April 1, 2020

3370 words 16 mins read

Paper Group ANR 455

Considering discrepancy when calibrating a mechanistic electrophysiology model. Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification. PBRnet: Pyramidal Bounding Box Refinement to Improve Object Localization Accuracy. “Who is Driving around Me?” Unique Vehicle Instance Classification using Deep Neural Features. Deep …

Considering discrepancy when calibrating a mechanistic electrophysiology model


Title	Considering discrepancy when calibrating a mechanistic electrophysiology model
Authors	Chon Lok Lei, Sanmitra Ghosh, Dominic G. Whittaker, Yasser Aboelkassem, Kylie A. Beattie, Chris D. Cantwell, Tammo Delhaas, Charles Houston, Gustavo Montes Novaes, Alexander V. Panfilov, Pras Pathmanathan, Marina Riabiz, Rodrigo Weber dos Santos, Keith Worden, Gary R. Mirams, Richard D. Wilkinson
Abstract	Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterise uncertainty in model inputs and how that propagates through to outputs or predictions. In this perspective piece we draw attention to an important and under-addressed source of uncertainty in our predictions — that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes (GPs) and autoregressive-moving-average (ARMA) models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided.
Tasks	Gaussian Processes
Published	2020-01-13
URL	https://arxiv.org/abs/2001.04230v1
PDF	https://arxiv.org/pdf/2001.04230v1.pdf
PWC	https://paperswithcode.com/paper/considering-discrepancy-when-calibrating-a
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Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification


Title	Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification
Authors	Wuraola Fisayo Oyewusi, Olubayo Adekanmbi, Olalekan Akinsande
Abstract	Nigerian English adaptation, Pidgin, has evolved over the years through multi-language code switching, code mixing and linguistic adaptation. While Pidgin preserves many of the words in the normal English language corpus, both in spelling and pronunciation, the fundamental meaning of these words have changed significantly. For example,‘ginger’ is not a plant but an expression of motivation and ‘tank’ is not a container but an expression of gratitude. The implication is that the current approach of using direct English sentiment analysis of social media text from Nigeria is sub-optimal, as it will not be able to capture the semantic variation and contextual evolution in the contemporary meaning of these words. In practice, while many words in Nigerian Pidgin adaptation are the same as the standard English, the full English language based sentiment analysis models are not designed to capture the full intent of the Nigerian pidgin when used alone or code-mixed. By augmenting scarce human labelled code-changed text with ample synthetic code-reformatted text and meaning, we achieve significant improvements in sentiment scoring. Our research explores how to understand sentiment in an intrasentential code mixing and switching context where there has been significant word localization.This work presents a 300 VADER lexicon compatible Nigerian Pidgin sentiment tokens and their scores and a 14,000 gold standard Nigerian Pidgin tweets and their sentiments labels.
Tasks	Sentiment Analysis
Published	2020-03-27
URL	https://arxiv.org/abs/2003.12450v1
PDF	https://arxiv.org/pdf/2003.12450v1.pdf
PWC	https://paperswithcode.com/paper/semantic-enrichment-of-nigerian-pidgin
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Title	PBRnet: Pyramidal Bounding Box Refinement to Improve Object Localization Accuracy
Authors	Li Xiao, Yufan Luo, Chunlong Luo, Lianhe Zhao, Quanshui Fu, Guoqing Yang, Anpeng Huang, Yi Zhao
Abstract	Many recently developed object detectors focused on coarse-to-fine framework which contains several stages that classify and regress proposals from coarse-grain to fine-grain, and obtains more accurate detection gradually. Multi-resolution models such as Feature Pyramid Network(FPN) integrate information of different levels of resolution and effectively improve the performance. Previous researches also have revealed that localization can be further improved by: 1) using fine-grained information which is more translational variant; 2) refining local areas which is more focused on local boundary information. Based on these principles, we designed a novel boundary refinement architecture to improve localization accuracy by combining coarse-to-fine framework with feature pyramid structure, named as Pyramidal Bounding Box Refinement network(PBRnet), which parameterizes gradually focused boundary areas of objects and leverages lower-level feature maps to extract finer local information when refining the predicted bounding boxes. Extensive experiments are performed on the MS-COCO dataset. The PBRnet brings a significant performance gains by roughly 3 point of $mAP$ when added to FPN or Libra R-CNN. Moreover, by treating Cascade R-CNN as a coarse-to-fine detector and replacing its localization branch by the regressor of PBRnet, it leads an extra performance improvement by 1.5 $mAP$, yielding a total performance boosting by as high as 5 point of $mAP$.
Tasks	Object Localization
Published	2020-03-10
URL	https://arxiv.org/abs/2003.04541v1
PDF	https://arxiv.org/pdf/2003.04541v1.pdf
PWC	https://paperswithcode.com/paper/pbrnet-pyramidal-bounding-box-refinement-to
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“Who is Driving around Me?” Unique Vehicle Instance Classification using Deep Neural Features


Title	“Who is Driving around Me?” Unique Vehicle Instance Classification using Deep Neural Features
Authors	Tim Oosterhuis, Lambert Schomaker
Abstract	Being aware of other traffic is a prerequisite for self-driving cars to operate in the real world. In this paper, we show how the intrinsic feature maps of an object detection CNN can be used to uniquely identify vehicles from a dash-cam feed. Feature maps of a pretrained `YOLO' network are used to create 700 deep integrated feature signatures (DIFS) from 20 different images of 35 vehicles from a high resolution dataset and 340 signatures from 20 different images of 17 vehicles of a lower resolution tracking benchmark dataset. The YOLO network was trained to classify general object categories, e.g. classify a detected object as a` car’ or `truck’. 5-Fold nearest neighbor (1NN) classification was used on DIFS created from feature maps in the middle layers of the network to correctly identify unique vehicles at a rate of 96.7% for the high resolution data and with a rate of 86.8% for the lower resolution data. We conclude that a deep neural detection network trained to distinguish between different classes can be successfully used to identify different instances belonging to the same class, through the creation of deep integrated feature signatures (DIFS). \|
Tasks	Object Detection, Self-Driving Cars
Published	2020-02-29
URL	https://arxiv.org/abs/2003.08771v1
PDF	https://arxiv.org/pdf/2003.08771v1.pdf
PWC	https://paperswithcode.com/paper/who-is-driving-around-me-unique-vehicle
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Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems


Title	Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
Authors	Ahmet M. Elbir, A Papazafeiropoulos, P. Kourtessis, S. Chatzinotas
Abstract	This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated.
Tasks
Published	2020-01-29
URL	https://arxiv.org/abs/2001.11085v1
PDF	https://arxiv.org/pdf/2001.11085v1.pdf
PWC	https://paperswithcode.com/paper/deep-channel-learning-for-large-intelligent
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Adversarial Attacks on Multivariate Time Series


Title	Adversarial Attacks on Multivariate Time Series
Authors	Samuel Harford, Fazle Karim, Houshang Darabi
Abstract	Classification models for the multivariate time series have gained significant importance in the research community, but not much research has been done on generating adversarial samples for these models. Such samples of adversaries could become a security concern. In this paper, we propose transforming the existing adversarial transformation network (ATN) on a distilled model to attack various multivariate time series classification models. The proposed attack on the classification model utilizes a distilled model as a surrogate that mimics the behavior of the attacked classical multivariate time series classification models. The proposed methodology is tested onto 1-Nearest Neighbor Dynamic Time Warping (1-NN DTW) and a Fully Convolutional Network (FCN), all of which are trained on 18 University of East Anglia (UEA) and University of California Riverside (UCR) datasets. We show both models were susceptible to attacks on all 18 datasets. To the best of our knowledge, adversarial attacks have only been conducted in the domain of univariate time series and have not been conducted on multivariate time series. such an attack on time series classification models has never been done before. Additionally, we recommend future researchers that develop time series classification models to incorporating adversarial data samples into their training data sets to improve resilience on adversarial samples and to consider model robustness as an evaluative metric.
Tasks	Time Series, Time Series Classification
Published	2020-03-31
URL	https://arxiv.org/abs/2004.00410v1
PDF	https://arxiv.org/pdf/2004.00410v1.pdf
PWC	https://paperswithcode.com/paper/adversarial-attacks-on-multivariate-time
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Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies


Title	Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies
Authors	Sungryull Sohn, Hyunjae Woo, Jongwook Choi, Honglak Lee
Abstract	We propose and address a novel few-shot RL problem, where a task is characterized by a subtask graph which describes a set of subtasks and their dependencies that are unknown to the agent. The agent needs to quickly adapt to the task over few episodes during adaptation phase to maximize the return in the test phase. Instead of directly learning a meta-policy, we develop a Meta-learner with Subtask Graph Inference(MSGI), which infers the latent parameter of the task by interacting with the environment and maximizes the return given the latent parameter. To facilitate learning, we adopt an intrinsic reward inspired by upper confidence bound (UCB) that encourages efficient exploration. Our experiment results on two grid-world domains and StarCraft II environments show that the proposed method is able to accurately infer the latent task parameter, and to adapt more efficiently than existing meta RL and hierarchical RL methods.
Tasks	Efficient Exploration, Starcraft, Starcraft II
Published	2020-01-01
URL	https://arxiv.org/abs/2001.00248v1
PDF	https://arxiv.org/pdf/2001.00248v1.pdf
PWC	https://paperswithcode.com/paper/meta-reinforcement-learning-with-autonomous-1
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Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks


Title	Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks
Authors	Chankyu Lee, Adarsh Kosta, Alex Zihao Zhu, Kenneth Chaney, Kostas Daniilidis, Kaushik Roy
Abstract	Event-based cameras display great potential for a variety of conditions such as high-speed motion detection and enabling navigation in low-light environments where conventional frame-based cameras suffer critically. This is attributed to their high temporal resolution, high dynamic range, and low-power consumption. However, conventional computer vision methods as well as deep Analog Neural Networks (ANNs) are not suited to work well with the asynchronous and discrete nature of event camera outputs. Spiking Neural Networks (SNNs) serve as ideal paradigms to handle event camera outputs, but deep SNNs suffer in terms of performance due to spike vanishing phenomenon. To overcome these issues, we present Spike-FlowNet, a deep hybrid neural network architecture integrating SNNs and ANNs for efficiently estimating optical flow from sparse event camera outputs without sacrificing the performance. The network is end-to-end trained with self-supervised learning on Multi-Vehicle Stereo Event Camera (MVSEC) dataset. Spike-FlowNet outperforms its corresponding ANN-based method in terms of the optical flow prediction capability while providing significant computational efficiency.
Tasks	Motion Detection, Optical Flow Estimation
Published	2020-03-14
URL	https://arxiv.org/abs/2003.06696v1
PDF	https://arxiv.org/pdf/2003.06696v1.pdf
PWC	https://paperswithcode.com/paper/spike-flownet-event-based-optical-flow
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Neural Mesh Refiner for 6-DoF Pose Estimation


Title	Neural Mesh Refiner for 6-DoF Pose Estimation
Authors	Di Wu, Yihao Chen, Xianbiao Qi, Yongjian Yu, Weixuan Chen, Rong Xiao
Abstract	How can we effectively utilise the 2D monocular image information for recovering the 6D pose (6-DoF) of the visual objects? Deep learning has shown to be effective for robust and real-time monocular pose estimation. Oftentimes, the network learns to regress the 6-DoF pose using a naive loss function. However, due to a lack of geometrical scene understanding from the directly regressed pose estimation, there are misalignments between the rendered mesh from the 3D object and the 2D instance segmentation result, e.g., bounding boxes and masks prediction. This paper bridges the gap between 2D mask generation and 3D location prediction via a differentiable neural mesh renderer. We utilise the overlay between the accurate mask prediction and less accurate mesh prediction to iteratively optimise the direct regressed 6D pose information with a focus on translation estimation. By leveraging geometry, we demonstrate that our technique significantly improves direct regression performance on the difficult task of translation estimation and achieve the state of the art results on Peking University/Baidu - Autonomous Driving dataset and the ApolloScape 3D Car Instance dataset. The code can be found at \url{https://bit.ly/2IRihfU}.
Tasks	Autonomous Driving, Instance Segmentation, Pose Estimation, Scene Understanding, Semantic Segmentation
Published	2020-03-17
URL	https://arxiv.org/abs/2003.07561v3
PDF	https://arxiv.org/pdf/2003.07561v3.pdf
PWC	https://paperswithcode.com/paper/neural-mesh-refiner-for-6-dof-pose-estimation
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Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data


Title	Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data
Authors	Priya Sundaresan, Jennifer Grannen, Brijen Thananjeyan, Ashwin Balakrishna, Michael Laskey, Kevin Stone, Joseph E. Gonzalez, Ken Goldberg
Abstract	Robotic manipulation of deformable 1D objects such as ropes, cables, and hoses is challenging due to the lack of high-fidelity analytic models and large configuration spaces. Furthermore, learning end-to-end manipulation policies directly from images and physical interaction requires significant time on a robot and can fail to generalize across tasks. We address these challenges using interpretable deep visual representations for rope, extending recent work on dense object descriptors for robot manipulation. This facilitates the design of interpretable and transferable geometric policies built on top of the learned representations, decoupling visual reasoning and control. We present an approach that learns point-pair correspondences between initial and goal rope configurations, which implicitly encodes geometric structure, entirely in simulation from synthetic depth images. We demonstrate that the learned representation – dense depth object descriptors (DDODs) – can be used to manipulate a real rope into a variety of different arrangements either by learning from demonstrations or using interpretable geometric policies. In 50 trials of a knot-tying task with the ABB YuMi Robot, the system achieves a 66% knot-tying success rate from previously unseen configurations. See https://tinyurl.com/rope-learning for supplementary material and videos.
Tasks	Visual Reasoning
Published	2020-03-03
URL	https://arxiv.org/abs/2003.01835v1
PDF	https://arxiv.org/pdf/2003.01835v1.pdf
PWC	https://paperswithcode.com/paper/learning-rope-manipulation-policies-using
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Cops-Ref: A new Dataset and Task on Compositional Referring Expression Comprehension


Title	Cops-Ref: A new Dataset and Task on Compositional Referring Expression Comprehension
Authors	Zhenfang Chen, Peng Wang, Lin Ma, Kwan-Yee K. Wong, Qi Wu
Abstract	Referring expression comprehension (REF) aims at identifying a particular object in a scene by a natural language expression. It requires joint reasoning over the textual and visual domains to solve the problem. Some popular referring expression datasets, however, fail to provide an ideal test bed for evaluating the reasoning ability of the models, mainly because 1) their expressions typically describe only some simple distinctive properties of the object and 2) their images contain limited distracting information. To bridge the gap, we propose a new dataset for visual reasoning in context of referring expression comprehension with two main features. First, we design a novel expression engine rendering various reasoning logics that can be flexibly combined with rich visual properties to generate expressions with varying compositionality. Second, to better exploit the full reasoning chain embodied in an expression, we propose a new test setting by adding additional distracting images containing objects sharing similar properties with the referent, thus minimising the success rate of reasoning-free cross-domain alignment. We evaluate several state-of-the-art REF models, but find none of them can achieve promising performance. A proposed modular hard mining strategy performs the best but still leaves substantial room for improvement. We hope this new dataset and task can serve as a benchmark for deeper visual reasoning analysis and foster the research on referring expression comprehension.
Tasks	Visual Reasoning
Published	2020-03-01
URL	https://arxiv.org/abs/2003.00403v1
PDF	https://arxiv.org/pdf/2003.00403v1.pdf
PWC	https://paperswithcode.com/paper/cops-ref-a-new-dataset-and-task-on
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Hierarchical Rule Induction Network for Abstract Visual Reasoning


Title	Hierarchical Rule Induction Network for Abstract Visual Reasoning
Authors	Sheng Hu, Yuqing Ma, Xianglong Liu, Yanlu Wei, Shihao Bai
Abstract	Abstract reasoning refers to the ability to analyze information, discover rules at an intangible level, and solve problems in innovative ways. Raven’s Progressive Matrices (RPM) test is typically used to examine the capability of abstract reasoning. In the test, the subject is asked to identify the correct choice from the answer set to fill the missing panel at the bottom right of RPM (e.g., a 3$\times$3 matrix), following the underlying rules inside the matrix. Recent studies, taking advantage of Convolutional Neural Networks (CNNs), have achieved encouraging progress to accomplish the RPM test problems. Unfortunately, simply relying on the relation extraction at the matrix level, they fail to recognize the complex attribute patterns inside or across rows/columns of RPM. To address this problem, in this paper we propose a Hierarchical Rule Induction Network (HriNet), by intimating human induction strategies. HriNet extracts multiple granularity rule embeddings at different levels and integrates them through a gated embedding fusion module. We further introduce a rule similarity metric based on the embeddings, so that HriNet can not only be trained using a tuplet loss but also infer the best answer according to the similarity score. To comprehensively evaluate HriNet, we first fix the defects contained in the very recent RAVEN dataset and generate a new one named Balanced-RAVEN. Then extensive experiments are conducted on the large-scale dataset PGM and our Balanced-RAVEN, the results of which show that HriNet outperforms the state-of-the-art models by a large margin.
Tasks	Relation Extraction, Visual Reasoning
Published	2020-02-17
URL	https://arxiv.org/abs/2002.06838v1
PDF	https://arxiv.org/pdf/2002.06838v1.pdf
PWC	https://paperswithcode.com/paper/hierarchical-rule-induction-network-for
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An Analysis of Word2Vec for the Italian Language


Title	An Analysis of Word2Vec for the Italian Language
Authors	Giovanni Di Gennaro, Amedeo Buonanno, Antonio Di Girolamo, Armando Ospedale, Francesco A. N. Palmieri, Gianfranco Fedele
Abstract	Word representation is fundamental in NLP tasks, because it is precisely from the coding of semantic closeness between words that it is possible to think of teaching a machine to understand text. Despite the spread of word embedding concepts, still few are the achievements in linguistic contexts other than English. In this work, analysing the semantic capacity of the Word2Vec algorithm, an embedding for the Italian language is produced. Parameter setting such as the number of epochs, the size of the context window and the number of negatively backpropagated samples is explored.
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Published	2020-01-25
URL	https://arxiv.org/abs/2001.09332v1
PDF	https://arxiv.org/pdf/2001.09332v1.pdf
PWC	https://paperswithcode.com/paper/an-analysis-of-word2vec-for-the-italian
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Distributionally Robust Bayesian Optimization


Title	Distributionally Robust Bayesian Optimization
Authors	Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka, Andreas Krause
Abstract	Robustness to distributional shift is one of the key challenges of contemporary machine learning. Attaining such robustness is the goal of distributionally robust optimization, which seeks a solution to an optimization problem that is worst-case robust under a specified distributional shift of an uncontrolled covariate. In this paper, we study such a problem when the distributional shift is measured via the maximum mean discrepancy (MMD). For the setting of zeroth-order, noisy optimization, we present a novel distributionally robust Bayesian optimization algorithm (DRBO). Our algorithm provably obtains sub-linear robust regret in various settings that differ in how the uncertain covariate is observed. We demonstrate the robust performance of our method on both synthetic and real-world benchmarks.
Tasks
Published	2020-02-20
URL	https://arxiv.org/abs/2002.09038v3
PDF	https://arxiv.org/pdf/2002.09038v3.pdf
PWC	https://paperswithcode.com/paper/distributionally-robust-bayesian-optimization
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Weakly Supervised Visual Semantic Parsing


Title	Weakly Supervised Visual Semantic Parsing
Authors	Alireza Zareian, Svebor Karaman, Shih-Fu Chang
Abstract	Scene Graph Generation (SGG) aims to extract entities, predicates and their semantic structure from images, enabling deep understanding of visual content, with many applications such as visual reasoning and image retrieval. Nevertheless, existing SGG methods require millions of manually annotated bounding boxes for training, and are computationally inefficient, as they exhaustively process all pairs of object proposals to detect predicates. In this paper, we address those two limitations by first proposing a generalized formulation of SGG, namely Visual Semantic Parsing, which disentangles entity and predicate recognition, and enables sub-quadratic performance. Then we propose the Visual Semantic Parsing Network, VSPNet, based on a dynamic, attention-based, bipartite message passing framework that jointly infers graph nodes and edges through an iterative process. Additionally, we propose the first graph-based weakly supervised learning framework, based on a novel graph alignment algorithm, which enables training without bounding box annotations. Through extensive experiments, we show that VSPNet outperforms weakly supervised baselines significantly and approaches fully supervised performance, while being several times faster. We publicly release the source code of our method.
Tasks	Graph Generation, Image Retrieval, Scene Graph Generation, Semantic Parsing, Visual Reasoning
Published	2020-01-08
URL	https://arxiv.org/abs/2001.02359v2
PDF	https://arxiv.org/pdf/2001.02359v2.pdf
PWC	https://paperswithcode.com/paper/weakly-supervised-visual-semantic-parsing
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