January 25, 2020

3207 words 16 mins read

Paper Group ANR 1680

On the Efficiency of the Neuro-Fuzzy Classifier for User Knowledge Modeling Systems. Ancient Painting to Natural Image: A New Solution for Painting Processing. Sparse and Imperceivable Adversarial Attacks. Graph-Revised Convolutional Network. Improved Generalization of Heading Direction Estimation for Aerial Filming Using Semi-supervised Regression …

On the Efficiency of the Neuro-Fuzzy Classifier for User Knowledge Modeling Systems


Title	On the Efficiency of the Neuro-Fuzzy Classifier for User Knowledge Modeling Systems
Authors	Ehsan Jeihaninejad, Azam Rabiee
Abstract	User knowledge modeling systems are used as the most effective technology for grabbing new user’s attention. Moreover, the quality of service (QOS) is increased by these intelligent services. This paper proposes two user knowledge classifiers based on artificial neural networks used as one of the influential parts of knowledge modeling systems. We employed multi-layer perceptron (MLP) and adaptive neural fuzzy inference system (ANFIS) as the classifiers. Moreover, we used real data contains the user’s degree of study time, repetition number, their performance in exam, as well as the learning percentage, as our classifier’s inputs. Compared with well-known methods like KNN and Bayesian classifiers used in other research with the same data sets, our experiments present better performance. Although, the number of samples in the train set is not large enough, the performance of the neuro-fuzzy classifier in the test set is 98.6% which is the best result in comparison with others. However, the comparison of MLP toward the ANFIS results presents performance reduction, although the MLP performance is more efficient than other methods like Bayesian and KNN. As our goal is evaluating and reporting the efficiency of a neuro-fuzzy classifier for user knowledge modeling systems, we utilized many different evaluation metrics such as Receiver Operating Characteristic and the Area Under its Curve, Total Accuracy, and Kappa statistics.
Tasks
Published	2019-10-26
URL	https://arxiv.org/abs/1910.12025v1
PDF	https://arxiv.org/pdf/1910.12025v1.pdf
PWC	https://paperswithcode.com/paper/on-the-efficiency-of-the-neuro-fuzzy
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Ancient Painting to Natural Image: A New Solution for Painting Processing


Title	Ancient Painting to Natural Image: A New Solution for Painting Processing
Authors	Tingting Qiao, Weijing Zhang, Miao Zhang, Zixuan Ma, Duanqing Xu
Abstract	Collecting a large-scale and well-annotated dataset for image processing has become a common practice in computer vision. However, in the ancient painting area, this task is not practical as the number of paintings is limited and their style is greatly diverse. We, therefore, propose a novel solution for the problems that come with ancient painting processing. This is to use domain transfer to convert ancient paintings to photo-realistic natural images. By doing so, the ancient painting processing problems become natural image processing problems and models trained on natural images can be directly applied to the transferred paintings. Specifically, we focus on Chinese ancient flower, bird and landscape paintings in this work. A novel Domain Style Transfer Network (DSTN) is proposed to transfer ancient paintings to natural images which employ a compound loss to ensure that the transferred paintings still maintain the color composition and content of the input paintings. The experiment results show that the transferred paintings generated by the DSTN have a better performance in both the human perceptual test and other image processing tasks than other state-of-art methods, indicating the authenticity of the transferred paintings and the superiority of the proposed method.
Tasks	Style Transfer
Published	2019-01-02
URL	http://arxiv.org/abs/1901.00224v2
PDF	http://arxiv.org/pdf/1901.00224v2.pdf
PWC	https://paperswithcode.com/paper/ancient-painting-to-natural-image-a-new
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Sparse and Imperceivable Adversarial Attacks


Title	Sparse and Imperceivable Adversarial Attacks
Authors	Francesco Croce, Matthias Hein
Abstract	Neural networks have been proven to be vulnerable to a variety of adversarial attacks. From a safety perspective, highly sparse adversarial attacks are particularly dangerous. On the other hand the pixelwise perturbations of sparse attacks are typically large and thus can be potentially detected. We propose a new black-box technique to craft adversarial examples aiming at minimizing $l_0$-distance to the original image. Extensive experiments show that our attack is better or competitive to the state of the art. Moreover, we can integrate additional bounds on the componentwise perturbation. Allowing pixels to change only in region of high variation and avoiding changes along axis-aligned edges makes our adversarial examples almost non-perceivable. Moreover, we adapt the Projected Gradient Descent attack to the $l_0$-norm integrating componentwise constraints. This allows us to do adversarial training to enhance the robustness of classifiers against sparse and imperceivable adversarial manipulations.
Tasks
Published	2019-09-11
URL	https://arxiv.org/abs/1909.05040v1
PDF	https://arxiv.org/pdf/1909.05040v1.pdf
PWC	https://paperswithcode.com/paper/sparse-and-imperceivable-adversarial-attacks
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Graph-Revised Convolutional Network


Title	Graph-Revised Convolutional Network
Authors	Donghan Yu, Ruohong Zhang, Zhengbao Jiang, Yuexin Wu, Yiming Yang
Abstract	Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveraging both the content features of nodes and the linkage patterns across graphs in various applications. As real-world graphs are often incomplete and noisy, treating them as ground-truth information, which is a common practice in most GCNs, unavoidably leads to sub-optimal solutions. Existing efforts for addressing this problem either involve an over-parameterized model which is difficult to scale, or simply re-weight observed edges without dealing with the missing-edge issue. This paper proposes a novel framework called Graph-Revised Convolutional Network (GRCN), which avoids both extremes. Specifically, a GCN-based graph revision module is introduced for predicting missing edges and revising edge weights w.r.t. downstream tasks via joint optimization. A theoretical analysis reveals the connection between GRCN and previous work on multigraph belief propagation. Experiments on six benchmark datasets show that GRCN consistently outperforms strong baseline methods by a large margin, especially when the original graphs are severely incomplete or the labeled instances for model training are highly sparse.
Tasks
Published	2019-11-17
URL	https://arxiv.org/abs/1911.07123v2
PDF	https://arxiv.org/pdf/1911.07123v2.pdf
PWC	https://paperswithcode.com/paper/graph-revised-convolutional-network
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Improved Generalization of Heading Direction Estimation for Aerial Filming Using Semi-supervised Regression


Title	Improved Generalization of Heading Direction Estimation for Aerial Filming Using Semi-supervised Regression
Authors	Wenshan Wang, Aayush Ahuja, Yanfu Zhang, Rogerio Bonatti, Sebastian Scherer
Abstract	In the task of Autonomous aerial filming of a moving actor (e.g. a person or a vehicle), it is crucial to have a good heading direction estimation for the actor from the visual input. However, the models obtained in other similar tasks, such as pedestrian collision risk analysis and human-robot interaction, are very difficult to generalize to the aerial filming task, because of the difference in data distributions. Towards improving generalization with less amount of labeled data, this paper presents a semi-supervised algorithm for heading direction estimation problem. We utilize temporal continuity as the unsupervised signal to regularize the model and achieve better generalization ability. This semi-supervised algorithm is applied to both training and testing phases, which increases the testing performance by a large margin. We show that by leveraging unlabeled sequences, the amount of labeled data required can be significantly reduced. We also discuss several important details on improving the performance by balancing labeled and unlabeled loss, and making good combinations. Experimental results show that our approach robustly outputs the heading direction for different types of actor. The aesthetic value of the video is also improved in the aerial filming task.
Tasks
Published	2019-03-26
URL	http://arxiv.org/abs/1903.11174v1
PDF	http://arxiv.org/pdf/1903.11174v1.pdf
PWC	https://paperswithcode.com/paper/improved-generalization-of-heading-direction
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Target-Based Temporal Difference Learning


Title	Target-Based Temporal Difference Learning
Authors	Donghwan Lee, Niao He
Abstract	The use of target networks has been a popular and key component of recent deep Q-learning algorithms for reinforcement learning, yet little is known from the theory side. In this work, we introduce a new family of target-based temporal difference (TD) learning algorithms and provide theoretical analysis on their convergences. In contrast to the standard TD-learning, target-based TD algorithms maintain two separate learning parameters-the target variable and online variable. Particularly, we introduce three members in the family, called the averaging TD, double TD, and periodic TD, where the target variable is updated through an averaging, symmetric, or periodic fashion, mirroring those techniques used in deep Q-learning practice. We establish asymptotic convergence analyses for both averaging TD and double TD and a finite sample analysis for periodic TD. In addition, we also provide some simulation results showing potentially superior convergence of these target-based TD algorithms compared to the standard TD-learning. While this work focuses on linear function approximation and policy evaluation setting, we consider this as a meaningful step towards the theoretical understanding of deep Q-learning variants with target networks.
Tasks	Q-Learning
Published	2019-04-24
URL	https://arxiv.org/abs/1904.10945v3
PDF	https://arxiv.org/pdf/1904.10945v3.pdf
PWC	https://paperswithcode.com/paper/target-based-temporal-difference-learning
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Dynamically Expanded CNN Array for Video Coding


Title	Dynamically Expanded CNN Array for Video Coding
Authors	Everett Fall, Kai-wei Chang, Liang-Gee Chen
Abstract	Video coding is a critical step in all popular methods of streaming video. Marked progress has been made in video quality, compression, and computational efficiency. Recently, there has been an interest in finding ways to apply techniques form the fast-progressing field of machine learning to further improve video coding. We present a method that uses convolutional neural networks to help refine the output of various standard coding methods. The novelty of our approach is to train multiple different sets of network parameters, with each set corresponding to a specific, short segment of video. The array of network parameter sets expands dynamically to match a video of any length. We show that our method can improve the quality and compression efficiency of standard video codecs.
Tasks
Published	2019-05-10
URL	https://arxiv.org/abs/1905.04326v1
PDF	https://arxiv.org/pdf/1905.04326v1.pdf
PWC	https://paperswithcode.com/paper/dynamically-expanded-cnn-array-for-video
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Defensive Few-shot Adversarial Learning


Title	Defensive Few-shot Adversarial Learning
Authors	Wenbin Li, Lei Wang, Xingxing Zhang, Jing Huo, Yang Gao, Jiebo Luo
Abstract	The robustness of deep learning models against adversarial attacks has received increasing attention in recent years. However, both deep learning and adversarial training rely on the availability of a large amount of labeled data and usually do not generalize well to new, unseen classes when only a few training samples are accessible. To address this problem, we explicitly introduce a new challenging problem – how to learn a robust deep model with limited training samples per class, called defensive few-shot learning in this paper. Simply employing the existing adversarial training techniques in the literature cannot solve this problem. This is because few-shot learning needs to learn transferable knowledge from disjoint auxiliary data, and thus it is invalid to assume the sample-level distribution consistency between the training and test sets as commonly assumed in existing adversarial training techniques. In this paper, instead of assuming such a distribution consistency, we propose to make this assumption at a task-level in the episodic training paradigm in order to better transfer the defense knowledge. Furthermore, inside each task, we design a task-conditioned distribution constraint to narrow the distribution gap between clean and adversarial examples at a sample-level. These give rise to a novel mechanism called multi-level distribution based adversarial training (MDAT) for learning transferable adversarial defense. In addition, a unified $\mathcal{F}_{\beta}$ score is introduced to evaluate different defense methods under the same principle. Extensive experiments demonstrate that MDAT achieves higher effectiveness and robustness over existing alternatives in the few-shot case.
Tasks	Adversarial Defense, Few-Shot Learning
Published	2019-11-16
URL	https://arxiv.org/abs/1911.06968v1
PDF	https://arxiv.org/pdf/1911.06968v1.pdf
PWC	https://paperswithcode.com/paper/defensive-few-shot-adversarial-learning
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Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning


Title	Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning
Authors	Eivind Meyer, Haakon Robinson, Adil Rasheed, Omer San
Abstract	In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the dual-objective problem of controlling an underactuated autonomous surface vehicle to follow an a priori known path while avoiding collisions with non-moving obstacles along the way. The artificial intelligent agent, which is equipped with multiple rangefinder sensors for obstacle detection, is trained and evaluated in a challenging, stochastically generated simulation environment based on the OpenAI gym python toolkit. Notably, the agent is provided with real-time insight into its own reward function, allowing it to dynamically adapt its guidance strategy. Depending on its strategy, which ranges from radical path-adherence to radical obstacle avoidance, the trained agent achieves an episodic success rate between 84 and 100%.
Tasks	Continuous Control
Published	2019-12-18
URL	https://arxiv.org/abs/1912.08578v1
PDF	https://arxiv.org/pdf/1912.08578v1.pdf
PWC	https://paperswithcode.com/paper/taming-an-autonomous-surface-vehicle-for-path
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A Bayesian Hierarchical Model for Criminal Investigations


Title	A Bayesian Hierarchical Model for Criminal Investigations
Authors	F. O. Bunnin, J. Q. Smith
Abstract	Potential violent criminals will often need to go through a sequence of preparatory steps before they can execute their plans. During this escalation process police have the opportunity to evaluate the threat posed by such people through what they know, observe and learn from intelligence reports about their activities. In this paper we customise a three-level Bayesian hierarchical model to describe this process. This is able to propagate both routine and unexpected evidence in real time. We discuss how to set up such a model so that it calibrates to domain expert judgments. The model illustrations include a hypothetical example based on a potential vehicle based terrorist attack.
Tasks
Published	2019-07-03
URL	https://arxiv.org/abs/1907.01894v2
PDF	https://arxiv.org/pdf/1907.01894v2.pdf
PWC	https://paperswithcode.com/paper/a-bayesian-hierarchical-model-for-criminal
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The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning


Title	The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning
Authors	Suyun Liu, Luis Nunes Vicente
Abstract	Optimization of conflicting functions is of paramount importance in decision making, and real world applications frequently involve data that is uncertain or unknown, resulting in multi-objective optimization (MOO) problems of stochastic type. We study the stochastic multi-gradient (SMG) method, seen as an extension of the classical stochastic gradient method for single-objective optimization. At each iteration of the SMG method, a stochastic multi-gradient direction is calculated by solving a quadratic subproblem, and it is shown that this direction is biased even when all individual gradient estimators are unbiased. We establish rates to compute a point in the Pareto front, of order similar to what is known for stochastic gradient in both convex and strongly convex cases. The analysis handles the bias in the multi-gradient and the unknown a priori weights of the limiting Pareto point. The SMG method is framed into a Pareto-front type algorithm for the computation of the entire Pareto front. The Pareto-front SMG algorithm is capable of robustly determining Pareto fronts for a number of synthetic test problems. One can apply it to any stochastic MOO problem arising from supervised machine learning, and we report results for logistic binary classification where multiple objectives correspond to distinct-sources data groups.
Tasks	Decision Making
Published	2019-07-10
URL	https://arxiv.org/abs/1907.04472v2
PDF	https://arxiv.org/pdf/1907.04472v2.pdf
PWC	https://paperswithcode.com/paper/the-stochastic-multi-gradient-algorithm-for
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Sim-to-Real Transfer for Biped Locomotion


Title	Sim-to-Real Transfer for Biped Locomotion
Authors	Wenhao Yu, Visak CV Kumar, Greg Turk, C. Karen Liu
Abstract	We present a new approach for transfer of dynamic robot control policies such as biped locomotion from simulation to real hardware. Key to our approach is to perform system identification of the model parameters {\mu} of the hardware (e.g. friction, center-of-mass) in two distinct stages, before policy learning (pre-sysID) and after policy learning (post-sysID). Pre-sysID begins by collecting trajectories from the physical hardware based on a set of generic motion sequences. Because the trajectories may not be related to the task of interest, presysID does not attempt to accurately identify the true value of {\mu}, but only to approximate the range of {\mu} to guide the policy learning. Next, a Projected Universal Policy (PUP) is created by simultaneously training a network that projects {\mu} to a low-dimensional latent variable {\eta} and a family of policies that are conditioned on {\eta}. The second round of system identification (post-sysID) is then carried out by deploying the PUP on the robot hardware using task-relevant trajectories. We use Bayesian Optimization to determine the values for {\eta} that optimizes the performance of PUP on the real hardware. We have used this approach to create three successful biped locomotion controllers (walk forward, walk backwards, walk sideways) on the Darwin OP2 robot.
Tasks
Published	2019-03-04
URL	https://arxiv.org/abs/1903.01390v2
PDF	https://arxiv.org/pdf/1903.01390v2.pdf
PWC	https://paperswithcode.com/paper/sim-to-real-transfer-for-biped-locomotion
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Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning


Title	Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning
Authors	Pedro Hermosilla, Tobias Ritschel, Timo Ropinski
Abstract	We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only. This is achieved by extending recent ideas from learning of unsupervised image denoisers to unstructured 3D point clouds. Unsupervised image denoisers operate under the assumption that a noisy pixel observation is a random realization of a distribution around a clean pixel value, which allows appropriate learning on this distribution to eventually converge to the correct value. Regrettably, this assumption is not valid for unstructured points: 3D point clouds are subject to total noise, i. e., deviations in all coordinates, with no reliable pixel grid. Thus, an observation can be the realization of an entire manifold of clean 3D points, which makes a na"ive extension of unsupervised image denoisers to 3D point clouds impractical. Overcoming this, we introduce a spatial prior term, that steers converges to the unique closest out of the many possible modes on a manifold. Our results demonstrate unsupervised denoising performance similar to that of supervised learning with clean data when given enough training examples - whereby we do not need any pairs of noisy and clean training data.
Tasks	Denoising
Published	2019-04-16
URL	https://arxiv.org/abs/1904.07615v2
PDF	https://arxiv.org/pdf/1904.07615v2.pdf
PWC	https://paperswithcode.com/paper/total-denoising-unsupervised-learning-of-3d
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The Scientific Method in the Science of Machine Learning


Title	The Scientific Method in the Science of Machine Learning
Authors	Jessica Zosa Forde, Michela Paganini
Abstract	In the quest to align deep learning with the sciences to address calls for rigor, safety, and interpretability in machine learning systems, this contribution identifies key missing pieces: the stages of hypothesis formulation and testing, as well as statistical and systematic uncertainty estimation – core tenets of the scientific method. This position paper discusses the ways in which contemporary science is conducted in other domains and identifies potentially useful practices. We present a case study from physics and describe how this field has promoted rigor through specific methodological practices, and provide recommendations on how machine learning researchers can adopt these practices into the research ecosystem. We argue that both domain-driven experiments and application-agnostic questions of the inner workings of fundamental building blocks of machine learning models ought to be examined with the tools of the scientific method, to ensure we not only understand effect, but also begin to understand cause, which is the raison d’^{e}tre of science.
Tasks
Published	2019-04-24
URL	http://arxiv.org/abs/1904.10922v1
PDF	http://arxiv.org/pdf/1904.10922v1.pdf
PWC	https://paperswithcode.com/paper/the-scientific-method-in-the-science-of
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Are You Doing What I Think You Are Doing? Criticising Uncertain Agent Models


Title	Are You Doing What I Think You Are Doing? Criticising Uncertain Agent Models
Authors	Stefano V. Albrecht, S. Ramamoorthy
Abstract	The key for effective interaction in many multiagent applications is to reason explicitly about the behaviour of other agents, in the form of a hypothesised behaviour. While there exist several methods for the construction of a behavioural hypothesis, there is currently no universal theory which would allow an agent to contemplate the correctness of a hypothesis. In this work, we present a novel algorithm which decides this question in the form of a frequentist hypothesis test. The algorithm allows for multiple metrics in the construction of the test statistic and learns its distribution during the interaction process, with asymptotic correctness guarantees. We present results from a comprehensive set of experiments, demonstrating that the algorithm achieves high accuracy and scalability at low computational costs.
Tasks
Published	2019-07-02
URL	https://arxiv.org/abs/1907.01912v1
PDF	https://arxiv.org/pdf/1907.01912v1.pdf
PWC	https://paperswithcode.com/paper/are-you-doing-what-i-think-you-are-doing
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