Neural Paraphrase Generation |
| Authors | Zichao Li, Xin Jiang, Lifeng Shang, Qun Liu |
| Abstract | Paraphrasing exists at different granularity levels, such as lexical level, phrasal level and sentential level. This paper presents Decomposable Neural Paraphrase Generator (DNPG), a Transformer-based model that can learn and generate paraphrases of a sentence at different levels of granularity in a disentangled way. Specifically, the model is composed of multiple encoders and decoders with different structures, each of which corresponds to a specific granularity. The empirical study shows that the decomposition mechanism of DNPG makes paraphrase generation more interpretable and controllable. Based on DNPG, we further develop an unsupervised domain adaptation method for paraphrase generation. Experimental results show that the proposed model achieves competitive in-domain performance compared to the state-of-the-art neural models, and significantly better performance when adapting to a new domain. |
| Tasks | Domain Adaptation, Paraphrase Generation, Unsupervised Domain Adaptation |
| Published | 2019-06-24 |
| URL | https://arxiv.org/abs/1906.09741v1 |
| https://arxiv.org/pdf/1906.09741v1.pdf | |
| PWC | https://paperswithcode.com/paper/decomposable-neural-paraphrase-generation |
| Repo | |
| Framework | |
DeepFlash: Turning a Flash Selfie into a Studio Portrait
| Title | DeepFlash: Turning a Flash Selfie into a Studio Portrait |
| Authors | Nicola Capece, Francesco Banterle, Paolo Cignoni, Fabio Ganovelli, Roberto Scopigno, Ugo Erra |
| Abstract | We present a method for turning a flash selfie taken with a smartphone into a photograph as if it was taken in a studio setting with uniform lighting. Our method uses a convolutional neural network trained on a set of pairs of photographs acquired in an ad-hoc acquisition campaign. Each pair consists of one photograph of a subject’s face taken with the camera flash enabled and another one of the same subject in the same pose illuminated using a photographic studio-lighting setup. We show how our method can amend defects introduced by a close-up camera flash, such as specular highlights, shadows, skin shine, and flattened images. |
| Tasks | |
| Published | 2019-01-14 |
| URL | https://arxiv.org/abs/1901.04252v2 |
| https://arxiv.org/pdf/1901.04252v2.pdf | |
| PWC | https://paperswithcode.com/paper/deepflash-turning-a-flash-selfie-into-a |
| Repo | |
| Framework | |
Mechanically Powered Motion Imaging Phantoms: Proof of Concept
| Title | Mechanically Powered Motion Imaging Phantoms: Proof of Concept |
| Authors | Alberto Gomez, Cornelia Schmitz, Markus Henningsson, James Housden, Yohan Noh, Veronika A. Zimmer, James R. Clough, Ilkay Oksuz, Nicolas Toussaint, Andrew P. King, Julia A. Schnabel |
| Abstract | Motion imaging phantoms are expensive, bulky and difficult to transport and set-up. The purpose of this paper is to demonstrate a simple approach to the design of multi-modality motion imaging phantoms that use mechanically stored energy to produce motion. We propose two phantom designs that use mainsprings and elastic bands to store energy. A rectangular piece was attached to an axle at the end of the transmission chain of each phantom, and underwent a rotary motion upon release of the mechanical motor. The phantoms were imaged with MRI and US, and the image sequences were embedded in a 1D non linear manifold (Laplacian Eigenmap) and the spectrogram of the embedding was used to derive the angular velocity over time. The derived velocities were consistent and reproducible within a small error. The proposed motion phantom concept showed great potential for the construction of simple and affordable motion phantoms |
| Tasks | |
| Published | 2019-05-17 |
| URL | https://arxiv.org/abs/1905.07198v1 |
| https://arxiv.org/pdf/1905.07198v1.pdf | |
| PWC | https://paperswithcode.com/paper/mechanically-powered-motion-imaging-phantoms |
| Repo | |
| Framework | |
Enabling Hyper-Personalisation: Automated Ad Creative Generation and Ranking for Fashion e-Commerce
| Title | Enabling Hyper-Personalisation: Automated Ad Creative Generation and Ranking for Fashion e-Commerce |
| Authors | Sreekanth Vempati, Korah T Malayil, Sruthi V, Sandeep R |
| Abstract | Homepage is the first touch point in the customer’s journey and is one of the prominent channels of revenue for many e-commerce companies. A user’s attention is mostly captured by homepage banner images (also called Ads/Creatives). The set of banners shown and their design, influence the customer’s interest and plays a key role in optimizing the click through rates of the banners. Presently, massive and repetitive effort is put in, to manually create aesthetically pleasing banner images. Due to the large amount of time and effort involved in this process, only a small set of banners are made live at any point. This reduces the number of banners created as well as the degree of personalization that can be achieved. This paper thus presents a method to generate creatives automatically on a large scale in a short duration. The availability of diverse banners generated helps in improving personalization as they can cater to the taste of larger audience. The focus of our paper is on generating wide variety of homepage banners that can be made as an input for user level personalization engine. Following are the main contributions of this paper: 1) We introduce and explain the need for large scale banner generation for e-commerce 2) We present on how we utilize existing deep learning based detectors which can automatically annotate the required objects/tags from the image. 3) We also propose a Genetic Algorithm based method to generate an optimal banner layout for the given image content, input components and other design constraints. 4) Further, to aid the process of picking the right set of banners, we designed a ranking method and evaluated multiple models. All our experiments have been performed on data from Myntra (http://www.myntra.com), one of the top fashion e-commerce players in India. |
| Tasks | |
| Published | 2019-08-27 |
| URL | https://arxiv.org/abs/1908.10139v1 |
| https://arxiv.org/pdf/1908.10139v1.pdf | |
| PWC | https://paperswithcode.com/paper/enabling-hyper-personalisation-automated-ad |
| Repo | |
| Framework | |
