Paper Group ANR 1533
The African Wildlife Ontology tutorial ontologies: requirements, design, and content. Neural Network Verification for the Masses (of AI graduates). Challenging On Car Racing Problem from OpenAI gym. Real-Time and Embedded Deep Learning on FPGA for RF Signal Classification. Short-Term Prediction and Multi-Camera Fusion on Semantic Grids. VASE: Varia …
The African Wildlife Ontology tutorial ontologies: requirements, design, and content
| Title | The African Wildlife Ontology tutorial ontologies: requirements, design, and content |
| Authors | C Maria Keet |
| Abstract | Background. Most tutorial ontologies focus on illustrating one aspect of ontology development, notably language features and automated reasoners, but ignore ontology development factors, such as emergent modelling guidelines and ontological principles. Yet, novices replicate examples from the exercises they carry out. Not providing good examples holistically causes the propagation of sub-optimal ontology development, which may negatively affect the quality of a real domain ontology. Results. We identified 22 requirements that a good tutorial ontology should satisfy regarding subject domain, logics and reasoning, and engineering aspects. We developed a set of ontologies about African Wildlife to serve as tutorial ontologies. A majority of the requirements have been met with the set of African Wildlife Ontology tutorial ontologies, which are introduced in this paper. The African Wildlife Ontology is mature and has been used yearly in an ontology engineering course or tutorial since 2010 and is included in a recent ontology engineering textbook with relevant examples and exercises. Conclusion. The African Wildlife Ontology provides a wide range of options concerning examples and exercises for ontology engineering well beyond illustrating only language features and automated reasoning. It assists in demonstrating tasks about ontology quality, such as alignment to a foundational ontology and satisfying competency questions, versioning, and multilingual ontologies. |
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| Published | 2019-05-23 |
| URL | https://arxiv.org/abs/1905.09519v1 |
| https://arxiv.org/pdf/1905.09519v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-african-wildlife-ontology-tutorial |
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Neural Network Verification for the Masses (of AI graduates)
| Title | Neural Network Verification for the Masses (of AI graduates) |
| Authors | Ekaterina Komendantskaya, Rob Stewart, Kirsy Duncan, Daniel Kienitz, Pierre Le Hen, Pascal Bacchus |
| Abstract | Rapid development of AI applications has stimulated demand for, and has given rise to, the rapidly growing number and diversity of AI MSc degrees. AI and Robotics research communities, industries and students are becoming increasingly aware of the problems caused by unsafe or insecure AI applications. Among them, perhaps the most famous example is vulnerability of deep neural networks to ``adversarial attacks’'. Owing to wide-spread use of neural networks in all areas of AI, this problem is seen as particularly acute and pervasive. Despite of the growing number of research papers about safety and security vulnerabilities of AI applications, there is a noticeable shortage of accessible tools, methods and teaching materials for incorporating verification into AI programs. LAIV – the Lab for AI and Verification – is a newly opened research lab at Heriot-Watt university that engages AI and Robotics MSc students in verification projects, as part of their MSc dissertation work. In this paper, we will report on successes and unexpected difficulties LAIV faces, many of which arise from limitations of existing programming languages used for verification. We will discuss future directions for incorporating verification into AI degrees. | |
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| Published | 2019-07-02 |
| URL | https://arxiv.org/abs/1907.01297v1 |
| https://arxiv.org/pdf/1907.01297v1.pdf | |
| PWC | https://paperswithcode.com/paper/neural-network-verification-for-the-masses-of |
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Challenging On Car Racing Problem from OpenAI gym
| Title | Challenging On Car Racing Problem from OpenAI gym |
| Authors | Changmao Li |
| Abstract | This project challenges the car racing problem from OpenAI gym environment. The problem is very challenging since it requires computer to finish the continuous control task by learning from pixels. To tackle this challenging problem, we explored two approaches including evolutionary algorithm based genetic multi-layer perceptron and double deep Q-learning network. The result shows that the genetic multi-layer perceptron can converge fast but when training many episodes, double deep Q-learning can get better score. We analyze the result and draw a conclusion that for limited hardware resources, using genetic multi-layer perceptron sometimes can be more efficient. |
| Tasks | Car Racing, Continuous Control, Q-Learning |
| Published | 2019-11-02 |
| URL | https://arxiv.org/abs/1911.04868v1 |
| https://arxiv.org/pdf/1911.04868v1.pdf | |
| PWC | https://paperswithcode.com/paper/challenging-on-car-racing-problem-from-openai |
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Real-Time and Embedded Deep Learning on FPGA for RF Signal Classification
| Title | Real-Time and Embedded Deep Learning on FPGA for RF Signal Classification |
| Authors | Sohraab Soltani, Yalin E. Sagduyu, Raqibul Hasan, Kemal Davaslioglu, Hongmei Deng, Tugba Erpek |
| Abstract | We designed and implemented a deep learning based RF signal classifier on the Field Programmable Gate Array (FPGA) of an embedded software-defined radio platform, DeepRadio, that classifies the signals received through the RF front end to different modulation types in real time and with low power. This classifier implementation successfully captures complex characteristics of wireless signals to serve critical applications in wireless security and communications systems such as identifying spoofing signals in signal authentication systems, detecting target emitters and jammers in electronic warfare (EW) applications, discriminating primary and secondary users in cognitive radio networks, interference hunting, and adaptive modulation. Empowered by low-power and low-latency embedded computing, the deep neural network runs directly on the FPGA fabric of DeepRadio, while maintaining classifier accuracy close to the software performance. We evaluated the performance when another SDR (USRP) transmits signals with different modulation types at different power levels and DeepRadio receives the signals and classifies them in real time on its FPGA. A smartphone with a mobile app is connected to DeepRadio to initiate the experiment and visualize the classification results. With real radio transmissions over the air, we show that the classifier implemented on DeepRadio achieves high accuracy with low latency (microsecond per sample) and low energy consumption (microJoule per sample), and this performance is not matched by other embedded platforms such as embedded graphics processing unit (GPU). |
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| Published | 2019-10-13 |
| URL | https://arxiv.org/abs/1910.05765v1 |
| https://arxiv.org/pdf/1910.05765v1.pdf | |
| PWC | https://paperswithcode.com/paper/real-time-and-embedded-deep-learning-on-fpga |
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Short-Term Prediction and Multi-Camera Fusion on Semantic Grids
| Title | Short-Term Prediction and Multi-Camera Fusion on Semantic Grids |
| Authors | Lukas Hoyer, Patrick Kesper, Anna Khoreva, Volker Fischer |
| Abstract | An environment representation (ER) is a substantial part of every autonomous system. It introduces a common interface between perception and other system components, such as decision making, and allows downstream algorithms to deal with abstracted data without knowledge of the used sensor. In this work, we propose and evaluate a novel architecture that generates an egocentric, grid-based, predictive, and semantically-interpretable ER. In particular, we provide a proof of concept for the spatio-temporal fusion of multiple camera sequences and short-term prediction in such an ER. Our design utilizes a strong semantic segmentation network together with depth and egomotion estimates to first extract semantic information from multiple camera streams and then transform these separately into egocentric temporally-aligned bird’s-eye view grids. A deep encoder-decoder network is trained to fuse a stack of these grids into a unified semantic grid representation and to predict the dynamics of its surrounding. We evaluate this representation on real-world sequences of the Cityscapes dataset and show that our architecture can make accurate predictions in complex sensor fusion scenarios and significantly outperforms a model-driven baseline in a category-based evaluation. |
| Tasks | Decision Making, Semantic Segmentation, Sensor Fusion |
| Published | 2019-03-21 |
| URL | https://arxiv.org/abs/1903.08960v2 |
| https://arxiv.org/pdf/1903.08960v2.pdf | |
| PWC | https://paperswithcode.com/paper/short-term-prediction-and-multi-camera-fusion |
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VASE: Variational Assorted Surprise Exploration for Reinforcement Learning
| Title | VASE: Variational Assorted Surprise Exploration for Reinforcement Learning |
| Authors | Haitao Xu, Brendan McCane, Lech Szymanski |
| Abstract | Exploration in environments with continuous control and sparse rewards remains a key challenge in reinforcement learning (RL). Recently, surprise has been used as an intrinsic reward that encourages systematic and efficient exploration. We introduce a new definition of surprise and its RL implementation named Variational Assorted Surprise Exploration (VASE). VASE uses a Bayesian neural network as a model of the environment dynamics and is trained using variational inference, alternately updating the accuracy of the agent’s model and policy. Our experiments show that in continuous control sparse reward environments VASE outperforms other surprise-based exploration techniques. |
| Tasks | Continuous Control, Efficient Exploration |
| Published | 2019-10-31 |
| URL | https://arxiv.org/abs/1910.14351v1 |
| https://arxiv.org/pdf/1910.14351v1.pdf | |
| PWC | https://paperswithcode.com/paper/vase-variational-assorted-surprise |
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Using Statistics to Automate Stochastic Optimization
| Title | Using Statistics to Automate Stochastic Optimization |
| Authors | Hunter Lang, Pengchuan Zhang, Lin Xiao |
| Abstract | Despite the development of numerous adaptive optimizers, tuning the learning rate of stochastic gradient methods remains a major roadblock to obtaining good practical performance in machine learning. Rather than changing the learning rate at each iteration, we propose an approach that automates the most common hand-tuning heuristic: use a constant learning rate until “progress stops,” then drop. We design an explicit statistical test that determines when the dynamics of stochastic gradient descent reach a stationary distribution. This test can be performed easily during training, and when it fires, we decrease the learning rate by a constant multiplicative factor. Our experiments on several deep learning tasks demonstrate that this statistical adaptive stochastic approximation (SASA) method can automatically find good learning rate schedules and match the performance of hand-tuned methods using default settings of its parameters. The statistical testing helps to control the variance of this procedure and improves its robustness. |
| Tasks | Stochastic Optimization |
| Published | 2019-09-21 |
| URL | https://arxiv.org/abs/1909.09785v1 |
| https://arxiv.org/pdf/1909.09785v1.pdf | |
| PWC | https://paperswithcode.com/paper/190909785 |
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Abstractive Dialog Summarization with Semantic Scaffolds
| Title | Abstractive Dialog Summarization with Semantic Scaffolds |
| Authors | Lin Yuan, Zhou Yu |
| Abstract | The demand for abstractive dialog summary is growing in real-world applications. For example, customer service center or hospitals would like to summarize customer service interaction and doctor-patient interaction. However, few researchers explored abstractive summarization on dialogs due to the lack of suitable datasets. We propose an abstractive dialog summarization dataset based on MultiWOZ. If we directly apply previous state-of-the-art document summarization methods on dialogs, there are two significant drawbacks: the informative entities such as restaurant names are difficult to preserve, and the contents from different dialog domains are sometimes mismatched. To address these two drawbacks, we propose Scaffold Pointer Network (SPNet)to utilize the existing annotation on speaker role, semantic slot and dialog domain. SPNet incorporates these semantic scaffolds for dialog summarization. Since ROUGE cannot capture the two drawbacks mentioned, we also propose a new evaluation metric that considers critical informative entities in the text. On MultiWOZ, our proposed SPNet outperforms state-of-the-art abstractive summarization methods on all the automatic and human evaluation metrics. |
| Tasks | Abstractive Text Summarization, Document Summarization |
| Published | 2019-10-02 |
| URL | https://arxiv.org/abs/1910.00825v1 |
| https://arxiv.org/pdf/1910.00825v1.pdf | |
| PWC | https://paperswithcode.com/paper/abstractive-dialog-summarization-with |
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Better Exploration with Optimistic Actor-Critic
| Title | Better Exploration with Optimistic Actor-Critic |
| Authors | Kamil Ciosek, Quan Vuong, Robert Loftin, Katja Hofmann |
| Abstract | Actor-critic methods, a type of model-free Reinforcement Learning, have been successfully applied to challenging tasks in continuous control, often achieving state-of-the art performance. However, wide-scale adoption of these methods in real-world domains is made difficult by their poor sample efficiency. We address this problem both theoretically and empirically. On the theoretical side, we identify two phenomena preventing efficient exploration in existing state-of-the-art algorithms such as Soft Actor Critic. First, combining a greedy actor update with a pessimistic estimate of the critic leads to the avoidance of actions that the agent does not know about, a phenomenon we call pessimistic underexploration. Second, current algorithms are directionally uninformed, sampling actions with equal probability in opposite directions from the current mean. This is wasteful, since we typically need actions taken along certain directions much more than others. To address both of these phenomena, we introduce a new algorithm, Optimistic Actor Critic, which approximates a lower and upper confidence bound on the state-action value function. This allows us to apply the principle of optimism in the face of uncertainty to perform directed exploration using the upper bound while still using the lower bound to avoid overestimation. We evaluate OAC in several challenging continuous control tasks, achieving state-of the art sample efficiency. |
| Tasks | Continuous Control, Efficient Exploration |
| Published | 2019-10-28 |
| URL | https://arxiv.org/abs/1910.12807v1 |
| https://arxiv.org/pdf/1910.12807v1.pdf | |
| PWC | https://paperswithcode.com/paper/better-exploration-with-optimistic-actor |
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Building chatbots from large scale domain-specific knowledge bases: challenges and opportunities
| Title | Building chatbots from large scale domain-specific knowledge bases: challenges and opportunities |
| Authors | Walid Shalaby, Adriano Arantes, Teresa GonzalezDiaz, Chetan Gupta |
| Abstract | Popular conversational agents frameworks such as Alexa Skills Kit (ASK) and Google Actions (gActions) offer unprecedented opportunities for facilitating the development and deployment of voice-enabled AI solutions in various verticals. Nevertheless, understanding user utterances with high accuracy remains a challenging task with these frameworks. Particularly, when building chatbots with large volume of domain-specific entities. In this paper, we describe the challenges and lessons learned from building a large scale virtual assistant for understanding and responding to equipment-related complaints. In the process, we describe an alternative scalable framework for: 1) extracting the knowledge about equipment components and their associated problem entities from short texts, and 2) learning to identify such entities in user utterances. We show through evaluation on a real dataset that the proposed framework, compared to off-the-shelf popular ones, scales better with large volume of entities being up to 30% more accurate, and is more effective in understanding user utterances with domain-specific entities. |
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| Published | 2019-12-31 |
| URL | https://arxiv.org/abs/2001.00100v1 |
| https://arxiv.org/pdf/2001.00100v1.pdf | |
| PWC | https://paperswithcode.com/paper/building-chatbots-from-large-scale-domain |
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Analyzing Sentence Fusion in Abstractive Summarization
| Title | Analyzing Sentence Fusion in Abstractive Summarization |
| Authors | Logan Lebanoff, John Muchovej, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, Fei Liu |
| Abstract | While recent work in abstractive summarization has resulted in higher scores in automatic metrics, there is little understanding on how these systems combine information taken from multiple document sentences. In this paper, we analyze the outputs of five state-of-the-art abstractive summarizers, focusing on summary sentences that are formed by sentence fusion. We ask assessors to judge the grammaticality, faithfulness, and method of fusion for summary sentences. Our analysis reveals that system sentences are mostly grammatical, but often fail to remain faithful to the original article. |
| Tasks | Abstractive Text Summarization |
| Published | 2019-10-01 |
| URL | https://arxiv.org/abs/1910.00203v1 |
| https://arxiv.org/pdf/1910.00203v1.pdf | |
| PWC | https://paperswithcode.com/paper/analyzing-sentence-fusion-in-abstractive |
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Volumetric Lung Nodule Segmentation using Adaptive ROI with Multi-View Residual Learning
| Title | Volumetric Lung Nodule Segmentation using Adaptive ROI with Multi-View Residual Learning |
| Authors | Muhammad Usman, Byoung-Dai Lee, Shi Sub Byon, Sung Hyun Kim, Byung-ilLee |
| Abstract | Accurate quantification of pulmonary nodules can greatly assist the early diagnosis of lung cancer, which can enhance patient survival possibilities. A number of nodule segmentation techniques have been proposed, however, all of the existing techniques rely on radiologist 3-D volume of interest (VOI) input or use the constant region of interest (ROI) and only investigate the presence of nodule voxels within the given VOI. Such approaches restrain the solutions to investigate the nodule presence outside the given VOI and also include the redundant structures into VOI, which may lead to inaccurate nodule segmentation. In this work, a novel semi-automated approach for 3-D segmentation of nodule in volumetric computerized tomography (CT) lung scans has been proposed. The proposed technique can be segregated into two stages, at the first stage, it takes a 2-D ROI containing the nodule as input and it performs patch-wise investigation along the axial axis with a novel adaptive ROI strategy. The adaptive ROI algorithm enables the solution to dynamically select the ROI for the surrounding slices to investigate the presence of nodule using deep residual U-Net architecture. The first stage provides the initial estimation of nodule which is further utilized to extract the VOI. At the second stage, the extracted VOI is further investigated along the coronal and sagittal axis with two different networks and finally, all the estimated masks are fed into the consensus module to produce the final volumetric segmentation of nodule. The proposed approach has been rigorously evaluated on the LIDC dataset, which is the largest publicly available dataset. The result suggests that the approach is significantly robust and accurate as compared to the previous state of the art techniques. |
| Tasks | Lung Nodule Segmentation |
| Published | 2019-12-31 |
| URL | https://arxiv.org/abs/1912.13335v2 |
| https://arxiv.org/pdf/1912.13335v2.pdf | |
| PWC | https://paperswithcode.com/paper/volumetric-lung-nodule-segmentation-using |
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Identification of Rhetorical Roles of Sentences in Indian Legal Judgments
| Title | Identification of Rhetorical Roles of Sentences in Indian Legal Judgments |
| Authors | Paheli Bhattacharya, Shounak Paul, Kripabandhu Ghosh, Saptarshi Ghosh, Adam Wyner |
| Abstract | Automatically understanding the rhetorical roles of sentences in a legal case judgement is an important problem to solve, since it can help in several downstream tasks like summarization of legal judgments, legal search, and so on. The task is challenging since legal case documents are usually not well-structured, and these rhetorical roles may be subjective (as evident from variation of opinions between legal experts). In this paper, we address this task for judgments from the Supreme Court of India. We label sentences in 50 documents using multiple human annotators, and perform an extensive analysis of the human-assigned labels. We also attempt automatic identification of the rhetorical roles of sentences. While prior approaches towards this task used Conditional Random Fields over manually handcrafted features, we explore the use of deep neural models which do not require hand-crafting of features. Experiments show that neural models perform much better in this task than baseline methods which use handcrafted features. |
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| Published | 2019-11-13 |
| URL | https://arxiv.org/abs/1911.05405v1 |
| https://arxiv.org/pdf/1911.05405v1.pdf | |
| PWC | https://paperswithcode.com/paper/identification-of-rhetorical-roles-of |
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On Universal Equivariant Set Networks
| Title | On Universal Equivariant Set Networks |
| Authors | Nimrod Segol, Yaron Lipman |
| Abstract | Using deep neural networks that are either invariant or equivariant to permutations in order to learn functions on unordered sets has become prevalent. The most popular, basic models are DeepSets [Zaheer et al. 2017] and PointNet [Qi et al. 2017]. While known to be universal for approximating invariant functions, DeepSets and PointNet are not known to be universal when approximating \emph{equivariant} set functions. On the other hand, several recent equivariant set architectures have been proven equivariant universal [Sannai et al. 2019], [Keriven et al. 2019], however these models either use layers that are not permutation equivariant (in the standard sense) and/or use higher order tensor variables which are less practical. There is, therefore, a gap in understanding the universality of popular equivariant set models versus theoretical ones. In this paper we close this gap by proving that: (i) PointNet is not equivariant universal; and (ii) adding a single linear transmission layer makes PointNet universal. We call this architecture PointNetST and argue it is the simplest permutation equivariant universal model known to date. Another consequence is that DeepSets is universal, and also PointNetSeg, a popular point cloud segmentation network (used eg, in [Qi et al. 2017]) is universal. The key theoretical tool used to prove the above results is an explicit characterization of all permutation equivariant polynomial layers. Lastly, we provide numerical experiments validating the theoretical results and comparing different permutation equivariant models. |
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| Published | 2019-10-06 |
| URL | https://arxiv.org/abs/1910.02421v2 |
| https://arxiv.org/pdf/1910.02421v2.pdf | |
| PWC | https://paperswithcode.com/paper/on-universal-equivariant-set-networks-1 |
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Understanding and Improving Proximity Graph based Maximum Inner Product Search
| Title | Understanding and Improving Proximity Graph based Maximum Inner Product Search |
| Authors | Jie Liu, Xiao Yan, Xinyan Dai, Zhirong Li, James Cheng, Ming-Chang Yang |
| Abstract | The inner-product navigable small world graph (ip-NSW) represents the state-of-the-art method for approximate maximum inner product search (MIPS) and it can achieve an order of magnitude speedup over the fastest baseline. However, to date it is still unclear where its exceptional performance comes from. In this paper, we show that there is a strong norm bias in the MIPS problem, which means that the large norm items are very likely to become the result of MIPS. Then we explain the good performance of ip-NSW as matching the norm bias of the MIPS problem - large norm items have big in-degrees in the ip-NSW proximity graph and a walk on the graph spends the majority of computation on these items, thus effectively avoids unnecessary computation on small norm items. Furthermore, we propose the ip-NSW+ algorithm, which improves ip-NSW by introducing an additional angular proximity graph. Search is first conducted on the angular graph to find the angular neighbors of a query and then the MIPS neighbors of these angular neighbors are used to initialize the candidate pool for search on the inner-product proximity graph. Experiment results show that ip-NSW+ consistently and significantly outperforms ip-NSW and provides more robust performance under different data distributions. |
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| Published | 2019-09-30 |
| URL | https://arxiv.org/abs/1909.13459v2 |
| https://arxiv.org/pdf/1909.13459v2.pdf | |
| PWC | https://paperswithcode.com/paper/understanding-and-improving-proximity-graph |
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