Paper Group ANR 645
Building Information Modeling and Classification by Visual Learning At A City Scale. 3D convolutional neural network for abdominal aortic aneurysm segmentation. Provable Approximations for Constrained $\ell_p$ Regression. Voyageur: An Experiential Travel Search Engine. Powerset Convolutional Neural Networks. XNOR-Net++: Improved Binary Neural Netwo …
Building Information Modeling and Classification by Visual Learning At A City Scale
| Title | Building Information Modeling and Classification by Visual Learning At A City Scale |
| Authors | Qian Yu, Chaofeng Wang, Barbaros Cetiner, Stella X. Yu, Frank Mckenna, Ertugrul Taciroglu, Kincho H. Law |
| Abstract | In this paper, we provide two case studies to demonstrate how artificial intelligence can empower civil engineering. In the first case, a machine learning-assisted framework, BRAILS, is proposed for city-scale building information modeling. Building information modeling (BIM) is an efficient way of describing buildings, which is essential to architecture, engineering, and construction. Our proposed framework employs deep learning technique to extract visual information of buildings from satellite/street view images. Further, a novel machine learning (ML)-based statistical tool, SURF, is proposed to discover the spatial patterns in building metadata. The second case focuses on the task of soft-story building classification. Soft-story buildings are a type of buildings prone to collapse during a moderate or severe earthquake. Hence, identifying and retrofitting such buildings is vital in the current earthquake preparedness efforts. For this task, we propose an automated deep learning-based procedure for identifying soft-story buildings from street view images at a regional scale. We also create a large-scale building image database and a semi-automated image labeling approach that effectively annotates new database entries. Through extensive computational experiments, we demonstrate the effectiveness of the proposed method. |
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| Published | 2019-10-14 |
| URL | https://arxiv.org/abs/1910.06391v1 |
| https://arxiv.org/pdf/1910.06391v1.pdf | |
| PWC | https://paperswithcode.com/paper/building-information-modeling-and |
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3D convolutional neural network for abdominal aortic aneurysm segmentation
| Title | 3D convolutional neural network for abdominal aortic aneurysm segmentation |
| Authors | Karen López-Linares, Inmaculada García, Ainhoa García-Familiar, Iván Macía, Miguel A. González Ballester |
| Abstract | An abdominal aortic aneurysm (AAA) is a focal dilation of the aorta that, if not treated, tends to grow and may rupture. A significant unmet need in the assessment of AAA disease, for the diagnosis, prognosis and follow-up, is the determination of rupture risk, which is currently based on the manual measurement of the aneurysm diameter in a selected Computed Tomography Angiography (CTA) scan. However, there is a lack of standardization determining the degree and rate of disease progression, due to the lack of robust, automated aneurysm segmentation tools that allow quantitatively analyzing the AAA. In this work, we aim at proposing the first 3D convolutional neural network for the segmentation of aneurysms both from preoperative and postoperative CTA scans. We extensively validate its performance in terms of diameter measurements, to test its applicability in the clinical practice, as well as regarding the relative volume difference, and Dice and Jaccard scores. The proposed method yields a mean diameter measurement error of 3.3 mm, a relative volume difference of 8.58 %, and Dice and Jaccard scores of 87 % and 77 %, respectively. At a clinical level, an aneurysm enlargement of 10 mm is considered relevant, thus, our method is suitable to automatically determine the AAA diameter and opens up the opportunity for more complex aneurysm analysis. |
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| Published | 2019-03-03 |
| URL | http://arxiv.org/abs/1903.00879v1 |
| http://arxiv.org/pdf/1903.00879v1.pdf | |
| PWC | https://paperswithcode.com/paper/3d-convolutional-neural-network-for-abdominal |
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Provable Approximations for Constrained $\ell_p$ Regression
| Title | Provable Approximations for Constrained $\ell_p$ Regression |
| Authors | Ibrahim Jubran, David Cohn, Dan Feldman |
| Abstract | The $\ell_p$ linear regression problem is to minimize $f(x)=Ax-b_p$ over $x\in\mathbb{R}^d$, where $A\in\mathbb{R}^{n\times d}$, $b\in \mathbb{R}^n$, and $p>0$. To avoid overfitting and bound $x_2$, the constrained $\ell_p$ regression minimizes $f(x)$ over every unit vector $x\in\mathbb{R}^d$. This makes the problem non-convex even for the simplest case $d=p=2$. Instead, ridge regression is used to minimize the Lagrange form $f(x)+\lambda x_2$ over $x\in\mathbb{R}^d$, which yields a convex problem in the price of calibrating the regularization parameter $\lambda>0$. We provide the first provable constant factor approximation algorithm that solves the constrained $\ell_p$ regression directly, for every constant $p,d\geq 1$. Using core-sets, its running time is $O(n \log n)$ including extensions for streaming and distributed (big) data. In polynomial time, it can handle outliers, $p\in (0,1)$ and minimize $f(x)$ over every $x$ and permutation of rows in $A$. Experimental results are also provided, including open source and comparison to existing software. |
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| Published | 2019-02-27 |
| URL | http://arxiv.org/abs/1902.10407v1 |
| http://arxiv.org/pdf/1902.10407v1.pdf | |
| PWC | https://paperswithcode.com/paper/provable-approximations-for-constrained-ell_p |
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Voyageur: An Experiential Travel Search Engine
| Title | Voyageur: An Experiential Travel Search Engine |
| Authors | Sara Evensen, Aaron Feng, Alon Halevy, Jinfeng Li, Vivian Li, Yuliang Li, Huining Liu, George Mihaila, John Morales, Natalie Nuno, Ekaterina Pavlovic, Wang-Chiew Tan, Xiaolan Wang |
| Abstract | We describe Voyageur, which is an application of experiential search to the domain of travel. Unlike traditional search engines for online services, experiential search focuses on the experiential aspects of the service under consideration. In particular, Voyageur needs to handle queries for subjective aspects of the service (e.g., quiet hotel, friendly staff) and combine these with objective attributes, such as price and location. Voyageur also highlights interesting facts and tips about the services the user is considering to provide them with further insights into their choices. |
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| Published | 2019-03-04 |
| URL | http://arxiv.org/abs/1903.01498v1 |
| http://arxiv.org/pdf/1903.01498v1.pdf | |
| PWC | https://paperswithcode.com/paper/voyageur-an-experiential-travel-search-engine |
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Powerset Convolutional Neural Networks
| Title | Powerset Convolutional Neural Networks |
| Authors | Chris Wendler, Dan Alistarh, Markus Püschel |
| Abstract | We present a novel class of convolutional neural networks (CNNs) for set functions, i.e., data indexed with the powerset of a finite set. The convolutions are derived as linear, shift-equivariant functions for various notions of shifts on set functions. The framework is fundamentally different from graph convolutions based on the Laplacian, as it provides not one but several basic shifts, one for each element in the ground set. Prototypical experiments with several set function classification tasks on synthetic datasets and on datasets derived from real-world hypergraphs demonstrate the potential of our new powerset CNNs. |
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| Published | 2019-09-05 |
| URL | https://arxiv.org/abs/1909.02253v4 |
| https://arxiv.org/pdf/1909.02253v4.pdf | |
| PWC | https://paperswithcode.com/paper/powerset-convolutional-neural-networks |
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XNOR-Net++: Improved Binary Neural Networks
| Title | XNOR-Net++: Improved Binary Neural Networks |
| Authors | Adrian Bulat, Georgios Tzimiropoulos |
| Abstract | This paper proposes an improved training algorithm for binary neural networks in which both weights and activations are binary numbers. A key but fairly overlooked feature of the current state-of-the-art method of XNOR-Net is the use of analytically calculated real-valued scaling factors for re-weighting the output of binary convolutions. We argue that analytic calculation of these factors is sub-optimal. Instead, in this work, we make the following contributions: (a) we propose to fuse the activation and weight scaling factors into a single one that is learned discriminatively via backpropagation. (b) More importantly, we explore several ways of constructing the shape of the scale factors while keeping the computational budget fixed. (c) We empirically measure the accuracy of our approximations and show that they are significantly more accurate than the analytically calculated one. (d) We show that our approach significantly outperforms XNOR-Net within the same computational budget when tested on the challenging task of ImageNet classification, offering up to 6% accuracy gain. |
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| Published | 2019-09-30 |
| URL | https://arxiv.org/abs/1909.13863v1 |
| https://arxiv.org/pdf/1909.13863v1.pdf | |
| PWC | https://paperswithcode.com/paper/xnor-net-improved-binary-neural-networks |
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Imitation Learning as $f$-Divergence Minimization
| Title | Imitation Learning as $f$-Divergence Minimization |
| Authors | Liyiming Ke, Matt Barnes, Wen Sun, Gilwoo Lee, Sanjiban Choudhury, Siddhartha Srinivasa |
| Abstract | We address the problem of imitation learning with multi-modal demonstrations. Instead of attempting to learn all modes, we argue that in many tasks it is sufficient to imitate any one of them. We show that the state-of-the-art methods such as GAIL and behavior cloning, due to their choice of loss function, often incorrectly interpolate between such modes. Our key insight is to minimize the right divergence between the learner and the expert state-action distributions, namely the reverse KL divergence or I-projection. We propose a general imitation learning framework for estimating and minimizing any f-Divergence. By plugging in different divergences, we are able to recover existing algorithms such as Behavior Cloning (Kullback-Leibler), GAIL (Jensen Shannon) and Dagger (Total Variation). Empirical results show that our approximate I-projection technique is able to imitate multi-modal behaviors more reliably than GAIL and behavior cloning. |
| Tasks | Imitation Learning |
| Published | 2019-05-30 |
| URL | https://arxiv.org/abs/1905.12888v1 |
| https://arxiv.org/pdf/1905.12888v1.pdf | |
| PWC | https://paperswithcode.com/paper/imitation-learning-as-f-divergence |
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Dynamic Epistemic Logic with ASP Updates: Application to Conditional Planning
| Title | Dynamic Epistemic Logic with ASP Updates: Application to Conditional Planning |
| Authors | Pedro Cabalar, Jorge Fandinno, Luis Fariñas del Cerro |
| Abstract | Dynamic Epistemic Logic (DEL) is a family of multimodal logics that has proved to be very successful for epistemic reasoning in planning tasks. In this logic, the agent’s knowledge is captured by modal epistemic operators whereas the system evolution is described in terms of (some subset of) dynamic logic modalities in which actions are usually represented as semantic objects called event models. In this paper, we study a variant of DEL, that wecall DEL[ASP], where actions are syntactically described by using an Answer Set Programming (ASP) representation instead of event models. This representation directly inherits high level expressive features like indirect effects, qualifications, state constraints, defaults, or recursive fluents that are common in ASP descriptions of action domains. Besides, we illustrate how this approach can be applied for obtaining conditional plans in single-agent, partially observable domains where knowledge acquisition may be represented as indirect effects of actions. |
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| Published | 2019-05-25 |
| URL | https://arxiv.org/abs/1905.10621v1 |
| https://arxiv.org/pdf/1905.10621v1.pdf | |
| PWC | https://paperswithcode.com/paper/dynamic-epistemic-logic-with-asp-updates |
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Autonomous Vehicle Control: End-to-end Learning in Simulated Urban Environments
| Title | Autonomous Vehicle Control: End-to-end Learning in Simulated Urban Environments |
| Authors | Hege Haavaldsen, Max Aasboe, Frank Lindseth |
| Abstract | In recent years, considerable progress has been made towards a vehicle’s ability to operate autonomously. An end-to-end approach attempts to achieve autonomous driving using a single, comprehensive software component. Recent breakthroughs in deep learning have significantly increased end-to-end systems’ capabilities, and such systems are now considered a possible alternative to the current state-of-the-art solutions. This paper examines end-to-end learning for autonomous vehicles in simulated urban environments containing other vehicles, traffic lights, and speed limits. Furthermore, the paper explores end-to-end systems’ ability to execute navigational commands and examines whether improved performance can be achieved by utilizing temporal dependencies between subsequent visual cues. Two end-to-end architectures are proposed: a traditional Convolutional Neural Network and an extended design combining a Convolutional Neural Network with a recurrent layer. The models are trained using expert driving data from a simulated urban setting, and are evaluated by their driving performance in an unseen simulated environment. The results of this paper indicate that end-to-end systems can operate autonomously in simple urban environments. Moreover, it is found that the exploitation of temporal information in subsequent images enhances a system’s ability to judge movement and distance. |
| Tasks | Autonomous Driving, Autonomous Vehicles |
| Published | 2019-05-16 |
| URL | https://arxiv.org/abs/1905.06712v1 |
| https://arxiv.org/pdf/1905.06712v1.pdf | |
| PWC | https://paperswithcode.com/paper/autonomous-vehicle-control-end-to-end |
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PerfVis: Pervasive Visualization in Immersive AugmentedReality for Performance Awareness
| Title | PerfVis: Pervasive Visualization in Immersive AugmentedReality for Performance Awareness |
| Authors | Leonel Merino, Mario Hess, Alexandre Bergel, Oscar Nierstrasz, Daniel Weiskopf |
| Abstract | Developers are usually unaware of the impact of code changes to the performance of software systems. Although developers can analyze the performance of a system by executing, for instance, a performance test to compare the performance of two consecutive versions of the system, changing from a programming task to a testing task would disrupt the development flow. In this paper, we propose the use of a city visualization that dynamically provides developers with a pervasive view of the continuous performance of a system. We use an immersive augmented reality device (Microsoft HoloLens) to display our visualization and extend the integrated development environment on a computer screen to use the physical space. We report on technical details of the design and implementation of our visualization tool, and discuss early feedback that we collected of its usability. Our investigation explores a new visual metaphor to support the exploration and analysis of possibly very large and multidimensional performance data. Our initial result indicates that the city metaphor can be adequate to analyze dynamic performance data on a large and non-trivial software system. |
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| Published | 2019-04-05 |
| URL | http://arxiv.org/abs/1904.06399v1 |
| http://arxiv.org/pdf/1904.06399v1.pdf | |
| PWC | https://paperswithcode.com/paper/190406399 |
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Learning First-Order Symbolic Representations for Planning from the Structure of the State Space
| Title | Learning First-Order Symbolic Representations for Planning from the Structure of the State Space |
| Authors | Blai Bonet, Hector Geffner |
| Abstract | One of the main obstacles for developing flexible AI systems is the split between data-based learners and model-based solvers. Solvers such as classical planners are very flexible and can deal with a variety of problem instances and goals but require first-order symbolic models. Data-based learners, on the other hand, are robust but do not produce such representations. In this work we address this split by showing how the first-order symbolic representations that are used by planners can be learned from non-symbolic inputs that encode the structure of the state space. The representation learning problem is formulated as the problem of inferring planning instances over a common but unknown first-order domain that account for the structure of the observed state space. This means to infer a complete first-order representation (i.e. general action schemas, relational symbols, and objects) that explains the observed state space structures. The inference problem is cast as a two-level combinatorial search where the outer level searches for values of a small set of hyperparameters and the inner level, solved via SAT, searches for a first-order symbolic model. The framework is shown to produce general and correct first-order representations for standard problems like Gripper, Blocksworld, and Hanoi from input graphs that encode the flat state-space structure of a single instance. |
| Tasks | Representation Learning |
| Published | 2019-09-12 |
| URL | https://arxiv.org/abs/1909.05546v3 |
| https://arxiv.org/pdf/1909.05546v3.pdf | |
| PWC | https://paperswithcode.com/paper/learning-first-order-symbolic-planning |
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On the Behaviour of Differential Evolution for Problems with Dynamic Linear Constraints
| Title | On the Behaviour of Differential Evolution for Problems with Dynamic Linear Constraints |
| Authors | Maryam Hasani-Shoreh, María-Yaneli Ameca-Alducin, Wilson Blaikie, Frank Neumann, Marc Schoenauer |
| Abstract | Evolutionary algorithms have been widely applied for solving dynamic constrained optimization problems (DCOPs) as a common area of research in evolutionary optimization. Current benchmarks proposed for testing these problems in the continuous spaces are either not scalable in problem dimension or the settings for the environmental changes are not flexible. Moreover, they mainly focus on non-linear environmental changes on the objective function. While the dynamism in some real-world problems exists in the constraints and can be emulated with linear constraint changes. The purpose of this paper is to introduce a framework which produces benchmarks in which a dynamic environment is created with simple changes in linear constraints (rotation and translation of constraint’s hyperplane). Our proposed framework creates dynamic benchmarks that are flexible in terms of number of changes, dimension of the problem and can be applied to test any objective function. Different constraint handling techniques will then be used to compare with our benchmark. The results reveal that with these changes set, there was an observable effect on the performance of the constraint handling techniques. |
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| Published | 2019-02-27 |
| URL | https://arxiv.org/abs/1905.04099v3 |
| https://arxiv.org/pdf/1905.04099v3.pdf | |
| PWC | https://paperswithcode.com/paper/190504099 |
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Some Research Problems in Biometrics: The Future Beckons
| Title | Some Research Problems in Biometrics: The Future Beckons |
| Authors | Arun Ross, Sudipta Banerjee, Cunjian Chen, Anurag Chowdhury, Vahid Mirjalili, Renu Sharma, Thomas Swearingen, Shivangi Yadav |
| Abstract | The need for reliably determining the identity of a person is critical in a number of different domains ranging from personal smartphones to border security; from autonomous vehicles to e-voting; from tracking child vaccinations to preventing human trafficking; from crime scene investigation to personalization of customer service. Biometrics, which entails the use of biological attributes such as face, fingerprints and voice for recognizing a person, is being increasingly used in several such applications. While biometric technology has made rapid strides over the past decade, there are several fundamental issues that are yet to be satisfactorily resolved. In this article, we will discuss some of these issues and enumerate some of the exciting challenges in this field. |
| Tasks | Autonomous Vehicles |
| Published | 2019-05-12 |
| URL | https://arxiv.org/abs/1905.04717v1 |
| https://arxiv.org/pdf/1905.04717v1.pdf | |
| PWC | https://paperswithcode.com/paper/some-research-problems-in-biometrics-the |
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Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
| Title | Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information |
| Authors | Eric Larsen, Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien, Andrea Lodi |
| Abstract | This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a methodology to quickly predict tactical solutions to a given operational problem. In this context, the tactical solution is less detailed than the operational one but it has to be computed in very short time and under imperfect information. The problem is of importance in various applications where tactical and operational planning problems are interrelated and information about the operational problem is revealed over time. This is for instance the case in certain capacity planning and demand management systems. We formulate the problem as a two-stage optimal prediction stochastic program whose solution we predict with a supervised machine learning algorithm. The training data set consists of a large number of deterministic (second stage) problems generated by controlled probabilistic sampling. The labels are computed based on solutions to the deterministic problems (solved independently and offline) employing appropriate aggregation and subselection methods to address uncertainty. Results on our motivating application in load planning for rail transportation show that deep learning algorithms produce highly accurate predictions in very short computing time (milliseconds or less). The prediction accuracy is comparable to solutions computed by sample average approximation of the stochastic program. |
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| Published | 2019-01-22 |
| URL | https://arxiv.org/abs/1901.07935v3 |
| https://arxiv.org/pdf/1901.07935v3.pdf | |
| PWC | https://paperswithcode.com/paper/predicting-tactical-solutions-to-operational |
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CORE: Automating Review Recommendation for Code Changes
| Title | CORE: Automating Review Recommendation for Code Changes |
| Authors | JingKai Siow, Cuiyun Gao, Lingling Fan, Sen Chen, Yang Liu |
| Abstract | Code review is a common process that is used by developers, in which a reviewer provides useful comments or points out defects in the submitted source code changes via pull request. Code review has been widely used for both industry and open-source projects due to its capacity in early defect identification, project maintenance, and code improvement. With rapid updates on project developments, code review becomes a non-trivial and labor-intensive task for reviewers. Thus, an automated code review engine can be beneficial and useful for project development in practice. Although there exist prior studies on automating the code review process by adopting static analysis tools or deep learning techniques, they often require external sources such as partial or full source code for accurate review suggestion. In this paper, we aim at automating the code review process only based on code changes and the corresponding reviews but with better performance. The hinge of accurate code review suggestion is to learn good representations for both code changes and reviews. To achieve this with limited source, we design a multi-level embedding (i.e., word embedding and character embedding) approach to represent the semantics provided by code changes and reviews. The embeddings are then well trained through a proposed attentional deep learning model, as a whole named CORE. We evaluate the effectiveness of CORE on code changes and reviews collected from 19 popular Java projects hosted on Github. Experimental results show that our model CORE can achieve significantly better performance than the state-of-the-art model (DeepMem), with an increase of 131.03% in terms of Recall@10 and 150.69% in terms of Mean Reciprocal Rank. Qualitative general word analysis among project developers also demonstrates the performance of CORE in automating code review. |
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| Published | 2019-12-20 |
| URL | https://arxiv.org/abs/1912.09652v1 |
| https://arxiv.org/pdf/1912.09652v1.pdf | |
| PWC | https://paperswithcode.com/paper/core-automating-review-recommendation-for |
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