pwc-archive/papers-with-abstracts huggingface dataset:
Learning fine-grained search space pruning and heuristics for combinatorial optimization
Authors: Juho Lauri, Sourav Dutta, Marco Grassia, Deepak Ajwani
Date: 2020-01-05 00:00:00
Tasks: Combinatorial Optimization, Reinforcement Learning

Combinatorial optimization problems arise in a wide range of applications from diverse domains. Many of these problems are NP-hard and designing efficient heuristics for them requires considerable time and experimentation. On the other hand, the number of optimization problems in the industry continues to grow. In recent years, machine learning techniques have been explored to address this gap. We propose a framework for leveraging machine learning techniques to scale-up exact combinatorial optimization algorithms. In contrast to the existing approaches based on deep-learning, reinforcement learning and restricted Boltzmann machines that attempt to directly learn the output of the optimization problem from its input (with limited success), our framework learns the relatively simpler task of pruning the elements in order to reduce the size of the problem instances. In addition, our framework uses only interpretable learning models based on intuitive features and thus the learning process provides deeper insights into the optimization problem and the instance class, that can be used for designing better heuristics. For the classical maximum clique enumeration problem, we show that our framework can prune a large fraction of the input graph (around 99 % of nodes in case of sparse graphs) and still detect almost all of the maximum cliques. This results in several fold speedups of state-of-the-art algorithms. Furthermore, the model used in our framework highlights that the chi-squared value of neighborhood degree has a statistically significant correlation with the presence of a node in a maximum clique, particularly in dense graphs which constitute a significant challenge for modern solvers. We leverage this insight to design a novel heuristic for this problem outperforming the state-of-the-art. Our heuristic is also of independent interest for maximum clique detection and enumeration.
Automatic Business Process Structure Discovery using Ordered Neurons LSTM: A Preliminary Study
Authors: Xue Han, Lianxue Hu, Yabin Dang, Shivali Agarwal, Lijun Mei, Shaochun Li, Xin Zhou
Date: 2020-01-05 00:00:00
Tasks: Language Modeling, Language Modelling, Management

Automatic process discovery from textual process documentations is highly desirable to reduce time and cost of Business Process Management (BPM) implementation in organizations. However, existing automatic process discovery approaches mainly focus on identifying activities out of the documentations. Deriving the structural relationships between activities, which is important in the whole process discovery scope, is still a challenge. In fact, a business process has latent semantic hierarchical structure which defines different levels of detail to reflect the complex business logic. Recent findings in neural ma-chine learning area show that the meaningful linguistic structure can be induced by joint language modeling and structure learning. Inspired by these findings, we propose to retrieve the latent hierarchical structure present in the textual business process documents by building a neural network that leverages a novel recurrent architecture, Ordered Neurons LSTM (ON-LSTM), with process-level language model objective. We tested the proposed approach on data set of Process Description Documents (PDD) from our practical Robotic Process Automation (RPA) projects. Preliminary experiments showed promising results.
Evolutionary Approach to Collectible Card Game Arena Deckbuilding using Active Genes
Authors: Jakub Kowalski, Radosław Miernik
Date: 2020-01-05 00:00:00
Tasks: Card Games

In this paper, we evolve a card-choice strategy for the arena mode of Legends of Code and Magic, a programming game inspired by popular collectible card games like Hearthstone or TES: Legends. In the arena game mode, before each match, a player has to construct his deck choosing cards one by one from the previously unknown options. Such a scenario is difficult from the optimization point of view, as not only the fitness function is non-deterministic, but its value, even for a given problem instance, is impossible to be calculated directly and can only be estimated with simulation-based approaches. We propose a variant of the evolutionary algorithm that uses a concept of an active gene to reduce the range of the operators only to generation-specific subsequences of the genotype. Thus, we batched learning process and constrained evolutionary updates only to the cards relevant for the particular draft, without forgetting the knowledge from the previous tests. We developed and tested various implementations of this idea, investigating their performance by taking into account the computational cost of each variant. Performed experiments show that some of the introduced active-genes algorithms tend to learn faster and produce statistically better draft policies than the compared methods.
A Block-based Generative Model for Attributed Networks Embedding
Authors: Xueyan Liu, Bo Yang, Wenzhuo Song, Katarzyna Musial, Wanli Zuo, Hongxu Chen, Hongzhi Yin
Date: 2020-01-06 00:00:00
Tasks: Attribute, Clustering, Network Embedding

Attributed network embedding has attracted plenty of interest in recent years. It aims to learn task-independent, low-dimensional, and continuous vectors for nodes preserving both topology and attribute information. Most of the existing methods, such as random-walk based methods and GCNs, mainly focus on the local information, i.e., the attributes of the neighbours. Thus, they have been well studied for assortative networks (i.e., networks with communities) but ignored disassortative networks (i.e., networks with multipartite, hubs, and hybrid structures), which are common in the real world. To enable model both assortative and disassortative networks, we propose a block-based generative model for attributed network embedding from a probability perspective. Specifically, the nodes are assigned to several blocks wherein the nodes in the same block share the similar linkage patterns. These patterns can define assortative networks containing communities or disassortative networks with the multipartite, hub, or any hybrid structures. To preserve the attribute information, we assume that each node has a hidden embedding related to its assigned block. We use a neural network to characterize the nonlinearity between node embeddings and node attributes. We perform extensive experiments on real-world and synthetic attributed networks. The results show that our proposed method consistently outperforms state-of-the-art embedding methods for both clustering and classification tasks, especially on disassortative networks.
A Rule-Based Model for Victim Prediction
Authors: Murat Ozer, Nelly Elsayed, Said Varlioglu, Chengcheng Li, Niyazi Ekici
Date: 2020-01-06 00:00:00
Tasks: model, Prediction

In this paper, we proposed a novel automated model, called Vulnerability Index for Population at Risk (VIPAR) scores, to identify rare populations for their future shooting victimizations. Likewise, the focused deterrence approach identifies vulnerable individuals and offers certain types of treatments (e.g., outreach services) to prevent violence in communities. The proposed rule-based engine model is the first AI-based model for victim prediction. This paper aims to compare the list of focused deterrence strategy with the VIPAR score list regarding their predictive power for the future shooting victimizations. Drawing on the criminological studies, the model uses age, past criminal history, and peer influence as the main predictors of future violence. Social network analysis is employed to measure the influence of peers on the outcome variable. The model also uses logistic regression analysis to verify the variable selections. Our empirical results show that VIPAR scores predict 25.8% of future shooting victims and 32.2% of future shooting suspects, whereas focused deterrence list predicts 13% of future shooting victims and 9.4% of future shooting suspects. The model outperforms the intelligence list of focused deterrence policies in predicting the future fatal and non-fatal shootings. Furthermore, we discuss the concerns about the presumption of innocence right.
TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images
Authors: Shubham Paliwal, Vishwanath D, Rohit Rahul, Monika Sharma, Lovekesh Vig
Date: 2020-01-06 00:00:00
Tasks: Table Detection, Table Extraction, Transfer Learning

With the widespread use of mobile phones and scanners to photograph and upload documents, the need for extracting the information trapped in unstructured document images such as retail receipts, insurance claim forms and financial invoices is becoming more acute. A major hurdle to this objective is that these images often contain information in the form of tables and extracting data from tabular sub-images presents a unique set of challenges. This includes accurate detection of the tabular region within an image, and subsequently detecting and extracting information from the rows and columns of the detected table. While some progress has been made in table detection, extracting the table contents is still a challenge since this involves more fine grained table structure(rows & columns) recognition. Prior approaches have attempted to solve the table detection and structure recognition problems independently using two separate models. In this paper, we propose TableNet: a novel end-to-end deep learning model for both table detection and structure recognition. The model exploits the interdependence between the twin tasks of table detection and table structure recognition to segment out the table and column regions. This is followed by semantic rule-based row extraction from the identified tabular sub-regions. The proposed model and extraction approach was evaluated on the publicly available ICDAR 2013 and Marmot Table datasets obtaining state of the art results. Additionally, we demonstrate that feeding additional semantic features further improves model performance and that the model exhibits transfer learning across datasets. Another contribution of this paper is to provide additional table structure annotations for the Marmot data, which currently only has annotations for table detection.
Learning Reusable Options for Multi-Task Reinforcement Learning
Authors: Francisco M. Garcia, Chris Nota, Philip S. Thomas
Date: 2020-01-06 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) has become an increasingly active area of research in recent years. Although there are many algorithms that allow an agent to solve tasks efficiently, they often ignore the possibility that prior experience related to the task at hand might be available. For many practical applications, it might be unfeasible for an agent to learn how to solve a task from scratch, given that it is generally a computationally expensive process; however, prior experience could be leveraged to make these problems tractable in practice. In this paper, we propose a framework for exploiting existing experience by learning reusable options. We show that after an agent learns policies for solving a small number of problems, we are able to use the trajectories generated from those policies to learn reusable options that allow an agent to quickly learn how to solve novel and related problems.
Teach Me What You Want to Play: Learning Variants of Connect Four through Human-Robot Interaction
Authors: Ali Ayub, Alan R. Wagner
Date: 2020-01-03 00:00:00
Tasks: Active Learning

This paper investigates the use of game theoretic representations to represent and learn how to play interactive games such as Connect Four. We combine aspects of learning by demonstration, active learning, and game theory allowing a robot to leverage its developing representation of the game to conduct question/answer sessions with a person, thus filling in gaps in its knowledge. The paper demonstrates a method for teaching a robot the win conditions of the game Connect Four and its variants using a single demonstration and a few trial examples with a question and answer session led by the robot. Our results show that the robot can learn arbitrary win conditions for the game with little prior knowledge of the win conditions and then play the game with a human utilizing the learned win conditions. Our experiments also show that some questions are more important for learning the game's win conditions. We believe that this method could be broadly applied to a variety of interactive learning scenarios.
selp: A Single-Shot Epistemic Logic Program Solver
Authors: Manuel Bichler, Michael Morak, Stefan Woltran
Date: 2020-01-04 00:00:00
Tasks: Translation

Epistemic Logic Programs (ELPs) are an extension of Answer Set Programming (ASP) with epistemic operators that allow for a form of meta-reasoning, that is, reasoning over multiple possible worlds. Existing ELP solving approaches generally rely on making multiple calls to an ASP solver in order to evaluate the ELP. However, in this paper, we show that there also exists a direct translation from ELPs into non-ground ASP with bounded arity. The resulting ASP program can thus be solved in a single shot. We then implement this encoding method, using recently proposed techniques to handle large, non-ground ASP rules, into the prototype ELP solving system "selp", which we present in this paper. This solver exhibits competitive performance on a set of ELP benchmark instances. Under consideration in Theory and Practice of Logic Programming (TPLP).
Speech Enhancement based on Denoising Autoencoder with Multi-branched Encoders
Authors: Cheng Yu, Ryandhimas E. Zezario, Jonathan Sherman, Yi-Yen Hsieh, Xugang Lu, Hsin-Min Wang, Yu Tsao
Date: 2020-01-06 00:00:00
Tasks: Decoder, Denoising, Speech Enhancement

Deep learning-based models have greatly advanced the performance of speech enhancement (SE) systems. However, two problems remain unsolved, which are closely related to model generalizability to noisy conditions: (1) mismatched noisy condition during testing, i.e., the performance is generally sub-optimal when models are tested with unseen noise types that are not involved in the training data; (2) local focus on specific noisy conditions, i.e., models trained using multiple types of noises cannot optimally remove a specific noise type even though the noise type has been involved in the training data. These problems are common in real applications. In this paper, we propose a novel denoising autoencoder with a multi-branched encoder (termed DAEME) model to deal with these two problems. In the DAEME model, two stages are involved: offline and online. In the offline stage, we build multiple component models to form a multi-branched encoder based on a dynamically-sized decision tree(DSDT). The DSDT is built based on a prior knowledge of speech and noisy conditions (the speaker, environment, and signal factors are considered in this paper), where each component of the multi-branched encoder performs a particular mapping from noisy to clean speech along the branch in the DSDT. Finally, a decoder is trained on top of the multi-branched encoder. In the online stage, noisy speech is first processed by the tree and fed to each component model. The multiple outputs from these models are then integrated into the decoder to determine the final enhanced speech. Experimental results show that DAEME is superior to several baseline models in terms of objective evaluation metrics and the quality of subjective human listening tests.
Audio-visual Recognition of Overlapped speech for the LRS2 dataset
Authors: Jianwei Yu, Shi-Xiong Zhang, Jian Wu, Shahram Ghorbani, Bo Wu, Shiyin Kang, Shansong Liu, Xunying Liu, Helen Meng, Dong Yu
Date: 2020-01-06 00:00:00
Tasks: Audio-Visual Speech Recognition, Automatic Speech Recognition (ASR), Lipreading, speech-recognition, Speech Recognition, Speech Separation, Visual Speech Recognition

Automatic recognition of overlapped speech remains a highly challenging task to date. Motivated by the bimodal nature of human speech perception, this paper investigates the use of audio-visual technologies for overlapped speech recognition. Three issues associated with the construction of audio-visual speech recognition (AVSR) systems are addressed. First, the basic architecture designs i.e. end-to-end and hybrid of AVSR systems are investigated. Second, purposefully designed modality fusion gates are used to robustly integrate the audio and visual features. Third, in contrast to a traditional pipelined architecture containing explicit speech separation and recognition components, a streamlined and integrated AVSR system optimized consistently using the lattice-free MMI (LF-MMI) discriminative criterion is also proposed. The proposed LF-MMI time-delay neural network (TDNN) system establishes the state-of-the-art for the LRS2 dataset. Experiments on overlapped speech simulated from the LRS2 dataset suggest the proposed AVSR system outperformed the audio only baseline LF-MMI DNN system by up to 29.98\% absolute in word error rate (WER) reduction, and produced recognition performance comparable to a more complex pipelined system. Consistent performance improvements of 4.89\% absolute in WER reduction over the baseline AVSR system using feature fusion are also obtained.
Ridge Regression: Structure, Cross-Validation, and Sketching
Authors: Sifan Liu, Edgar Dobriban
Date: 2019-10-06 00:00:00
Proceeding: ICLR 2020 1
Tasks: regression

We study the following three fundamental problems about ridge regression: (1) what is the structure of the estimator? (2) how to correctly use cross-validation to choose the regularization parameter? and (3) how to accelerate computation without losing too much accuracy? We consider the three problems in a unified large-data linear model. We give a precise representation of ridge regression as a covariance matrix-dependent linear combination of the true parameter and the noise. We study the bias of $K$-fold cross-validation for choosing the regularization parameter, and propose a simple bias-correction. We analyze the accuracy of primal and dual sketching for ridge regression, showing they are surprisingly accurate. Our results are illustrated by simulations and by analyzing empirical data.
Impact of non-stationarity on estimating and modeling empirical copulas of daily stock returns
Authors: Marcel Wollschläger, Rudi Schäfer
Date: 2015-06-26 00:00:00
Tasks: Time Series, Time Series Analysis

All too often measuring statistical dependencies between financial time series is reduced to a linear correlation coefficient. However this may not capture all facets of reality. We study empirical dependencies of daily stock returns by their pairwise copulas. Here we investigate particularly to which extent the non-stationarity of financial time series affects both the estimation and the modeling of empirical copulas. We estimate empirical copulas from the non-stationary, original return time series and stationary, locally normalized ones. Thereby we are able to explore the empirical dependence structure on two different scales: a global and a local one. Additionally the asymmetry of the empirical copulas is emphasized as a fundamental characteristic. We compare our empirical findings with a single Gaussian copula, with a correlation-weighted average of Gaussian copulas, with the K-copula directly addressing the non-stationarity of dependencies as a model parameter, and with the skewed Student's t-copula. The K-copula covers the empirical dependence structure on the local scale most adequately, whereas the skewed Student's t-copula best captures the asymmetry of the empirical copula on the global scale.
Dependence structure of market states
Authors: Desislava Chetalova, Marcel Wollschläger, Rudi Schäfer
Date: 2015-03-31 00:00:00

We study the dependence structure of market states by estimating empirical pairwise copulas of daily stock returns. We consider both original returns, which exhibit time-varying trends and volatilities, as well as locally normalized ones, where the non-stationarity has been removed. The empirical pairwise copula for each state is compared with a bivariate K-copula. This copula arises from a recently introduced random matrix model, in which non-stationary correlations between returns are modeled by an ensemble of random matrices. The comparison reveals overall good agreement between empirical and analytical copulas, especially for locally normalized returns. Still, there are some deviations in the tails. Furthermore, we find an asymmetry in the dependence structure of market states. The empirical pairwise copulas exhibit a stronger lower tail dependence, particularly in times of crisis.
Convolutional Subspace Clustering Network with Block Diagonal Prior
Authors: Junjian Zhang, Chun-Guang Li, Tianming Du, Honggang Zhang, Jun Guo
Date: 2019-12-31 00:00:00
Proceeding: IEEE Access 2019 12
Tasks: Clustering

Standard methods of subspace clustering are based on self-expressiveness in the original data space, which states that a data point in a subspace can be expressed as a linear combination of other points. However, the real data in raw form are usually not well aligned with the linear subspace model. Therefore, it is crucial to obtain a proper feature space for performing high quality subspace clustering. Inspired by the success of Convolutional Neural Networks (CNN) for extraction powerful features from visual data and the block diagonal prior for learning a good affinity matrix from self-expression coefficients, in this paper, we propose a jointly trainable feature extraction and affinity learning framework with the block diagonal prior, termed as Convolutional Subspace Clustering Network with Block Diagonal prior (ConvSCN-BD), in which we solve the joint optimization problem in ConvSCN-BD via an alternating minimization algorithm, which updates the parameters in the convolutional modules and the self-expression coefficients with stochastic gradients descent and updates other variables with close-form solutions alternatingly. In addition, we derive the connection between the block diagonal prior and the subspace structured norm, and reveal that using the block diagonal prior on the affinity matrix is essentially incorporating the feedback information from spectral clustering. Experiments on three benchmark datasets demonstrated the effectiveness of our proposal.