pwc-archive/papers-with-abstracts huggingface dataset:
Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image Classification
Authors: Jose Carranza-Rojas, Saul Calderon-Ramirez, Adán Mora-Fallas, Michael Granados-Menani, Jordina Torrents-Barrena
Date: 2019-09-29 00:00:00
Proceeding: ICANN 2019 2019 9
Tasks: General Classification, image-classification, Image Classification, Image Enhancement, Scene Text Detection

Image enhancement refers to the enrichment of certain image features such as edges, boundaries, or contrast. The main objective is to process the original image so that the overall performance of visualization, classification and segmentation tasks is considerably improved. Traditional techniques require manual fine-tuning of the parameters to control enhancement behavior. To date, recent Convolutional Neural Network (CNN) approaches frequently employ the aforementioned techniques as an enriched pre-processing step. In this work, we present the first intrinsic CNN pre-processing layer based on the well-known unsharp masking algorithm. The proposed layer injects prior knowledge about how to enhance the image, by adding high frequency information to the input, to subsequently emphasize meaningful image features. The layer optimizes the unsharp masking parameters during model training, without any manual intervention. We evaluate the network performance and impact on two applications: CIFAR100 image classification, and the PlantCLEF identification challenge. Results obtained show a significant improvement over popular CNNs, yielding 9.49% and 2.42% for PlantCLEF and general-purpose CIFAR100, respectively. The design of an unsharp enhancement layer plainly boosts the accuracy with negligible performance cost on simple CNN models, as prior knowledge is directly injected to improve its robustness.
Linking convolutional neural networks with graph convolutional networks: application in pulmonary artery-vein separation
Authors: Zhiwei Zhai, Marius Staring, Xuhui Zhou, Qiuxia Xie, Xiaojuan Xiao, M. Els Bakker, Lucia J. Kroft, Boudewijn P. F. Lelieveldt, Gudula J.A.M. Boon, Frederikus A. Klok, Berend C. Stoel
Date: 2019-09-01 00:00:00
Proceeding: Preprint 2019 9
Tasks: 3D Medical Imaging Segmentation, Medical Image Segmentation, Pulmonary Artery–Vein Classification, Pulmorary Vessel Segmentation

Graph Convolutional Networks (GCNs) are a novel and powerful method for dealing with non-Euclidean data, while Convolutional Neural Networks (CNNs) can learn features from Euclidean data such as images. In this work, we propose a novel method to combine CNNs with GCNs (CNN-GCN), that can consider both Euclidean and non-Euclidean features and can be trained end-to-end. We applied this method to separate the pulmonary vascular trees into arteries and veins (A/V). Chest CT scans were pre-processed by vessel segmentation and skeletonization, from which a graph was constructed: voxels on the skeletons resulting in a vertex set and their connections in an adjacency matrix. 3D patches centered around each vertex were extracted from the CT scans, oriented perpendicularly to the vessel. The proposed CNN-GCN classifier was trained and applied on the constructed vessel graphs, where each node is then labeled as artery or vein. The proposed method was trained and validated on data from one hospital (11 patient, 22 lungs), and tested on independent data from a different hospital (10 patients, 10 lungs). A baseline CNN method and human observer performance were used for comparison. The CNN-GCN method obtained a median accuracy of 0.773 (0.738) in the validation (test) set, compared to a median accuracy of 0.817 by the observers, and 0.727 (0.693) by the CNN. In conclusion, the proposed CNN-GCN method combines local image information with graph connectivity information, improving pulmonary A/V separation over a baseline CNN method, approaching the performance of human observers.
Pulmonary Artery–Vein Classification in CT Images Using Deep Learning
Authors: Pietro Nardelli, Daniel Jimenez-Carretero, David Bermejo-Pelaez, George R. Washko, Farbod N. Rahaghi, Maria J. Ledesma-Carbayo, Raúl San José Estépar
Date: 2018-05-04 00:00:00
Proceeding: IEEE Transactions on Medical Imaging ( Volume: 37 , Issue: 11 , Nov. 2018 ) 2018 5
Tasks: 3D Medical Imaging Segmentation, Computed Tomography (CT), Decision Making, Deep Learning, General Classification, Medical Image Segmentation, Pulmonary Artery–Vein Classification, Pulmorary Vessel Segmentation

Recent studies show that pulmonary vascular diseases may specifically affect arteries or veins through different physiologic mechanisms. To detect changes in the two vascular trees, physicians manually analyze the chest computed tomography (CT) image of the patients in search of abnormalities. This process is time consuming, difficult to standardize, and thus not feasible for large clinical studies or useful in real-world clinical decision making. Therefore, automatic separation of arteries and veins in CT images is becoming of great interest, as it may help physicians to accurately diagnose pathological conditions. In this paper, we present a novel, fully automatic approach to classify vessels from chest CT images into arteries and veins. The algorithm follows three main steps: first, a scale-space particles segmentation to isolate vessels; then a 3-D convolutional neural network (CNN) to obtain a first classification of vessels; finally, graph-cuts' optimization to refine the results. To justify the usage of the proposed CNN architecture, we compared different 2-D and 3-D CNNs that may use local information from bronchus- and vessel-enhanced images provided to the network with different strategies. We also compared the proposed CNN approach with a random forests (RFs) classifier. The methodology was trained and evaluated on the superior and inferior lobes of the right lung of 18 clinical cases with noncontrast chest CT scans, in comparison with manual classification. The proposed algorithm achieves an overall accuracy of 94%, which is higher than the accuracy obtained using other CNN architectures and RF. Our method was also validated with contrast-enhanced CT scans of patients with chronic thromboembolic pulmonary hypertension to demonstrate that our model generalizes well to contrast-enhanced modalities. The proposed method outperforms state-of-the-art methods, paving the way for future use of 3-D CNN for artery/vein classification in CT images.
A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems
Authors: S. Narendhar, T. Amudha
Date: 2012-11-21 00:00:00
Tasks: Job Shop Scheduling, Scheduling

Bio-Inspired computing is the subset of Nature-Inspired computing. Job Shop Scheduling Problem is categorized under popular scheduling problems. In this research work, Bacterial Foraging Optimization was hybridized with Ant Colony Optimization and a new technique Hybrid Bacterial Foraging Optimization for solving Job Shop Scheduling Problem was proposed. The optimal solutions obtained by proposed Hybrid Bacterial Foraging Optimization algorithms are much better when compared with the solutions obtained by Bacterial Foraging Optimization algorithm for well-known test problems of different sizes. From the implementation of this research work, it could be observed that the proposed Hybrid Bacterial Foraging Optimization was effective than Bacterial Foraging Optimization algorithm in solving Job Shop Scheduling Problems. Hybrid Bacterial Foraging Optimization is used to implement real world Job Shop Scheduling Problems.
Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering
Authors: Shiyue Zhang, Mohit Bansal
Date: 2019-09-13 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Articles, Question Answering, Question Generation, Question-Generation, valid

Text-based Question Generation (QG) aims at generating natural and relevant questions that can be answered by a given answer in some context. Existing QG models suffer from a "semantic drift" problem, i.e., the semantics of the model-generated question drifts away from the given context and answer. In this paper, we first propose two semantics-enhanced rewards obtained from downstream question paraphrasing and question answering tasks to regularize the QG model to generate semantically valid questions. Second, since the traditional evaluation metrics (e.g., BLEU) often fall short in evaluating the quality of generated questions, we propose a QA-based evaluation method which measures the QG model's ability to mimic human annotators in generating QA training data. Experiments show that our method achieves the new state-of-the-art performance w.r.t. traditional metrics, and also performs best on our QA-based evaluation metrics. Further, we investigate how to use our QG model to augment QA datasets and enable semi-supervised QA. We propose two ways to generate synthetic QA pairs: generate new questions from existing articles or collect QA pairs from new articles. We also propose two empirically effective strategies, a data filter and mixing mini-batch training, to properly use the QG-generated data for QA. Experiments show that our method improves over both BiDAF and BERT QA baselines, even without introducing new articles.
Probing the Information Encoded in X-vectors
Authors: Desh Raj, David Snyder, Daniel Povey, Sanjeev Khudanpur
Date: 2019-09-13 00:00:00
Tasks: Data Augmentation, Sentence, Speaker Recognition, Speaker Verification, Text-Independent Speaker Recognition

Deep neural network based speaker embeddings, such as x-vectors, have been shown to perform well in text-independent speaker recognition/verification tasks. In this paper, we use simple classifiers to investigate the contents encoded by x-vector embeddings. We probe these embeddings for information related to the speaker, channel, transcription (sentence, words, phones), and meta information about the utterance (duration and augmentation type), and compare these with the information encoded by i-vectors across a varying number of dimensions. We also study the effect of data augmentation during extractor training on the information captured by x-vectors. Experiments on the RedDots data set show that x-vectors capture spoken content and channel-related information, while performing well on speaker verification tasks.
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices
Authors: Vincent S. Chen, Sen Wu, Zhenzhen Weng, Alexander Ratner, Christopher Ré
Date: 2019-09-13 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Autonomous Driving, BIG-bench Machine Learning

In real-world machine learning applications, data subsets correspond to especially critical outcomes: vulnerable cyclist detections are safety-critical in an autonomous driving task, and "question" sentences might be important to a dialogue agent's language understanding for product purposes. While machine learning models can achieve high quality performance on coarse-grained metrics like F1-score and overall accuracy, they may underperform on critical subsets---we define these as slices, the key abstraction in our approach. To address slice-level performance, practitioners often train separate "expert" models on slice subsets or use multi-task hard parameter sharing. We propose Slice-based Learning, a new programming model in which the slicing function (SF), a programming interface, specifies critical data subsets for which the model should commit additional capacity. Any model can leverage SFs to learn slice expert representations, which are combined with an attention mechanism to make slice-aware predictions. We show that our approach maintains a parameter-efficient representation while improving over baselines by up to 19.0 F1 on slices and 4.6 F1 overall on datasets spanning language understanding (e.g. SuperGLUE), computer vision, and production-scale industrial systems.
Explainable Machine Learning in Deployment
Authors: Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, Peter Eckersley
Date: 2019-09-13 00:00:00
Tasks: BIG-bench Machine Learning, counterfactual, Feature Importance

Explainable machine learning offers the potential to provide stakeholders with insights into model behavior by using various methods such as feature importance scores, counterfactual explanations, or influential training data. Yet there is little understanding of how organizations use these methods in practice. This study explores how organizations view and use explainability for stakeholder consumption. We find that, currently, the majority of deployments are not for end users affected by the model but rather for machine learning engineers, who use explainability to debug the model itself. There is thus a gap between explainability in practice and the goal of transparency, since explanations primarily serve internal stakeholders rather than external ones. Our study synthesizes the limitations of current explainability techniques that hamper their use for end users. To facilitate end user interaction, we develop a framework for establishing clear goals for explainability. We end by discussing concerns raised regarding explainability.
MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks
Authors: Mohammadreza Soltaninejad, Lei Zhang, Tryphon Lambrou, Guang Yang, Nigel Allinson, Xujiong Ye
Date: 2019-09-13 00:00:00
Tasks: Brain Tumor Segmentation, Segmentation, Tumor Segmentation

In this paper, we propose a novel learning based method for automated segmentation of brain tumor in multimodal MRI images, which incorporates two sets of machine -learned and hand crafted features. Fully convolutional networks (FCN) forms the machine learned features and texton based features are considered as hand-crafted features. Random forest (RF) is used to classify the MRI image voxels into normal brain tissues and different parts of tumors, i.e. edema, necrosis and enhancing tumor. The method was evaluated on BRATS 2017 challenge dataset. The results show that the proposed method provides promising segmentations. The mean Dice overlap measure for automatic brain tumor segmentation against ground truth is 0.86, 0.78 and 0.66 for whole tumor, core and enhancing tumor, respectively.
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
Authors: Tzu-Ming Harry Hsu, Hang Qi, Matthew Brown
Date: 2019-09-13 00:00:00
Tasks: Federated Learning, General Classification, Privacy Preserving

Federated Learning enables visual models to be trained in a privacy-preserving way using real-world data from mobile devices. Given their distributed nature, the statistics of the data across these devices is likely to differ significantly. In this work, we look at the effect such non-identical data distributions has on visual classification via Federated Learning. We propose a way to synthesize datasets with a continuous range of identicalness and provide performance measures for the Federated Averaging algorithm. We show that performance degrades as distributions differ more, and propose a mitigation strategy via server momentum. Experiments on CIFAR-10 demonstrate improved classification performance over a range of non-identicalness, with classification accuracy improved from 30.1% to 76.9% in the most skewed settings.
A Knowledge Transfer Framework for Differentially Private Sparse Learning
Authors: Lingxiao Wang, Quanquan Gu
Date: 2019-09-13 00:00:00
Tasks: regression, Sparse Learning, Transfer Learning

We study the problem of estimating high dimensional models with underlying sparse structures while preserving the privacy of each training example. We develop a differentially private high-dimensional sparse learning framework using the idea of knowledge transfer. More specifically, we propose to distill the knowledge from a "teacher" estimator trained on a private dataset, by creating a new dataset from auxiliary features, and then train a differentially private "student" estimator using this new dataset. In addition, we establish the linear convergence rate as well as the utility guarantee for our proposed method. For sparse linear regression and sparse logistic regression, our method achieves improved utility guarantees compared with the best known results (Kifer et al., 2012; Wang and Gu, 2019). We further demonstrate the superiority of our framework through both synthetic and real-world data experiments.
End-to-End Bias Mitigation by Modelling Biases in Corpora
Authors: Rabeeh Karimi Mahabadi, Yonatan Belinkov, James Henderson
Date: 2019-09-13 00:00:00
Proceeding: ACL 2020 6
Tasks: Fact Verification, Natural Language Inference, Natural Language Understanding

Several recent studies have shown that strong natural language understanding (NLU) models are prone to relying on unwanted dataset biases without learning the underlying task, resulting in models that fail to generalize to out-of-domain datasets and are likely to perform poorly in real-world scenarios. We propose two learning strategies to train neural models, which are more robust to such biases and transfer better to out-of-domain datasets. The biases are specified in terms of one or more bias-only models, which learn to leverage the dataset biases. During training, the bias-only models' predictions are used to adjust the loss of the base model to reduce its reliance on biases by down-weighting the biased examples and focusing the training on the hard examples. We experiment on large-scale natural language inference and fact verification benchmarks, evaluating on out-of-domain datasets that are specifically designed to assess the robustness of models against known biases in the training data. Results show that our debiasing methods greatly improve robustness in all settings and better transfer to other textual entailment datasets. Our code and data are publicly available in \url{https://github.com/rabeehk/robust-nli}.
Flow Models for Arbitrary Conditional Likelihoods
Authors: Yang Li, Shoaib Akbar, Junier B. Oliva
Date: 2019-09-13 00:00:00
Tasks: Image Inpainting, Imputation

Understanding the dependencies among features of a dataset is at the core of most unsupervised learning tasks. However, a majority of generative modeling approaches are focused solely on the joint distribution $p(x)$ and utilize models where it is intractable to obtain the conditional distribution of some arbitrary subset of features $x_u$ given the rest of the observed covariates $x_o$: $p(x_u \mid x_o)$. Traditional conditional approaches provide a model for a fixed set of covariates conditioned on another fixed set of observed covariates. Instead, in this work we develop a model that is capable of yielding all conditional distributions $p(x_u \mid x_o)$ (for arbitrary $x_u$) via tractable conditional likelihoods. We propose a novel extension of (change of variables based) flow generative models, arbitrary conditioning flow models (AC-Flow), that can be conditioned on arbitrary subsets of observed covariates, which was previously infeasible. We apply AC-Flow to the imputation of features, and also develop a unified platform for both multiple and single imputation by introducing an auxiliary objective that provides a principled single "best guess" for flow models. Extensive empirical evaluations show that our models achieve state-of-the-art performance in both single and multiple imputation across image inpainting and feature imputation in synthetic and real-world datasets. Code is available at https://github.com/lupalab/ACFlow.
A Comparative Study on Transformer vs RNN in Speech Applications
Authors: Shigeki Karita, Nanxin Chen, Tomoki Hayashi, Takaaki Hori, Hirofumi Inaguma, Ziyan Jiang, Masao Someki, Nelson Enrique Yalta Soplin, Ryuichi Yamamoto, Xiaofei Wang, Shinji Watanabe, Takenori Yoshimura, Wangyou Zhang
Date: 2019-09-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition, text-to-speech, Text to Speech, Translation

Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS). This paper focuses on an emergent sequence-to-sequence model called Transformer, which achieves state-of-the-art performance in neural machine translation and other natural language processing applications. We undertook intensive studies in which we experimentally compared and analyzed Transformer and conventional recurrent neural networks (RNN) in a total of 15 ASR, one multilingual ASR, one ST, and two TTS benchmarks. Our experiments revealed various training tips and significant performance benefits obtained with Transformer for each task including the surprising superiority of Transformer in 13/15 ASR benchmarks in comparison with RNN. We are preparing to release Kaldi-style reproducible recipes using open source and publicly available datasets for all the ASR, ST, and TTS tasks for the community to succeed our exciting outcomes.
Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data
Authors: Sergei Popov, Stanislav Morozov, Artem Babenko
Date: 2019-09-13 00:00:00
Proceeding: ICLR 2020 1
Tasks: BIG-bench Machine Learning, Deep Learning, Representation Learning

Nowadays, deep neural networks (DNNs) have become the main instrument for machine learning tasks within a wide range of domains, including vision, NLP, and speech. Meanwhile, in an important case of heterogenous tabular data, the advantage of DNNs over shallow counterparts remains questionable. In particular, there is no sufficient evidence that deep learning machinery allows constructing methods that outperform gradient boosting decision trees (GBDT), which are often the top choice for tabular problems. In this paper, we introduce Neural Oblivious Decision Ensembles (NODE), a new deep learning architecture, designed to work with any tabular data. In a nutshell, the proposed NODE architecture generalizes ensembles of oblivious decision trees, but benefits from both end-to-end gradient-based optimization and the power of multi-layer hierarchical representation learning. With an extensive experimental comparison to the leading GBDT packages on a large number of tabular datasets, we demonstrate the advantage of the proposed NODE architecture, which outperforms the competitors on most of the tasks. We open-source the PyTorch implementation of NODE and believe that it will become a universal framework for machine learning on tabular data.
AITuning: Machine Learning-based Tuning Tool for Run-Time Communication Libraries
Authors: Alessandro Fanfarillo, Davide Del Vento
Date: 2019-09-13 00:00:00
Tasks: BIG-bench Machine Learning, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this work, we address the problem of tuning communication libraries by using a deep reinforcement learning approach. Reinforcement learning is a machine learning technique incredibly effective in solving game-like situations. In fact, tuning a set of parameters in a communication library in order to get better performance in a parallel application can be expressed as a game: Find the right combination/path that provides the best reward. Even though AITuning has been designed to be utilized with different run-time libraries, we focused this work on applying it to the OpenCoarrays run-time communication library, built on top of MPI-3. This work not only shows the potential of using a reinforcement learning algorithm for tuning communication libraries, but also demonstrates how the MPI Tool Information Interface, introduced by the MPI-3 standard, can be used effectively by run-time libraries to improve the performance without human intervention.