October 19, 2019

3121 words 15 mins read

Paper Group ANR 248

Logical Explanations for Deep Relational Machines Using Relevance Information. Anytime Neural Prediction via Slicing Networks Vertically. Sounding Board: A User-Centric and Content-Driven Social Chatbot. Cylindrical Transform: 3D Semantic Segmentation of Kidneys With Limited Annotated Images. Looking Deeper into Deep Learning Model: Attribution-bas …

Logical Explanations for Deep Relational Machines Using Relevance Information


Title	Logical Explanations for Deep Relational Machines Using Relevance Information
Authors	Ashwin Srinivasan, Lovekesh Vig, Michael Bain
Abstract	Our interest in this paper is in the construction of symbolic explanations for predictions made by a deep neural network. We will focus attention on deep relational machines (DRMs, first proposed by H. Lodhi). A DRM is a deep network in which the input layer consists of Boolean-valued functions (features) that are defined in terms of relations provided as domain, or background, knowledge. Our DRMs differ from those proposed by Lodhi, which use an Inductive Logic Programming (ILP) engine to first select features (we use random selections from a space of features that satisfies some approximate constraints on logical relevance and non-redundancy). But why do the DRMs predict what they do? One way of answering this is the LIME setting, in which readable proxies for a black-box predictor. The proxies are intended only to model the predictions of the black-box in local regions of the instance-space. But readability alone may not enough: to be understandable, the local models must use relevant concepts in an meaningful manner. We investigate the use of a Bayes-like approach to identify logical proxies for local predictions of a DRM. We show: (a) DRM’s with our randomised propositionalization method achieve state-of-the-art predictive performance; (b) Models in first-order logic can approximate the DRM’s prediction closely in a small local region; and (c) Expert-provided relevance information can play the role of a prior to distinguish between logical explanations that perform equivalently on prediction alone.
Tasks
Published	2018-07-02
URL	http://arxiv.org/abs/1807.00595v1
PDF	http://arxiv.org/pdf/1807.00595v1.pdf
PWC	https://paperswithcode.com/paper/logical-explanations-for-deep-relational
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Anytime Neural Prediction via Slicing Networks Vertically


Title	Anytime Neural Prediction via Slicing Networks Vertically
Authors	Hankook Lee, Jinwoo Shin
Abstract	The pioneer deep neural networks (DNNs) have emerged to be deeper or wider for improving their accuracy in various applications of artificial intelligence. However, DNNs are often too heavy to deploy in practice, and it is often required to control their architectures dynamically given computing resource budget, i.e., anytime prediction. While most existing approaches have focused on training multiple shallow sub-networks jointly, we study training thin sub-networks instead. To this end, we first build many inclusive thin sub-networks (of the same depth) under a minor modification of existing multi-branch DNNs, and found that they can significantly outperform the state-of-art dense architecture for anytime prediction. This is remarkable due to their simplicity and effectiveness, but training many thin sub-networks jointly faces a new challenge on training complexity. To address the issue, we also propose a novel DNN architecture by forcing a certain sparsity pattern on multi-branch network parameters, making them train efficiently for the purpose of anytime prediction. In our experiments on the ImageNet dataset, its sub-networks have up to $43.3%$ smaller sizes (FLOPs) compared to those of the state-of-art anytime model with respect to the same accuracy. Finally, we also propose an alternative task under the proposed architecture using a hierarchical taxonomy, which brings a new angle for anytime prediction.
Tasks
Published	2018-07-07
URL	http://arxiv.org/abs/1807.02609v1
PDF	http://arxiv.org/pdf/1807.02609v1.pdf
PWC	https://paperswithcode.com/paper/anytime-neural-prediction-via-slicing
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Title	Sounding Board: A User-Centric and Content-Driven Social Chatbot
Authors	Hao Fang, Hao Cheng, Maarten Sap, Elizabeth Clark, Ari Holtzman, Yejin Choi, Noah A. Smith, Mari Ostendorf
Abstract	We present Sounding Board, a social chatbot that won the 2017 Amazon Alexa Prize. The system architecture consists of several components including spoken language processing, dialogue management, language generation, and content management, with emphasis on user-centric and content-driven design. We also share insights gained from large-scale online logs based on 160,000 conversations with real-world users.
Tasks	Chatbot, Dialogue Management, Text Generation
Published	2018-04-26
URL	http://arxiv.org/abs/1804.10202v1
PDF	http://arxiv.org/pdf/1804.10202v1.pdf
PWC	https://paperswithcode.com/paper/sounding-board-a-user-centric-and-content
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Cylindrical Transform: 3D Semantic Segmentation of Kidneys With Limited Annotated Images


Title	Cylindrical Transform: 3D Semantic Segmentation of Kidneys With Limited Annotated Images
Authors	Hojjat Salehinejad, Sumeya Naqvi, Errol Colak, Joseph Barfett, Shahrokh Valaee
Abstract	In this paper, we propose a novel technique for sampling sequential images using a cylindrical transform in a cylindrical coordinate system for kidney semantic segmentation in abdominal computed tomography (CT). The images generated from a cylindrical transform augment a limited annotated set of images in three dimensions. This approach enables us to train contemporary classification deep convolutional neural networks (DCNNs) instead of fully convolutional networks (FCNs) for semantic segmentation. Typical semantic segmentation models segment a sequential set of images (e.g. CT or video) by segmenting each image independently. However, the proposed method not only considers the spatial dependency in the x-y plane, but also the spatial sequential dependency along the z-axis. The results show that classification DCNNs, trained on cylindrical transformed images, can achieve a higher segmentation performance value than FCNs using a limited number of annotated images.
Tasks	3D Semantic Segmentation, Computed Tomography (CT), Semantic Segmentation
Published	2018-09-24
URL	http://arxiv.org/abs/1809.10245v1
PDF	http://arxiv.org/pdf/1809.10245v1.pdf
PWC	https://paperswithcode.com/paper/cylindrical-transform-3d-semantic
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Looking Deeper into Deep Learning Model: Attribution-based Explanations of TextCNN


Title	Looking Deeper into Deep Learning Model: Attribution-based Explanations of TextCNN
Authors	Wenting Xiong, Iftitahu Ni’mah, Juan M. G. Huesca, Werner van Ipenburg, Jan Veldsink, Mykola Pechenizkiy
Abstract	Layer-wise Relevance Propagation (LRP) and saliency maps have been recently used to explain the predictions of Deep Learning models, specifically in the domain of text classification. Given different attribution-based explanations to highlight relevant words for a predicted class label, experiments based on word deleting perturbation is a common evaluation method. This word removal approach, however, disregards any linguistic dependencies that may exist between words or phrases in a sentence, which could semantically guide a classifier to a particular prediction. In this paper, we present a feature-based evaluation framework for comparing the two attribution methods on customer reviews (public data sets) and Customer Due Diligence (CDD) extracted reports (corporate data set). Instead of removing words based on the relevance score, we investigate perturbations based on embedded features removal from intermediate layers of Convolutional Neural Networks. Our experimental study is carried out on embedded-word, embedded-document, and embedded-ngrams explanations. Using the proposed framework, we provide a visualization tool to assist analysts in reasoning toward the model’s final prediction.
Tasks	Text Classification
Published	2018-11-08
URL	http://arxiv.org/abs/1811.03970v2
PDF	http://arxiv.org/pdf/1811.03970v2.pdf
PWC	https://paperswithcode.com/paper/looking-deeper-into-deep-learning-model
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Explainable PCGML via Game Design Patterns


Title	Explainable PCGML via Game Design Patterns
Authors	Matthew Guzdial, Joshua Reno, Jonathan Chen, Gillian Smith, Mark Riedl
Abstract	Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning. One of the benefits of PCGML is that, unlike search or grammar-based PCG, it does not require hand authoring of initial content or rules. Instead, PCGML relies on existing content and black box models, which can be difficult to tune or tweak without expert knowledge. This is especially problematic when a human designer needs to understand how to manipulate their data or models to achieve desired results. We present an approach to Explainable PCGML via Design Patterns in which the design patterns act as a vocabulary and mode of interaction between user and model. We demonstrate that our technique outperforms non-explainable versions of our system in interactions with five expert designers, four of whom lack any machine learning expertise.
Tasks
Published	2018-09-25
URL	http://arxiv.org/abs/1809.09419v1
PDF	http://arxiv.org/pdf/1809.09419v1.pdf
PWC	https://paperswithcode.com/paper/explainable-pcgml-via-game-design-patterns
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BLEU is Not Suitable for the Evaluation of Text Simplification


Title	BLEU is Not Suitable for the Evaluation of Text Simplification
Authors	Elior Sulem, Omri Abend, Ari Rappoport
Abstract	BLEU is widely considered to be an informative metric for text-to-text generation, including Text Simplification (TS). TS includes both lexical and structural aspects. In this paper we show that BLEU is not suitable for the evaluation of sentence splitting, the major structural simplification operation. We manually compiled a sentence splitting gold standard corpus containing multiple structural paraphrases, and performed a correlation analysis with human judgments. We find low or no correlation between BLEU and the grammaticality and meaning preservation parameters where sentence splitting is involved. Moreover, BLEU often negatively correlates with simplicity, essentially penalizing simpler sentences.
Tasks	Text Generation, Text Simplification
Published	2018-10-14
URL	http://arxiv.org/abs/1810.05995v1
PDF	http://arxiv.org/pdf/1810.05995v1.pdf
PWC	https://paperswithcode.com/paper/bleu-is-not-suitable-for-the-evaluation-of
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Smallify: Learning Network Size while Training


Title	Smallify: Learning Network Size while Training
Authors	Guillaume Leclerc, Manasi Vartak, Raul Castro Fernandez, Tim Kraska, Samuel Madden
Abstract	As neural networks become widely deployed in different applications and on different hardware, it has become increasingly important to optimize inference time and model size along with model accuracy. Most current techniques optimize model size, model accuracy and inference time in different stages, resulting in suboptimal results and computational inefficiency. In this work, we propose a new technique called Smallify that optimizes all three of these metrics at the same time. Specifically we present a new method to simultaneously optimize network size and model performance by neuron-level pruning during training. Neuron-level pruning not only produces much smaller networks but also produces dense weight matrices that are amenable to efficient inference. By applying our technique to convolutional as well as fully connected models, we show that Smallify can reduce network size by 35X with a 6X improvement in inference time with similar accuracy as models found by traditional training techniques.
Tasks
Published	2018-06-10
URL	http://arxiv.org/abs/1806.03723v1
PDF	http://arxiv.org/pdf/1806.03723v1.pdf
PWC	https://paperswithcode.com/paper/smallify-learning-network-size-while-training
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Recommending Scientific Videos based on Metadata Enrichment using Linked Open Data


Title	Recommending Scientific Videos based on Metadata Enrichment using Linked Open Data
Authors	Justyna Medrek, Christian Otto, Ralph Ewerth
Abstract	The amount of available videos in the Web has significantly increased not only for entertainment etc., but also to convey educational or scientific information in an effective way. There are several web portals that offer access to the latter kind of video material. One of them is the TIB AV-Portal of the Leibniz Information Centre for Science and Technology (TIB), which hosts scientific and educational video content. In contrast to other video portals, automatic audiovisual analysis (visual concept classification, optical character recognition, speech recognition) is utilized to enhance metadata information and semantic search. In this paper, we propose to further exploit and enrich this automatically generated information by linking it to the Integrated Authority File (GND) of the German National Library. This information is used to derive a measure to compare the similarity of two videos which serves as a basis for recommending semantically similar videos. A user study demonstrates the feasibility of the proposed approach.
Tasks	Optical Character Recognition, Speech Recognition
Published	2018-06-19
URL	http://arxiv.org/abs/1806.07309v2
PDF	http://arxiv.org/pdf/1806.07309v2.pdf
PWC	https://paperswithcode.com/paper/recommending-scientific-videos-based-on
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An Investigation of Supervised Learning Methods for Authorship Attribution in Short Hinglish Texts using Char & Word N-grams


Title	An Investigation of Supervised Learning Methods for Authorship Attribution in Short Hinglish Texts using Char & Word N-grams
Authors	Abhay Sharma, Ananya Nandan, Reetika Ralhan
Abstract	The writing style of a person can be affirmed as a unique identity indicator; the words used, and the structuring of the sentences are clear measures which can identify the author of a specific work. Stylometry and its subset - Authorship Attribution, have a long history beginning from the 19th century, and we can still find their use in modern times. The emergence of the Internet has shifted the application of attribution studies towards non-standard texts that are comparatively shorter to and different from the long texts on which most research has been done. The aim of this paper focuses on the study of short online texts, retrieved from messaging application called WhatsApp and studying the distinctive features of a macaronic language (Hinglish), using supervised learning methods and then comparing the models. Various features such as word n-gram and character n-gram are compared via methods viz., Naive Bayes Classifier, Support Vector Machine, Conditional Tree, and Random Forest, to find the best discriminator for such corpora. Our results showed that SVM attained a test accuracy of up to 95.079% while similarly, Naive Bayes attained an accuracy of up to 94.455% for the dataset. Conditional Tree & Random Forest failed to perform as well as expected. We also found that word unigram and character 3-grams features were more likely to distinguish authors accurately than other features.
Tasks
Published	2018-12-26
URL	http://arxiv.org/abs/1812.10281v1
PDF	http://arxiv.org/pdf/1812.10281v1.pdf
PWC	https://paperswithcode.com/paper/an-investigation-of-supervised-learning
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Framework


Title	Blind Channel Equalization using Variational Autoencoders
Authors	Avi Caciularu, David Burshtein
Abstract	A new maximum likelihood estimation approach for blind channel equalization, using variational autoencoders (VAEs), is introduced. Significant and consistent improvements in the error rate of the reconstructed symbols, compared to constant modulus equalizers, are demonstrated. In fact, for the channels that were examined, the performance of the new VAE blind channel equalizer was close to the performance of a nonblind adaptive linear minimum mean square error equalizer. The new equalization method enables a significantly lower latency channel acquisition compared to the constant modulus algorithm (CMA). The VAE uses a convolutional neural network with two layers and a very small number of free parameters. Although the computational complexity of the new equalizer is higher compared to CMA, it is still reasonable, and the number of free parameters to estimate is small.
Tasks
Published	2018-03-05
URL	http://arxiv.org/abs/1803.01526v1
PDF	http://arxiv.org/pdf/1803.01526v1.pdf
PWC	https://paperswithcode.com/paper/blind-channel-equalization-using-variational
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Time Series Segmentation through Automatic Feature Learning


Title	Time Series Segmentation through Automatic Feature Learning
Authors	Wei-Han Lee, Jorge Ortiz, Bongjun Ko, Ruby Lee
Abstract	Internet of things (IoT) applications have become increasingly popular in recent years, with applications ranging from building energy monitoring to personal health tracking and activity recognition. In order to leverage these data, automatic knowledge extraction - whereby we map from observations to interpretable states and transitions - must be done at scale. As such, we have seen many recent IoT data sets include annotations with a human expert specifying states, recorded as a set of boundaries and associated labels in a data sequence. These data can be used to build automatic labeling algorithms that produce labels as an expert would. Here, we refer to human-specified boundaries as breakpoints. Traditional changepoint detection methods only look for statistically-detectable boundaries that are defined as abrupt variations in the generative parameters of a data sequence. However, we observe that breakpoints occur on more subtle boundaries that are non-trivial to detect with these statistical methods. In this work, we propose a new unsupervised approach, based on deep learning, that outperforms existing techniques and learns the more subtle, breakpoint boundaries with a high accuracy. Through extensive experiments on various real-world data sets - including human-activity sensing data, speech signals, and electroencephalogram (EEG) activity traces - we demonstrate the effectiveness of our algorithm for practical applications. Furthermore, we show that our approach achieves significantly better performance than previous methods.
Tasks	Activity Recognition, EEG, Time Series
Published	2018-01-16
URL	http://arxiv.org/abs/1801.05394v2
PDF	http://arxiv.org/pdf/1801.05394v2.pdf
PWC	https://paperswithcode.com/paper/time-series-segmentation-through-automatic
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A Self-Supervised Bootstrap Method for Single-Image 3D Face Reconstruction


Title	A Self-Supervised Bootstrap Method for Single-Image 3D Face Reconstruction
Authors	Yifan Xing, Rahul Tewari, Paulo R. S. Mendonca
Abstract	State-of-the-art methods for 3D reconstruction of faces from a single image require 2D-3D pairs of ground-truth data for supervision. Such data is costly to acquire, and most datasets available in the literature are restricted to pairs for which the input 2D images depict faces in a near fronto-parallel pose. Therefore, many data-driven methods for single-image 3D facial reconstruction perform poorly on profile and near-profile faces. We propose a method to improve the performance of single-image 3D facial reconstruction networks by utilizing the network to synthesize its own training data for fine-tuning, comprising: (i) single-image 3D reconstruction of faces in near-frontal images without ground-truth 3D shape; (ii) application of a rigid-body transformation to the reconstructed face model; (iii) rendering of the face model from new viewpoints; and (iv) use of the rendered image and corresponding 3D reconstruction as additional data for supervised fine-tuning. The new 2D-3D pairs thus produced have the same high-quality observed for near fronto-parallel reconstructions, thereby nudging the network towards more uniform performance as a function of the viewing angle of input faces. Application of the proposed technique to the fine-tuning of a state-of-the-art single-image 3D-reconstruction network for faces demonstrates the usefulness of the method, with particularly significant gains for profile or near-profile views.
Tasks	3D Face Reconstruction, 3D Reconstruction, Face Reconstruction
Published	2018-12-14
URL	http://arxiv.org/abs/1812.05806v2
PDF	http://arxiv.org/pdf/1812.05806v2.pdf
PWC	https://paperswithcode.com/paper/a-self-supervised-bootstrap-method-for-single
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RumourEval 2019: Determining Rumour Veracity and Support for Rumours


Title	RumourEval 2019: Determining Rumour Veracity and Support for Rumours
Authors	Genevieve Gorrell, Kalina Bontcheva, Leon Derczynski, Elena Kochkina, Maria Liakata, Arkaitz Zubiaga
Abstract	This is the proposal for RumourEval-2019, which will run in early 2019 as part of that year’s SemEval event. Since the first RumourEval shared task in 2017, interest in automated claim validation has greatly increased, as the dangers of “fake news” have become a mainstream concern. Yet automated support for rumour checking remains in its infancy. For this reason, it is important that a shared task in this area continues to provide a focus for effort, which is likely to increase. We therefore propose a continuation in which the veracity of further rumours is determined, and as previously, supportive of this goal, tweets discussing them are classified according to the stance they take regarding the rumour. Scope is extended compared with the first RumourEval, in that the dataset is substantially expanded to include Reddit as well as Twitter data, and additional languages are also included.
Tasks	Rumour Detection
Published	2018-09-18
URL	http://arxiv.org/abs/1809.06683v1
PDF	http://arxiv.org/pdf/1809.06683v1.pdf
PWC	https://paperswithcode.com/paper/rumoureval-2019-determining-rumour-veracity
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Pose Invariant 3D Face Reconstruction


Title	Pose Invariant 3D Face Reconstruction
Authors	Lei Jiang, XiaoJun Wu, Josef Kittler
Abstract	3D face reconstruction is an important task in the field of computer vision. Although 3D face reconstruction has being developing rapidly in recent years, it is still a challenge for face reconstruction under large pose. That is because much of the information about a face in a large pose will be unknowable. In order to address this issue, this paper proposes a novel 3D face reconstruction algorithm (PIFR) based on 3D Morphable Model (3DMM). After input a single face image, it generates a frontal image by normalizing the image. Then we set weighted sum of the 3D parameters of the two images. Our method solves the problem of face reconstruction of a single image of a traditional method in a large pose, works on arbitrary Pose and Expressions, greatly improves the accuracy of reconstruction. Experiments on the challenging AFW, LFPW and AFLW database show that our algorithm significantly improves the accuracy of 3D face reconstruction even under extreme poses .
Tasks	3D Face Reconstruction, Face Reconstruction
Published	2018-11-13
URL	http://arxiv.org/abs/1811.05295v1
PDF	http://arxiv.org/pdf/1811.05295v1.pdf
PWC	https://paperswithcode.com/paper/pose-invariant-3d-face-reconstruction
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