January 29, 2020

3352 words 16 mins read

Paper Group ANR 661

ASP-Core-2 Input Language Format. Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches. Syntactic Interchangeability in Word Embedding Models. Learning to see across Domains and Modalities. Patch-based Generative Adversarial Network Towards Retinal Vessel Segmentation. On Learning Invariant Representation for …

ASP-Core-2 Input Language Format


Title	ASP-Core-2 Input Language Format
Authors	Francesco Calimeri, Wolfgang Faber, Martin Gebser, Giovambattista Ianni, Roland Kaminski, Thomas Krennwallner, Nicola Leone, Marco Maratea, Francesco Ricca, Torsten Schaub
Abstract	Standardization of solver input languages has been a main driver for the growth of several areas within knowledge representation and reasoning, fostering the exploitation in actual applications. In this document we present the ASP-Core-2 standard input language for Answer Set Programming, which has been adopted in ASP Competition events since 2013.
Tasks
Published	2019-11-11
URL	https://arxiv.org/abs/1911.04326v1
PDF	https://arxiv.org/pdf/1911.04326v1.pdf
PWC	https://paperswithcode.com/paper/asp-core-2-input-language-format
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Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches


Title	Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches
Authors	Kacper Sokol, Peter Flach
Abstract	Explanations in Machine Learning come in many forms, but a consensus regarding their desired properties is yet to emerge. In this paper we introduce a taxonomy and a set of descriptors that can be used to characterise and systematically assess explainable systems along five key dimensions: functional, operational, usability, safety and validation. In order to design a comprehensive and representative taxonomy and associated descriptors we surveyed the eXplainable Artificial Intelligence literature, extracting the criteria and desiderata that other authors have proposed or implicitly used in their research. The survey includes papers introducing new explainability algorithms to see what criteria are used to guide their development and how these algorithms are evaluated, as well as papers proposing such criteria from both computer science and social science perspectives. This novel framework allows to systematically compare and contrast explainability approaches, not just to better understand their capabilities but also to identify discrepancies between their theoretical qualities and properties of their implementations. We developed an operationalisation of the framework in the form of Explainability Fact Sheets, which enable researchers and practitioners alike to quickly grasp capabilities and limitations of a particular explainable method. When used as a Work Sheet, our taxonomy can guide the development of new explainability approaches by aiding in their critical evaluation along the five proposed dimensions.
Tasks
Published	2019-12-11
URL	https://arxiv.org/abs/1912.05100v1
PDF	https://arxiv.org/pdf/1912.05100v1.pdf
PWC	https://paperswithcode.com/paper/explainability-fact-sheets-a-framework-for
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Syntactic Interchangeability in Word Embedding Models


Title	Syntactic Interchangeability in Word Embedding Models
Authors	Daniel Hershcovich, Assaf Toledo, Alon Halfon, Noam Slonim
Abstract	Nearest neighbors in word embedding models are commonly observed to be semantically similar, but the relations between them can vary greatly. We investigate the extent to which word embedding models preserve syntactic interchangeability, as reflected by distances between word vectors, and the effect of hyper-parameters—context window size in particular. We use part of speech (POS) as a proxy for syntactic interchangeability, as generally speaking, words with the same POS are syntactically valid in the same contexts. We also investigate the relationship between interchangeability and similarity as judged by commonly-used word similarity benchmarks, and correlate the result with the performance of word embedding models on these benchmarks. Our results will inform future research and applications in the selection of word embedding model, suggesting a principle for an appropriate selection of the context window size parameter depending on the use-case.
Tasks
Published	2019-04-01
URL	http://arxiv.org/abs/1904.00669v2
PDF	http://arxiv.org/pdf/1904.00669v2.pdf
PWC	https://paperswithcode.com/paper/syntactic-interchangeability-in-word
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Learning to see across Domains and Modalities


Title	Learning to see across Domains and Modalities
Authors	Fabio Maria Carlucci
Abstract	Deep learning has raised hopes and expectations as a general solution for many applications; indeed it has proven effective, but it also showed a strong dependence on large quantities of data. Luckily, it has been shown that, even when data is scarce, a successful model can be trained by reusing prior knowledge. Thus, developing techniques for transfer learning, in its broadest definition, is a crucial element towards the deployment of effective and accurate intelligent systems. This thesis will focus on a family of transfer learning methods applied to the task of visual object recognition, specifically image classification. Transfer learning is a general term, and specific settings have been given specific names: when the learner has only access to unlabeled data from the a target domain and labeled data from a different domain (the source), the problem is known as that of “unsupervised domain adaptation” (DA). The first part of this work will focus on three methods for this setting: one of these methods deals with features, one with images while the third one uses both. The second part will focus on the real life issues of robotic perception, specifically RGB-D recognition. Robotic platforms are usually not limited to color perception; very often they also carry a Depth camera. Unfortunately, the depth modality is rarely used for visual recognition due to the lack of pretrained models from which to transfer and little data to train one on from scratch. Two methods for dealing with this scenario will be presented: one using synthetic data and the other exploiting cross-modality transfer learning.
Tasks	Domain Adaptation, Image Classification, Object Recognition, Transfer Learning, Unsupervised Domain Adaptation
Published	2019-02-13
URL	http://arxiv.org/abs/1902.04992v1
PDF	http://arxiv.org/pdf/1902.04992v1.pdf
PWC	https://paperswithcode.com/paper/learning-to-see-across-domains-and-modalities
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Patch-based Generative Adversarial Network Towards Retinal Vessel Segmentation


Title	Patch-based Generative Adversarial Network Towards Retinal Vessel Segmentation
Authors	Waseem Abbas, Muhammad Haroon Shakeel, Numan Khurshid, Murtaza Taj
Abstract	Retinal blood vessels are considered to be the reliable diagnostic biomarkers of ophthalmologic and diabetic retinopathy. Monitoring and diagnosis totally depends on expert analysis of both thin and thick retinal vessels which has recently been carried out by various artificial intelligent techniques. Existing deep learning methods attempt to segment retinal vessels using a unified loss function optimized for both thin and thick vessels with equal importance. Due to variable thickness, biased distribution, and difference in spatial features of thin and thick vessels, unified loss function are more influential towards identification of thick vessels resulting in weak segmentation. To address this problem, a conditional patch-based generative adversarial network is proposed which utilizes a generator network and a patch-based discriminator network conditioned on the sample data with an additional loss function to learn both thin and thick vessels. Experiments are conducted on publicly available STARE and DRIVE datasets which show that the proposed model outperforms the state-of-the-art methods.
Tasks	Retinal Vessel Segmentation
Published	2019-12-22
URL	https://arxiv.org/abs/1912.10377v1
PDF	https://arxiv.org/pdf/1912.10377v1.pdf
PWC	https://paperswithcode.com/paper/patch-based-generative-adversarial-network
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On Learning Invariant Representation for Domain Adaptation


Title	On Learning Invariant Representation for Domain Adaptation
Authors	Han Zhao, Remi Tachet des Combes, Kun Zhang, Geoffrey J. Gordon
Abstract	Due to the ability of deep neural nets to learn rich representations, recent advances in unsupervised domain adaptation have focused on learning domain-invariant features that achieve a small error on the source domain. The hope is that the learnt representation, together with the hypothesis learnt from the source domain, can generalize to the target domain. In this paper, we first construct a simple counterexample showing that, contrary to common belief, the above conditions are not sufficient to guarantee successful domain adaptation. In particular, the counterexample exhibits \emph{conditional shift}: the class-conditional distributions of input features change between source and target domains. To give a sufficient condition for domain adaptation, we propose a natural and interpretable generalization upper bound that explicitly takes into account the aforementioned shift. Moreover, we shed new light on the problem by proving an information-theoretic lower bound on the joint error of \emph{any} domain adaptation method that attempts to learn invariant representations. Our result characterizes a fundamental tradeoff between learning invariant representations and achieving small joint error on both domains when the marginal label distributions differ from source to target. Finally, we conduct experiments on real-world datasets that corroborate our theoretical findings. We believe these insights are helpful in guiding the future design of domain adaptation and representation learning algorithms.
Tasks	Domain Adaptation, Representation Learning, Unsupervised Domain Adaptation
Published	2019-01-27
URL	https://arxiv.org/abs/1901.09453v2
PDF	https://arxiv.org/pdf/1901.09453v2.pdf
PWC	https://paperswithcode.com/paper/on-learning-invariant-representation-for
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Using Contextual Information to Improve Blood Glucose Prediction


Title	Using Contextual Information to Improve Blood Glucose Prediction
Authors	Mohammad Akbari, Rumi Chunara
Abstract	Blood glucose value prediction is an important task in diabetes management. While it is reported that glucose concentration is sensitive to social context such as mood, physical activity, stress, diet, alongside the influence of diabetes pathologies, we need more research on data and methodologies to incorporate and evaluate signals about such temporal context into prediction models. Person-generated data sources, such as actively contributed surveys as well as passively mined data from social media offer opportunity to capture such context, however the self-reported nature and sparsity of such data mean that such data are noisier and less specific than physiological measures such as blood glucose values themselves. Therefore, here we propose a Gaussian Process model to both address these data challenges and combine blood glucose and latent feature representations of contextual data for a novel multi-signal blood glucose prediction task. We find this approach outperforms common methods for multi-variate data, as well as using the blood glucose values in isolation. Given a robust evaluation across two blood glucose datasets with different forms of contextual information, we conclude that multi-signal Gaussian Processes can improve blood glucose prediction by using contextual information and may provide a significant shift in blood glucose prediction research and practice.
Tasks	Gaussian Processes
Published	2019-08-24
URL	https://arxiv.org/abs/1909.01735v1
PDF	https://arxiv.org/pdf/1909.01735v1.pdf
PWC	https://paperswithcode.com/paper/using-contextual-information-to-improve-blood
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Axonal Conduction Velocity Impacts Neuronal Network Oscillations


Title	Axonal Conduction Velocity Impacts Neuronal Network Oscillations
Authors	Vladimir A. Ivanov, Ioannis E. Polykretis, Konstantinos P. Michmizos
Abstract	Increasing experimental evidence suggests that axonal action potential conduction velocity is a highly adaptive parameter in the adult central nervous system. Yet, the effects of this newfound plasticity on global brain dynamics is poorly understood. In this work, we analyzed oscillations in biologically plausible neuronal networks with different conduction velocity distributions. Changes of 1-2 (ms) in network mean signal transmission time resulted in substantial network oscillation frequency changes ranging in 0-120 (Hz). Our results suggest that changes in axonal conduction velocity may significantly affect both the frequency and synchrony of brain rhythms, which have well established connections to learning, memory, and other cognitive processes.
Tasks
Published	2019-03-22
URL	http://arxiv.org/abs/1903.09671v1
PDF	http://arxiv.org/pdf/1903.09671v1.pdf
PWC	https://paperswithcode.com/paper/axonal-conduction-velocity-impacts-neuronal
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A Refined Equilibrium Generative Adversarial Network for Retinal Vessel Segmentation


Title	A Refined Equilibrium Generative Adversarial Network for Retinal Vessel Segmentation
Authors	Yukun Zhou, Zailiang Chen, Hailan Shen, Xianxian Zheng, Rongchang Zhao, Xuanchu Duan
Abstract	Objective: Recognizing retinal vessel abnormity is vital to early diagnosis of ophthalmological diseases and cardiovascular events. However, segmentation results are highly influenced by elusive vessels, especially in low-contrast background and lesion region. In this work, we present an end-to-end synthetic neural network, containing a symmetric equilibrium generative adversarial network (SEGAN), multi-scale features refine blocks (MSFRB), and attention mechanism (AM) to enhance the performance on vessel segmentation. Method: The proposed network is granted powerful multi-scale representation capability to extract detail information. First, SEGAN constructs a symmetric adversarial architecture, which forces generator to produce more realistic images with local details. Second, MSFRB are devised to prevent high-resolution features from being obscured, thereby merging multi-scale features better. Finally, the AM is employed to encourage the network to concentrate on discriminative features. Results: On public dataset DRIVE, STARE, CHASEDB1, and HRF, we evaluate our network quantitatively and compare it with state-of-the-art works. The ablation experiment shows that SEGAN, MSFRB, and AM both contribute to the desirable performance. Conclusion: The proposed network outperforms the mature methods and effectively functions in elusive vessels segmentation, achieving highest scores in Sensitivity, G-Mean, Precision, and F1-Score while maintaining the top level in other metrics. Significance: The appreciable performance and computational efficiency offer great potential in clinical retinal vessel segmentation application. Meanwhile, the network could be utilized to extract detail information in other biomedical issues
Tasks	Retinal Vessel Segmentation
Published	2019-09-26
URL	https://arxiv.org/abs/1909.11936v2
PDF	https://arxiv.org/pdf/1909.11936v2.pdf
PWC	https://paperswithcode.com/paper/a-symmetric-equilibrium-generative
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Convolutional Neural Networks Considering Local and Global features for Image Enhancement


Title	Convolutional Neural Networks Considering Local and Global features for Image Enhancement
Authors	Yuma Kinoshita, Hitoshi Kiya
Abstract	In this paper, we propose a novel convolutional neural network (CNN) architecture considering both local and global features for image enhancement. Most conventional image enhancement methods, including Retinex-based methods, cannot restore lost pixel values caused by clipping and quantizing. CNN-based methods have recently been proposed to solve the problem, but they still have a limited performance due to network architectures not handling global features. To handle both local and global features, the proposed architecture consists of three networks: a local encoder, a global encoder, and a decoder. In addition, high dynamic range (HDR) images are used for generating training data for our networks. The use of HDR images makes it possible to train CNNs with better-quality images than images directly captured with cameras. Experimental results show that the proposed method can produce higher-quality images than conventional image enhancement methods including CNN-based methods, in terms of various objective quality metrics: TMQI, entropy, NIQE, and BRISQUE.
Tasks	Image Enhancement
Published	2019-05-07
URL	https://arxiv.org/abs/1905.02899v1
PDF	https://arxiv.org/pdf/1905.02899v1.pdf
PWC	https://paperswithcode.com/paper/convolutional-neural-networks-considering
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Automated retinal vessel segmentation based on morphological preprocessing and 2D-Gabor wavelets


Title	Automated retinal vessel segmentation based on morphological preprocessing and 2D-Gabor wavelets
Authors	Kundan Kumar, Debashisa Samal, Suraj
Abstract	Automated segmentation of vascular map in retinal images endeavors a potential benefit in diagnostic procedure of different ocular diseases. In this paper, we suggest a new unsupervised retinal blood vessel segmentation approach using top-hat transformation, contrast-limited adaptive histogram equalization (CLAHE), and 2-D Gabor wavelet filters. Initially, retinal image is preprocessed using top-hat morphological transformation followed by CLAHE to enhance only the blood vessel pixels in the presence of exudates, optic disc, and fovea. Then, multiscale 2-D Gabor wavelet filters are applied on preprocessed image for better representation of thick and thin blood vessels located at different orientations. The efficacy of the presented algorithm is assessed on publicly available DRIVE database with manually labeled images. On DRIVE database, we achieve an average accuracy of 94.32% with a small standard deviation of 0.004. In comparison with major algorithms, our algorithm produces better performance concerning the accuracy, sensitivity, and kappa agreement.
Tasks	Retinal Vessel Segmentation
Published	2019-08-12
URL	https://arxiv.org/abs/1908.04123v1
PDF	https://arxiv.org/pdf/1908.04123v1.pdf
PWC	https://paperswithcode.com/paper/automated-retinal-vessel-segmentation-based
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Cooperative image captioning


Title	Cooperative image captioning
Authors	Gilad Vered, Gal Oren, Yuval Atzmon, Gal Chechik
Abstract	When describing images with natural language, the descriptions can be made more informative if tuned using downstream tasks. This is often achieved by training two networks: a “speaker network” that generates sentences given an image, and a “listener network” that uses them to perform a task. Unfortunately, training multiple networks jointly to communicate to achieve a joint task, faces two major challenges. First, the descriptions generated by a speaker network are discrete and stochastic, making optimization very hard and inefficient. Second, joint training usually causes the vocabulary used during communication to drift and diverge from natural language. We describe an approach that addresses both challenges. We first develop a new effective optimization based on partial-sampling from a multinomial distribution combined with straight-through gradient updates, which we name PSST for Partial-Sampling Straight-Through. Second, we show that the generated descriptions can be kept close to natural by constraining them to be similar to human descriptions. Together, this approach creates descriptions that are both more discriminative and more natural than previous approaches. Evaluations on the standard COCO benchmark show that PSST Multinomial dramatically improve the recall@10 from 60% to 86% maintaining comparable language naturalness, and human evaluations show that it also increases naturalness while keeping the discriminative power of generated captions.
Tasks	Image Captioning
Published	2019-07-26
URL	https://arxiv.org/abs/1907.11565v1
PDF	https://arxiv.org/pdf/1907.11565v1.pdf
PWC	https://paperswithcode.com/paper/cooperative-image-captioning
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Revenue, Relevance, Arbitrage and More: Joint Optimization Framework for Search Experiences in Two-Sided Marketplaces


Title	Revenue, Relevance, Arbitrage and More: Joint Optimization Framework for Search Experiences in Two-Sided Marketplaces
Authors	Andrew Stanton, Akhila Ananthram, Congzhe Su, Liangjie Hong
Abstract	Two-sided marketplaces such as eBay, Etsy and Taobao have two distinct groups of customers: buyers who use the platform to seek the most relevant and interesting item to purchase and sellers who view the same platform as a tool to reach out to their audience and grow their business. Additionally, platforms have their own objectives ranging from growing both buyer and seller user bases to revenue maximization. It is not difficult to see that it would be challenging to obtain a globally favorable outcome for all parties. Taking the search experience as an example, any interventions are likely to impact either buyers or sellers unfairly to course correct for a greater perceived need. In this paper, we address how a company-aligned search experience can be provided with competing business metrics that E-commerce companies typically tackle. As far as we know, this is a pioneering work to consider multiple different aspects of business indicators in two-sided marketplaces to optimize a search experience. We demonstrate that many problems are difficult or impossible to decompose down to credit assigned scores on individual documents, rendering traditional methods inadequate. Instead, we express market-level metrics as constraints and discuss to what degree multiple potentially conflicting metrics can be tuned to business needs. We further explore the use of policy learners in the form of Evolutionary Strategies to jointly optimize both group-level and market-level metrics simultaneously, side-stepping traditional cascading methods and manual interventions. We empirically evaluate the effectiveness of the proposed method on Etsy data and demonstrate its potential with insights.
Tasks
Published	2019-05-15
URL	https://arxiv.org/abs/1905.06452v1
PDF	https://arxiv.org/pdf/1905.06452v1.pdf
PWC	https://paperswithcode.com/paper/revenue-relevance-arbitrage-and-more-joint
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PerceptNet: A Human Visual System Inspired Neural Network for Estimating Perceptual Distance


Title	PerceptNet: A Human Visual System Inspired Neural Network for Estimating Perceptual Distance
Authors	Alexander Hepburn, Valero Laparra, Jesús Malo, Ryan McConville, Raul Santos-Rodriguez
Abstract	Traditionally, the vision community has devised algorithms to estimate the distance between an original image and images that have been subject to perturbations. Inspiration was usually taken from the human visual perceptual system and how the system processes different perturbations in order to replicate to what extent it determines our ability to judge image quality. While recent works have presented deep neural networks trained to predict human perceptual quality, very few borrow any intuitions from the human visual system. To address this, we present PerceptNet, a convolutional neural network where the architecture has been chosen to reflect the structure and various stages in the human visual system. We evaluate PerceptNet on various traditional perception datasets and note strong performance on a number of them as compared with traditional image quality metrics. We also show that including a nonlinearity inspired by the human visual system in classical deep neural networks architectures can increase their ability to judge perceptual similarity.
Tasks
Published	2019-10-28
URL	https://arxiv.org/abs/1910.12548v1
PDF	https://arxiv.org/pdf/1910.12548v1.pdf
PWC	https://paperswithcode.com/paper/perceptnet-a-human-visual-system-inspired
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Entity-Relation Extraction as Multi-Turn Question Answering


Title	Entity-Relation Extraction as Multi-Turn Question Answering
Authors	Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, Jiwei Li
Abstract	In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast the task as a multi-turn question answering problem, i.e., the extraction of entities and relations is transformed to the task of identifying answer spans from the context. This multi-turn QA formalization comes with several key advantages: firstly, the question query encodes important information for the entity/relation class we want to identify; secondly, QA provides a natural way of jointly modeling entity and relation; and thirdly, it allows us to exploit the well developed machine reading comprehension (MRC) models. Experiments on the ACE and the CoNLL04 corpora demonstrate that the proposed paradigm significantly outperforms previous best models. We are able to obtain the state-of-the-art results on all of the ACE04, ACE05 and CoNLL04 datasets, increasing the SOTA results on the three datasets to 49.4 (+1.0), 60.2 (+0.6) and 68.9 (+2.1), respectively. Additionally, we construct a newly developed dataset RESUME in Chinese, which requires multi-step reasoning to construct entity dependencies, as opposed to the single-step dependency extraction in the triplet exaction in previous datasets. The proposed multi-turn QA model also achieves the best performance on the RESUME dataset.
Tasks	Machine Reading Comprehension, Question Answering, Reading Comprehension, Relation Extraction
Published	2019-05-14
URL	https://arxiv.org/abs/1905.05529v4
PDF	https://arxiv.org/pdf/1905.05529v4.pdf
PWC	https://paperswithcode.com/paper/entity-relation-extraction-as-multi-turn
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