Paper Group ANR 212
Learning fashion compatibility across apparel categories for outfit recommendation. Hybrid Approaches for our Participation to the n2c2 Challenge on Cohort Selection for Clinical Trials. The What-If Tool: Interactive Probing of Machine Learning Models. Deep Trajectory for Recognition of Human Behaviours. On Approximate Nonlinear Gaussian Message Pa …
Learning fashion compatibility across apparel categories for outfit recommendation
| Title | Learning fashion compatibility across apparel categories for outfit recommendation |
| Authors | Luisa F. Polania, Satyajit Gupte |
| Abstract | This paper addresses the problem of generating recommendations for completing the outfit given that a user is interested in a particular apparel item. The proposed method is based on a siamese network used for feature extraction followed by a fully-connected network used for learning a fashion compatibility metric. The embeddings generated by the siamese network are augmented with color histogram features motivated by the important role that color plays in determining fashion compatibility. The training of the network is formulated as a maximum a posteriori (MAP) problem where Laplacian distributions are assumed for the filters of the siamese network to promote sparsity and matrix-variate normal distributions are assumed for the weights of the metric network to efficiently exploit correlations between the input units of each fully-connected layer. |
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| Published | 2019-05-01 |
| URL | http://arxiv.org/abs/1905.03703v1 |
| http://arxiv.org/pdf/1905.03703v1.pdf | |
| PWC | https://paperswithcode.com/paper/190503703 |
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Hybrid Approaches for our Participation to the n2c2 Challenge on Cohort Selection for Clinical Trials
| Title | Hybrid Approaches for our Participation to the n2c2 Challenge on Cohort Selection for Clinical Trials |
| Authors | Xavier Tannier, Nicolas Paris, Hugo Cisneros, Christel Daniel, Matthieu Doutreligne, Catherine Duclos, Nicolas Griffon, Claire Hassen-Khodja, Ivan Lerner, Adrien Parrot, Éric Sadou, Cyril Saussol, Pascal Vaillant |
| Abstract | Objective: Natural language processing can help minimize human intervention in identifying patients meeting eligibility criteria for clinical trials, but there is still a long way to go to obtain a general and systematic approach that is useful for researchers. We describe two methods taking a step in this direction and present their results obtained during the n2c2 challenge on cohort selection for clinical trials. Materials and Methods: The first method is a weakly supervised method using an unlabeled corpus (MIMIC) to build a silver standard, by producing semi-automatically a small and very precise set of rules to detect some samples of positive and negative patients. This silver standard is then used to train a traditional supervised model. The second method is a terminology-based approach where a medical expert selects the appropriate concepts, and a procedure is defined to search the terms and check the structural or temporal constraints. Results: On the n2c2 dataset containing annotated data about 13 selection criteria on 288 patients, we obtained an overall F1-measure of 0.8969, which is the third best result out of 45 participant teams, with no statistically significant difference with the best-ranked team. Discussion: Both approaches obtained very encouraging results and apply to different types of criteria. The weakly supervised method requires explicit descriptions of positive and negative examples in some reports. The terminology-based method is very efficient when medical concepts carry most of the relevant information. Conclusion: It is unlikely that much more annotated data will be soon available for the task of identifying a wide range of patient phenotypes. One must focus on weakly or non-supervised learning methods using both structured and unstructured data and relying on a comprehensive representation of the patients. |
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| Published | 2019-03-19 |
| URL | http://arxiv.org/abs/1903.07879v1 |
| http://arxiv.org/pdf/1903.07879v1.pdf | |
| PWC | https://paperswithcode.com/paper/hybrid-approaches-for-our-participation-to |
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The What-If Tool: Interactive Probing of Machine Learning Models
| Title | The What-If Tool: Interactive Probing of Machine Learning Models |
| Authors | James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda Viegas, Jimbo Wilson |
| Abstract | A key challenge in developing and deploying Machine Learning (ML) systems is understanding their performance across a wide range of inputs. To address this challenge, we created the What-If Tool, an open-source application that allows practitioners to probe, visualize, and analyze ML systems, with minimal coding. The What-If Tool lets practitioners test performance in hypothetical situations, analyze the importance of different data features, and visualize model behavior across multiple models and subsets of input data. It also lets practitioners measure systems according to multiple ML fairness metrics. We describe the design of the tool, and report on real-life usage at different organizations. |
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| Published | 2019-07-09 |
| URL | https://arxiv.org/abs/1907.04135v2 |
| https://arxiv.org/pdf/1907.04135v2.pdf | |
| PWC | https://paperswithcode.com/paper/the-what-if-tool-interactive-probing-of |
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Deep Trajectory for Recognition of Human Behaviours
| Title | Deep Trajectory for Recognition of Human Behaviours |
| Authors | Tauseef Ali, Eissa Jaber Alreshidi |
| Abstract | Identifying human actions in complex scenes is widely considered as a challenging research problem due to the unpredictable behaviors and variation of appearances and postures. For extracting variations in motion and postures, trajectories provide meaningful way. However, simple trajectories are normally represented by vector of spatial coordinates. In order to identify human actions, we must exploit structural relationship between different trajectories. In this paper, we propose a method that divides the video into N number of segments and then for each segment we extract trajectories. We then compute trajectory descriptor for each segment which capture the structural relationship among different trajectories in the video segment. For trajectory descriptor, we project all extracted trajectories on the canvas. This will result in texture image which can store the relative motion and structural relationship among the trajectories. We then train Convolution Neural Network (CNN) to capture and learn the representation from dense trajectories. . Experimental results shows that our proposed method out performs state of the art methods by 90.01% on benchmark data set. |
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| Published | 2019-05-24 |
| URL | https://arxiv.org/abs/1905.10357v1 |
| https://arxiv.org/pdf/1905.10357v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-trajectory-for-recognition-of-human |
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On Approximate Nonlinear Gaussian Message Passing On Factor Graphs
| Title | On Approximate Nonlinear Gaussian Message Passing On Factor Graphs |
| Authors | Eike Petersen, Christian Hoffmann, Philipp Rostalski |
| Abstract | Factor graphs have recently gained increasing attention as a unified framework for representing and constructing algorithms for signal processing, estimation, and control. One capability that does not seem to be well explored within the factor graph tool kit is the ability to handle deterministic nonlinear transformations, such as those occurring in nonlinear filtering and smoothing problems, using tabulated message passing rules. In this contribution, we provide general forward (filtering) and backward (smoothing) approximate Gaussian message passing rules for deterministic nonlinear transformation nodes in arbitrary factor graphs fulfilling a Markov property, based on numerical quadrature procedures for the forward pass and a Rauch-Tung-Striebel-type approximation of the backward pass. These message passing rules can be employed for deriving many algorithms for solving nonlinear problems using factor graphs, as is illustrated by the proposition of a nonlinear modified Bryson-Frazier (MBF) smoother based on the presented message passing rules. |
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| Published | 2019-03-21 |
| URL | http://arxiv.org/abs/1903.09136v1 |
| http://arxiv.org/pdf/1903.09136v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-approximate-nonlinear-gaussian-message |
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A New Corpus for Low-Resourced Sindhi Language with Word Embeddings
| Title | A New Corpus for Low-Resourced Sindhi Language with Word Embeddings |
| Authors | Wazir Ali, Jay Kumar, Junyu Lu, Zenglin Xu |
| Abstract | Representing words and phrases into dense vectors of real numbers which encode semantic and syntactic properties is a vital constituent in natural language processing (NLP). The success of neural network (NN) models in NLP largely rely on such dense word representations learned on the large unlabeled corpus. Sindhi is one of the rich morphological language, spoken by large population in Pakistan and India lacks corpora which plays an essential role of a test-bed for generating word embeddings and developing language independent NLP systems. In this paper, a large corpus of more than 61 million words is developed for low-resourced Sindhi language for training neural word embeddings. The corpus is acquired from multiple web-resources using web-scrappy. Due to the unavailability of open source preprocessing tools for Sindhi, the prepossessing of such large corpus becomes a challenging problem specially cleaning of noisy data extracted from web resources. Therefore, a preprocessing pipeline is employed for the filtration of noisy text. Afterwards, the cleaned vocabulary is utilized for training Sindhi word embeddings with state-of-the-art GloVe, Skip-Gram (SG), and Continuous Bag of Words (CBoW) word2vec algorithms. The intrinsic evaluation approach of cosine similarity matrix and WordSim-353 are employed for the evaluation of generated Sindhi word embeddings. Moreover, we compare the proposed word embeddings with recently revealed Sindhi fastText (SdfastText) word representations. Our intrinsic evaluation results demonstrate the high quality of our generated Sindhi word embeddings using SG, CBoW, and GloVe as compare to SdfastText word representations. |
| Tasks | Word Embeddings |
| Published | 2019-11-28 |
| URL | https://arxiv.org/abs/1911.12579v2 |
| https://arxiv.org/pdf/1911.12579v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-new-corpus-for-low-resourced-sindhi |
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INSET: Sentence Infilling with Inter-sentential Generative Pre-training
| Title | INSET: Sentence Infilling with Inter-sentential Generative Pre-training |
| Authors | Yichen Huang, Yizhe Zhang, Oussama Elachqar, Yu Cheng |
| Abstract | Missing sentence generation (or sentence infilling) fosters a wide range of applications in natural language generation, such as document auto-completion and meeting note expansion. Such a task asks the model to generate intermediate missing sentence that can semantically and syntactically bridge the surrounding context. Solving the sentence infilling task requires techniques in NLP ranging from natural language understanding, discourse-level planning and natural language generation. In this paper, we present a framework to decouple this challenge and address these three aspects respectively, leveraging the power of existing large-scale pre-trained models such as BERT and GPT-2. Our empirical results demonstrate the effectiveness of our proposed model in learning a sentence representation for generation, and further generating a missing sentence that bridges the context. |
| Tasks | Text Generation |
| Published | 2019-11-10 |
| URL | https://arxiv.org/abs/1911.03892v1 |
| https://arxiv.org/pdf/1911.03892v1.pdf | |
| PWC | https://paperswithcode.com/paper/inset-sentence-infilling-with-inter |
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Bag of Negatives for Siamese Architectures
| Title | Bag of Negatives for Siamese Architectures |
| Authors | Bojana Gajic, Ariel Amato, Ramon Baldrich, Carlo Gatta |
| Abstract | Training a Siamese architecture for re-identification with a large number of identities is a challenging task due to the difficulty of finding relevant negative samples efficiently. In this work we present Bag of Negatives (BoN), a method for accelerated and improved training of Siamese networks that scales well on datasets with a very large number of identities. BoN is an efficient and loss-independent method, able to select a bag of high quality negatives, based on a novel online hashing strategy. |
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| Published | 2019-08-06 |
| URL | https://arxiv.org/abs/1908.02391v1 |
| https://arxiv.org/pdf/1908.02391v1.pdf | |
| PWC | https://paperswithcode.com/paper/bag-of-negatives-for-siamese-architectures |
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An empirical study of neural networks for trend detection in time series
| Title | An empirical study of neural networks for trend detection in time series |
| Authors | Alexandre Miot, Gilles Drigout |
| Abstract | Detecting structure in noisy time series is a difficult task. One intuitive feature is the notion of trend. From theoretical hints and using simulated time series, we empirically investigate the efficiency of standard recurrent neural networks (RNNs) to detect trends. We show the overall superiority and versatility of certain standard RNNs structures over various other estimators. These RNNs could be used as basic blocks to build more complex time series trend estimators. |
| Tasks | Time Series |
| Published | 2019-12-09 |
| URL | https://arxiv.org/abs/1912.04009v2 |
| https://arxiv.org/pdf/1912.04009v2.pdf | |
| PWC | https://paperswithcode.com/paper/an-empirical-study-of-neural-networks-for |
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Reporting on Decision-Making Algorithms and some Related Ethical Questions
| Title | Reporting on Decision-Making Algorithms and some Related Ethical Questions |
| Authors | Benoît Otjacques |
| Abstract | Companies report on their financial performance for decades. More recently they have also started to report on their environmental impact and their social responsibility. The latest trend is now to deliver one single integrated report where all stakeholders of the company can easily connect all facets of the business with their impact considered in a broad sense. The main purpose of this integrated approach is to avoid delivering data related to disconnected silos, which consequently makes it very difficult to globally assess the overall performance of an entity or a business line. In this paper, we focus on how companies report on risks and ethical issues related to the increasing use of Artificial Intelligence (AI). We explain some of these risks and potential issues. Next, we identify some recent initiatives by various stakeholders to define a global ethical framework for AI. Finally, we illustrate with four cases that companies are very shy to report on these facets of AI. |
| Tasks | Decision Making |
| Published | 2019-11-04 |
| URL | https://arxiv.org/abs/1911.05731v1 |
| https://arxiv.org/pdf/1911.05731v1.pdf | |
| PWC | https://paperswithcode.com/paper/reporting-on-decision-making-algorithms-and |
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AiAds: Automated and Intelligent Advertising System for Sponsored Search
| Title | AiAds: Automated and Intelligent Advertising System for Sponsored Search |
| Authors | Xiao Yang, Daren Sun, Ruiwei Zhu, Tao Deng, Zhi Guo, Jiao Ding, Shouke Qin, Zongyao Ding, Yanfeng Zhu |
| Abstract | Sponsored search has more than 20 years of history, and it has been proven to be a successful business model for online advertising. Based on the pay-per-click pricing model and the keyword targeting technology, the sponsored system runs online auctions to determine the allocations and prices of search advertisements. In the traditional setting, advertisers should manually create lots of ad creatives and bid on some relevant keywords to target their audience. Due to the huge amount of search traffic and a wide variety of ad creations, the limits of manual optimizations from advertisers become the main bottleneck for improving the efficiency of this market. Moreover, as many emerging advertising forms and supplies are growing, it’s crucial for sponsored search platform to pay more attention to the ROI metrics of ads for getting the marketing budgets of advertisers. In this paper, we present the AiAds system developed at Baidu, which use machine learning techniques to build an automated and intelligent advertising system. By designing and implementing the automated bidding strategy, the intelligent targeting and the intelligent creation models, the AiAds system can transform the manual optimizations into multiple automated tasks and optimize these tasks in advanced methods. AiAds is a brand-new architecture of sponsored search system which changes the bidding language and allocation mechanism, breaks the limit of keyword targeting with end-to-end ad retrieval framework and provides global optimization of ad creation. This system can increase the advertiser’s campaign performance, the user experience and the revenue of the advertising platform simultaneously and significantly. We present the overall architecture and modeling techniques for each module of the system and share our lessons learned in solving several key challenges. |
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| Published | 2019-07-28 |
| URL | https://arxiv.org/abs/1907.12118v1 |
| https://arxiv.org/pdf/1907.12118v1.pdf | |
| PWC | https://paperswithcode.com/paper/aiads-automated-and-intelligent-advertising |
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Investigating Flight Envelope Variation Predictability of Impaired Aircraft using Least-Squares Regression Analysis
| Title | Investigating Flight Envelope Variation Predictability of Impaired Aircraft using Least-Squares Regression Analysis |
| Authors | Ramin Norouzi, Amirreza Kosari, Mohammad Hossein Sabour |
| Abstract | Aircraft failures alter the aircraft dynamics and cause maneuvering flight envelope to change. Such envelope variations are nonlinear and generally unpredictable by the pilot as they are governed by the aircraft’s complex dynamics. Hence, in order to prevent in-flight Loss of Control it is crucial to practically predict the impaired aircraft’s flight envelope variation due to any a-priori unknown failure degree. This paper investigates the predictability of the number of trim points within the maneuvering flight envelope and its centroid using both linear and nonlinear least-squares estimation methods. To do so, various polynomial models and nonlinear models based on hyperbolic tangent function are developed and compared which incorporate the influencing factors on the envelope variations as the inputs and estimate the centroid and the number of trim points of the maneuvering flight envelope at any intended failure degree. Results indicate that both the polynomial and hyperbolic tangent function-based models are capable of predicting the impaired fight envelope variation with good precision. Furthermore, it is shown that the regression equation of the best polynomial fit enables direct assessment of the impaired aircraft’s flight envelope contraction and displacement sensitivity to the specific parameters characterizing aircraft failure and flight condition. |
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| Published | 2019-05-20 |
| URL | https://arxiv.org/abs/1905.07875v2 |
| https://arxiv.org/pdf/1905.07875v2.pdf | |
| PWC | https://paperswithcode.com/paper/investigating-flight-envelope-variation |
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Sentence-Level Content Planning and Style Specification for Neural Text Generation
| Title | Sentence-Level Content Planning and Style Specification for Neural Text Generation |
| Authors | Xinyu Hua, Lu Wang |
| Abstract | Building effective text generation systems requires three critical components: content selection, text planning, and surface realization, and traditionally they are tackled as separate problems. Recent all-in-one style neural generation models have made impressive progress, yet they often produce outputs that are incoherent and unfaithful to the input. To address these issues, we present an end-to-end trained two-step generation model, where a sentence-level content planner first decides on the keyphrases to cover as well as a desired language style, followed by a surface realization decoder that generates relevant and coherent text. For experiments, we consider three tasks from domains with diverse topics and varying language styles: persuasive argument construction from Reddit, paragraph generation for normal and simple versions of Wikipedia, and abstract generation for scientific articles. Automatic evaluation shows that our system can significantly outperform competitive comparisons. Human judges further rate our system generated text as more fluent and correct, compared to the generations by its variants that do not consider language style. |
| Tasks | Text Generation |
| Published | 2019-09-02 |
| URL | https://arxiv.org/abs/1909.00734v1 |
| https://arxiv.org/pdf/1909.00734v1.pdf | |
| PWC | https://paperswithcode.com/paper/sentence-level-content-planning-and-style |
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Data adaptation in HANDY economy-ideology model
| Title | Data adaptation in HANDY economy-ideology model |
| Authors | Marcin Sendera |
| Abstract | The concept of mathematical modeling is widespread across almost all of the fields of contemporary science and engineering. Because of the existing necessity of predictions the behavior of natural phenomena, the researchers develop more and more complex models. However, despite their ability to better forecasting, the problem of an appropriate fitting ground truth data to those, high-dimensional and nonlinear models seems to be inevitable. In order to deal with this demanding problem the entire discipline of data assimilation has been developed. Basing on the Human and Nature Dynamics (HANDY) model, we have presented a detailed and comprehensive comparison of Approximate Bayesian Computation (classic data assimilation method) and a novelty approach of Supermodeling. Furthermore, with the usage of Sensitivity Analysis, we have proposed the methodology to reduce the number of coupling coefficients between submodels and as a consequence to increase the speed of the Supermodel converging. In addition, we have demonstrated that usage of Approximate Bayesian Computation method with the knowledge about parameters’ sensitivities could result with satisfactory estimation of the initial parameters. However, we have also presented the mentioned methodology as unable to achieve similar predictions to Approximate Bayesian Computation. Finally, we have proved that Supermodeling with synchronization via the most sensitive variable could effect with the better forecasting for chaotic as well as more stable systems than the Approximate Bayesian Computation. What is more, we have proposed the adequate methodologies. |
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| Published | 2019-04-08 |
| URL | http://arxiv.org/abs/1904.04309v1 |
| http://arxiv.org/pdf/1904.04309v1.pdf | |
| PWC | https://paperswithcode.com/paper/data-adaptation-in-handy-economy-ideology |
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Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
| Title | Skew-Fit: State-Covering Self-Supervised Reinforcement Learning |
| Authors | Vitchyr H. Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, Sergey Levine |
| Abstract | Autonomous agents that must exhibit flexible and broad capabilities will need to be equipped with large repertoires of skills. Defining each skill with a manually-designed reward function limits this repertoire and imposes a manual engineering burden. Self-supervised agents that set their own goals can automate this process, but designing appropriate goal setting objectives can be difficult, and often involves heuristic design decisions. In this paper, we propose a formal exploration objective for goal-reaching policies that maximizes state coverage. We show that this objective is equivalent to maximizing goal reaching performance together with the entropy of the goal distribution, where goals correspond to full state observations. To instantiate this principle, we present an algorithm called Skew-Fit for learning a maximum-entropy goal distributions. We prove that, under regularity conditions, Skew-Fit converges to a uniform distribution over the set of valid states, even when we do not know this set beforehand. Our experiments show that combining Skew-Fit for learning goal distributions with existing goal-reaching methods outperforms a variety of prior methods on open-sourced visual goal-reaching tasks. Moreover, we demonstrate that \METHOD enables a real-world robot to learn to open a door, entirely from scratch, from pixels, and without any manually-designed reward function. |
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| Published | 2019-03-08 |
| URL | https://arxiv.org/abs/1903.03698v3 |
| https://arxiv.org/pdf/1903.03698v3.pdf | |
| PWC | https://paperswithcode.com/paper/skew-fit-state-covering-self-supervised |
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