Paper Group ANR 1505
The importance of evaluating the complete automated knowledge-based planning pipeline. Learning Optimal and Near-Optimal Lexicographic Preference Lists. Similar Image Search for Histopathology: SMILY. Generalized Control Functions via Variational Decoupling. Field typing for improved recognition on heterogeneous handwritten forms. Cross-Modal Messa …
The importance of evaluating the complete automated knowledge-based planning pipeline
| Title | The importance of evaluating the complete automated knowledge-based planning pipeline |
| Authors | Aaron Babier, Rafid Mahmood, Andrea L. McNiven, Adam Diamant, Timothy C. Y. Chan |
| Abstract | We determine how prediction methods combine with optimization methods in two-stage knowledge-based planning (KBP) pipelines to produce radiation therapy treatment plans. We trained two dose prediction methods, a generative adversarial network (GAN) and a random forest (RF) with the same 130 treatment plans. The models were applied to 87 out-of-sample patients to create two sets of predicted dose distributions that were used as input to two optimization models. The first optimization model, inverse planning (IP), estimates weights for dose-objectives from a predicted dose distribution and generates new plans using conventional inverse planning. The second optimization model, dose mimicking (DM), minimizes the sum of one-sided quadratic penalties between the predictions and the generated plans using several dose-objectives. Altogether, four KBP pipelines (GAN-IP, GAN-DM, RF-IP, and RF-DM) were constructed and benchmarked against the corresponding clinical plans using clinical criteria; the error of both prediction methods was also evaluated. The best performing plans were GAN-IP plans, which satisfied the same criteria as their corresponding clinical plans (78%) more often than any other KBP pipeline. However, GAN did not necessarily provide the best prediction for the second-stage optimization models. Specifically, both the RF-IP and RF-DM plans satisfied all clinical criteria 25% and 15% more often than GAN-DM plans (the worst performing planning), respectively. GAN predictions also had a higher mean absolute error (3.9 Gy) than those from RF (3.6 Gy). We find that state-of-the-art prediction methods when paired with different optimization algorithms, produce treatment plans with considerable variation in quality. |
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| Published | 2019-10-31 |
| URL | https://arxiv.org/abs/1910.14257v1 |
| https://arxiv.org/pdf/1910.14257v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-importance-of-evaluating-the-complete |
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Learning Optimal and Near-Optimal Lexicographic Preference Lists
| Title | Learning Optimal and Near-Optimal Lexicographic Preference Lists |
| Authors | Ahmed Moussa, Xudong Liu |
| Abstract | We consider learning problems of an intuitive and concise preference model, called lexicographic preference lists (LP-lists). Given a set of examples that are pairwise ordinal preferences over a universe of objects built of attributes of discrete values, we want to learn (1) an optimal LP-list that decides the maximum number of these examples, or (2) a near-optimal LP-list that decides as many examples as it can. To this end, we introduce a dynamic programming based algorithm and a genetic algorithm for these two learning problems, respectively. Furthermore, we empirically demonstrate that the sub-optimal models computed by the genetic algorithm very well approximate the de facto optimal models computed by our dynamic programming based algorithm, and that the genetic algorithm outperforms the baseline greedy heuristic with higher accuracy predicting new preferences. |
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| Published | 2019-09-19 |
| URL | https://arxiv.org/abs/1909.09072v1 |
| https://arxiv.org/pdf/1909.09072v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-optimal-and-near-optimal |
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Similar Image Search for Histopathology: SMILY
| Title | Similar Image Search for Histopathology: SMILY |
| Authors | Narayan Hegde, Jason D. Hipp, Yun Liu, Michael E. Buck, Emily Reif, Daniel Smilkov, Michael Terry, Carrie J. Cai, Mahul B. Amin, Craig H. Mermel, Phil Q. Nelson, Lily H. Peng, Greg S. Corrado, Martin C. Stumpe |
| Abstract | The increasing availability of large institutional and public histopathology image datasets is enabling the searching of these datasets for diagnosis, research, and education. Though these datasets typically have associated metadata such as diagnosis or clinical notes, even carefully curated datasets rarely contain annotations of the location of regions of interest on each image. Because pathology images are extremely large (up to 100,000 pixels in each dimension), further laborious visual search of each image may be needed to find the feature of interest. In this paper, we introduce a deep learning based reverse image search tool for histopathology images: Similar Medical Images Like Yours (SMILY). We assessed SMILY’s ability to retrieve search results in two ways: using pathologist-provided annotations, and via prospective studies where pathologists evaluated the quality of SMILY search results. As a negative control in the second evaluation, pathologists were blinded to whether search results were retrieved by SMILY or randomly. In both types of assessments, SMILY was able to retrieve search results with similar histologic features, organ site, and prostate cancer Gleason grade compared with the original query. SMILY may be a useful general-purpose tool in the pathologist’s arsenal, to improve the efficiency of searching large archives of histopathology images, without the need to develop and implement specific tools for each application. |
| Tasks | Image Retrieval |
| Published | 2019-01-30 |
| URL | http://arxiv.org/abs/1901.11112v3 |
| http://arxiv.org/pdf/1901.11112v3.pdf | |
| PWC | https://paperswithcode.com/paper/similar-image-search-for-histopathology-smily |
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Generalized Control Functions via Variational Decoupling
| Title | Generalized Control Functions via Variational Decoupling |
| Authors | Aahlad Manas Puli, Rajesh Ranganath |
| Abstract | Causal estimation relies on separating the variation in the outcome due to the confounders from that due to the treatment. To achieve this separation, practitioners can use external sources of randomness that only influence the treatment called instrumental variables (IVs). Traditional IV-methods rely on structural assumptions that limit the effect that the confounders can have on both outcome and treatment. To relax these assumptions we develop a new estimator called the generalized control-function method (GCFN). GCFN’s first stage called variational decoupling (VDE) recovers the residual variation in the treatment given the IV. In the second stage, GCFN regresses the outcome on the treatment and residual variation to compute the causal effect. We evaluate GCFN on simulated data and on recovering the causal effect of slave export on community trust. We show how VDE can help unify IV-estimators and non-IV-estimators. |
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| Published | 2019-07-08 |
| URL | https://arxiv.org/abs/1907.03451v1 |
| https://arxiv.org/pdf/1907.03451v1.pdf | |
| PWC | https://paperswithcode.com/paper/generalized-control-functions-via-variational |
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Field typing for improved recognition on heterogeneous handwritten forms
| Title | Field typing for improved recognition on heterogeneous handwritten forms |
| Authors | Ciprian Tomoiaga, Paul Feng, Mathieu Salzmann, Patrick Jayet |
| Abstract | Offline handwriting recognition has undergone continuous progress over the past decades. However, existing methods are typically benchmarked on free-form text datasets that are biased towards good-quality images and handwriting styles, and homogeneous content. In this paper, we show that state-of-the-art algorithms, employing long short-term memory (LSTM) layers, do not readily generalize to real-world structured documents, such as forms, due to their highly heterogeneous and out-of-vocabulary content, and to the inherent ambiguities of this content. To address this, we propose to leverage the content type within an LSTM-based architecture. Furthermore, we introduce a procedure to generate synthetic data to train this architecture without requiring expensive manual annotations. We demonstrate the effectiveness of our approach at transcribing text on a challenging, real-world dataset of European Accident Statements. |
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| Published | 2019-09-23 |
| URL | https://arxiv.org/abs/1909.10120v1 |
| https://arxiv.org/pdf/1909.10120v1.pdf | |
| PWC | https://paperswithcode.com/paper/190910120 |
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Cross-Modal Message Passing for Two-stream Fusion
| Title | Cross-Modal Message Passing for Two-stream Fusion |
| Authors | Dong Wang, Yuan Yuan, Qi Wang |
| Abstract | Processing and fusing information among multi-modal is a very useful technique for achieving high performance in many computer vision problems. In order to tackle multi-modal information more effectively, we introduce a novel framework for multi-modal fusion: Cross-modal Message Passing (CMMP). Specifically, we propose a cross-modal message passing mechanism to fuse two-stream network for action recognition, which composes of an appearance modal network (RGB image) and a motion modal (optical flow image) network. The objectives of individual networks in this framework are two-fold: a standard classification objective and a competing objective. The classification object ensures that each modal network predicts the true action category while the competing objective encourages each modal network to outperform the other one. We quantitatively show that the proposed CMMP fuses the traditional two-stream network more effectively, and outperforms all existing two-stream fusion method on UCF-101 and HMDB-51 datasets. |
| Tasks | Optical Flow Estimation, Temporal Action Localization |
| Published | 2019-04-30 |
| URL | http://arxiv.org/abs/1904.13072v1 |
| http://arxiv.org/pdf/1904.13072v1.pdf | |
| PWC | https://paperswithcode.com/paper/cross-modal-message-passing-for-two-stream |
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Encoding Selection for Solving Hamiltonian Cycle Problems with ASP
| Title | Encoding Selection for Solving Hamiltonian Cycle Problems with ASP |
| Authors | Liu Liu, Miroslaw Truszczynski |
| Abstract | It is common for search and optimization problems to have alternative equivalent encodings in ASP. Typically none of them is uniformly better than others when evaluated on broad classes of problem instances. We claim that one can improve the solving ability of ASP by using machine learning techniques to select encodings likely to perform well on a given instance. We substantiate this claim by studying the hamiltonian cycle problem. We propose several equivalent encodings of the problem and several classes of hard instances. We build models to predict the behavior of each encoding, and then show that selecting encodings for a given instance using the learned performance predictors leads to significant performance gains. |
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| Published | 2019-09-18 |
| URL | https://arxiv.org/abs/1909.08252v1 |
| https://arxiv.org/pdf/1909.08252v1.pdf | |
| PWC | https://paperswithcode.com/paper/encoding-selection-for-solving-hamiltonian |
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On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis
| Title | On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis |
| Authors | Kohei Hayashi, Masaaki Imaizumi, Yuichi Yoshida |
| Abstract | In this paper, we study random subsampling of Gaussian process regression, one of the simplest approximation baselines, from a theoretical perspective. Although subsampling discards a large part of training data, we show provable guarantees on the accuracy of the predictive mean/variance and its generalization ability. For analysis, we consider embedding kernel matrices into graphons, which encapsulate the difference of the sample size and enables us to evaluate the approximation and generalization errors in a unified manner. The experimental results show that the subsampling approximation achieves a better trade-off regarding accuracy and runtime than the Nystr"{o}m and random Fourier expansion methods. |
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| Published | 2019-01-28 |
| URL | http://arxiv.org/abs/1901.09541v1 |
| http://arxiv.org/pdf/1901.09541v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-random-subsampling-of-gaussian-process |
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A Conjoint Application of Data Mining Techniques for Analysis of Global Terrorist Attacks – Prevention and Prediction for Combating Terrorism
| Title | A Conjoint Application of Data Mining Techniques for Analysis of Global Terrorist Attacks – Prevention and Prediction for Combating Terrorism |
| Authors | Vivek Kumar, Manuel Mazzara, Maj. Gen., Angelo Messina, JooYoung Lee |
| Abstract | Terrorism has become one of the most tedious problems to deal with and a prominent threat to mankind. To enhance counter-terrorism, several research works are developing efficient and precise systems, data mining is not an exception. Immense data is floating in our lives, though the scarce availability of authentic terrorist attack data in the public domain makes it complicated to fight terrorism. This manuscript focuses on data mining classification techniques and discusses the role of United Nations in counter-terrorism. It analyzes the performance of classifiers such as Lazy Tree, Multilayer Perceptron, Multiclass and Na"ive Bayes classifiers for observing the trends for terrorist attacks around the world. The database for experiment purpose is created from different public and open access sources for years 1970-2015 comprising of 156,772 reported attacks causing massive losses of lives and property. This work enumerates the losses occurred, trends in attack frequency and places more prone to it, by considering the attack responsibilities taken as evaluation class. |
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| Published | 2019-01-19 |
| URL | http://arxiv.org/abs/1901.06483v3 |
| http://arxiv.org/pdf/1901.06483v3.pdf | |
| PWC | https://paperswithcode.com/paper/a-conjoint-application-of-data-mining |
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Defending Against Adversarial Machine Learning
| Title | Defending Against Adversarial Machine Learning |
| Authors | Alison Jenkins |
| Abstract | An Adversarial System to attack and an Authorship Attribution System (AAS) to defend itself against the attacks are analyzed. Defending a system against attacks from an adversarial machine learner can be done by randomly switching between models for the system, by detecting and reacting to changes in the distribution of normal inputs, or by using other methods. Adversarial machine learning is used to identify a system that is being used to map system inputs to outputs. Three types of machine learners are using for the model that is being attacked. The machine learners that are used to model the system being attacked are a Radial Basis Function Support Vector Machine, a Linear Support Vector Machine, and a Feedforward Neural Network. The feature masks are evolved using accuracy as the fitness measure. The system defends itself against adversarial machine learning attacks by identifying inputs that do not match the probability distribution of normal inputs. The system also defends itself against adversarial attacks by randomly switching between the feature masks being used to map system inputs to outputs. |
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| Published | 2019-11-26 |
| URL | https://arxiv.org/abs/1911.11746v1 |
| https://arxiv.org/pdf/1911.11746v1.pdf | |
| PWC | https://paperswithcode.com/paper/defending-against-adversarial-machine |
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Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey
| Title | Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey |
| Authors | Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart |
| Abstract | Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance. Traditionally, machine learning models for graphs have been mostly designed for static graphs. However, many applications involve evolving graphs. This introduces important challenges for learning and inference since nodes, attributes, and edges change over time. In this survey, we review the recent advances in representation learning for dynamic graphs, including dynamic knowledge graphs. We describe existing models from an encoder-decoder perspective, categorize these encoders and decoders based on the techniques they employ, and analyze the approaches in each category. We also review several prominent applications and widely used datasets, and highlight directions for future research. |
| Tasks | Knowledge Graphs, Recommendation Systems, Representation Learning |
| Published | 2019-05-27 |
| URL | https://arxiv.org/abs/1905.11485v1 |
| https://arxiv.org/pdf/1905.11485v1.pdf | |
| PWC | https://paperswithcode.com/paper/relational-representation-learning-for |
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KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
| Title | KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation |
| Authors | Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhiyuan Liu, Juanzi Li, Jian Tang |
| Abstract | Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relational facts in knowledge graphs (KGs) with informative entity embeddings, but conventional KE models do not utilize the rich text data. In this paper, we propose a unified model for Knowledge Embedding and Pre-trained LanguagE Representation (KEPLER), which can not only better integrate factual knowledge into PLMs but also effectively learn KE through the abundant information in text. In KEPLER, we encode textual descriptions of entities with a PLM as their embeddings, and then jointly optimize the KE and language modeling objectives. Experimental results show that KEPLER achieves state-of-the-art performance on various NLP tasks, and also works remarkably well as an inductive KE model on the link prediction task. Furthermore, for pre-training KEPLER and evaluating the KE performance, we construct Wikidata5M, a large-scale KG dataset with aligned entity descriptions, and benchmark state-of-the-art KE methods on it. It shall serve as a new KE benchmark and facilitate the research on large KG, inductive KE, and KG with text. The dataset can be obtained from https://deepgraphlearning.github.io/project/wikidata5m. |
| Tasks | Entity Embeddings, Entity Typing, Knowledge Graph Completion, Knowledge Graph Embeddings, Knowledge Graphs, Language Modelling, Link Prediction, Relation Extraction |
| Published | 2019-11-13 |
| URL | https://arxiv.org/abs/1911.06136v2 |
| https://arxiv.org/pdf/1911.06136v2.pdf | |
| PWC | https://paperswithcode.com/paper/kepler-a-unified-model-for-knowledge |
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Time-Aware Gated Recurrent Unit Networks for Road Surface Friction Prediction Using Historical Data
| Title | Time-Aware Gated Recurrent Unit Networks for Road Surface Friction Prediction Using Historical Data |
| Authors | Ziyuan Pu, Zhiyong Cui, Shuo Wang, Qianmu Li, Yinhai Wang |
| Abstract | An accurate road surface friction prediction algorithm can enable intelligent transportation systems to share timely road surface condition to the public for increasing the safety of the road users. Previously, scholars developed multiple prediction models for forecasting road surface conditions using historical data. However, road surface condition data cannot be perfectly collected at every timestamp, e.g. the data collected by on-vehicle sensors may be influenced when vehicles cannot travel due to economic cost issue or weather issues. Such resulted missing values in the collected data can damage the effectiveness and accuracy of the existing prediction methods since they are assumed to have the input data with a fixed temporal resolution. This study proposed a road surface friction prediction model employing a Gated Recurrent Unit network-based decay mechanism (GRU-D) to handle the missing values. The evaluation results present that the proposed GRU-D networks outperform all baseline models. The impact of missing rate on predictive accuracy, learning efficiency and learned decay rate are analyzed as well. The findings can help improve the prediction accuracy and efficiency of forecasting road surface friction using historical data sets with missing values, therefore mitigating the impact of wet or icy road conditions on traffic safety. |
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| Published | 2019-11-01 |
| URL | https://arxiv.org/abs/1911.00605v1 |
| https://arxiv.org/pdf/1911.00605v1.pdf | |
| PWC | https://paperswithcode.com/paper/time-aware-gated-recurrent-unit-networks-for |
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Learning to solve the credit assignment problem
| Title | Learning to solve the credit assignment problem |
| Authors | Benjamin James Lansdell, Prashanth Ravi Prakash, Konrad Paul Kording |
| Abstract | Backpropagation is driving today’s artificial neural networks (ANNs). However, despite extensive research, it remains unclear if the brain implements this algorithm. Among neuroscientists, reinforcement learning (RL) algorithms are often seen as a realistic alternative: neurons can randomly introduce change, and use unspecific feedback signals to observe their effect on the cost and thus approximate their gradient. However, the convergence rate of such learning scales poorly with the number of involved neurons. Here we propose a hybrid learning approach. Each neuron uses an RL-type strategy to learn how to approximate the gradients that backpropagation would provide. We provide proof that our approach converges to the true gradient for certain classes of networks. In both feedforward and convolutional networks, we empirically show that our approach learns to approximate the gradient, and can match the performance of gradient-based learning. Learning feedback weights provides a biologically plausible mechanism of achieving good performance, without the need for precise, pre-specified learning rules. |
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| Published | 2019-06-03 |
| URL | https://arxiv.org/abs/1906.00889v3 |
| https://arxiv.org/pdf/1906.00889v3.pdf | |
| PWC | https://paperswithcode.com/paper/190600889 |
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Visual analytics for team-based invasion sports with significant events and Markov reward process
| Title | Visual analytics for team-based invasion sports with significant events and Markov reward process |
| Authors | Kun Zhao, Takayuki Osogami, Tetsuro Morimura |
| Abstract | In team-based invasion sports such as soccer and basketball, analytics is important for teams to understand their performance and for audiences to understand matches better. The present work focuses on performing visual analytics to evaluate the value of any kind of event occurring in a sports match with a continuous parameter space. Here, the continuous parameter space involves the time, location, score, and other parameters. Because the spatiotemporal data used in such analytics is a low-level representation and has a very large size, however, traditional analytics may need to discretize the continuous parameter space (e.g., subdivide the playing area) or use a local feature to limit the analysis to specific events (e.g., only shots). These approaches make evaluation impossible for any kind of event with a continuous parameter space. To solve this problem, we consider a whole match as a Markov chain of significant events, so that event values can be estimated with a continuous parameter space by solving the Markov chain with a machine learning model. The significant events are first extracted by considering the time-varying distribution of players to represent the whole match. Then, the extracted events are redefined as different states with the continuous parameter space and built as a Markov chain so that a Markov reward process can be applied. Finally, the Markov reward process is solved by a customized fitted-value iteration algorithm so that the event values with the continuous parameter space can be predicted by a regression model. As a result, the event values can be visually inspected over the whole playing field under arbitrary given conditions. Experimental results with real soccer data show the effectiveness of the proposed system. |
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| Published | 2019-07-02 |
| URL | https://arxiv.org/abs/1907.01221v1 |
| https://arxiv.org/pdf/1907.01221v1.pdf | |
| PWC | https://paperswithcode.com/paper/visual-analytics-for-team-based-invasion |
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