pwc-archive/papers-with-abstracts huggingface dataset:
Finite-Time 4-Expert Prediction Problem
Authors: Erhan Bayraktar, Ibrahim Ekren, Xin Zhang
Date: 2019-11-22 00:00:00
Tasks: Prediction

We explicitly solve the nonlinear PDE that is the continuous limit of dynamic programming of \emph{expert prediction problem} in finite horizon setting with $N=4$ experts. The \emph{expert prediction problem} is formulated as a zero sum game between a player and an adversary. By showing that the solution is $\mathcal{C}^2$, we are able to show that the strategies conjectured in arXiv:1409.3040G form an asymptotic Nash equilibrium. We also prove the "Finite vs Geometric regret" conjecture proposed in arXiv:1409.3040G for $N=4$, and and show that this conjecture in fact follows from the conjecture that the comb strategies are optimal.
State Alignment-based Imitation Learning
Authors: Fangchen Liu, Zhan Ling, Tongzhou Mu, Hao Su
Date: 2019-11-21 00:00:00
Proceeding: ICLR 2020 1
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Consider an imitation learning problem that the imitator and the expert have different dynamics models. Most of the current imitation learning methods fail because they focus on imitating actions. We propose a novel state alignment-based imitation learning method to train the imitator to follow the state sequences in expert demonstrations as much as possible. The state alignment comes from both local and global perspectives and we combine them into a reinforcement learning framework by a regularized policy update objective. We show the superiority of our method on standard imitation learning settings and imitation learning settings where the expert and imitator have different dynamics models.
A Survey on Adversarial Information Retrieval on the Web
Authors: Saad Farooq
Date: 2019-11-21 00:00:00
Tasks: Information Retrieval, Retrieval, Survey

This survey paper discusses different forms of malicious techniques that can affect how an information retrieval model retrieves documents for a query and their remedies.
A Coefficient of Determination for Probabilistic Topic Models
Authors: Tommy Jones
Date: 2019-11-20 00:00:00
Tasks: Topic Models

This research proposes a new (old) metric for evaluating goodness of fit in topic models, the coefficient of determination, or $R^2$. Within the context of topic modeling, $R^2$ has the same interpretation that it does when used in a broader class of statistical models. Reporting $R^2$ with topic models addresses two current problems in topic modeling: a lack of standard cross-contextual evaluation metrics for topic modeling and ease of communication with lay audiences. The author proposes that $R^2$ should be reported as a standard metric when constructing topic models.
Automatic Detection of Satire in Bangla Documents: A CNN Approach Based on Hybrid Feature Extraction Model
Authors: Arnab Sen Sharma, Maruf Ahmed Mridul, Md. Saiful Islam
Date: 2019-11-19 00:00:00

Widespread of satirical news in online communities is an ongoing trend. The nature of satires is so inherently ambiguous that sometimes it's too hard even for humans to understand whether it's actually satire or not. So, research interest has grown in this field. The purpose of this research is to detect Bangla satirical news spread in online news portals as well as social media. In this paper, we propose a hybrid technique for extracting features from text documents combining Word2Vec and TF-IDF. Using our proposed feature extraction technique, with standard CNN architecture we could detect whether a Bangla text document is satire or not with an accuracy of more than 96%.
Simple yet Effective Way for Improving the Performance of GAN
Authors: Yong-Goo Shin, Yoon-Jae Yeo, Sung-Jea Ko
Date: 2019-11-19 00:00:00
Tasks: Diversity, Generative Adversarial Network

In adversarial learning, discriminator often fails to guide the generator successfully since it distinguishes between real and generated images using silly or non-robust features. To alleviate this problem, this brief presents a simple but effective way that improves the performance of generative adversarial network (GAN) without imposing the training overhead or modifying the network architectures of existing methods. The proposed method employs a novel cascading rejection (CR) module for discriminator, which extracts multiple non-overlapped features in an iterative manner using the vector rejection operation. Since the extracted diverse features prevent the discriminator from concentrating on non-meaningful features, the discriminator can guide the generator effectively to produce the images that are more similar to the real images. In addition, since the proposed CR module requires only a few simple vector operations, it can be readily applied to existing frameworks with marginal training overheads. Quantitative evaluations on various datasets including CIFAR-10, CelebA, CelebA-HQ, LSUN, and tiny-ImageNet confirm that the proposed method significantly improves the performance of GAN and conditional GAN in terms of Frechet inception distance (FID) indicating the diversity and visual appearance of the generated images.
Voice Search and Typed Search Performance Comparison on Baidu Search System
Authors: Hanqing Huang, Kezia Irene, Nahyun Ryu
Date: 2019-11-17 00:00:00

Although the voice search system is getting more and more developed, some people still have difficulties when searching for information with the voice search system. This paper is a pilot study to compare the search performance of people using voice search and typed search using Baidu search system. We surveyed and interviewed 40 Chinese students who have been using the Baidu search system. Afterward, we analyzed 8 people who had a middle to advanced searching ability by their behaviors, search results, and average query length. We found that there are a lot of variations among the participants' time when searching for different queries, and there were some interesting behaviors that were displayed by a number of participants. We conclude that more participants are needed to make a firm conclusion on the performance comparison between the voice search and typed search.
DE/RM-MEDA: A New Hybrid Multi-Objective Generator
Authors: Abel SaÂ J. R Malano, Guanjun Du, Guoxiang Tong, Naixue Xiong
Date: 2019-11-15 00:00:00
Tasks: Diversity

Under the condition of Karush-Kuhn-Tucker, the Pareto Set (PS) in the decision area of an m-objective optimization problem is a piecewise continuous (m-1)-D manifold. For illustrate the degree of convergence of the population, we employed the ratio of the sum of the first (m-1) largest eigenvalue of the population's covariance matrix of the sum of all eigenvalue. Based on this property, this paper proposes a new algorithm, called DE/RM-MEDA, which mix differential evolutionary (DE) and the estimation of distribution algorithm (EDA) to generate and adaptively adjusts the number of new solutions by the ratio. The proposed algorithm is experimented on nine tec09 problems. The comparison results between DE/RM-MEDA and the others algorithms, called NSGA-II-DE and RM-MEDA, show that the proposed algorithm perform better in terms of convergence and diversity metric.
Sparsity through evolutionary pruning prevents neuronal networks from overfitting
Authors: Richard C. Gerum, André Erpenbeck, Patrick Krauss, Achim Schilling
Date: 2019-11-07 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Modern Machine learning techniques take advantage of the exponentially rising calculation power in new generation processor units. Thus, the number of parameters which are trained to resolve complex tasks was highly increased over the last decades. However, still the networks fail - in contrast to our brain - to develop general intelligence in the sense of being able to solve several complex tasks with only one network architecture. This could be the case because the brain is not a randomly initialized neural network, which has to be trained by simply investing a lot of calculation power, but has from birth some fixed hierarchical structure. To make progress in decoding the structural basis of biological neural networks we here chose a bottom-up approach, where we evolutionarily trained small neural networks in performing a maze task. This simple maze task requires dynamical decision making with delayed rewards. We were able to show that during the evolutionary optimization random severance of connections lead to better generalization performance of the networks compared to fully connected networks. We conclude that sparsity is a central property of neural networks and should be considered for modern Machine learning approaches.
Trend-responsive User Segmentation Enabling Traceable Publishing Insights. A Case Study of a Real-world Large-scale News Recommendation System
Authors: Joanna Misztal-Radecka, Dominik Rusiecki, Michał Żmuda, Artur Bujak
Date: 2019-10-28 00:00:00
Tasks: Diversity, global-optimization, Multi-Armed Bandits, News Recommendation, Recommendation Systems

The traditional offline approaches are no longer sufficient for building modern recommender systems in domains such as online news services, mainly due to the high dynamics of environment changes and necessity to operate on a large scale with high data sparsity. The ability to balance exploration with exploitation makes the multi-armed bandits an efficient alternative to the conventional methods, and a robust user segmentation plays a crucial role in providing the context for such online recommendation algorithms. In this work, we present an unsupervised and trend-responsive method for segmenting users according to their semantic interests, which has been integrated with a real-world system for large-scale news recommendations. The results of an online A/B test show significant improvements compared to a global-optimization algorithm on several services with different characteristics. Based on the experimental results as well as the exploration of segments descriptions and trend dynamics, we propose extensions to this approach that address particular real-world challenges for different use-cases. Moreover, we describe a method of generating traceable publishing insights facilitating the creation of content that serves the diversity of all users needs.
Knowledge Distillation in Document Retrieval
Authors: Siamak Shakeri, Abhinav Sethy, Cheng Cheng
Date: 2019-11-11 00:00:00
Tasks: Knowledge Distillation, Retrieval

Complex deep learning models now achieve state of the art performance for many document retrieval tasks. The best models process the query or claim jointly with the document. However for fast scalable search it is desirable to have document embeddings which are independent of the claim. In this paper we show that knowledge distillation can be used to encourage a model that generates claim independent document encodings to mimic the behavior of a more complex model which generates claim dependent encodings. We explore this approach in document retrieval for a fact extraction and verification task. We show that by using the soft labels from a complex cross attention teacher model, the performance of claim independent student LSTM or CNN models is improved across all the ranking metrics. The student models we use are 12x faster in runtime and 20x smaller in number of parameters than the teacher
Query Expansion for Patent Searching using Word Embedding and Professional Crowdsourcing
Authors: Arthi Krishna, Ye Jin, Christine Foster, Greg Gabel, Britt Hanley, Abdou Youssef
Date: 2019-11-14 00:00:00

The patent examination process includes a search of previous work to verify that a patent application describes a novel invention. Patent examiners primarily use keyword-based searches to uncover prior art. A critical part of keyword searching is query expansion, which is the process of including alternate terms such as synonyms and other related words, since the same concepts are often described differently in the literature. Patent terminology is often domain specific. By curating technology-specific corpora and training word embedding models based on these corpora, we are able to automatically identify the most relevant expansions of a given word or phrase. We compare the performance of several automated query expansion techniques against expert specified expansions. Furthermore, we explore a novel mechanism to extract related terms not just based on one input term but several terms in conjunction by computing their centroid and identifying the nearest neighbors to this centroid. Highly skilled patent examiners are often the best and most reliable source of identifying related terms. By designing a user interface that allows examiners to interact with the word embedding suggestions, we are able to use these interactions to power crowdsourced modes of related terms. Learning from users allows us to overcome several challenges such as identifying words that are bleeding edge and have not been published in the corpus yet. This paper studies the effectiveness of word embedding and crowdsourced models across 11 disparate technical areas.
Generating Stereotypes Automatically For Complex Categorical Features
Authors: Nourah ALRossais, Daniel Kudenko
Date: 2019-11-13 00:00:00
Tasks: Clustering, Recommendation Systems

In the context of stereotypes creation for recommender systems, we found that certain types of categorical variables pose particular challenges if simple clustering procedures were employed with the objective to create stereotypes. A categorical variable is defined to be complex when it cannot be easily translated into a numerical variable, when the semantic of the categories potentially plays an important role in the optimal determination of stereotypes, and when it is also multi-choice (e.g., each item can be labelled with one or more categories that may be applicable, in a non pre-defined number). The main objective of this paper is to analyse the possibility of obtaining a viable recommendation system that operates on stereotypes generated directly via the feature's metadata similarities, without using ratings information at the time the generation of the classes. The encouraging results using integrated MovieLens and Imdb data set show that the proposed algorithm performs better than other categorical clustering algorithms like k-modes when clustering complex categorical features. Notably, the representation of complex categorical features can help to alleviate cold-start issues in recommender systems.
Crawler for Image Acquisition from World Wide Web
Authors: R Rajkumar, Dr. M V Sudhamani
Date: 2019-11-11 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

Due to the advancement in computer communication and storage technologies, large amount of image data is available on World Wide Web (WWW). In order to locate a particular set of images the available search engines may be used with the help of keywords. Here, the filtering of unwanted data is not done. For the purpose of retrieving relevant images with appropriate keyword(s) an image crawler is designed and implemented. Here, keyword(s) are submitted as query and with the help of sender engine, images are downloaded along with metadata like URL, filename, file size, file access date and time etc.,. Later, with the help of URL, images already present in repository and newly downloaded are compared for uniqueness. Only unique URLs are in turn considered and stored in repository. The images in the repository are used to build novel Content Based Image Retrieval (CBIR) system in future. This repository may be used for various purposes. This image crawler tool is useful in building image datasets which can be used by any CBIR system for training and testing purposes.
Analysing Russian Trolls via NLP tools
Authors: Bokun Kong
Date: 2019-11-11 00:00:00
Tasks: Sentiment Analysis

The fifty-eighth American presidential election in 2016 still arouse fierce controversyat present. A portion of politicians as well as medium and voters believe that theRussian government interfered with the election of 2016 by controlling malicioussocial media accounts on twitter, such as trolls and bots accounts. Both of them willbroadcast fake news, derail the conversations about election, and mislead people.Therefore, this paper will focus on analysing some of the twitter dataset about theelection of 2016 by using NLP methods and looking for some interesting patterns ofwhether the Russian government interfered with the election or not. We apply topicmodel on the given twitter dataset to extract some interesting topics and analysethe meaning, then we implement supervised topic model to retrieve the relationshipbetween topics to category which is left troll or right troll, and analyse the pattern.Additionally, we will do sentiment analysis to analyse the attitude of the tweet. Afterextracting typical tweets from interesting topic, sentiment analysis offers the ability toknow whether the tweet supports this topic or not. Based on comprehensive analysisand evaluation, we find interesting patterns of the dataset as well as some meaningfultopics.
Trojan Horses in Amazon's Castle: Understanding the Incentivized Online Reviews
Authors: S Jamshidi, R Rejaie, J. Li
Date: 2018-06-06 00:00:00
Proceeding: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) 2018 6

During the past few years, sellers have increasingly offered discounted or free products to selected reviewers of e-commerce platforms in exchange for their reviews. Such incentivized (and often very positive) reviews can improve the rating of a product which in turn sways other users' opinions about the product. Despite their importance, the prevalence, characteristics, and the influence of incentivized reviews in a major e-commerce platform have not been systematically and quantitatively studied. This paper examines the problem of detecting and characterizing incentivized reviews in two primary categories of Amazon products. We describe a new method to identify Explicitly Incentivized Reviews (EIRs) and then collect a few datasets to capture an extensive collection of EIRs along with their associated products and reviewers. We show that the key features of EIRs and normal reviews exhibit different …
Deep learning for minimum mean-square error approaches to speech enhancement
Authors: Aaron Nicolson, Kuldip K. Paliwal
Date: 2019-08-01 00:00:00
Proceeding: Speech communication 2019 8
Tasks: Deep Learning, Speech Enhancement

Recently, the focus of speech enhancement research has shifted from minimum mean-square error (MMSE) approaches, like the MMSE short-time spectral amplitude (MMSE-STSA) estimator, to state-of-the-art masking- and mapping-based deep learning approaches. We aim to bridge the gap between these two differing speech enhancement approaches. Deep learning methods for MMSE approaches are investigated in this work, with the objective of producing intelligible enhanced speech at a high quality. Since the speech enhancement performance of an MMSE approach improves with the accuracy of the used a priori signal-to-noise ratio (SNR) estimator, a residual long short-term memory (ResLSTM) network is utilised here to accurately estimate the a priori SNR. MMSE approaches utilising the ResLSTM a priori SNR estimator are evaluated using subjective and objective measures of speech quality and intelligibility. The tested conditions include real-world non-stationary and coloured noise sources at multiple SNR levels. MMSE approaches utilising the proposed a priori SNR estimator are able to achieve higher enhanced speech quality and intelligibility scores than recent masking- and mapping-based deep learning approaches. The results presented in this work show that the performance of an MMSE approach to speech enhancement significantly increases when utilising deep learning.

Availability: The proposed a priori SNR estimator is available at: https://github.com/anicolson/DeepXi.
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
Authors: Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, Andrew Markham
Date: 2019-11-25 00:00:00
Proceeding: CVPR 2020 6
Tasks: 3D Semantic Segmentation, LIDAR Semantic Segmentation, Segmentation, Semantic Segmentation

We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing steps, most existing approaches are only able to be trained and operate over small-scale point clouds. In this paper, we introduce RandLA-Net, an efficient and lightweight neural architecture to directly infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Although remarkably computation and memory efficient, random sampling can discard key features by chance. To overcome this, we introduce a novel local feature aggregation module to progressively increase the receptive field for each 3D point, thereby effectively preserving geometric details. Extensive experiments show that our RandLA-Net can process 1 million points in a single pass with up to 200X faster than existing approaches. Moreover, our RandLA-Net clearly surpasses state-of-the-art approaches for semantic segmentation on two large-scale benchmarks Semantic3D and SemanticKITTI.
SuperGlue: Learning Feature Matching with Graph Neural Networks
Authors: Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich
Date: 2019-11-26 00:00:00
Proceeding: CVPR 2020 6
Tasks: Camera Pose Estimation, GPU, Graph Neural Network, Image Matching, Pose Estimation, Visual Localization, Visual Place Recognition

This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points. Assignments are estimated by solving a differentiable optimal transport problem, whose costs are predicted by a graph neural network. We introduce a flexible context aggregation mechanism based on attention, enabling SuperGlue to reason about the underlying 3D scene and feature assignments jointly. Compared to traditional, hand-designed heuristics, our technique learns priors over geometric transformations and regularities of the 3D world through end-to-end training from image pairs. SuperGlue outperforms other learned approaches and achieves state-of-the-art results on the task of pose estimation in challenging real-world indoor and outdoor environments. The proposed method performs matching in real-time on a modern GPU and can be readily integrated into modern SfM or SLAM systems. The code and trained weights are publicly available at https://github.com/magicleap/SuperGluePretrainedNetwork.
Password-conditioned Anonymization and Deanonymization with Face Identity Transformers
Authors: Xiuye Gu, Weixin Luo, Michael S. Ryoo, Yong Jae Lee
Date: 2019-11-26 00:00:00

Cameras are prevalent in our daily lives, and enable many useful systems built upon computer vision technologies such as smart cameras and home robots for service applications. However, there is also an increasing societal concern as the captured images/videos may contain privacy-sensitive information (e.g., face identity). We propose a novel face identity transformer which enables automated photo-realistic password-based anonymization as well as deanonymization of human faces appearing in visual data. Our face identity transformer is trained to (1) remove face identity information after anonymization, (2) make the recovery of the original face possible when given the correct password, and (3) return a wrong--but photo-realistic--face given a wrong password. Extensive experiments show that our approach enables multimodal password-conditioned face anonymizations and deanonymizations, without sacrificing privacy compared to existing anonymization approaches.