pwc-archive/papers-with-abstracts huggingface dataset:
An Approach for Process Model Extraction By Multi-Grained Text Classification
Authors: Chen Qian, Lijie Wen, Akhil Kumar, Leilei Lin, Li Lin, Zan Zong, Shuang Li, Jian-Min Wang
Date: 2019-05-16 00:00:00
Tasks: General Classification, Management, Model extraction, Multi-Task Learning, Semantic Role Labeling, Sentence Classification, text-classification, Text Classification

Process model extraction (PME) is a recently emerged interdiscipline between natural language processing (NLP) and business process management (BPM), which aims to extract process models from textual descriptions. Previous process extractors heavily depend on manual features and ignore the potential relations between clues of different text granularities. In this paper, we formalize the PME task into the multi-grained text classification problem, and propose a hierarchical neural network to effectively model and extract multi-grained information without manually-defined procedural features. Under this structure, we accordingly propose the coarse-to-fine (grained) learning mechanism, training multi-grained tasks in coarse-to-fine grained order to share the high-level knowledge for the low-level tasks. To evaluate our approach, we construct two multi-grained datasets from two different domains and conduct extensive experiments from different dimensions. The experimental results demonstrate that our approach outperforms the state-of-the-art methods with statistical significance and further investigations demonstrate its effectiveness.
Recovering Dropped Pronouns in Chinese Conversations via Modeling Their Referents
Authors: Jingxuan Yang, Jianzhuo Tong, Si Li, Sheng Gao, Jun Guo, Nianwen Xue
Date: 2019-05-17 00:00:00
Proceeding: NAACL 2019 6
Tasks: Machine Translation, Sentence, Translation

Pronouns are often dropped in Chinese sentences, and this happens more frequently in conversational genres as their referents can be easily understood from context. Recovering dropped pronouns is essential to applications such as Information Extraction where the referents of these dropped pronouns need to be resolved, or Machine Translation when Chinese is the source language. In this work, we present a novel end-to-end neural network model to recover dropped pronouns in conversational data. Our model is based on a structured attention mechanism that models the referents of dropped pronouns utilizing both sentence-level and word-level information. Results on three different conversational genres show that our approach achieves a significant improvement over the current state of the art.
Ex-Twit: Explainable Twitter Mining on Health Data
Authors: Tunazzina Islam
Date: 2019-05-24 00:00:00
Tasks: BIG-bench Machine Learning

Since most machine learning models provide no explanations for the predictions, their predictions are obscure for the human. The ability to explain a model's prediction has become a necessity in many applications including Twitter mining. In this work, we propose a method called Explainable Twitter Mining (Ex-Twit) combining Topic Modeling and Local Interpretable Model-agnostic Explanation (LIME) to predict the topic and explain the model predictions. We demonstrate the effectiveness of Ex-Twit on Twitter health-related data.
LMF Reloaded
Authors: Laurent Romary, Mohamed Khemakhem, Fahad Khan, Jack Bowers, Nicoletta Calzolari, Monte George, Mandy Pet, Piotr Bański
Date: 2019-05-23 00:00:00

Lexical Markup Framework (LMF) or ISO 24613 [1] is a de jure standard that provides a framework for modelling and encoding lexical information in retrodigitised print dictionaries and NLP lexical databases. An in-depth review is currently underway within the standardisation subcommittee , ISO-TC37/SC4/WG4, to find a more modular, flexible and durable follow up to the original LMF standard published in 2008. In this paper we will present some of the major improvements which have so far been implemented in the new version of LMF.
Automatic Detection of Cortical Arousals in Sleep and their Contribution to Daytime Sleepiness
Authors: Andreas Brink-Kjaer, Alexander Neergaard Olesen, Paul E. Peppard, Katie L. Stone, Poul Jennum, Emmanuel Mignot, Helge B. D. Sorensen
Date: 2019-03-15 00:00:00

Cortical arousals are transient events of disturbed sleep that occur
spontaneously or in response to stimuli such as apneic events. The gold
standard for arousal detection in human polysomnographic recordings (PSGs) is
manual annotation by expert human scorers, a method with significant
interscorer variability. In this study, we developed an automated method, the
Multimodal Arousal Detector (MAD), to detect arousals using deep learning
methods. The MAD was trained on 2,889 PSGs to detect both cortical arousals and
wakefulness in 1 second intervals. Furthermore, the relationship between
MAD-predicted labels on PSGs and next day mean sleep latency (MSL) on a
multiple sleep latency test (MSLT), a reflection of daytime sleepiness, was
analyzed in 1447 MSLT instances in 873 subjects. In a dataset of 1,026 PSGs,
the MAD achieved a F1 score of 0.76 for arousal detection, while wakefulness
was predicted with an accuracy of 0.95. In 60 PSGs scored by multiple human
expert technicians, the MAD significantly outperformed the average human scorer
for arousal detection with a difference in F1 score of 0.09. After controlling
for other known covariates, a doubling of the arousal index was associated with
an average decrease in MSL of 40 seconds ($\beta$ = -0.67, p = 0.0075). The MAD
outperformed the average human expert and the MAD-predicted arousals were shown
to be significant predictors of MSL, which demonstrate clinical validity the
MAD.
Towards an efficient deep learning model for musical onset detection
Authors: Rong Gong, Xavier Serra
Date: 2018-06-18 00:00:00
Tasks: Deep Learning, Onset Detection, Transfer Learning

In this paper, we propose an efficient and reproducible deep learning model
for musical onset detection (MOD). We first review the state-of-the-art deep
learning models for MOD, and identify their shortcomings and challenges: (i)
the lack of hyper-parameter tuning details, (ii) the non-availability of code
for training models on other datasets, and (iii) ignoring the network
capability when comparing different architectures. Taking the above issues into
account, we experiment with seven deep learning architectures. The most
efficient one achieves equivalent performance to our implementation of the
state-of-the-art architecture. However, it has only 28.3% of the total number
of trainable parameters compared to the state-of-the-art. Our experiments are
conducted using two different datasets: one mainly consists of instrumental
music excerpts, and another developed by ourselves includes only solo singing
voice excerpts. Further, inter-dataset transfer learning experiments are
conducted. The results show that the model pre-trained on one dataset fails to
detect onsets on another dataset, which denotes the importance of providing the
implementation code to enable re-training the model for a different dataset.
Datasets, code and a Jupyter notebook running on Google Colab are publicly
available to make this research understandable and easy to reproduce.
Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders
Authors: Agostina J. Larrazabal, Cesar Martinez, Enzo Ferrante
Date: 2019-06-05 00:00:00
Tasks: Denoising, Image Segmentation, Segmentation, Semantic Segmentation, valid

Deep convolutional neural networks (CNN) proved to be highly accurate to perform anatomical segmentation of medical images. However, some of the most popular CNN architectures for image segmentation still rely on post-processing strategies (e.g. Conditional Random Fields) to incorporate connectivity constraints into the resulting masks. These post-processing steps are based on the assumption that objects are usually continuous and therefore nearby pixels should be assigned the same object label. Even if it is a valid assumption in general, these methods do not offer a straightforward way to incorporate more complex priors like convexity or arbitrary shape restrictions. In this work we propose Post-DAE, a post-processing method based on denoising autoencoders (DAE) trained using only segmentation masks. We learn a low-dimensional space of anatomically plausible segmentations, and use it as a post-processing step to impose shape constraints on the resulting masks obtained with arbitrary segmentation methods. Our approach is independent of image modality and intensity information since it employs only segmentation masks for training. This enables the use of anatomical segmentations that do not need to be paired with intensity images, making the approach very flexible. Our experimental results on anatomical segmentation of X-ray images show that Post-DAE can improve the quality of noisy and incorrect segmentation masks obtained with a variety of standard methods, by bringing them back to a feasible space, with almost no extra computational time.
Generative Model-Based Ischemic Stroke Lesion Segmentation
Authors: Tao Song
Date: 2019-06-06 00:00:00
Tasks: Image Segmentation, Ischemic Stroke Lesion Segmentation, Lesion Segmentation, Medical Image Segmentation, model, Segmentation, Semantic Segmentation

CT perfusion (CTP) has been used to triage ischemic stroke patients in the early stage, because of its speed, availability, and lack of contraindications. Perfusion parameters including cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT) and time of peak (Tmax) could also be computed from CTP data. However, CTP data or the perfusion parameters, are ambiguous to locate the infarct core or tissue at risk (penumbra), which is normally confirmed by the follow-up Diffusion Weighted Imaging (DWI) or perfusion diffusion mismatch. In this paper, we propose a novel generative modelbased segmentation framework composed of an extractor, a generator and a segmentor for ischemic stroke lesion segmentation. First, an extractor is used to directly extract the representative feature images from the CTP feature images. Second, a generator is used to generate the clinical relevant DWI images using the output from the extractor and perfusion parameters. Finally, the segmentor is used to precisely segment the ischemic stroke lesion using the generated DWI from the generator. Meanwhile, a novel pixel-region loss function, generalized dice combined with weighted cross entropy, is used to handle data unbalance problem which is commonly encountered in medical image segmentation. All networks are trained end-to-end from scratch using the 2018 Ischemic Stroke Lesion Segmentation Challenge (ISLES) dataset and our method won the first place in the 2018 ischemic stroke lesions segmentation challenge in the test stage.
Mesh R-CNN
Authors: Georgia Gkioxari, Jitendra Malik, Justin Johnson
Date: 2019-06-06 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Shape Modeling, Prediction

Rapid advances in 2D perception have led to systems that accurately detect objects in real-world images. However, these systems make predictions in 2D, ignoring the 3D structure of the world. Concurrently, advances in 3D shape prediction have mostly focused on synthetic benchmarks and isolated objects. We unify advances in these two areas. We propose a system that detects objects in real-world images and produces a triangle mesh giving the full 3D shape of each detected object. Our system, called Mesh R-CNN, augments Mask R-CNN with a mesh prediction branch that outputs meshes with varying topological structure by first predicting coarse voxel representations which are converted to meshes and refined with a graph convolution network operating over the mesh's vertices and edges. We validate our mesh prediction branch on ShapeNet, where we outperform prior work on single-image shape prediction. We then deploy our full Mesh R-CNN system on Pix3D, where we jointly detect objects and predict their 3D shapes.
Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
Authors: Lianhui Qin, Michel Galley, Chris Brockett, Xiaodong Liu, Xiang Gao, Bill Dolan, Yejin Choi, Jianfeng Gao
Date: 2019-06-06 00:00:00
Proceeding: ACL 2019 7
Tasks: Diversity, Informativeness, Reading Comprehension, Response Generation

Although neural conversation models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous. We present a new end-to-end approach to contentful neural conversation that jointly models response generation and on-demand machine reading. The key idea is to provide the conversation model with relevant long-form text on the fly as a source of external knowledge. The model performs QA-style reading comprehension on this text in response to each conversational turn, thereby allowing for more focused integration of external knowledge than has been possible in prior approaches. To support further research on knowledge-grounded conversation, we introduce a new large-scale conversation dataset grounded in external web pages (2.8M turns, 7.4M sentences of grounding). Both human evaluation and automated metrics show that our approach results in more contentful responses compared to a variety of previous methods, improving both the informativeness and diversity of generated output.
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Authors: Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare
Date: 2019-06-06 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Many reinforcement learning (RL) tasks provide the agent with high-dimensional observations that can be simplified into low-dimensional continuous states. To formalize this process, we introduce the concept of a DeepMDP, a parameterized latent space model that is trained via the minimization of two tractable losses: prediction of rewards and prediction of the distribution over next latent states. We show that the optimization of these objectives guarantees (1) the quality of the latent space as a representation of the state space and (2) the quality of the DeepMDP as a model of the environment. We connect these results to prior work in the bisimulation literature, and explore the use of a variety of metrics. Our theoretical findings are substantiated by the experimental result that a trained DeepMDP recovers the latent structure underlying high-dimensional observations on a synthetic environment. Finally, we show that learning a DeepMDP as an auxiliary task in the Atari 2600 domain leads to large performance improvements over model-free RL.
Residual Flows for Invertible Generative Modeling
Authors: Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen
Date: 2019-06-06 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Density Estimation, Image Generation

Flow-based generative models parameterize probability distributions through an invertible transformation and can be trained by maximum likelihood. Invertible residual networks provide a flexible family of transformations where only Lipschitz conditions rather than strict architectural constraints are needed for enforcing invertibility. However, prior work trained invertible residual networks for density estimation by relying on biased log-density estimates whose bias increased with the network's expressiveness. We give a tractable unbiased estimate of the log density using a "Russian roulette" estimator, and reduce the memory required during training by using an alternative infinite series for the gradient. Furthermore, we improve invertible residual blocks by proposing the use of activation functions that avoid derivative saturation and generalizing the Lipschitz condition to induced mixed norms. The resulting approach, called Residual Flows, achieves state-of-the-art performance on density estimation amongst flow-based models, and outperforms networks that use coupling blocks at joint generative and discriminative modeling.
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation
Authors: Raphael Gontijo Lopes, Dong Yin, Ben Poole, Justin Gilmer, Ekin D. Cubuk
Date: 2019-06-06 00:00:00
Tasks: Data Augmentation, object-detection, Object Detection

Deploying machine learning systems in the real world requires both high accuracy on clean data and robustness to naturally occurring corruptions. While architectural advances have led to improved accuracy, building robust models remains challenging. Prior work has argued that there is an inherent trade-off between robustness and accuracy, which is exemplified by standard data augment techniques such as Cutout, which improves clean accuracy but not robustness, and additive Gaussian noise, which improves robustness but hurts accuracy. To overcome this trade-off, we introduce Patch Gaussian, a simple augmentation scheme that adds noise to randomly selected patches in an input image. Models trained with Patch Gaussian achieve state of the art on the CIFAR-10 and ImageNetCommon Corruptions benchmarks while also improving accuracy on clean data. We find that this augmentation leads to reduced sensitivity to high frequency noise(similar to Gaussian) while retaining the ability to take advantage of relevant high frequency information in the image (similar to Cutout). Finally, we show that Patch Gaussian can be used in conjunction with other regularization methods and data augmentation policies such as AutoAugment, and improves performance on the COCO object detection benchmark.
A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis
Authors: Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li, Peer-Timo Bremer
Date: 2019-06-06 00:00:00
Tasks: valid

This paper provides a general framework to study the effect of sampling properties of training data on the generalization error of the learned machine learning (ML) models. Specifically, we propose a new spectral analysis of the generalization error, expressed in terms of the power spectra of the sampling pattern and the function involved. The framework is build in the Euclidean space using Fourier analysis and establishes a connection between some high dimensional geometric objects and optimal spectral form of different state-of-the-art sampling patterns. Subsequently, we estimate the expected error bounds and convergence rate of different state-of-the-art sampling patterns, as the number of samples and dimensions increase. We make several observations about generalization error which are valid irrespective of the approximation scheme (or learning architecture) and training (or optimization) algorithms. Our result also sheds light on ways to formulate design principles for constructing optimal sampling methods for particular problems.
3D-RelNet: Joint Object and Relational Network for 3D Prediction
Authors: Nilesh Kulkarni, Ishan Misra, Shubham Tulsiani, Abhinav Gupta
Date: 2019-06-06 00:00:00
Proceeding: ICCV 2019 10
Tasks: Object, Prediction

We propose an approach to predict the 3D shape and pose for the objects present in a scene. Existing learning based methods that pursue this goal make independent predictions per object, and do not leverage the relationships amongst them. We argue that reasoning about these relationships is crucial, and present an approach to incorporate these in a 3D prediction framework. In addition to independent per-object predictions, we predict pairwise relations in the form of relative 3D pose, and demonstrate that these can be easily incorporated to improve object level estimates. We report performance across different datasets (SUNCG, NYUv2), and show that our approach significantly improves over independent prediction approaches while also outperforming alternate implicit reasoning methods.
Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild
Authors: Jie Cai, Zibo Meng, Ahmed Shehab Khan, Zhiyuan Li, James O'Reilly, Shizhong Han, Ping Liu, Min Chen, Yan Tong
Date: 2019-06-06 00:00:00
Tasks: Emotion Recognition

Emotion recognition plays an important role in human-computer interaction (HCI) and has been extensively studied for decades. Although tremendous improvements have been achieved for posed expressions, recognizing human emotions in "close-to-real-world" environments remains a challenge. In this paper, we proposed two strategies to fuse information extracted from different modalities, i.e., audio and visual. Specifically, we utilized LBP-TOP, an ensemble of CNNs, and a bi-directional LSTM (BLSTM) to extract features from the visual channel, and the OpenSmile toolkit to extract features from the audio channel. Two kinds of fusion methods, i,e., feature-level fusion and model-level fusion, were developed to utilize the information extracted from the two channels. Experimental results on the EmotiW2018 AFEW dataset have shown that the proposed fusion methods outperform the baseline methods significantly and achieve better or at least comparable performance compared with the state-of-the-art methods, where the model-level fusion performs better when one of the channels totally fails.
Learning Gaussian Graphical Models with Ordered Weighted L1 Regularization
Authors: Cody Mazza-Anthony, Bogdan Mazoure, Mark Coates
Date: 2019-06-06 00:00:00
Tasks: Computational Efficiency

We address the task of identifying densely connected subsets of multivariate Gaussian random variables within a graphical model framework. We propose two novel estimators based on the Ordered Weighted $\ell_1$ (OWL) norm: 1) The Graphical OWL (GOWL) is a penalized likelihood method that applies the OWL norm to the lower triangle components of the precision matrix. 2) The column-by-column Graphical OWL (ccGOWL) estimates the precision matrix by performing OWL regularized linear regressions. Both methods can simultaneously identify highly correlated groups of variables and control the sparsity in the resulting precision matrix. We formulate GOWL such that it solves a composite optimization problem and establish that the estimator has a unique global solution. In addition, we prove sufficient grouping conditions for each column of the ccGOWL precision matrix estimate. We propose proximal descent algorithms to find the optimum for both estimators. For synthetic data where group structure is present, the ccGOWL estimator requires significantly reduced computation and achieves similar or greater accuracy than state-of-the-art estimators. Timing comparisons are presented and demonstrates the superior computational efficiency of the ccGOWL. We illustrate the grouping performance of the ccGOWL method on a cancer gene expression data set and an equities data set.
Adaptive Gradient-Based Meta-Learning Methods
Authors: Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar
Date: 2019-06-06 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Federated Learning, Few-Shot Learning, Meta-Learning

We build a theoretical framework for designing and understanding practical meta-learning methods that integrates sophisticated formalizations of task-similarity with the extensive literature on online convex optimization and sequential prediction algorithms. Our approach enables the task-similarity to be learned adaptively, provides sharper transfer-risk bounds in the setting of statistical learning-to-learn, and leads to straightforward derivations of average-case regret bounds for efficient algorithms in settings where the task-environment changes dynamically or the tasks share a certain geometric structure. We use our theory to modify several popular meta-learning algorithms and improve their meta-test-time performance on standard problems in few-shot learning and federated learning.