pwc-archive/papers-with-abstracts huggingface dataset:
Deep neural network or dermatologist?
Authors: Kyle Young, Gareth Booth, Becks Simpson, Reuben Dutton, Sally Shrapnel
Date: 2019-08-19 00:00:00
Tasks: Cancer Classification, Diagnostic, Skin Cancer Classification

Deep learning techniques have proven high accuracy for identifying melanoma in digitised dermoscopic images. A strength is that these methods are not constrained by features that are pre-defined by human semantics. A down-side is that it is difficult to understand the rationale of the model predictions and to identify potential failure modes. This is a major barrier to adoption of deep learning in clinical practice. In this paper we ask if two existing local interpretability methods, Grad-CAM and Kernel SHAP, can shed light on convolutional neural networks trained in the context of melanoma detection. Our contributions are (i) we first explore the domain space via a reproducible, end-to-end learning framework that creates a suite of 30 models, all trained on a publicly available data set (HAM10000), (ii) we next explore the reliability of GradCAM and Kernel SHAP in this context via some basic sanity check experiments (iii) finally, we investigate a random selection of models from our suite using GradCAM and Kernel SHAP. We show that despite high accuracy, the models will occasionally assign importance to features that are not relevant to the diagnostic task. We also show that models of similar accuracy will produce different explanations as measured by these methods. This work represents first steps in bridging the gap between model accuracy and interpretability in the domain of skin cancer classification.
Video synthesis of human upper body with realistic face
Authors: Zhaoxiang Liu, Huan Hu, Zipeng Wang, Kai Wang, Jinqiang Bai, Shiguo Lian
Date: 2019-08-19 00:00:00

This paper presents a generative adversarial learning-based human upper body video synthesis approach to generate an upper body video of target person that is consistent with the body motion, face expression, and pose of the person in source video. We use upper body keypoints, facial action units and poses as intermediate representations between source video and target video. Instead of directly transferring the source video to the target video, we firstly map the source person's facial action units and poses into the target person's facial landmarks, then combine the normalized upper body keypoints and generated facial landmarks with spatio-temporal smoothing to generate the corresponding target video's image. Experimental results demonstrated the effectiveness of our method.
Question Answering based Clinical Text Structuring Using Pre-trained Language Model
Authors: Jiahui Qiu, Yangming Zhou, Zhiyuan Ma, Tong Ruan, Jinlin Liu, Jing Sun
Date: 2019-08-19 00:00:00
Tasks: Language Modeling, Language Modelling, Question Answering

Clinical text structuring is a critical and fundamental task for clinical research. Traditional methods such as taskspecific end-to-end models and pipeline models usually suffer from the lack of dataset and error propagation. In this paper, we present a question answering based clinical text structuring (QA-CTS) task to unify different specific tasks and make dataset shareable. A novel model that aims to introduce domain-specific features (e.g., clinical named entity information) into pre-trained language model is also proposed for QA-CTS task. Experimental results on Chinese pathology reports collected from Ruijing Hospital demonstrate our presented QA-CTS task is very effective to improve the performance on specific tasks. Our proposed model also competes favorably with strong baseline models in specific tasks.
Long and Diverse Text Generation with Planning-based Hierarchical Variational Model
Authors: Zhihong Shao, Minlie Huang, Jiangtao Wen, Wenfei Xu, Xiaoyan Zhu
Date: 2019-08-19 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Data-to-Text Generation, Diversity, Sentence, Text Generation

Existing neural methods for data-to-text generation are still struggling to produce long and diverse texts: they are insufficient to model input data dynamically during generation, to capture inter-sentence coherence, or to generate diversified expressions. To address these issues, we propose a Planning-based Hierarchical Variational Model (PHVM). Our model first plans a sequence of groups (each group is a subset of input items to be covered by a sentence) and then realizes each sentence conditioned on the planning result and the previously generated context, thereby decomposing long text generation into dependent sentence generation sub-tasks. To capture expression diversity, we devise a hierarchical latent structure where a global planning latent variable models the diversity of reasonable planning and a sequence of local latent variables controls sentence realization. Experiments show that our model outperforms state-of-the-art baselines in long and diverse text generation.
Transfer Learning-Based Label Proportions Method with Data of Uncertainty
Authors: Yanshan Xiao, HuaiPei Wang, Bo Liu
Date: 2019-08-19 00:00:00
Tasks: Transfer Learning

Learning with label proportions (LLP), which is a learning task that only provides unlabeled data in bags and each bag's label proportion, has widespread successful applications in practice. However, most of the existing LLP methods don't consider the knowledge transfer for uncertain data. This paper presents a transfer learning-based approach for the problem of learning with label proportions(TL-LLP) to transfer knowledge from source task to target task where both the source and target tasks contain uncertain data. Our approach first formulates objective model for the uncertain data and deals with transfer learning at the same time, and then proposes an iterative framework to build an accurate classifier for the target task. Extensive experiments have shown that the proposed TL-LLP method can obtain the better accuracies and is less sensitive to noise compared with the existing LLP methods.
Mitigating Multi-Stage Cascading Failure by Reinforcement Learning
Authors: Yongli Zhu, Chengxi Liu
Date: 2019-08-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL) method. Firstly, the principles of RL are introduced. Then, the Multi-Stage Cascading Failure (MSCF) problem is presented and its challenges are investigated. The problem is then tackled by the RL based on DC-OPF (Optimal Power Flow). Designs of the key elements of the RL framework (rewards, states, etc.) are also discussed in detail. Experiments on the IEEE 118-bus system by both shallow and deep neural networks demonstrate promising results in terms of reduced system collapse rates.
Model-free Feature Screening and FDR Control with Knockoff Features
Authors: Wanjun Liu, Yuan Ke, Jingyuan Liu, Runze Li
Date: 2019-08-19 00:00:00

This paper proposes a model-free and data-adaptive feature screening method for ultra-high dimensional datasets. The proposed method is based on the projection correlation which measures the dependence between two random vectors. This projection correlation based method does not require specifying a regression model and applies to the data in the presence of heavy-tailed errors and multivariate response. It enjoys both sure screening and rank consistency properties under weak assumptions. Further, a two-step approach is proposed to control the false discovery rate (FDR) in feature screening with the help of knockoff features. It can be shown that the proposed two-step approach enjoys both sure screening and FDR control if the pre-specified FDR level $\alpha$ is greater or equal to $1/s$, where $s$ is the number of active features. The superior empirical performance of the proposed methods is justified by various numerical experiments and real data applications.
Seq-SG2SL: Inferring Semantic Layout from Scene Graph Through Sequence to Sequence Learning
Authors: Boren Li, Boyu Zhuang, Mingyang Li, Jian Gu
Date: 2019-08-19 00:00:00
Proceeding: ICCV 2019 10

Generating semantic layout from scene graph is a crucial intermediate task connecting text to image. We present a conceptually simple, flexible and general framework using sequence to sequence (seq-to-seq) learning for this task. The framework, called Seq-SG2SL, derives sequence proxies for the two modality and a Transformer-based seq-to-seq model learns to transduce one into the other. A scene graph is decomposed into a sequence of semantic fragments (SF), one for each relationship. A semantic layout is represented as the consequence from a series of brick-action code segments (BACS), dictating the position and scale of each object bounding box in the layout. Viewing the two building blocks, SF and BACS, as corresponding terms in two different vocabularies, a seq-to-seq model is fittingly used to translate. A new metric, semantic layout evaluation understudy (SLEU), is devised to evaluate the task of semantic layout prediction inspired by BLEU. SLEU defines relationships within a layout as unigrams and looks at the spatial distribution for n-grams. Unlike the binary precision of BLEU, SLEU allows for some tolerances spatially through thresholding the Jaccard Index and is consequently more adapted to the task. Experimental results on the challenging Visual Genome dataset show improvement over a non-sequential approach based on graph convolution.
How far should self-driving cars see? Effect of observation range on vehicle self-localization
Authors: Mahdi Javanmardi, Ehsan Javanmardi, Shunsuke Kamijo
Date: 2019-08-19 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Self-Driving Cars, Simultaneous Localization and Mapping

Accuracy and time efficiency are two essential requirements for the self-localization of autonomous vehicles. While the observation range considered for simultaneous localization and mapping (SLAM) has a significant effect on both accuracy and computation time, its effect is not well investigated in the literature. In this paper, we will answer the question: How far should a driverless car observe during self-localization? We introduce a framework to dynamically define the observation range for localization to meet the accuracy requirement for autonomous driving, while keeping the computation time low. To model the effect of scanning range on the localization accuracy for every point on the map, several map factors were employed. The capability of the proposed framework was verified using field data, demonstrating that it is able to improve the average matching time from 142.2 ms to 39.3 ms while keeping the localization accuracy around 8.1 cm.
Latent User Linking for Collaborative Cross Domain Recommendation
Authors: Sapumal Ahangama, Danny Chiang-Choon Poo
Date: 2019-08-19 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems, Transfer Learning

With the widespread adoption of information systems, recommender systems are widely used for better user experience. Collaborative filtering is a popular approach in implementing recommender systems. Yet, collaborative filtering methods are highly dependent on user feedback, which is often highly sparse and hard to obtain. However, such issues could be alleviated if knowledge from a much denser and a related secondary domain could be used to enhance the recommendation accuracy in the sparse target domain. In this publication, we propose a deep learning method for cross-domain recommender systems through the linking of cross-domain user latent representations as a form of knowledge transfer across domains. We assume that cross-domain similarities of user tastes and behaviors are clearly observable in the low dimensional user latent representations. These user similarities are used to link the domains. As a result, we propose a Variational Autoencoder based network model for cross-domain linking with added contextualization to handle sparse data and for better transfer of cross-domain knowledge. We further extend the model to be more suitable in cold start scenarios and to utilize auxiliary user information for additional gains in recommendation accuracy. The effectiveness of the proposed model was empirically evaluated using multiple datasets. The experiments proved that the proposed model outperforms the state of the art techniques.
A Co-analysis Framework for Exploring Multivariate Scientific Data
Authors: Xiangyang He, Yubo Tao, Qirui Wang, Hai Lin
Date: 2019-08-19 00:00:00
Tasks: Diversity

In complex multivariate data sets, different features usually include diverse associations with different variables, and different variables are associated within different regions. Therefore, exploring the associations between variables and voxels locally becomes necessary to better understand the underlying phenomena. In this paper, we propose a co-analysis framework based on biclusters, which are two subsets of variables and voxels with close scalar-value relationships, to guide the process of visually exploring multivariate data. We first automatically extract all meaningful biclusters, each of which only contains voxels with a similar scalar-value pattern over a subset of variables. These biclusters are organized according to their variable sets, and biclusters in each variable set are further grouped by a similarity metric to reduce redundancy and support diversity during visual exploration. Biclusters are visually represented in coordinated views to facilitate interactive exploration of multivariate data based on the similarity between biclusters and the correlation of scalar values with different variables. Experiments on several representative multivariate scientific data sets demonstrate the effectiveness of our framework in exploring local relationships among variables, biclusters and scalar values in the data.
PolyGAN: High-Order Polynomial Generators
Authors: Grigorios Chrysos, Stylianos Moschoglou, Yannis Panagakis, Stefanos Zafeiriou
Date: 2019-08-19 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Generative Adversarial Networks (GANs) have become the gold standard when it comes to learning generative models for high-dimensional distributions. Since their advent, numerous variations of GANs have been introduced in the literature, primarily focusing on utilization of novel loss functions, optimization/regularization strategies and network architectures. In this paper, we turn our attention to the generator and investigate the use of high-order polynomials as an alternative class of universal function approximators. Concretely, we propose PolyGAN, where we model the data generator by means of a high-order polynomial whose unknown parameters are naturally represented by high-order tensors. We introduce two tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks that only employ linear/convolutional blocks. We exhibit for the first time that by using our approach a GAN generator can approximate the data distribution without using any activation functions. Thorough experimental evaluation on both synthetic and real data (images and 3D point clouds) demonstrates the merits of PolyGAN against the state of the art.
Adversarial Defense by Suppressing High-frequency Components
Authors: Zhendong Zhang, Cheolkon Jung, Xiaolong Liang
Date: 2019-08-19 00:00:00
Tasks: Adversarial Defense, Classification, General Classification, image-classification, Image Classification, Vocal Bursts Intensity Prediction

Recent works show that deep neural networks trained on image classification dataset bias towards textures. Those models are easily fooled by applying small high-frequency perturbations to clean images. In this paper, we learn robust image classification models by removing high-frequency components. Specifically, we develop a differentiable high-frequency suppression module based on discrete Fourier transform (DFT). Combining with adversarial training, we won the 5th place in the IJCAI-2019 Alibaba Adversarial AI Challenge. Our code is available online.
Recurrent Graph Syntax Encoder for Neural Machine Translation
Authors: Liang Ding, DaCheng Tao
Date: 2019-08-19 00:00:00
Tasks: Machine Translation, NMT, Sentence, Translation

Syntax-incorporated machine translation models have been proven successful in improving the model's reasoning and meaning preservation ability. In this paper, we propose a simple yet effective graph-structured encoder, the Recurrent Graph Syntax Encoder, dubbed \textbf{RGSE}, which enhances the ability to capture useful syntactic information. The RGSE is done over a standard encoder (recurrent or self-attention encoder), regarding recurrent network units as graph nodes and injects syntactic dependencies as edges, such that RGSE models syntactic dependencies and sequential information (\textit{i.e.}, word order) simultaneously. Our approach achieves considerable improvements over several syntax-aware NMT models in English$\Rightarrow$German and English$\Rightarrow$Czech translation tasks. And RGSE-equipped big model obtains competitive result compared with the state-of-the-art model in WMT14 En-De task. Extensive analysis further verifies that RGSE could benefit long sentence modeling, and produces better translations.
Transfer in Deep Reinforcement Learning using Knowledge Graphs
Authors: Prithviraj Ammanabrolu, Mark O. Riedl
Date: 2019-08-19 00:00:00
Proceeding: WS 2019 11
Tasks: Deep Reinforcement Learning, Knowledge Graphs, Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Text adventure games, in which players must make sense of the world through text descriptions and declare actions through text descriptions, provide a stepping stone toward grounding action in language. Prior work has demonstrated that using a knowledge graph as a state representation and question-answering to pre-train a deep Q-network facilitates faster control policy transfer. In this paper, we explore the use of knowledge graphs as a representation for domain knowledge transfer for training text-adventure playing reinforcement learning agents. Our methods are tested across multiple computer generated and human authored games, varying in domain and complexity, and demonstrate that our transfer learning methods let us learn a higher-quality control policy faster.
Weakly-supervised Action Localization with Background Modeling
Authors: Phuc Xuan Nguyen, Deva Ramanan, Charless C. Fowlkes
Date: 2019-08-19 00:00:00
Proceeding: ICCV 2019 10
Tasks: Action Localization, Weakly Supervised Action Localization, Weakly-supervised Learning

We describe a latent approach that learns to detect actions in long sequences given training videos with only whole-video class labels. Our approach makes use of two innovations to attention-modeling in weakly-supervised learning. First, and most notably, our framework uses an attention model to extract both foreground and background frames whose appearance is explicitly modeled. Most prior works ignore the background, but we show that modeling it allows our system to learn a richer notion of actions and their temporal extents. Second, we combine bottom-up, class-agnostic attention modules with top-down, class-specific activation maps, using the latter as form of self-supervision for the former. Doing so allows our model to learn a more accurate model of attention without explicit temporal supervision. These modifications lead to 10% AP@IoU=0.5 improvement over existing systems on THUMOS14. Our proposed weaklysupervised system outperforms recent state-of-the-arts by at least 4.3% AP@IoU=0.5. Finally, we demonstrate that weakly-supervised learning can be used to aggressively scale-up learning to in-the-wild, uncurated Instagram videos. The addition of these videos significantly improves localization performance of our weakly-supervised model
HumanMeshNet: Polygonal Mesh Recovery of Humans
Authors: Abbhinav Venkat, Chaitanya Patel, Yudhik Agrawal, Avinash Sharma
Date: 2019-08-19 00:00:00
Tasks: Surface Reconstruction

3D Human Body Reconstruction from a monocular image is an important problem in computer vision with applications in virtual and augmented reality platforms, animation industry, en-commerce domain, etc. While several of the existing works formulate it as a volumetric or parametric learning with complex and indirect reliance on re-projections of the mesh, we would like to focus on implicitly learning the mesh representation. To that end, we propose a novel model, HumanMeshNet, that regresses a template mesh's vertices, as well as receives a regularization by the 3D skeletal locations in a multi-branch, multi-task setup. The image to mesh vertex regression is further regularized by the neighborhood constraint imposed by mesh topology ensuring smooth surface reconstruction. The proposed paradigm can theoretically learn local surface deformations induced by body shape variations and can therefore learn high-resolution meshes going ahead. We show comparable performance with SoA (in terms of surface and joint error) with far lesser computational complexity, modeling cost and therefore real-time reconstructions on three publicly available datasets. We also show the generalizability of the proposed paradigm for a similar task of predicting hand mesh models. Given these initial results, we would like to exploit the mesh topology in an explicit manner going ahead.
Benchmarks for Graph Embedding Evaluation
Authors: Palash Goyal, Di Huang, Ankita Goswami, Sujit Rokka Chhetri, Arquimedes Canedo, Emilio Ferrara
Date: 2019-08-19 00:00:00
Tasks: Benchmarking, Graph Embedding, Link Prediction

Graph embedding is the task of representing nodes of a graph in a low-dimensional space and its applications for graph tasks have gained significant traction in academia and industry. The primary difference among the many recently proposed graph embedding methods is the way they preserve the inherent properties of the graphs. However, in practice, comparing these methods is very challenging. The majority of methods report performance boosts on few selected real graphs. Therefore, it is difficult to generalize these performance improvements to other types of graphs. Given a graph, it is currently impossible to quantify the advantages of one approach over another. In this work, we introduce a principled framework to compare graph embedding methods. Our goal is threefold: (i) provide a unifying framework for comparing the performance of various graph embedding methods, (ii) establish a benchmark with real-world graphs that exhibit different structural properties, and (iii) provide users with a tool to identify the best graph embedding method for their data. This paper evaluates 4 of the most influential graph embedding methods and 4 traditional link prediction methods against a corpus of 100 real-world networks with varying properties. We organize the 100 networks in terms of their properties to get a better understanding of the embedding performance of these popular methods. We use the comparisons on our 100 benchmark graphs to define GFS-score, that can be applied to any embedding method to quantify its performance. We rank the state-of-the-art embedding approaches using the GFS-score and show that it can be used to understand and evaluate novel embedding approaches. We envision that the proposed framework (https://www.github.com/palash1992/GEM-Benchmark) will serve the community as a benchmarking platform to test and compare the performance of future graph embedding techniques.