January 26, 2020

3189 words 15 mins read

Paper Group ANR 1509

Cribriform pattern detection in prostate histopathological images using deep learning models. Policy Optimization with Stochastic Mirror Descent. A Bayesian Finite Mixture Model with Variable Selection for Data with Mixed-type Variables. Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models. …

Cribriform pattern detection in prostate histopathological images using deep learning models


Title	Cribriform pattern detection in prostate histopathological images using deep learning models
Authors	Malay Singh, Emarene Mationg Kalaw, Wang Jie, Mundher Al-Shabi, Chin Fong Wong, Danilo Medina Giron, Kian-Tai Chong, Maxine Tan, Zeng Zeng, Hwee Kuan Lee
Abstract	Architecture, size, and shape of glands are most important patterns used by pathologists for assessment of cancer malignancy in prostate histopathological tissue slides. Varying structures of glands along with cumbersome manual observations may result in subjective and inconsistent assessment. Cribriform gland with irregular border is an important feature in Gleason pattern 4. We propose using deep neural networks for cribriform pattern classification in prostate histopathological images. $163708$ Hematoxylin and Eosin (H&E) stained images were extracted from histopathologic tissue slides of $19$ patients with prostate cancer and annotated for cribriform patterns. Our automated image classification system analyses the H&E images to classify them as either `Cribriform' or` Non-cribriform’. Our system uses various deep learning approaches and hand-crafted image pixel intensity-based features. We present our results for cribriform pattern detection across various parameters and configuration allowed by our system. The combination of fine-tuned deep learning models outperformed the state-of-art nuclei feature based methods. Our image classification system achieved the testing accuracy of $85.93~\pm~7.54$ (cross-validated) and $88.04~\pm~5.63$ ( additional unseen test set) across three folds. In this paper, we present an annotated cribriform dataset along with analysis of deep learning models and hand-crafted features for cribriform pattern detection in prostate histopathological images.
Tasks	Image Classification
Published	2019-10-09
URL	https://arxiv.org/abs/1910.04030v1
PDF	https://arxiv.org/pdf/1910.04030v1.pdf
PWC	https://paperswithcode.com/paper/cribriform-pattern-detection-in-prostate
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Framework

Policy Optimization with Stochastic Mirror Descent


Title	Policy Optimization with Stochastic Mirror Descent
Authors	Long Yang, Gang Zheng, Haotian Zhang, Yu Zhang, Qian Zheng, Jun Wen, Gang Pan
Abstract	Improving sample efficiency has been a longstanding goal in reinforcement learning. In this paper, we propose the $\mathtt{VRMPO}$: a sample efficient policy gradient method with stochastic mirror descent. A novel variance reduced policy gradient estimator is the key of $\mathtt{VRMPO}$ to improve sample efficiency. Our $\mathtt{VRMPO}$ needs only $\mathcal{O}(\epsilon^{-3})$ sample trajectories to achieve an $\epsilon$-approximate first-order stationary point, which matches the best-known sample complexity. We conduct extensive experiments to show our algorithm outperforms state-of-the-art policy gradient methods in various settings.
Tasks	Continuous Control, Policy Gradient Methods
Published	2019-06-25
URL	https://arxiv.org/abs/1906.10462v2
PDF	https://arxiv.org/pdf/1906.10462v2.pdf
PWC	https://paperswithcode.com/paper/policy-optimization-with-stochastic-mirror
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A Bayesian Finite Mixture Model with Variable Selection for Data with Mixed-type Variables


Title	A Bayesian Finite Mixture Model with Variable Selection for Data with Mixed-type Variables
Authors	Shu Wang, Jonathan G. Yabes, Chung-Chou H. Chang
Abstract	Finite mixture model is an important branch of clustering methods and can be applied on data sets with mixed types of variables. However, challenges exist in its applications. First, it typically relies on the EM algorithm which could be sensitive to the choice of initial values. Second, biomarkers subject to limits of detection (LOD) are common to encounter in clinical data, which brings censored variables into finite mixture model. Additionally, researchers are recently getting more interest in variable importance due to the increasing number of variables that become available for clustering. To address these challenges, we propose a Bayesian finite mixture model to simultaneously conduct variable selection, account for biomarker LOD and obtain clustering results. We took a Bayesian approach to obtain parameter estimates and the cluster membership to bypass the limitation of the EM algorithm. To account for LOD, we added one more step in Gibbs sampling to iteratively fill in biomarker values below or above LODs. In addition, we put a spike-and-slab type of prior on each variable to obtain variable importance. Simulations across various scenarios were conducted to examine the performance of this method. Real data application on electronic health records was also conducted.
Tasks
Published	2019-05-09
URL	https://arxiv.org/abs/1905.03680v1
PDF	https://arxiv.org/pdf/1905.03680v1.pdf
PWC	https://paperswithcode.com/paper/190503680
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Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models


Title	Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models
Authors	Vishnu TV, Diksha, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff
Abstract	Prognostics or Remaining Useful Life (RUL) Estimation from multi-sensor time series data is useful to enable condition-based maintenance and ensure high operational availability of equipment. We propose a novel deep learning based approach for Prognostics with Uncertainty Quantification that is useful in scenarios where: (i) access to labeled failure data is scarce due to rarity of failures (ii) future operational conditions are unobserved and (iii) inherent noise is present in the sensor readings. All three scenarios mentioned are unavoidable sources of uncertainty in the RUL estimation process often resulting in unreliable RUL estimates. To address (i), we formulate RUL estimation as an Ordinal Regression (OR) problem, and propose LSTM-OR: deep Long Short Term Memory (LSTM) network based approach to learn the OR function. We show that LSTM-OR naturally allows for incorporation of censored operational instances in training along with the failed instances, leading to more robust learning. To address (ii), we propose a simple yet effective approach to quantify predictive uncertainty in the RUL estimation models by training an ensemble of LSTM-OR models. Through empirical evaluation on C-MAPSS turbofan engine benchmark datasets, we demonstrate that LSTM-OR is significantly better than the commonly used deep metric regression based approaches for RUL estimation, especially when failed training instances are scarce. Further, our uncertainty quantification approach yields high quality predictive uncertainty estimates while also leading to improved RUL estimates compared to single best LSTM-OR models.
Tasks	Time Series
Published	2019-03-23
URL	http://arxiv.org/abs/1903.09795v3
PDF	http://arxiv.org/pdf/1903.09795v3.pdf
PWC	https://paperswithcode.com/paper/data-driven-prognostics-with-predictive
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Discovering Latent Classes for Semi-Supervised Semantic Segmentation


Title	Discovering Latent Classes for Semi-Supervised Semantic Segmentation
Authors	Johann Sawatzky, Olga Zatsarynna, Juergen Gall
Abstract	High annotation costs are a major bottleneck for the training of semantic segmentation systems. Therefore, methods working with less annotation effort are of special interest. This paper studies the problem of semi-supervised semantic segmentation. This means that only a small subset of the training images is annotated while the other training images do not contain any annotation. In order to leverage the information present in the unlabeled images, we propose to learn a second task that is related to semantic segmentation but easier. On labeled images, we learn latent classes consistent with semantic classes so that the variety of semantic classes assigned to a latent class is as low as possible. On unlabeled images, we predict a probability map for latent classes and use it as a supervision signal to learn semantic segmentation. The latent classes, as well as the semantic classes, are simultaneously predicted by a two-branch network. In our experiments on Pascal VOC and Cityscapes, we show that the latent classes learned this way have an intuitive meaning and that the proposed method achieves state of the art results for semi-supervised semantic segmentation.
Tasks	Semantic Segmentation, Semi-Supervised Semantic Segmentation
Published	2019-12-30
URL	https://arxiv.org/abs/1912.12936v1
PDF	https://arxiv.org/pdf/1912.12936v1.pdf
PWC	https://paperswithcode.com/paper/discovering-latent-classes-for-semi
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Contrast Trees and Distribution Boosting


Title	Contrast Trees and Distribution Boosting
Authors	Jerome H. Friedman
Abstract	Often machine learning methods are applied and results reported in cases where there is little to no information concerning accuracy of the output. Simply because a computer program returns a result does not insure its validity. If decisions are to be made based on such results it is important to have some notion of their veracity. Contrast trees represent a new approach for assessing the accuracy of many types of machine learning estimates that are not amenable to standard (cross) validation methods. In situations where inaccuracies are detected boosted contrast trees can often improve performance. A special case, distribution boosting, provides an assumption free method for estimating the full probability distribution of an outcome variable given any set of joint input predictor variable values.
Tasks
Published	2019-12-08
URL	https://arxiv.org/abs/1912.03785v1
PDF	https://arxiv.org/pdf/1912.03785v1.pdf
PWC	https://paperswithcode.com/paper/contrast-trees-and-distribution-boosting
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Task-Oriented Conversation Generation Using Heterogeneous Memory Networks


Title	Task-Oriented Conversation Generation Using Heterogeneous Memory Networks
Authors	Zehao Lin, Xinjing Huang, Feng Ji, Haiqing Chen, Ying Zhang
Abstract	How to incorporate external knowledge into a neural dialogue model is critically important for dialogue systems to behave like real humans. To handle this problem, memory networks are usually a great choice and a promising way. However, existing memory networks do not perform well when leveraging heterogeneous information from different sources. In this paper, we propose a novel and versatile external memory networks called Heterogeneous Memory Networks (HMNs), to simultaneously utilize user utterances, dialogue history and background knowledge tuples. In our method, historical sequential dialogues are encoded and stored into the context-aware memory enhanced by gating mechanism while grounding knowledge tuples are encoded and stored into the context-free memory. During decoding, the decoder augmented with HMNs recurrently selects each word in one response utterance from these two memories and a general vocabulary. Experimental results on multiple real-world datasets show that HMNs significantly outperform the state-of-the-art data-driven task-oriented dialogue models in most domains.
Tasks
Published	2019-09-25
URL	https://arxiv.org/abs/1909.11287v1
PDF	https://arxiv.org/pdf/1909.11287v1.pdf
PWC	https://paperswithcode.com/paper/task-oriented-conversation-generation-using
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Framework

Better Technical Debt Detection via SURVEYing


Title	Better Technical Debt Detection via SURVEYing
Authors	Fahmid M. Fahid, Zhe Yu, Tim Menzies
Abstract	Software analytics can be improved by surveying; i.e. rechecking and (possibly) revising the labels offered by prior analysis. Surveying is a time-consuming task and effective surveyors must carefully manage their time. Specifically, they must balance the cost of further surveying against the additional benefits of that extra effort. This paper proposes SURVEY0, an incremental Logistic Regression estimation method that implements cost/benefit analysis. Some classifier is used to rank the as-yet-unvisited examples according to how interesting they might be. Humans then review the most interesting examples, after which their feedback is used to update an estimator for estimating how many examples are remaining. This paper evaluates SURVEY0 in the context of self-admitted technical debt. As software project mature, they can accumulate “technical debt” i.e. developer decisions which are sub-optimal and decrease the overall quality of the code. Such decisions are often commented on by programmers in the code; i.e. it is self-admitted technical debt (SATD). Recent results show that text classifiers can automatically detect such debt. We find that we can significantly outperform prior results by SURVEYing the data. Specifically, for ten open-source JAVA projects, we can find 83% of the technical debt via SURVEY0 using just 16% of the comments (and if higher levels of recall are required, SURVEY0can adjust towards that with some additional effort).
Tasks
Published	2019-05-20
URL	https://arxiv.org/abs/1905.08297v1
PDF	https://arxiv.org/pdf/1905.08297v1.pdf
PWC	https://paperswithcode.com/paper/better-technical-debt-detection-via-surveying
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Framework

Drone Path-Following in GPS-Denied Environments using Convolutional Networks


Title	Drone Path-Following in GPS-Denied Environments using Convolutional Networks
Authors	M. Samy, K. Amer, M. Shaker, M. ElHelw
Abstract	his paper presents a simple approach for drone navigation to follow a predetermined path using visual input only without reliance on a Global Positioning System (GPS). A Convolutional Neural Network (CNN) is used to output the steering command of the drone in an end-to-end approach. We tested our approach in two simulated environments in the Unreal Engine using the AirSim plugin for drone simulation. Results show that the proposed approach, despite its simplicity, has average cross track distance less than 2.9 meters in the simulated environment. We also investigate the significance of data augmentation in path following. Finally, we conclude by suggesting possible enhancements for extending our approach to more difficult paths in real life, in the hope that one day visual navigation will become the norm in GPS-denied zones.
Tasks	Data Augmentation, Drone navigation, Visual Navigation
Published	2019-05-05
URL	https://arxiv.org/abs/1905.01658v1
PDF	https://arxiv.org/pdf/1905.01658v1.pdf
PWC	https://paperswithcode.com/paper/drone-path-following-in-gps-denied
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Framework

PopEval: A Character-Level Approach to End-To-End Evaluation Compatible with Word-Level Benchmark Dataset


Title	PopEval: A Character-Level Approach to End-To-End Evaluation Compatible with Word-Level Benchmark Dataset
Authors	Hong-Seok Lee, Youngmin Yoon, Pil-Hoon Jang, Chankyu Choi
Abstract	The most prevalent scope of interest for OCR applications used to be scanned documents, but it has now shifted towards the natural scene. Despite the change of times, the existing evaluation methods are still based on the old criteria suited better for the past interests. In this paper, we propose PopEval, a novel evaluation approach for the recent OCR interests. The new and past evaluation algorithms were compared through the results on various datasets and OCR models. Compared to the other evaluation methods, the proposed evaluation algorithm was closer to the human’s qualitative evaluation than other existing methods. Although the evaluation algorithm was devised as a character-level approach, the comparative experiment revealed that PopEval is also compatible on existing benchmark datasets annotated at word-level. The proposed evaluation algorithm is not only applicable to current end-to-end tasks, but also suggests a new direction to redesign the evaluation concept for further OCR researches.
Tasks	Optical Character Recognition
Published	2019-08-29
URL	https://arxiv.org/abs/1908.11060v1
PDF	https://arxiv.org/pdf/1908.11060v1.pdf
PWC	https://paperswithcode.com/paper/popeval-a-character-level-approach-to-end-to
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Framework

Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning


Title	Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning
Authors	Frederik Benzing, Marcelo Matheus Gauy, Asier Mujika, Anders Martinsson, Angelika Steger
Abstract	One of the central goals of Recurrent Neural Networks (RNNs) is to learn long-term dependencies in sequential data. Nevertheless, the most popular training method, Truncated Backpropagation through Time (TBPTT), categorically forbids learning dependencies beyond the truncation horizon. In contrast, the online training algorithm Real Time Recurrent Learning (RTRL) provides untruncated gradients, with the disadvantage of impractically large computational costs. Recently published approaches reduce these costs by providing noisy approximations of RTRL. We present a new approximation algorithm of RTRL, Optimal Kronecker-Sum Approximation (OK). We prove that OK is optimal for a class of approximations of RTRL, which includes all approaches published so far. Additionally, we show that OK has empirically negligible noise: Unlike previous algorithms it matches TBPTT in a real world task (character-level Penn TreeBank) and can exploit online parameter updates to outperform TBPTT in a synthetic string memorization task. Code availiable on github.
Tasks
Published	2019-02-11
URL	https://arxiv.org/abs/1902.03993v2
PDF	https://arxiv.org/pdf/1902.03993v2.pdf
PWC	https://paperswithcode.com/paper/optimal-kronecker-sum-approximation-of-real
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Framework

Self-supervised Learning for ECG-based Emotion Recognition


Title	Self-supervised Learning for ECG-based Emotion Recognition
Authors	Pritam Sarkar, Ali Etemad
Abstract	We present an electrocardiogram (ECG) -based emotion recognition system using self-supervised learning. Our proposed architecture consists of two main networks, a signal transformation recognition network and an emotion recognition network. First, unlabelled data are used to successfully train the former network to detect specific pre-determined signal transformations in the self-supervised learning step. Next, the weights of the convolutional layers of this network are transferred to the emotion recognition network, and two dense layers are trained in order to classify arousal and valence scores. We show that our self-supervised approach helps the model learn the ECG feature manifold required for emotion recognition, performing equal or better than the fully-supervised version of the model. Our proposed method outperforms the state-of-the-art in ECG-based emotion recognition with two publicly available datasets, SWELL and AMIGOS. Further analysis highlights the advantage of our self-supervised approach in requiring significantly less data to achieve acceptable results.
Tasks	Emotion Recognition
Published	2019-10-14
URL	https://arxiv.org/abs/1910.07497v2
PDF	https://arxiv.org/pdf/1910.07497v2.pdf
PWC	https://paperswithcode.com/paper/self-supervised-learning-for-ecg-based
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Framework

Transferable Force-Torque Dynamics Model for Peg-in-hole Task


Title	Transferable Force-Torque Dynamics Model for Peg-in-hole Task
Authors	Junfeng Ding, Chen Wang, Cewu Lu
Abstract	We present a learning-based force-torque dynamics to achieve model-based control for contact-rich peg-in-hole task using force-only inputs. Learning the force-torque dynamics is challenging because of the ambiguity of the low-dimensional 6-d force signal and the requirement of excessive training data. To tackle these problems, we propose a multi-pose force-torque state representation, based on which a dynamics model is learned with the data generated in a sample-efficient offline fashion. In addition, by training the dynamics model with peg-and-holes of various shapes, scales, and elasticities, the model could quickly transfer to new peg-and-holes after a small number of trials. Extensive experiments show that our dynamics model could adapt to unseen peg-and-holes with 70% fewer samples required compared to learning from scratch. Along with the learned dynamics, model predictive control and model-based reinforcement learning policies achieve over 80% insertion success rate. Our video is available at https://youtu.be/ZAqldpVZgm4.
Tasks
Published	2019-11-30
URL	https://arxiv.org/abs/1912.00260v1
PDF	https://arxiv.org/pdf/1912.00260v1.pdf
PWC	https://paperswithcode.com/paper/transferable-force-torque-dynamics-model-for
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Framework

Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations


Title	Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations
Authors	Ethan Wilcox, Roger Levy, Richard Futrell
Abstract	Deep learning sequence models have led to a marked increase in performance for a range of Natural Language Processing tasks, but it remains an open question whether they are able to induce proper hierarchical generalizations for representing natural language from linear input alone. Work using artificial languages as training input has shown that LSTMs are capable of inducing the stack-like data structures required to represent context-free and certain mildly context-sensitive languages—formal language classes which correspond in theory to the hierarchical structures of natural language. Here we present a suite of experiments probing whether neural language models trained on linguistic data induce these stack-like data structures and deploy them while incrementally predicting words. We study two natural language phenomena: center embedding sentences and syntactic island constraints on the filler–gap dependency. In order to properly predict words in these structures, a model must be able to temporarily suppress certain expectations and then recover those expectations later, essentially pushing and popping these expectations on a stack. Our results provide evidence that models can successfully suppress and recover expectations in many cases, but do not fully recover their previous grammatical state.
Tasks
Published	2019-06-10
URL	https://arxiv.org/abs/1906.04068v1
PDF	https://arxiv.org/pdf/1906.04068v1.pdf
PWC	https://paperswithcode.com/paper/hierarchical-representation-in-neural
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A deep artificial neural network based model for underlying cause of death prediction from death certificates


Title	A deep artificial neural network based model for underlying cause of death prediction from death certificates
Authors	Louis Falissard, Claire Morgand, Sylvie Roussel, Claire Imbaud, Walid Ghosn, Karim Bounebache, Grégoire Rey
Abstract	Underlying cause of death coding from death certificates is a process that is nowadays undertaken mostly by humans with a potential assistance from expert systems such as the Iris software. It is as a consequence an expensive process that can in addition suffer from geospatial discrepancies, thus severely impairing the comparability of death statistics at the international level. The recent advances in artificial intelligence, specifically the raise of deep learning methods, has enabled computers to make efficient decisions on a number of complex problem that were typically considered as out of reach without human assistance. They however require a considerable amount of data to learn from, which is typically their main limiting factor. However, the C'epiDc stores an exhaustive database of death certificate at the French national scale, amounting to several millions training example available for the machine learning practitioner. This article presents a deep learning based tool for automated coding of the underlying cause of death from the data contained in death certificates with 97.8% accuracy, a substantial achievement compared to the Iris software and its 75% accuracy assessed on the same test examples. Such an improvement opens a whole field of new applications, from nosologist-level batch automated coding to international and temporal harmonization of cause of death statistics.
Tasks
Published	2019-08-26
URL	https://arxiv.org/abs/1908.09712v1
PDF	https://arxiv.org/pdf/1908.09712v1.pdf
PWC	https://paperswithcode.com/paper/a-deep-artificial-neural-network-based-model
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