pwc-archive/papers-with-abstracts huggingface dataset:
Scalable Semi-Supervised SVM via Triply Stochastic Gradients
Authors: Xiang Geng, Bin Gu, Xiang Li, Wanli Shi, Guansheng Zheng, Heng Huang
Date: 2019-07-26 00:00:00

Semi-supervised learning (SSL) plays an increasingly important role in the big data era because a large number of unlabeled samples can be used effectively to improve the performance of the classifier. Semi-supervised support vector machine (S$^3$VM) is one of the most appealing methods for SSL, but scaling up S$^3$VM for kernel learning is still an open problem. Recently, a doubly stochastic gradient (DSG) algorithm has been proposed to achieve efficient and scalable training for kernel methods. However, the algorithm and theoretical analysis of DSG are developed based on the convexity assumption which makes them incompetent for non-convex problems such as S$^3$VM. To address this problem, in this paper, we propose a triply stochastic gradient algorithm for S$^3$VM, called TSGS$^3$VM. Specifically, to handle two types of data instances involved in S$^3$VM, TSGS$^3$VM samples a labeled instance and an unlabeled instance as well with the random features in each iteration to compute a triply stochastic gradient. We use the approximated gradient to update the solution. More importantly, we establish new theoretic analysis for TSGS$^3$VM which guarantees that TSGS$^3$VM can converge to a stationary point. Extensive experimental results on a variety of datasets demonstrate that TSGS$^3$VM is much more efficient and scalable than existing S$^3$VM algorithms.
Sequential Learning of Active Subspaces
Authors: Nathan Wycoff, Mickael Binois, Stefan M. Wild
Date: 2019-07-26 00:00:00
Tasks: Gaussian Processes, Sensitivity, subspace methods

In recent years, active subspace methods (ASMs) have become a popular means of performing subspace sensitivity analysis on black-box functions. Naively applied, however, ASMs require gradient evaluations of the target function. In the event of noisy, expensive, or stochastic simulators, evaluating gradients via finite differencing may be infeasible. In such cases, often a surrogate model is employed, on which finite differencing is performed. When the surrogate model is a Gaussian process, we show that the ASM estimator is available in closed form, rendering the finite-difference approximation unnecessary. We use our closed-form solution to develop acquisition functions focused on sequential learning tailored to sensitivity analysis on top of ASMs. We also show that the traditional ASM estimator may be viewed as a method of moments estimator for a certain class of Gaussian processes. We demonstrate how uncertainty on Gaussian process hyperparameters may be propagated to uncertainty on the sensitivity analysis, allowing model-based confidence intervals on the active subspace. Our methodological developments are illustrated on several examples.
Making Neural Networks FAIR
Authors: Anna Nguyen, Tobias Weller, Michael Färber, York Sure-Vetter
Date: 2019-07-26 00:00:00

Research on neural networks has gained significant momentum over the past few years. Because training is a resource-intensive process and training data cannot always be made available to everyone, there has been a trend to reuse pre-trained neural networks. As such, neural networks themselves have become research data. In this paper, we first present the neural network ontology FAIRnets Ontology, an ontology to make existing neural network models findable, accessible, interoperable, and reusable according to the FAIR principles. Our ontology allows us to model neural networks on a meta-level in a structured way, including the representation of all network layers and their characteristics. Secondly, we have modeled over 18,400 neural networks from GitHub based on this ontology, which we provide to the public as a knowledge graph called FAIRnets, ready to be used for recommending suitable neural networks to data scientists.
Cooperative image captioning
Authors: Gilad Vered, Gal Oren, Yuval Atzmon, Gal Chechik
Date: 2019-07-26 00:00:00
Tasks: Image Captioning

When describing images with natural language, the descriptions can be made more informative if tuned using downstream tasks. This is often achieved by training two networks: a "speaker network" that generates sentences given an image, and a "listener network" that uses them to perform a task. Unfortunately, training multiple networks jointly to communicate to achieve a joint task, faces two major challenges. First, the descriptions generated by a speaker network are discrete and stochastic, making optimization very hard and inefficient. Second, joint training usually causes the vocabulary used during communication to drift and diverge from natural language. We describe an approach that addresses both challenges. We first develop a new effective optimization based on partial-sampling from a multinomial distribution combined with straight-through gradient updates, which we name PSST for Partial-Sampling Straight-Through. Second, we show that the generated descriptions can be kept close to natural by constraining them to be similar to human descriptions. Together, this approach creates descriptions that are both more discriminative and more natural than previous approaches. Evaluations on the standard COCO benchmark show that PSST Multinomial dramatically improve the recall@10 from 60% to 86% maintaining comparable language naturalness, and human evaluations show that it also increases naturalness while keeping the discriminative power of generated captions.
Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress
Authors: J. G. M. Esgario, R. A. Krohling, J. A. Ventura
Date: 2019-07-26 00:00:00
Tasks: Data Augmentation, General Classification

Biotic stress consists of damage to plants through other living organisms. Efficient control of biotic agents such as pests and pathogens (viruses, fungi, bacteria, etc.) is closely related to the concept of agricultural sustainability. Agricultural sustainability promotes the development of new technologies that allow the reduction of environmental impacts, greater accessibility to farmers and, consequently, increase on productivity. The use of computer vision with deep learning methods allows the early and correct identification of the stress-causing agent. So, corrective measures can be applied as soon as possible to mitigate the problem. The objective of this work is to design an effective and practical system capable of identifying and estimating the stress severity caused by biotic agents on coffee leaves. The proposed approach consists of a multi-task system based on convolutional neural networks. In addition, we have explored the use of data augmentation techniques to make the system more robust and accurate. The experimental results obtained for classification as well as for severity estimation indicate that the proposed system might be a suitable tool to assist both experts and farmers in the identification and quantification of biotic stresses in coffee plantations.
Bayesian Volumetric Autoregressive generative models for better semisupervised learning
Authors: Guilherme Pombo, Robert Gray, Tom Varsavsky, John Ashburner, Parashkev Nachev
Date: 2019-07-26 00:00:00
Tasks: General Classification, Semantic Segmentation

Deep generative models are rapidly gaining traction in medical imaging. Nonetheless, most generative architectures struggle to capture the underlying probability distributions of volumetric data, exhibit convergence problems, and offer no robust indices of model uncertainty. By comparison, the autoregressive generative model PixelCNN can be extended to volumetric data with relative ease, it readily attempts to learn the true underlying probability distribution and it still admits a Bayesian reformulation that provides a principled framework for reasoning about model uncertainty. Our contributions in this paper are two fold: first, we extend PixelCNN to work with volumetric brain magnetic resonance imaging data. Second, we show that reformulating this model to approximate a deep Gaussian process yields a measure of uncertainty that improves the performance of semi-supervised learning, in particular classification performance in settings where the proportion of labelled data is low. We quantify this improvement across classification, regression, and semantic segmentation tasks, training and testing on clinical magnetic resonance brain imaging data comprising T1-weighted and diffusion-weighted sequences.
Autoencoding with a Learning Classifier System: Initial Results
Authors: Larry Bull
Date: 2019-07-26 00:00:00
Tasks: Clustering, Dimensionality Reduction, Form

Autoencoders enable data dimensionality reduction and a key component of many (deep) learning systems. This short paper introduces a form of Holland's Learning Classifier System (LCS) to perform autoencoding building upon a previously presented form of LCS that utilises unsupervised learning for clustering. Initial results using a neural network representation suggest it is an effective approach to reduction.
Compressing deep quaternion neural networks with targeted regularization
Authors: Riccardo Vecchi, Simone Scardapane, Danilo Comminiello, Aurelio Uncini
Date: 2019-07-26 00:00:00
Tasks: Image Reconstruction

In recent years, hyper-complex deep networks (such as complex-valued and quaternion-valued neural networks) have received a renewed interest in the literature. They find applications in multiple fields, ranging from image reconstruction to 3D audio processing. Similar to their real-valued counterparts, quaternion neural networks (QVNNs) require custom regularization strategies to avoid overfitting. In addition, for many real-world applications and embedded implementations, there is the need of designing sufficiently compact networks, with few weights and neurons. However, the problem of regularizing and/or sparsifying QVNNs has not been properly addressed in the literature as of now. In this paper, we show how to address both problems by designing targeted regularization strategies, which are able to minimize the number of connections and neurons of the network during training. To this end, we investigate two extensions of l1 and structured regularization to the quaternion domain. In our experimental evaluation, we show that these tailored strategies significantly outperform classical (real-valued) regularization approaches, resulting in small networks especially suitable for low-power and real-time applications.
Towards the Enhancement of Body Standing Balance Recovery by Means of a Wireless Audio-Biofeedback System
Authors: Giovanni Costantini, Daniele Casali, Fabio Paolizzo, Marco Alessandrini, Alessandro Micarelli, Andrea Viziano, Giovanni Saggio
Date: 2019-07-26 00:00:00

Human maintain their body balance by sensorimotor controls mainly based on information gathered from vision, proprioception and vestibular systems. When there is a lack of information, caused by pathologies, diseases or aging, the subject may fall. In this context, we developed a system to augment information gathering, providing the subject with warning audio-feedback signals related to his/her equilibrium. The system comprises an inertial measurement unit (IMU), a data processing unit, a headphone audio device and a software application. The IMU is a low-weight, small-size wireless instrument that, body-back located between the L2 and L5 lumbar vertebrae, measures the subject's trunk kinematics. The application drives the data processing unit to feeding the headphone with electric signals related to the kinematic measures. Consequently, the user is audio-alerted, via headphone, of his/her own equilibrium, hearing a pleasant sound when in a stable equilibrium, or an increasing bothering sound when in an increasing unstable condition. Tests were conducted on a group of six older subjects (59y-61y, SD = 2.09y) and a group of four young subjects (21y-26y, SD = 2.88y) to underline difference in effectiveness of the system, if any, related to the age of the users. For each subject, standing balance tests were performed in normal or altered conditions, such as, open or closed eyes, and on a solid or foam surface The system was evaluated in terms of usability, reliability, and effectiveness in improving the subject's balance in all conditions. As a result, the system successfully helped the subjects in reducing the body swaying within 10.65%-65.90%, differences depending on subjects' age and test conditions.
Report on UG^2+ Challenge Track 1: Assessing Algorithms to Improve Video Object Detection and Classification from Unconstrained Mobility Platforms
Authors: Sreya Banerjee, Rosaura G. VidalMata, Zhangyang Wang, Walter J. Scheirer
Date: 2019-07-26 00:00:00
Tasks: Image Restoration, object-detection, Object Detection, Video Object Detection

How can we effectively engineer a computer vision system that is able to interpret videos from unconstrained mobility platforms like UAVs? One promising option is to make use of image restoration and enhancement algorithms from the area of computational photography to improve the quality of the underlying frames in a way that also improves automatic visual recognition. Along these lines, exploratory work is needed to find out which image pre-processing algorithms, in combination with the strongest features and supervised machine learning approaches, are good candidates for difficult scenarios like motion blur, weather, and mis-focus -- all common artifacts in UAV acquired images. This paper summarizes the protocols and results of Track 1 of the UG^2+ Challenge held in conjunction with IEEE/CVF CVPR 2019. The challenge looked at two separate problems: (1) object detection improvement in video, and (2) object classification improvement in video. The challenge made use of the UG^2 (UAV, Glider, Ground) dataset, which is an established benchmark for assessing the interplay between image restoration and enhancement and visual recognition. 16 algorithms were submitted by academic and corporate teams, and a detailed analysis of how they performed on each challenge problem is reported here.
Context-Aware Multipath Networks
Authors: Dumindu Tissera, Kumara Kahatapitiya, Rukshan Wijesinghe, Subha Fernando, Ranga Rodrigo
Date: 2019-07-26 00:00:00
Tasks: Image Classification

Making a single network effectively address diverse contexts---learning the variations within a dataset or multiple datasets---is an intriguing step towards achieving generalized intelligence. Existing approaches of deepening, widening, and assembling networks are not cost effective in general. In view of this, networks which can allocate resources according to the context of the input and regulate flow of information across the network are effective. In this paper, we present Context-Aware Multipath Network (CAMNet), a multi-path neural network with data-dependant routing between parallel tensors. We show that our model performs as a generalized model capturing variations in individual datasets and multiple different datasets, both simultaneously and sequentially. CAMNet surpasses the performance of classification and pixel-labeling tasks in comparison with the equivalent single-path, multi-path, and deeper single-path networks, considering datasets individually, sequentially, and in combination. The data-dependent routing between tensors in CAMNet enables the model to control the flow of information end-to-end, deciding which resources to be common or domain-specific.
Investigating Self-Attention Network for Chinese Word Segmentation
Authors: Leilei Gan, Yue Zhang
Date: 2019-07-26 00:00:00
Tasks: Chinese Word Segmentation, Segmentation

Neural network has become the dominant method for Chinese word segmentation. Most existing models cast the task as sequence labeling, using BiLSTM-CRF for representing the input and making output predictions. Recently, attention-based sequence models have emerged as a highly competitive alternative to LSTMs, which allow better running speed by parallelization of computation. We investigate self attention network for Chinese word segmentation, making comparisons between BiLSTM-CRF models. In addition, the influence of contextualized character embeddings is investigated using BERT, and a method is proposed for integrating word information into SAN segmentation. Results show that SAN gives highly competitive results compared with BiLSTMs, with BERT and word information further improving segmentation for in-domain and cross-domain segmentation. Our final models give the best results for 6 heterogenous domain benchmarks.
A close-up comparison of the misclassification error distance and the adjusted Rand index for external clustering evaluation
Authors: José E. Chacón
Date: 2019-07-26 00:00:00
Tasks: Clustering, Misconceptions

The misclassification error distance and the adjusted Rand index are two of the most commonly used criteria to evaluate the performance of clustering algorithms. This paper provides an in-depth comparison of the two criteria, aimed to better understand exactly what they measure, their properties and their differences. Starting from their population origins, the investigation includes many data analysis examples and the study of particular cases in great detail. An exhaustive simulation study allows inspecting the criteria distributions and reveals some previous misconceptions.
DCT-CompCNN: A Novel Image Classification Network Using JPEG Compressed DCT Coefficients
Authors: Bulla Rajesh, Mohammed Javed, Ratnesh, Shubham Srivastava
Date: 2019-07-26 00:00:00
Tasks: General Classification, image-classification, Image Classification

The popularity of Convolutional Neural Network (CNN) in the field of Image Processing and Computer Vision has motivated researchers and industrialist experts across the globe to solve different challenges with high accuracy. The simplest way to train a CNN classifier is to directly feed the original RGB pixels images into the network. However, if we intend to classify images directly with its compressed data, the same approach may not work better, like in case of JPEG compressed images. This research paper investigates the issues of modifying the input representation of the JPEG compressed data, and then feeding into the CNN. The architecture is termed as DCT-CompCNN. This novel approach has shown that CNNs can also be trained with JPEG compressed DCT coefficients, and subsequently can produce a better performance in comparison with the conventional CNN approach. The efficiency of the modified input representation is tested with the existing ResNet-50 architecture and the proposed DCT-CompCNN architecture on a public image classification datasets like Dog Vs Cat and CIFAR-10 datasets, reporting a better performance
Extensional Higher-Order Paramodulation in Leo-III
Authors: Alexander Steen, Christoph Benzmüller
Date: 2019-07-26 00:00:00

Leo-III is an automated theorem prover for extensional type theory with Henkin semantics and choice. Reasoning with primitive equality is enabled by adapting paramodulation-based proof search to higher-order logic. The prover may cooperate with multiple external specialist reasoning systems such as first-order provers and SMT solvers. Leo-III is compatible with the TPTP/TSTP framework for input formats, reporting results and proofs, and standardized communication between reasoning systems, enabling e.g. proof reconstruction from within proof assistants such as Isabelle/HOL. Leo-III supports reasoning in polymorphic first-order and higher-order logic, in all normal quantified modal logics, as well as in different deontic logics. Its development had initiated the ongoing extension of the TPTP infrastructure to reasoning within non-classical logics.
Weakly Supervised Domain Detection
Authors: Yumo Xu, Mirella Lapata
Date: 2019-07-26 00:00:00
Proceeding: TACL 2019 3
Tasks: Classification, General Classification, Multiple Instance Learning, text-classification, Text Classification, Text Summarization

In this paper we introduce domain detection as a new natural language processing task. We argue that the ability to detect textual segments which are domain-heavy, i.e., sentences or phrases which are representative of and provide evidence for a given domain could enhance the robustness and portability of various text classification applications. We propose an encoder-detector framework for domain detection and bootstrap classifiers with multiple instance learning (MIL). The model is hierarchically organized and suited to multilabel classification. We demonstrate that despite learning with minimal supervision, our model can be applied to text spans of different granularities, languages, and genres. We also showcase the potential of domain detection for text summarization.
Outfit Compatibility Prediction and Diagnosis with Multi-Layered Comparison Network
Authors: Xin Wang, Bo Wu, Yun Ye, Yueqi Zhong
Date: 2019-07-26 00:00:00
Tasks: Fashion Compatibility Learning, Persuasiveness, Prediction

Existing works about fashion outfit compatibility focus on predicting the overall compatibility of a set of fashion items with their information from different modalities. However, there are few works explore how to explain the prediction, which limits the persuasiveness and effectiveness of the model. In this work, we propose an approach to not only predict but also diagnose the outfit compatibility. We introduce an end-to-end framework for this goal, which features for: (1) The overall compatibility is learned from all type-specified pairwise similarities between items, and the backpropagation gradients are used to diagnose the incompatible factors. (2) We leverage the hierarchy of CNN and compare the features at different layers to take into account the compatibilities of different aspects from the low level (such as color, texture) to the high level (such as style). To support the proposed method, we build a new type-specified outfit dataset named Polyvore-T based on Polyvore dataset. We compare our method with the prior state-of-the-art in two tasks: outfit compatibility prediction and fill-in-the-blank. Experiments show that our approach has advantages in both prediction performance and diagnosis ability.
Multi-level Domain Adaptive learning for Cross-Domain Detection
Authors: Rongchang Xie, Fei Yu, Jiachao Wang, Yizhou Wang, Li Zhang
Date: 2019-07-26 00:00:00
Tasks: Object, object-detection, Object Detection

In recent years, object detection has shown impressive results using supervised deep learning, but it remains challenging in a cross-domain environment. The variations of illumination, style, scale, and appearance in different domains can seriously affect the performance of detection models. Previous works use adversarial training to align global features across the domain shift and to achieve image information transfer. However, such methods do not effectively match the distribution of local features, resulting in limited improvement in cross-domain object detection. To solve this problem, we propose a multi-level domain adaptive model to simultaneously align the distributions of local-level features and global-level features. We evaluate our method with multiple experiments, including adverse weather adaptation, synthetic data adaptation, and cross camera adaptation. In most object categories, the proposed method achieves superior performance against state-of-the-art techniques, which demonstrates the effectiveness and robustness of our method.