January 31, 2020

3129 words 15 mins read

Paper Group ANR 103

MOEA/D with Uniformly Randomly Adaptive Weights. Assessing the Sharpness of Satellite Images: Study of the PlanetScope Constellation. Embedding-based system for the Text part of CALL v3 shared task. Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization. Discovering Opioid Use Patterns from Social Media for Relapse Preven …

MOEA/D with Uniformly Randomly Adaptive Weights


Title	MOEA/D with Uniformly Randomly Adaptive Weights
Authors	Lucas R. C. de Farias, Pedro H. M. Braga, Hansenclever F. Bassani, Aluizio F. R. Araújo
Abstract	When working with decomposition-based algorithms, an appropriate set of weights might improve quality of the final solution. A set of uniformly distributed weights usually leads to well-distributed solutions on a Pareto front. However, there are two main difficulties with this approach. Firstly, it may fail depending on the problem geometry. Secondly, the population size becomes not flexible as the number of objectives increases. In this paper, we propose the MOEA/D with Uniformly Randomly Adaptive Weights (MOEA/DURAW) which uses the Uniformly Randomly method as an approach to subproblems generation, allowing a flexible population size even when working with many objective problems. During the evolutionary process, MOEA/D-URAW adds and removes subproblems as a function of the sparsity level of the population. Moreover, instead of requiring assumptions about the Pareto front shape, our method adapts its weights to the shape of the problem during the evolutionary process. Experimental results using WFG41-48 problem classes, with different Pareto front shapes, shows that the present method presents better or equal results in 77.5% of the problems evaluated from 2 to 6 objectives when compared with state-of-the-art methods in the literature.
Tasks
Published	2019-08-15
URL	https://arxiv.org/abs/1908.05383v1
PDF	https://arxiv.org/pdf/1908.05383v1.pdf
PWC	https://paperswithcode.com/paper/moead-with-uniformly-randomly-adaptive
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Assessing the Sharpness of Satellite Images: Study of the PlanetScope Constellation


Title	Assessing the Sharpness of Satellite Images: Study of the PlanetScope Constellation
Authors	Jérémy Anger, Carlo de Franchis, Gabriele Facciolo
Abstract	New micro-satellite constellations enable unprecedented systematic monitoring applications thanks to their wide coverage and short revisit capabilities. However, the large volumes of images that they produce have uneven qualities, creating the need for automatic quality assessment methods. In this work, we quantify the sharpness of images from the PlanetScope constellation by estimating the blur kernel from each image. Once the kernel has been estimated, it is possible to compute an absolute measure of sharpness which allows to discard low quality images and deconvolve blurry images before any further processing. The method is fully blind and automatic, and since it does not require the knowledge of any satellite specifications it can be ported to other constellations.
Tasks
Published	2019-04-19
URL	http://arxiv.org/abs/1904.09159v1
PDF	http://arxiv.org/pdf/1904.09159v1.pdf
PWC	https://paperswithcode.com/paper/assessing-the-sharpness-of-satellite-images
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Embedding-based system for the Text part of CALL v3 shared task


Title	Embedding-based system for the Text part of CALL v3 shared task
Authors	Volodymyr Sokhatskyi, Olga Zvyeryeva, Ievgen Karaulov, Dmytro Tkanov
Abstract	This paper presents a scoring system that has shown the top result on the text subset of CALL v3 shared task. The presented system is based on text embeddings, namely NNLM~\cite{nnlm} and BERT~\cite{Bert}. The distinguishing feature of the given approach is that it does not rely on the reference grammar file for scoring. The model is compared against approaches that use the grammar file and proves the possibility to achieve similar and even higher results without a predefined set of correct answers. The paper describes the model itself and the data preparation process that played a crucial role in the model training.
Tasks
Published	2019-08-07
URL	https://arxiv.org/abs/1908.02505v1
PDF	https://arxiv.org/pdf/1908.02505v1.pdf
PWC	https://paperswithcode.com/paper/embedding-based-system-for-the-text-part-of
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Framework

Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization


Title	Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization
Authors	Ruyi Ji, Longyin Wen, Libo Zhang, Dawei Du, Yanjun Wu, Chen Zhao, Xianglong Liu, Feiyue Huang
Abstract	Fine-grained visual categorization (FGVC) is an important but challenging task due to high intra-class variances and low inter-class variances caused by deformation, occlusion, illumination, etc. An attention convolutional binary neural tree architecture is presented to address those problems for weakly supervised FGVC. Specifically, we incorporate convolutional operations along edges of the tree structure, and use the routing functions in each node to determine the root-to-leaf computational paths within the tree. The final decision is computed as the summation of the predictions from leaf nodes. The deep convolutional operations learn to capture the representations of objects, and the tree structure characterizes the coarse-to-fine hierarchical feature learning process. In addition, we use the attention transformer module to enforce the network to capture discriminative features. The negative log-likelihood loss is used to train the entire network in an end-to-end fashion by SGD with back-propagation. Several experiments on the CUB-200-2011, Stanford Cars and Aircraft datasets demonstrate that the proposed method performs favorably against the state-of-the-arts.
Tasks	Fine-Grained Visual Categorization
Published	2019-09-25
URL	https://arxiv.org/abs/1909.11378v2
PDF	https://arxiv.org/pdf/1909.11378v2.pdf
PWC	https://paperswithcode.com/paper/attention-convolutional-binary-neural-tree
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Framework


Title	Discovering Opioid Use Patterns from Social Media for Relapse Prevention
Authors	Zhou Yang, Spencer Bradshaw, Rattikorn Hewett, Fang Jin
Abstract	The United States is currently experiencing an unprecedented opioid crisis, and opioid overdose has become a leading cause of injury and death. Effective opioid addiction recovery calls for not only medical treatments, but also behavioral interventions for impacted individuals. In this paper, we study communication and behavior patterns of patients with opioid use disorder (OUD) from social media, intending to demonstrate how existing information from common activities, such as online social networking, might lead to better prediction, evaluation, and ultimately prevention of relapses. Through a multi-disciplinary and advanced novel analytic perspective, we characterize opioid addiction behavior patterns by analyzing opioid groups from Reddit.com - including modeling online discussion topics, analyzing text co-occurrence and correlations, and identifying emotional states of people with OUD. These quantitative analyses are of practical importance and demonstrate innovative ways to use information from online social media, to create technology that can assist in relapse prevention.
Tasks
Published	2019-12-02
URL	https://arxiv.org/abs/1912.01122v1
PDF	https://arxiv.org/pdf/1912.01122v1.pdf
PWC	https://paperswithcode.com/paper/discovering-opioid-use-patterns-from-social
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A multiple testing framework for diagnostic accuracy studies with co-primary endpoints


Title	A multiple testing framework for diagnostic accuracy studies with co-primary endpoints
Authors	Max Westphal, Antonia Zapf, Werner Brannath
Abstract	Major advances have been made regarding the utilization of artificial intelligence in health care. In particular, deep learning approaches have been successfully applied for automated and assisted disease diagnosis and prognosis based on complex and high-dimensional data. However, despite all justified enthusiasm, overoptimistic assessments of predictive performance are still common. Automated medical testing devices based on machine-learned prediction models should thus undergo a throughout evaluation before being implemented into clinical practice. In this work, we propose a multiple testing framework for (comparative) phase III diagnostic accuracy studies with sensitivity and specificity as co-primary endpoints. Our approach challenges the frequent recommendation to strictly separate model selection and evaluation, i.e. to only assess a single diagnostic model in the evaluation study. We show that our parametric simultaneous test procedure asymptotically allows strong control of the family-wise error rate. Moreover, we demonstrate in extensive simulation studies that our multiple testing strategy on average leads to a better final diagnostic model and increased statistical power. To plan such studies, we propose a Bayesian approach to determine the optimal number of models to evaluate. For this purpose, our algorithm optimizes the expected final model performance given previous (hold-out) data from the model development phase. We conclude that an assessment of multiple promising diagnostic models in the same evaluation study has several advantages when suitable adjustments for multiple comparisons are implemented.
Tasks	Model Selection
Published	2019-11-08
URL	https://arxiv.org/abs/1911.02982v2
PDF	https://arxiv.org/pdf/1911.02982v2.pdf
PWC	https://paperswithcode.com/paper/a-multiple-testing-framework-for-diagnostic
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Realistic Image Generation using Region-phrase Attention


Title	Realistic Image Generation using Region-phrase Attention
Authors	Wanming Huang, Yida Xu, Ian Oppermann
Abstract	The Generative Adversarial Network (GAN) has recently been applied to generate synthetic images from text. Despite significant advances, most current state-of-the-art algorithms are regular-grid region based; when attention is used, it is mainly applied between individual regular-grid regions and a word. These approaches are sufficient to generate images that contain a single object in its foreground, such as a “bird” or “flower”. However, natural languages often involve complex foreground objects and the background may also constitute a variable portion of the generated image. Therefore, the regular-grid based image attention weights may not necessarily concentrate on the intended foreground region(s), which in turn, results in an unnatural looking image. Additionally, individual words such as “a”, “blue” and “shirt” do not necessarily provide a full visual context unless they are applied together. For this reason, in our paper, we proposed a novel method in which we introduced an additional set of attentions between true-grid regions and word phrases. The true-grid region is derived using a set of auxiliary bounding boxes. These auxiliary bounding boxes serve as superior location indicators to where the alignment and attention should be drawn with the word phrases. Word phrases are derived from analysing Part-of-Speech (POS) results. We perform experiments on this novel network architecture using the Microsoft Common Objects in Context (MSCOCO) dataset and the model generates $256 \times 256$ conditioned on a short sentence description. Our proposed approach is capable of generating more realistic images compared with the current state-of-the-art algorithms.
Tasks	Image Generation
Published	2019-02-04
URL	http://arxiv.org/abs/1902.05395v1
PDF	http://arxiv.org/pdf/1902.05395v1.pdf
PWC	https://paperswithcode.com/paper/realistic-image-generation-using-region
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Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity


Title	Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity
Authors	Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett
Abstract	We present an improved analysis of the Euler-Maruyama discretization of the Langevin diffusion. Our analysis does not require global contractivity, and yields polynomial dependence on the time horizon. Compared to existing approaches, we make an additional smoothness assumption, and improve the existing rate from $O(\eta)$ to $O(\eta^2)$ in terms of the KL divergence. This result matches the correct order for numerical SDEs, without suffering from exponential time dependence. When applied to algorithms for sampling and learning, this result simultaneously improves all those methods based on Dalayan’s approach.
Tasks
Published	2019-07-25
URL	https://arxiv.org/abs/1907.11331v2
PDF	https://arxiv.org/pdf/1907.11331v2.pdf
PWC	https://paperswithcode.com/paper/improved-bounds-for-discretization-of
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Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems


Title	Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems
Authors	Jiaxuan You, Yichen Wang, Aditya Pal, Pong Eksombatchai, Chuck Rosenberg, Jure Leskovec
Abstract	Recommender systems that can learn from cross-session data to dynamically predict the next item a user will choose are crucial for online platforms. However, existing approaches often use out-of-the-box sequence models which are limited by speed and memory consumption, are often infeasible for production environments, and usually do not incorporate cross-session information, which is crucial for effective recommendations. Here we propose Hierarchical Temporal Convolutional Networks (HierTCN), a hierarchical deep learning architecture that makes dynamic recommendations based on users’ sequential multi-session interactions with items. HierTCN is designed for web-scale systems with billions of items and hundreds of millions of users. It consists of two levels of models: The high-level model uses Recurrent Neural Networks (RNN) to aggregate users’ evolving long-term interests across different sessions, while the low-level model is implemented with Temporal Convolutional Networks (TCN), utilizing both the long-term interests and the short-term interactions within sessions to predict the next interaction. We conduct extensive experiments on a public XING dataset and a large-scale Pinterest dataset that contains 6 million users with 1.6 billion interactions. We show that HierTCN is 2.5x faster than RNN-based models and uses 90% less data memory compared to TCN-based models. We further develop an effective data caching scheme and a queue-based mini-batch generator, enabling our model to be trained within 24 hours on a single GPU. Our model consistently outperforms state-of-the-art dynamic recommendation methods, with up to 18% improvement in recall and 10% in mean reciprocal rank.
Tasks	Recommendation Systems, Session-Based Recommendations
Published	2019-04-08
URL	http://arxiv.org/abs/1904.04381v2
PDF	http://arxiv.org/pdf/1904.04381v2.pdf
PWC	https://paperswithcode.com/paper/hierarchical-temporal-convolutional-networks
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Bounds for the Number of Tests in Non-Adaptive Randomized Algorithms for Group Testing


Title	Bounds for the Number of Tests in Non-Adaptive Randomized Algorithms for Group Testing
Authors	Nader H. Bshouty, George Haddad, Catherine A. Haddad-Zaknoon
Abstract	We study the group testing problem with non-adaptive randomized algorithms. Several models have been discussed in the literature to determine how to randomly choose the tests. For a model ${\cal M}$, let $m_{\cal M}(n,d)$ be the minimum number of tests required to detect at most $d$ defectives within $n$ items, with success probability at least $1-\delta$, for some constant $\delta$. In this paper, we study the measures $$c_{\cal M}(d)=\lim_{n\to \infty} \frac{m_{\cal M}(n,d)}{\ln n} \mbox{ and } c_{\cal M}=\lim_{d\to \infty} \frac{c_{\cal M}(d)}{d}.$$ In the literature, the analyses of such models only give upper bounds for $c_{\cal M}(d)$ and $c_{\cal M}$, and for some of them, the bounds are not tight. We give new analyses that yield tight bounds for $c_{\cal M}(d)$ and $c_{\cal M}$ for all the known models~${\cal M}$.
Tasks
Published	2019-11-05
URL	https://arxiv.org/abs/1911.01694v1
PDF	https://arxiv.org/pdf/1911.01694v1.pdf
PWC	https://paperswithcode.com/paper/bounds-for-the-number-of-tests-in-non
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Framework

InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification


Title	InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification
Authors	Léonard Boussioux, Tomás Giro-Larraz, Charles Guille-Escuret, Mehdi Cherti, Balázs Kégl
Abstract	Insects play such a crucial role in ecosystems that a shift in demography of just a few species can have devastating consequences at environmental, social and economic levels. Despite this, evaluation of insect demography is strongly limited by the difficulty of collecting census data at sufficient scale. We propose a method to gather and leverage observations from bystanders, hikers, and entomology enthusiasts in order to provide researchers with data that could significantly help anticipate and identify environmental threats. Finally, we show that there is indeed interest on both sides for such collaboration.
Tasks
Published	2019-05-30
URL	https://arxiv.org/abs/1906.11898v2
PDF	https://arxiv.org/pdf/1906.11898v2.pdf
PWC	https://paperswithcode.com/paper/insectup-crowdsourcing-insect-observations-to
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Framework

A Case for Quantifying Statistical Robustness of Specialized Probabilistic AI Accelerators


Title	A Case for Quantifying Statistical Robustness of Specialized Probabilistic AI Accelerators
Authors	Xiangyu Zhang, Sayan Mukherjee, Alvin R. Lebeck
Abstract	Statistical machine learning often uses probabilistic algorithms, such as Markov Chain Monte Carlo (MCMC), to solve a wide range of problems. Many accelerators are proposed using specialized hardware to address sampling inefficiency, the critical performance bottleneck of probabilistic algorithms. These accelerators usually improve the hardware efficiency by using some approximation techniques, such as reducing bit representation, truncating small values to zero, or simplifying the Random Number Generator (RNG). Understanding the influence of these approximations on result quality is crucial to meeting the quality requirements of real applications. Although a common approach is to compare the end-point result quality using community-standard benchmarks and metrics, we claim a probabilistic architecture should provide some measure (or guarantee) of statistical robustness. This work takes a first step towards quantifying the statistical robustness of specialized hardware MCMC accelerators by proposing three pillars of statistical robustness: sampling quality, convergence diagnostic, and goodness of fit. Each pillar has at least one quantitative metric without the need to know the ground truth data. We apply this method to analyze the statistical robustness of an MCMC accelerator proposed by previous work, with some modifications, as a case study. The method also applies to other probabilistic accelerators and can be used in design space exploration.
Tasks
Published	2019-10-27
URL	https://arxiv.org/abs/1910.12346v2
PDF	https://arxiv.org/pdf/1910.12346v2.pdf
PWC	https://paperswithcode.com/paper/a-case-for-quantifying-statistical-robustness
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Bilinear Supervised Hashing Based on 2D Image Features


Title	Bilinear Supervised Hashing Based on 2D Image Features
Authors	Yujuan Ding, Wai Kueng Wong, Zhihui Lai, Zheng Zhang
Abstract	Hashing has been recognized as an efficient representation learning method to effectively handle big data due to its low computational complexity and memory cost. Most of the existing hashing methods focus on learning the low-dimensional vectorized binary features based on the high-dimensional raw vectorized features. However, studies on how to obtain preferable binary codes from the original 2D image features for retrieval is very limited. This paper proposes a bilinear supervised discrete hashing (BSDH) method based on 2D image features which utilizes bilinear projections to binarize the image matrix features such that the intrinsic characteristics in the 2D image space are preserved in the learned binary codes. Meanwhile, the bilinear projection approximation and vectorization binary codes regression are seamlessly integrated together to formulate the final robust learning framework. Furthermore, a discrete optimization strategy is developed to alternatively update each variable for obtaining the high-quality binary codes. In addition, two 2D image features, traditional SURF-based FVLAD feature and CNN-based AlexConv5 feature are designed for further improving the performance of the proposed BSDH method. Results of extensive experiments conducted on four benchmark datasets show that the proposed BSDH method almost outperforms all competing hashing methods with different input features by different evaluation protocols.
Tasks	Representation Learning
Published	2019-01-05
URL	http://arxiv.org/abs/1901.01474v1
PDF	http://arxiv.org/pdf/1901.01474v1.pdf
PWC	https://paperswithcode.com/paper/bilinear-supervised-hashing-based-on-2d-image
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Framework

Duality Regularization for Unsupervised Bilingual Lexicon Induction


Title	Duality Regularization for Unsupervised Bilingual Lexicon Induction
Authors	Xuefeng Bai, Yue Zhang, Hailong Cao, Tiejun Zhao
Abstract	Unsupervised bilingual lexicon induction naturally exhibits duality, which results from symmetry in back-translation. For example, EN-IT and IT-EN induction can be mutually primal and dual problems. Current state-of-the-art methods, however, consider the two tasks independently. In this paper, we propose to train primal and dual models jointly, using regularizers to encourage consistency in back translation cycles. Experiments across 6 language pairs show that the proposed method significantly outperforms competitive baselines, obtaining the best-published results on a standard benchmark.
Tasks
Published	2019-09-03
URL	https://arxiv.org/abs/1909.01013v1
PDF	https://arxiv.org/pdf/1909.01013v1.pdf
PWC	https://paperswithcode.com/paper/duality-regularization-for-unsupervised
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Harnessing Reinforcement Learning for Neural Motion Planning


Title	Harnessing Reinforcement Learning for Neural Motion Planning
Authors	Tom Jurgenson, Aviv Tamar
Abstract	Motion planning is an essential component in most of today’s robotic applications. In this work, we consider the learning setting, where a set of solved motion planning problems is used to improve the efficiency of motion planning on different, yet similar problems. This setting is important in applications with rapidly changing environments such as in e-commerce, among others. We investigate a general deep learning based approach, where a neural network is trained to map an image of the domain, the current robot state, and a goal robot state to the next robot state in the plan. We focus on the learning algorithm, and compare supervised learning methods with reinforcement learning (RL) algorithms. We first establish that supervised learning approaches are inferior in their accuracy due to insufficient data on the boundary of the obstacles, an issue that RL methods mitigate by actively exploring the domain. We then propose a modification of the popular DDPG RL algorithm that is tailored to motion planning domains, by exploiting the known model in the problem and the set of solved plans in the data. We show that our algorithm, dubbed DDPG-MP, significantly improves the accuracy of the learned motion planning policy. Finally, we show that given enough training data, our method can plan significantly faster on novel domains than off-the-shelf sampling based motion planners. Results of our experiments are shown in https://youtu.be/wHQ4Y4mBRb8.
Tasks	Motion Planning
Published	2019-06-01
URL	https://arxiv.org/abs/1906.00214v1
PDF	https://arxiv.org/pdf/1906.00214v1.pdf
PWC	https://paperswithcode.com/paper/190600214
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