pwc-archive/papers-with-abstracts huggingface dataset:
Improving Robustness of Task Oriented Dialog Systems
Authors: Arash Einolghozati, Sonal Gupta, Mrinal Mohit, Rushin Shah
Date: 2019-11-12 00:00:00
Tasks: Adversarial Attack, Data Augmentation, Intent Detection, Sentence, Sentence Classification, Translation

Task oriented language understanding in dialog systems is often modeled using intents (task of a query) and slots (parameters for that task). Intent detection and slot tagging are, in turn, modeled using sentence classification and word tagging techniques respectively. Similar to adversarial attack problems with computer vision models discussed in existing literature, these intent-slot tagging models are often over-sensitive to small variations in input -- predicting different and often incorrect labels when small changes are made to a query, thus reducing their accuracy and reliability. However, evaluating a model's robustness to these changes is harder for language since words are discrete and an automated change (e.g. adding `noise') to a query sometimes changes the meaning and thus labels of a query. In this paper, we first describe how to create an adversarial test set to measure the robustness of these models. Furthermore, we introduce and adapt adversarial training methods as well as data augmentation using back-translation to mitigate these issues. Our experiments show that both techniques improve the robustness of the system substantially and can be combined to yield the best results.
Learning from Data-Rich Problems: A Case Study on Genetic Variant Calling
Authors: Ren Yi, Pi-Chuan Chang, Gunjan Baid, Andrew Carroll
Date: 2019-11-12 00:00:00
Tasks: Data Augmentation

Next Generation Sequencing can sample the whole genome (WGS) or the 1-2% of the genome that codes for proteins called the whole exome (WES). Machine learning approaches to variant calling achieve high accuracy in WGS data, but the reduced number of training examples causes training with WES data alone to achieve lower accuracy. We propose and compare three different data augmentation strategies for improving performance on WES data: 1) joint training with WES and WGS data, 2) warmstarting the WES model from a WGS model, and 3) joint training with the sequencing type specified. All three approaches show improved accuracy over a model trained using just WES data, suggesting the ability of models to generalize insights from the greater WGS data while retaining performance on the specialized WES problem. These data augmentation approaches may apply to other problem areas in genomics, where several specialized models would each see only a subset of the genome.
HyPar-Flow: Exploiting MPI and Keras for Scalable Hybrid-Parallel DNN Training using TensorFlow
Authors: Ammar Ahmad Awan, Arpan Jain, Quentin Anthony, Hari Subramoni, Dhabaleswar K. Panda
Date: 2019-11-12 00:00:00

To reduce training time of large-scale DNNs, scientists have started to explore parallelization strategies like data-parallelism, model-parallelism, and hybrid-parallelism. While data-parallelism has been extensively studied and developed, several problems exist in realizing model-parallelism and hybrid-parallelism efficiently. Four major problems we focus on are: 1) defining a notion of a distributed model across processes, 2) implementing forward/back-propagation across process boundaries that requires explicit communication, 3) obtaining parallel speedup on an inherently sequential task, and 4) achieving scalability without losing out on a model's accuracy. To address these problems, we create HyPar-Flow --- a model-size/-type agnostic, scalable, practical, and user-transparent system for hybrid-parallel training by exploiting MPI, Keras, and TensorFlow. HyPar-Flow provides a single API that can be used to perform data, model, and hybrid parallel training of any Keras model at scale. We create an internal distributed representation of the user-provided Keras model, utilize TF's Eager execution features for distributed forward/back-propagation across processes, exploit pipelining to improve performance and leverage efficient MPI primitives for scalable communication. Between model partitions, we use send and recv to exchange layer-data/partial-errors while allreduce is used to accumulate/average gradients across model replicas. Beyond the design and implementation of HyPar-Flow, we also provide comprehensive correctness and performance results on three state-of-the-art HPC systems including TACC Frontera (#5 on Top500.org). For ResNet-1001, an ultra-deep model, HyPar-Flow provides: 1) Up to 1.6x speedup over Horovod-based data-parallel training, 2) 110x speedup over single-node on 128 Stampede2 nodes, and 3) 481x speedup over single-node on 512 Frontera nodes.
Incentivized Exploration for Multi-Armed Bandits under Reward Drift
Authors: Zhiyuan Liu, Huazheng Wang, Fan Shen, Kai Liu, Lijun Chen
Date: 2019-11-12 00:00:00
Tasks: Multi-Armed Bandits, Thompson Sampling

We study incentivized exploration for the multi-armed bandit (MAB) problem where the players receive compensation for exploring arms other than the greedy choice and may provide biased feedback on reward. We seek to understand the impact of this drifted reward feedback by analyzing the performance of three instantiations of the incentivized MAB algorithm: UCB, $\varepsilon$-Greedy, and Thompson Sampling. Our results show that they all achieve $\mathcal{O}(\log T)$ regret and compensation under the drifted reward, and are therefore effective in incentivizing exploration. Numerical examples are provided to complement the theoretical analysis.
Unsupervised Medical Image Segmentation with Adversarial Networks: From Edge Diagrams to Segmentation Maps
Authors: Umaseh Sivanesan, Luis H. Braga, Ranil R. Sonnadara, Kiret Dhindsa
Date: 2019-11-12 00:00:00
Tasks: Edge Detection, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

We develop and approach to unsupervised semantic medical image segmentation that extends previous work with generative adversarial networks. We use existing edge detection methods to construct simple edge diagrams, train a generative model to convert them into synthetic medical images, and construct a dataset of synthetic images with known segmentations using variations on extracted edge diagrams. This synthetic dataset is then used to train a supervised image segmentation model. We test our approach on a clinical dataset of kidney ultrasound images and the benchmark ISIC 2018 skin lesion dataset. We show that our unsupervised approach is more accurate than previous unsupervised methods, and performs reasonably compared to supervised image segmentation models. All code and trained models are available at https://github.com/kiretd/Unsupervised-MIseg.
Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning
Authors: Chufan Gao, Fabian Falck, Mononito Goswami, Anthony Wertz, Michael R. Pinsky, Artur Dubrawski
Date: 2019-11-12 00:00:00
Tasks: BIG-bench Machine Learning, Survival Prediction, Time Series, Time Series Analysis

Monitoring physiological responses to hemodynamic stress can help in determining appropriate treatment and ensuring good patient outcomes. Physicians' intuition suggests that the human body has a number of physiological response patterns to hemorrhage which escalate as blood loss continues, however the exact etiology and phenotypes of such responses are not well known or understood only at a coarse level. Although previous research has shown that machine learning models can perform well in hemorrhage detection and survival prediction, it is unclear whether machine learning could help to identify and characterize the underlying physiological responses in raw vital sign data. We approach this problem by first transforming the high-dimensional vital sign time series into a tractable, lower-dimensional latent space using a dilated, causal convolutional encoder model trained purely unsupervised. Second, we identify informative clusters in the embeddings. By analyzing the clusters of latent embeddings and visualizing them over time, we hypothesize that the clusters correspond to the physiological response patterns that match physicians' intuition. Furthermore, we attempt to evaluate the latent embeddings using a variety of methods, such as predicting the cluster labels using explainable features.
An Unethical Optimization Principle
Authors: Nicholas Beale, Heather Battey, Anthony C. Davison, Robert S. MacKay
Date: 2019-11-12 00:00:00
Tasks: Unity

If an artificial intelligence aims to maximise risk-adjusted return, then under mild conditions it is disproportionately likely to pick an unethical strategy unless the objective function allows sufficiently for this risk. Even if the proportion ${\eta}$ of available unethical strategies is small, the probability ${p_U}$ of picking an unethical strategy can become large; indeed unless returns are fat-tailed ${p_U}$ tends to unity as the strategy space becomes large. We define an Unethical Odds Ratio Upsilon (${\Upsilon}$) that allows us to calculate ${p_U}$ from ${\eta}$, and we derive a simple formula for the limit of ${\Upsilon}$ as the strategy space becomes large. We give an algorithm for estimating ${\Upsilon}$ and ${p_U}$ in finite cases and discuss how to deal with infinite strategy spaces. We show how this principle can be used to help detect unethical strategies and to estimate ${\eta}$. Finally we sketch some policy implications of this work.
Deep Multi-task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging
Authors: Riqiang Gao, Lingfeng li, Yucheng Tang, Sanja L. Antic, Alexis B. Paulson, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman
Date: 2019-11-12 00:00:00
Tasks: Computed Tomography (CT), Diagnostic, Multi-Task Learning, Prognosis, regression

Annual low dose computed tomography (CT) lung screening is currently advised for individuals at high risk of lung cancer (e.g., heavy smokers between 55 and 80 years old). The recommended screening practice significantly reduces all-cause mortality, but the vast majority of screening results are negative for cancer. If patients at very low risk could be identified based on individualized, image-based biomarkers, the health care resources could be more efficiently allocated to higher risk patients and reduce overall exposure to ionizing radiation. In this work, we propose a multi-task (diagnosis and prognosis) deep convolutional neural network to improve the diagnostic accuracy over a baseline model while simultaneously estimating a personalized cancer-free progression time (CFPT). A novel Censored Regression Loss (CRL) is proposed to perform weakly supervised regression so that even single negative screening scans can provide small incremental value. Herein, we study 2287 scans from 1433 de-identified patients from the Vanderbilt Lung Screening Program (VLSP) and Molecular Characterization Laboratories (MCL) cohorts. Using five-fold cross-validation, we train a 3D attention-based network under two scenarios: (1) single-task learning with only classification, and (2) multi-task learning with both classification and regression. The single-task learning leads to a higher AUC compared with the Kaggle challenge winner pre-trained model (0.878 v. 0.856), and multi-task learning significantly improves the single-task one (AUC 0.895, p<0.01, McNemar test). In summary, the image-based predicted CFPT can be used in follow-up year lung cancer prediction and data assessment.
Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision
Authors: Ho Hin Lee, Yucheng Tang, Olivia Tang, Yuchen Xu, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman
Date: 2019-11-12 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Organ Segmentation, Segmentation, Semantic Segmentation

Human in-the-loop quality assurance (QA) is typically performed after medical image segmentation to ensure that the systems are performing as intended, as well as identifying and excluding outliers. By performing QA on large-scale, previously unlabeled testing data, categorical QA scores can be generatedIn this paper, we propose a semi-supervised multi-organ segmentation deep neural network consisting of a traditional segmentation model generator and a QA involved discriminator. A large-scale dataset of 2027 volumes are used to train the generator, whose 2-D montage images and segmentation mask with QA scores are used to train the discriminator. To generate the QA scores, the 2-D montage images were reviewed manually and coded 0 (success), 1 (errors consistent with published performance), and 2 (gross failure). Then, the ResNet-18 network was trained with 1623 montage images in equal distribution of all three code labels and achieved an accuracy 94% for classification predictions with 404 montage images withheld for the test cohort. To assess the performance of using the QA supervision, the discriminator was used as a loss function in a multi-organ segmentation pipeline. The inclusion of QA-loss function boosted performance on the unlabeled test dataset from 714 patients to 951 patients over the baseline model. Additionally, the number of failures decreased from 606 (29.90%) to 402 (19.83%). The contributions of the proposed method are threefold: We show that (1) the QA scores can be used as a loss function to perform semi-supervised learning for unlabeled data, (2) the well trained discriminator is learnt by QA score rather than traditional true/false, and (3) the performance of multi-organ segmentation on unlabeled datasets can be fine-tuned with more robust and higher accuracy than the original baseline method.
Harmonic Mean Point Processes: Proportional Rate Error Minimization for Obtundation Prediction
Authors: Yoonjung Kim, Jeremy C. Weiss
Date: 2019-11-12 00:00:00
Tasks: Point Processes

In healthcare, the highest risk individuals for morbidity and mortality are rarely those with the greatest modifiable risk. By contrast, many machine learning formulations implicitly attend to the highest risk individuals. We focus on this problem in point processes, a popular modeling technique for the analysis of the temporal event sequences in electronic health records (EHR) data with applications in risk stratification and risk score systems. We show that optimization of the log-likelihood function also gives disproportionate attention to high risk individuals and leads to poor prediction results for low risk individuals compared to ones at high risk. We characterize the problem and propose an adjusted log-likelihood formulation as a new objective for point processes. We demonstrate the benefits of our method in simulations and in EHR data of patients admitted to the critical care unit for intracerebral hemorrhage.
Time-Aware Prospective Modeling of Users for Online Display Advertising
Authors: Djordje Gligorijevic, Jelena Gligorijevic, Aaron Flores
Date: 2019-11-12 00:00:00

Prospective display advertising poses a great challenge for large advertising platforms as the strongest predictive signals of users are not eligible to be used in the conversion prediction systems. To that end efforts are made to collect as much information as possible about each user from various data sources and to design powerful models that can capture weaker signals ultimately obtaining good quality of conversion prediction probability estimates. In this study we propose a novel time-aware approach to model heterogeneous sequences of users' activities and capture implicit signals of users' conversion intents. On two real-world datasets we show that our approach outperforms other, previously proposed approaches, while providing interpretability of signal impact to conversion probability.
Constant Curvature Graph Convolutional Networks
Authors: Gregor Bachmann, Gary Bécigneul, Octavian-Eugen Ganea
Date: 2019-11-12 00:00:00
Proceeding: ICML 2020 1
Tasks: Node Classification

Interest has been rising lately towards methods representing data in non-Euclidean spaces, e.g. hyperbolic or spherical, that provide specific inductive biases useful for certain real-world data properties, e.g. scale-free, hierarchical or cyclical. However, the popular graph neural networks are currently limited in modeling data only via Euclidean geometry and associated vector space operations. Here, we bridge this gap by proposing mathematically grounded generalizations of graph convolutional networks (GCN) to (products of) constant curvature spaces. We do this by i) introducing a unified formalism that can interpolate smoothly between all geometries of constant curvature, ii) leveraging gyro-barycentric coordinates that generalize the classic Euclidean concept of the center of mass. Our class of models smoothly recover their Euclidean counterparts when the curvature goes to zero from either side. Empirically, we outperform Euclidean GCNs in the tasks of node classification and distortion minimization for symbolic data exhibiting non-Euclidean behavior, according to their discrete curvature.
Schedule Earth Observation satellites with Deep Reinforcement Learning
Authors: Adrien Hadj-Salah, Rémi Verdier, Clément Caron, Mathieu Picard, Mikaël Capelle
Date: 2019-11-12 00:00:00
Tasks: Deep Reinforcement Learning, Earth Observation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling, valid

Optical Earth observation satellites acquire images worldwide , covering up to several million square kilometers every day. The complexity of scheduling acquisitions for such systems increases exponentially when considering the interoperabil-ity of several satellite constellations together with the uncertainties from weather forecasts. In order to deliver valid images to customers as fast as possible, it is crucial to acquire cloud-free images. Depending on weather forecasts, up to 50% of images acquired by operational satellites can be trashed due to excessive cloud covers, showing there is room for improvement. We propose an acquisition scheduling approach based on Deep Reinforcement Learning and experiment on a simplified environment. We find that it challenges classical methods relying on human-expert heuristic.
Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks
Authors: Kira Maag, Matthias Rottmann, Hanno Gottschalk
Date: 2019-11-12 00:00:00
Tasks: General Classification, Semantic Segmentation, Time Series, Time Series Analysis

In the semantic segmentation of street scenes with neural networks, the reliability of predictions is of highest interest. The assessment of neural networks by means of uncertainties is a common ansatz to prevent safety issues. As in applications like automated driving, video streams of images are available, we present a time-dynamic approach to investigating uncertainties and assessing the prediction quality of neural networks. We track segments over time and gather aggregated metrics per segment, thus obtaining time series of metrics from which we assess prediction quality. This is done by either classifying between intersection over union equal to 0 and greater than 0 or predicting the intersection over union directly. We study different models for these two tasks and analyze the influence of the time series length on the predictive power of our metrics.
Predictive Multi-level Patient Representations from Electronic Health Records
Authors: Zichang Wang, Haoran Li, Lu-chen Liu, Haoxian Wu, Ming Zhang
Date: 2019-11-12 00:00:00
Tasks: Prediction

The advent of the Internet era has led to an explosive growth in the Electronic Health Records (EHR) in the past decades. The EHR data can be regarded as a collection of clinical events, including laboratory results, medication records, physiological indicators, etc, which can be used for clinical outcome prediction tasks to support constructions of intelligent health systems. Learning patient representation from these clinical events for the clinical outcome prediction is an important but challenging step. Most related studies transform EHR data of a patient into a sequence of clinical events in temporal order and then use sequential models to learn patient representations for outcome prediction. However, clinical event sequence contains thousands of event types and temporal dependencies. We further make an observation that clinical events occurring in a short period are not constrained by any temporal order but events in a long term are influenced by temporal dependencies. The multi-scale temporal property makes it difficult for traditional sequential models to capture the short-term co-occurrence and the long-term temporal dependencies in clinical event sequences. In response to the above challenges, this paper proposes a Multi-level Representation Model (MRM). MRM first uses a sparse attention mechanism to model the short-term co-occurrence, then uses interval-based event pooling to remove redundant information and reduce sequence length and finally predicts clinical outcomes through Long Short-Term Memory (LSTM). Experiments on real-world datasets indicate that our proposed model largely improves the performance of clinical outcome prediction tasks using EHR data.
Learning Representations in Reinforcement Learning:An Information Bottleneck Approach
Authors: Pei Yingjun, Hou Xinwen
Date: 2019-11-12 00:00:00
Tasks: Reinforcement Learning, Representation Learning

The information bottleneck principle is an elegant and useful approach to representation learning. In this paper, we investigate the problem of representation learning in the context of reinforcement learning using the information bottleneck framework, aiming at improving the sample efficiency of the learning algorithms. %by accelerating the process of discarding irrelevant information when the %input states are extremely high-dimensional. We analytically derive the optimal conditional distribution of the representation, and provide a variational lower bound. Then, we maximize this lower bound with the Stein variational (SV) gradient method. We incorporate this framework in the advantageous actor critic algorithm (A2C) and the proximal policy optimization algorithm (PPO). Our experimental results show that our framework can improve the sample efficiency of vanilla A2C and PPO significantly. Finally, we study the information bottleneck (IB) perspective in deep RL with the algorithm called mutual information neural estimation(MINE) . We experimentally verify that the information extraction-compression process also exists in deep RL and our framework is capable of accelerating this process. We also analyze the relationship between MINE and our method, through this relationship, we theoretically derive an algorithm to optimize our IB framework without constructing the lower bound.
Relative contributions of Shakespeare and Fletcher in Henry VIII: An Analysis Based on Most Frequent Words and Most Frequent Rhythmic Patterns
Authors: Petr Plecháč
Date: 2019-10-30 00:00:00

The versified play Henry VIII is nowadays widely recognized to be a collaborative work not written solely by William Shakespeare. We employ combined analysis of vocabulary and versification together with machine learning techniques to determine which authors also took part in the writing of the play and what were their relative contributions. Unlike most previous studies, we go beyond the attribution of particular scenes and use the rolling attribution approach to determine the probabilities of authorship of pieces of texts, without respecting the scene boundaries. Our results highly support the canonical division of the play between William Shakespeare and John Fletcher proposed by James Spedding, but also bring new evidence supporting the modifications proposed later by Thomas Merriam.