October 18, 2019

2834 words 14 mins read

Paper Group ANR 609

Tensor-based Nonlinear Classifier for High-Order Data Analysis. Nested Reasoning About Autonomous Agents Using Probabilistic Programs. Expolring Architectures for CNN-Based Word Spotting. Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic. Multi-channel discourse as an indicator for Bitcoin price and volume movements. Ma …

Tensor-based Nonlinear Classifier for High-Order Data Analysis


Title	Tensor-based Nonlinear Classifier for High-Order Data Analysis
Authors	Konstantinos Makantasis, Anastasios Doulamis, Nikolaos Doulamis, Antonis Nikitakis, Athanasios Voulodimos
Abstract	In this paper we propose a tensor-based nonlinear model for high-order data classification. The advantages of the proposed scheme are that (i) it significantly reduces the number of weight parameters, and hence of required training samples, and (ii) it retains the spatial structure of the input samples. The proposed model, called \textit{Rank}-1 FNN, is based on a modification of a feedforward neural network (FNN), such that its weights satisfy the {\it rank}-1 canonical decomposition. We also introduce a new learning algorithm to train the model, and we evaluate the \textit{Rank}-1 FNN on third-order hyperspectral data. Experimental results and comparisons indicate that the proposed model outperforms state of the art classification methods, including deep learning based ones, especially in cases with small numbers of available training samples.
Tasks
Published	2018-02-15
URL	http://arxiv.org/abs/1802.05981v1
PDF	http://arxiv.org/pdf/1802.05981v1.pdf
PWC	https://paperswithcode.com/paper/tensor-based-nonlinear-classifier-for-high
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Nested Reasoning About Autonomous Agents Using Probabilistic Programs


Title	Nested Reasoning About Autonomous Agents Using Probabilistic Programs
Authors	Iris Rubi Seaman, Jan-Willem van de Meent, David Wingate
Abstract	As autonomous agents become more ubiquitous, they will eventually have to reason about the plans of other agents, which is known as theory of mind reasoning. We develop a planning-as-inference framework in which agents perform nested simulation to reason about the behavior of other agents in an online manner. As a concrete application of this framework, we use probabilistic programs to model a high-uncertainty variant of pursuit-evasion games in which an agent must make inferences about the other agents’ plans to craft counter-plans. Our probabilistic programs incorporate a variety of complex primitives such as field-of-view calculations and path planners, which enable us to model quasi-realistic scenarios in a computationally tractable manner. We perform extensive experimental evaluations which establish a variety of rational behaviors and quantify how allocating computation across levels of nesting affects the variance of our estimators.
Tasks	Probabilistic Programming
Published	2018-12-04
URL	https://arxiv.org/abs/1812.01569v2
PDF	https://arxiv.org/pdf/1812.01569v2.pdf
PWC	https://paperswithcode.com/paper/modeling-theory-of-mind-for-autonomous-agents
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Expolring Architectures for CNN-Based Word Spotting


Title	Expolring Architectures for CNN-Based Word Spotting
Authors	Eugen Rusakov, Sebastian Sudholt, Fabian Wolf, Gernot A. Fink
Abstract	The goal in word spotting is to retrieve parts of document images which are relevant with respect to a certain user-defined query. The recent past has seen attribute-based Convolutional Neural Networks take over this field of research. As is common for other fields of computer vision, the CNNs used for this task are already considerably deep. The question that arises, however, is: How complex does a CNN have to be for word spotting? Are increasingly deeper models giving increasingly bet- ter results or does performance behave asymptotically for these architectures? On the other hand, can similar results be obtained with a much smaller CNN? The goal of this paper is to give an answer to these questions. Therefore, the recently successful TPP- PHOCNet will be compared to a Residual Network, a Densely Connected Convolutional Network and a LeNet architecture empirically. As will be seen in the evaluation, a complex model can be beneficial for word spotting on harder tasks such as the IAM Offline Database but gives no advantage for easier benchmarks such as the George Washington Database.
Tasks
Published	2018-06-28
URL	http://arxiv.org/abs/1806.10866v1
PDF	http://arxiv.org/pdf/1806.10866v1.pdf
PWC	https://paperswithcode.com/paper/expolring-architectures-for-cnn-based-word
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Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic


Title	Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic
Authors	Oleh Bodunov, Florian Schmidt, André Martin, Andrey Brito, Christof Fetzer
Abstract	In this paper, we present our approach for solving the DEBS Grand Challenge 2018. The challenge asks to provide a prediction for (i) a destination and the (ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the maritime context. Novel aspects of our approach include the use of ensemble learning based on Random Forest, Gradient Boosting Decision Trees (GBDT), XGBoost Trees and Extremely Randomized Trees (ERT) in order to provide a prediction for a destination while for the arrival time, we propose the use of Feed-forward Neural Networks. In our evaluation, we were able to achieve an accuracy of 97% for the port destination classification problem and 90% (in mins) for the ETA prediction.
Tasks
Published	2018-10-12
URL	http://arxiv.org/abs/1810.05567v1
PDF	http://arxiv.org/pdf/1810.05567v1.pdf
PWC	https://paperswithcode.com/paper/grand-challenge-real-time-destination-and-eta
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Multi-channel discourse as an indicator for Bitcoin price and volume movements


Title	Multi-channel discourse as an indicator for Bitcoin price and volume movements
Authors	Marvin Aron Kennis
Abstract	This research aims to identify how Bitcoin-related news publications and online discourse are expressed in Bitcoin exchange movements of price and volume. Being inherently digital, all Bitcoin-related fundamental data (from exchanges, as well as transactional data directly from the blockchain) is available online, something that is not true for traditional businesses or currencies traded on exchanges. This makes Bitcoin an interesting subject for such research, as it enables the mapping of sentiment to fundamental events that might otherwise be inaccessible. Furthermore, Bitcoin discussion largely takes place on online forums and chat channels. In stock trading, the value of sentiment data in trading decisions has been demonstrated numerous times [1] [2] [3], and this research aims to determine whether there is value in such data for Bitcoin trading models. To achieve this, data over the year 2015 has been collected from Bitcointalk.org, (the biggest Bitcoin forum in post volume), established news sources such as Bloomberg and the Wall Street Journal, the complete /r/btc and /r/Bitcoin subreddits, and the bitcoin-otc and bitcoin-dev IRC channels. By analyzing this data on sentiment and volume, we find weak to moderate correlations between forum, news, and Reddit sentiment and movements in price and volume from 1 to 5 days after the sentiment was expressed. A Granger causality test confirms the predictive causality of the sentiment on the daily percentage price and volume movements, and at the same time underscores the predictive causality of market movements on sentiment expressions in online communities
Tasks
Published	2018-11-06
URL	http://arxiv.org/abs/1811.03146v1
PDF	http://arxiv.org/pdf/1811.03146v1.pdf
PWC	https://paperswithcode.com/paper/multi-channel-discourse-as-an-indicator-for
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MaskReID: A Mask Based Deep Ranking Neural Network for Person Re-identification


Title	MaskReID: A Mask Based Deep Ranking Neural Network for Person Re-identification
Authors	Lei Qi, Jing Huo, Lei Wang, Yinghuan Shi, Yang Gao
Abstract	Person retrieval faces many challenges including cluttered background, appearance variations (e.g., illumination, pose, occlusion) among different camera views and the similarity among different person’s images. To address these issues, we put forward a novel mask based deep ranking neural network with a skipped fusing layer. Firstly, to alleviate the problem of cluttered background, masked images with only the foreground regions are incorporated as input in the proposed neural network. Secondly, to reduce the impact of the appearance variations, the multi-layer fusion scheme is developed to obtain more discriminative fine-grained information. Lastly, considering person retrieval is a special image retrieval task, we propose a novel ranking loss to optimize the whole network. The proposed ranking loss can further mitigate the interference problem of similar negative samples when producing ranking results. The extensive experiments validate the superiority of the proposed method compared with the state-of-the-art methods on many benchmark datasets.
Tasks	Image Retrieval, Person Re-Identification, Person Retrieval
Published	2018-04-11
URL	http://arxiv.org/abs/1804.03864v2
PDF	http://arxiv.org/pdf/1804.03864v2.pdf
PWC	https://paperswithcode.com/paper/maskreid-a-mask-based-deep-ranking-neural
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Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model


Title	Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Authors	Atılım Güneş Baydin, Lukas Heinrich, Wahid Bhimji, Lei Shao, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip Torr, Victor Lee, Prabhat, Kyle Cranmer, Frank Wood
Abstract	We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way. The execution of existing simulators as probabilistic programs enables highly interpretable posterior inference in the structured model defined by the simulator code base. We demonstrate the technique in particle physics, on a scientifically accurate simulation of the tau lepton decay, which is a key ingredient in establishing the properties of the Higgs boson. Inference efficiency is achieved via inference compilation where a deep recurrent neural network is trained to parameterize proposal distributions and control the stochastic simulator in a sequential importance sampling scheme, at a fraction of the computational cost of a Markov chain Monte Carlo baseline.
Tasks	Bayesian Inference, Probabilistic Programming
Published	2018-07-20
URL	https://arxiv.org/abs/1807.07706v5
PDF	https://arxiv.org/pdf/1807.07706v5.pdf
PWC	https://paperswithcode.com/paper/efficient-probabilistic-inference-in-the
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Principles for Developing a Knowledge Graph of InterlinkedEvents from News Headlines on Twitter


Title	Principles for Developing a Knowledge Graph of InterlinkedEvents from News Headlines on Twitter
Authors	Saeedeh Shekarpour, Ankita Saxena, Krishnaprasad Thirunarayan, Valerie L. Shalin, Amit Sheth
Abstract	The ever-growing datasets published on Linked Open Data mainly contain encyclopedic information. However, there is a lack of quality structured and semantically annotated datasets extracted from unstructured real-time sources. In this paper, we present principles for developing a knowledge graph of interlinked events using the case study of news headlines published on Twitter which is a real-time and eventful source of fresh information. We represent the essential pipeline containing the required tasks ranging from choosing background data model, event annotation (i.e., event recognition and classification), entity annotation and eventually interlinking events. The state-of-the-art is limited to domain-specific scenarios for recognizing and classifying events, whereas this paper plays the role of a domain-agnostic road-map for developing a knowledge graph of interlinked events.
Tasks
Published	2018-08-06
URL	http://arxiv.org/abs/1808.02022v1
PDF	http://arxiv.org/pdf/1808.02022v1.pdf
PWC	https://paperswithcode.com/paper/principles-for-developing-a-knowledge-graph
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Analyzing Learned Representations of a Deep ASR Performance Prediction Model


Title	Analyzing Learned Representations of a Deep ASR Performance Prediction Model
Authors	Zied Elloumi, Laurent Besacier, Olivier Galibert, Benjamin Lecouteux
Abstract	This paper addresses a relatively new task: prediction of ASR performance on unseen broadcast programs. In a previous paper, we presented an ASR performance prediction system using CNNs that encode both text (ASR transcript) and speech, in order to predict word error rate. This work is dedicated to the analysis of speech signal embeddings and text embeddings learnt by the CNN while training our prediction model. We try to better understand which information is captured by the deep model and its relation with different conditioning factors. It is shown that hidden layers convey a clear signal about speech style, accent and broadcast type. We then try to leverage these 3 types of information at training time through multi-task learning. Our experiments show that this allows to train slightly more efficient ASR performance prediction systems that - in addition - simultaneously tag the analyzed utterances according to their speech style, accent and broadcast program origin.
Tasks	Multi-Task Learning
Published	2018-08-26
URL	http://arxiv.org/abs/1808.08573v2
PDF	http://arxiv.org/pdf/1808.08573v2.pdf
PWC	https://paperswithcode.com/paper/analyzing-learned-representations-of-a-deep
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The Adverse Effects of Code Duplication in Machine Learning Models of Code


Title	The Adverse Effects of Code Duplication in Machine Learning Models of Code
Authors	Miltiadis Allamanis
Abstract	The field of big code relies on mining large corpora of code to perform some learning task. A significant threat to this approach has been recently identified by Lopes et al. (2017) who found a large amount of near-duplicate code on GitHub. However, the impact of code duplication has not been noticed by researchers devising machine learning models for source code. In this work, we explore the effects of code duplication on machine learning models showing that reported performance metrics are sometimes inflated by up to 100% when testing on duplicated code corpora compared to the performance on de-duplicated corpora which more accurately represent how machine learning models of code are used by software engineers. We present a duplication index for widely used datasets, list best practices for collecting code corpora and evaluating machine learning models on them. Finally, we release tools to help the community avoid this problem in future research.
Tasks
Published	2018-12-16
URL	https://arxiv.org/abs/1812.06469v6
PDF	https://arxiv.org/pdf/1812.06469v6.pdf
PWC	https://paperswithcode.com/paper/the-adverse-effects-of-code-duplication-in
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Algorithmic Collusion in Cournot Duopoly Market: Evidence from Experimental Economics


Title	Algorithmic Collusion in Cournot Duopoly Market: Evidence from Experimental Economics
Authors	Nan Zhou, Li Zhang, Shijian Li, Zhijian Wang
Abstract	Algorithmic collusion is an emerging concept in current artificial intelligence age. Whether algorithmic collusion is a creditable threat remains as an argument. In this paper, we propose an algorithm which can extort its human rival to collude in a Cournot duopoly competing market. In experiments, we show that, the algorithm can successfully extorted its human rival and gets higher profit in long run, meanwhile the human rival will fully collude with the algorithm. As a result, the social welfare declines rapidly and stably. Both in theory and in experiment, our work confirms that, algorithmic collusion can be a creditable threat. In application, we hope, the frameworks, the algorithm design as well as the experiment environment illustrated in this work, can be an incubator or a test bed for researchers and policymakers to handle the emerging algorithmic collusion.
Tasks
Published	2018-02-21
URL	http://arxiv.org/abs/1802.08061v1
PDF	http://arxiv.org/pdf/1802.08061v1.pdf
PWC	https://paperswithcode.com/paper/algorithmic-collusion-in-cournot-duopoly
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Can Computers Create Art?


Title	Can Computers Create Art?
Authors	Aaron Hertzmann
Abstract	This essay discusses whether computers, using Artificial Intelligence (AI), could create art. First, the history of technologies that automated aspects of art is surveyed, including photography and animation. In each case, there were initial fears and denial of the technology, followed by a blossoming of new creative and professional opportunities for artists. The current hype and reality of Artificial Intelligence (AI) tools for art making is then discussed, together with predictions about how AI tools will be used. It is then speculated about whether it could ever happen that AI systems could be credited with authorship of artwork. It is theorized that art is something created by social agents, and so computers cannot be credited with authorship of art in our current understanding. A few ways that this could change are also hypothesized.
Tasks
Published	2018-01-13
URL	http://arxiv.org/abs/1801.04486v6
PDF	http://arxiv.org/pdf/1801.04486v6.pdf
PWC	https://paperswithcode.com/paper/can-computers-create-art
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Improving Landmark Recognition using Saliency detection and Feature classification


Title	Improving Landmark Recognition using Saliency detection and Feature classification
Authors	Akash Kumar, Sagnik Bhowmick, N. Jayanthi, S. Indu
Abstract	Image Landmark Recognition has been one of the most sought-after classification challenges in the field of vision and perception. After so many years of generic classification of buildings and monuments from images, people are now focussing upon fine-grained problems - recognizing the category of each building or monument. We proposed an ensemble network for the purpose of classification of Indian Landmark Images. To this end, our method gives robust classification by ensembling the predictions from Graph-Based Visual Saliency (GBVS) network alongwith supervised feature-based classification algorithms such as kNN and Random Forest. The final architecture is an adaptive learning of all the mentioned networks. The proposed network produces a reliable score to eliminate false category cases. Evaluation of our model was done on a new dataset, which involves challenges such as landmark clutter, variable scaling, partial occlusion, etc.
Tasks	Saliency Detection
Published	2018-11-30
URL	http://arxiv.org/abs/1811.12748v1
PDF	http://arxiv.org/pdf/1811.12748v1.pdf
PWC	https://paperswithcode.com/paper/improving-landmark-recognition-using-saliency
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Insights into End-to-End Learning Scheme for Language Identification


Title	Insights into End-to-End Learning Scheme for Language Identification
Authors	Weicheng Cai, Zexin Cai, Wenbo Liu, Xiaoqi Wang, Ming Li
Abstract	A novel interpretable end-to-end learning scheme for language identification is proposed. It is in line with the classical GMM i-vector methods both theoretically and practically. In the end-to-end pipeline, a general encoding layer is employed on top of the front-end CNN, so that it can encode the variable-length input sequence into an utterance level vector automatically. After comparing with the state-of-the-art GMM i-vector methods, we give insights into CNN, and reveal its role and effect in the whole pipeline. We further introduce a general encoding layer, illustrating the reason why they might be appropriate for language identification. We elaborate on several typical encoding layers, including a temporal average pooling layer, a recurrent encoding layer and a novel learnable dictionary encoding layer. We conducted experiment on NIST LRE07 closed-set task, and the results show that our proposed end-to-end systems achieve state-of-the-art performance.
Tasks	Language Identification
Published	2018-04-02
URL	http://arxiv.org/abs/1804.00381v1
PDF	http://arxiv.org/pdf/1804.00381v1.pdf
PWC	https://paperswithcode.com/paper/insights-into-end-to-end-learning-scheme-for
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A probabilistic framework for handwritten text line segmentation


Title	A probabilistic framework for handwritten text line segmentation
Authors	Francisco Cruz, Oriol Ramos Terrades
Abstract	We successfully combine Expectation-Maximization algorithm and variational approaches for parameter learning and computing inference on Markov random felds. This is a general method that can be applied to many computer vision tasks. In this paper, we apply it to handwritten text line segmentation. We conduct several experiments that demonstrate that our method deal with common issues of this task, such as complex document layout or non-latin scripts. The obtained results prove that our method achieve state-of-the-art performance on different benchmark datasets without any particular fine tuning step.
Tasks
Published	2018-05-07
URL	http://arxiv.org/abs/1805.02536v2
PDF	http://arxiv.org/pdf/1805.02536v2.pdf
PWC	https://paperswithcode.com/paper/a-probabilistic-framework-for-handwritten
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