pwc-archive/papers-with-abstracts huggingface dataset:
Context is Key: Grammatical Error Detection with Contextual Word Representations
Authors: Samuel Bell, Helen Yannakoudakis, Marek Rei
Date: 2019-06-15 00:00:00
Proceeding: WS 2019 8
Tasks: Grammatical Error Detection

Grammatical error detection (GED) in non-native writing requires systems to identify a wide range of errors in text written by language learners. Error detection as a purely supervised task can be challenging, as GED datasets are limited in size and the label distributions are highly imbalanced. Contextualized word representations offer a possible solution, as they can efficiently capture compositional information in language and can be optimized on large amounts of unsupervised data. In this paper, we perform a systematic comparison of ELMo, BERT and Flair embeddings (Peters et al., 2017; Devlin et al., 2018; Akbik et al., 2018) on a range of public GED datasets, and propose an approach to effectively integrate such representations in current methods, achieving a new state of the art on GED. We further analyze the strengths and weaknesses of different contextual embeddings for the task at hand, and present detailed analyses of their impact on different types of errors.
Membership Privacy for Machine Learning Models Through Knowledge Transfer
Authors: Virat Shejwalkar, Amir Houmansadr
Date: 2019-06-15 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Inference Attack, Knowledge Distillation, Model Compression, Transfer Learning

Large capacity machine learning (ML) models are prone to membership inference attacks (MIAs), which aim to infer whether the target sample is a member of the target model's training dataset. The serious privacy concerns due to the membership inference have motivated multiple defenses against MIAs, e.g., differential privacy and adversarial regularization. Unfortunately, these defenses produce ML models with unacceptably low classification performances. Our work proposes a new defense, called distillation for membership privacy (DMP), against MIAs that preserves the utility of the resulting models significantly better than prior defenses. DMP leverages knowledge distillation to train ML models with membership privacy. We provide a novel criterion to tune the data used for knowledge transfer in order to amplify the membership privacy of DMP. Our extensive evaluation shows that DMP provides significantly better tradeoffs between membership privacy and classification accuracies compared to state-of-the-art MIA defenses. For instance, DMP achieves ~100% accuracy improvement over adversarial regularization for DenseNet trained on CIFAR100, for similar membership privacy (measured using MIA risk): when the MIA risk is 53.7%, adversarially regularized DenseNet is 33.6% accurate, while DMP-trained DenseNet is 65.3% accurate.
Reinforcement Learning with Non-uniform State Representations for Adaptive Search
Authors: Sandeep Manjanna, Herke van Hoof, Gregory Dudek
Date: 2019-06-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Efficient spatial exploration is a key aspect of search and rescue. In this paper, we present a search algorithm that generates efficient trajectories that optimize the rate at which probability mass is covered by a searcher. This should allow an autonomous vehicle find one or more lost targets as rapidly as possible. We do this by performing non-uniform sampling of the search region. The path generated minimizes the expected time to locate the missing target by visiting high probability regions using non-myopic path generation based on reinforcement learning. We model the target probability distribution using a classic mixture of Gaussians model with means and mixture coefficients tuned according to the location and time of sightings of the lost target. Key features of our search algorithm are the ability to employ a very general non-deterministic action model and the ability to generate action plans for any new probability distribution using the parameters learned on other similar looking distributions. One of the key contributions of this paper is the use of non-uniform state aggregation for policy search in the context of robotics.
A Computational-Hermeneutic Approach for Conceptual Explicitation
Authors: David Fuenmayor, Christoph Benzmüller
Date: 2019-06-15 00:00:00

We present a computer-supported approach for the logical analysis and conceptual explicitation of argumentative discourse. Computational hermeneutics harnesses recent progresses in automated reasoning for higher-order logics and aims at formalizing natural-language argumentative discourse using flexible combinations of expressive non-classical logics. In doing so, it allows us to render explicit the tacit conceptualizations implicit in argumentative discursive practices. Our approach operates on networks of structured arguments and is iterative and two-layered. At one layer we search for logically correct formalizations for each of the individual arguments. At the next layer we select among those correct formalizations the ones which honor the argument's dialectic role, i.e. attacking or supporting other arguments as intended. We operate at these two layers in parallel and continuously rate sentences' formalizations by using, primarily, inferential adequacy criteria. An interpretive, logical theory will thus gradually evolve. This theory is composed of meaning postulates serving as explications for concepts playing a role in the analyzed arguments. Such a recursive, iterative approach to interpretation does justice to the inherent circularity of understanding: the whole is understood compositionally on the basis of its parts, while each part is understood only in the context of the whole (hermeneutic circle). We summarily discuss previous work on exemplary applications of human-in-the-loop computational hermeneutics in metaphysical discourse. We also discuss some of the main challenges involved in fully-automating our approach. By sketching some design ideas and reviewing relevant technologies, we argue for the technological feasibility of a highly-automated computational hermeneutics.
Practical User Feedback-driven Internal Search Using Online Learning to Rank
Authors: Rajhans Samdani, Pierre Rappolt, Ankit Goyal, Pratyus Patnaik
Date: 2019-06-15 00:00:00
Tasks: Articles, Information Retrieval, Learning-To-Rank, Retrieval

We present a system, Spoke, for creating and searching internal knowledge base (KB) articles for organizations. Spoke is available as a SaaS (Software-as-a-Service) product deployed across hundreds of organizations with a diverse set of domains. Spoke continually improves search quality using conversational user feedback which allows it to provide better search experience than standard information retrieval systems without encoding any explicit domain knowledge. We achieve this by using a real-time online learning-to-rank (L2R) algorithm that automatically customizes relevance scoring for each organization deploying Spoke by using a query similarity kernel. The focus of this paper is on incorporating practical considerations into our relevance scoring function and algorithm that make Spoke easy to deploy and suitable for handling events that naturally happen over the life-cycle of any KB deployment. We show that Spoke outperforms competitive baselines by up to 41% in offline F1 comparisons.
EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse
Authors: YoungJoon Yoo, Dongyoon Han, Sangdoo Yun
Date: 2019-06-15 00:00:00
Tasks: Face Detection

In this paper, we propose a new multi-scale face detector having an extremely tiny number of parameters (EXTD),less than 0.1 million, as well as achieving comparable performance to deep heavy detectors. While existing multi-scale face detectors extract feature maps with different scales from a single backbone network, our method generates the feature maps by iteratively reusing a shared lightweight and shallow backbone network. This iterative sharing of the backbone network significantly reduces the number of parameters, and also provides the abstract image semantics captured from the higher stage of the network layers to the lower-level feature map. The proposed idea is employed by various model architectures and evaluated by extensive experiments. From the experiments from WIDER FACE dataset, we show that the proposed face detector can handle faces with various scale and conditions, and achieved comparable performance to the more massive face detectors that few hundreds and tens times heavier in model size and floating point operations.
Injecting Prior Knowledge for Transfer Learning into Reinforcement Learning Algorithms using Logic Tensor Networks
Authors: Samy Badreddine, Michael Spranger
Date: 2019-06-15 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Tensor Networks, Transfer Learning

Human ability at solving complex tasks is helped by priors on object and event semantics of their environment. This paper investigates the use of similar prior knowledge for transfer learning in Reinforcement Learning agents. In particular, the paper proposes to use a first-order-logic language grounded in deep neural networks to represent facts about objects and their semantics in the real world. Facts are provided as background knowledge a priori to learning a policy for how to act in the world. The priors are injected with the conventional input in a single agent architecture. As proof-of-concept, the paper tests the system in simple experiments that show the importance of symbolic abstraction and flexible fact derivation. The paper shows that the proposed system can learn to take advantage of both the symbolic layer and the image layer in a single decision selection module.
Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis
Authors: Kewen Liu, Yuan Ma, Hongxia Xiong, Zejun Yan, Zhijun Zhou, Chaoyang Liu, Panpan Fang, Xiaojun Li, Yalei Chen
Date: 2019-06-15 00:00:00
Tasks: Generative Adversarial Network, GPU, Image Super-Resolution, Super-Resolution

During training phase, more connections (e.g. channel concatenation in last layer of DenseNet) means more occupied GPU memory and lower GPU utilization, requiring more training time. The increase of training time is also not conducive to launch application of SR algorithms. This's why we abandoned DenseNet as basic network. Futhermore, we abandoned this paper due to its limitation only applied on medical images. Please view our lastest work applied on general images at arXiv:1911.03464.
LioNets: Local Interpretation of Neural Networks through Penultimate Layer Decoding
Authors: Ioannis Mollas, Nikolaos Bassiliades, Grigorios Tsoumakas
Date: 2019-06-15 00:00:00
Tasks: General Classification, Self-Driving Cars

Technological breakthroughs on smart homes, self-driving cars, health care and robotic assistants, in addition to reinforced law regulations, have critically influenced academic research on explainable machine learning. A sufficient number of researchers have implemented ways to explain indifferently any black box model for classification tasks. A drawback of building agnostic explanators is that the neighbourhood generation process is universal and consequently does not guarantee true adjacency between the generated neighbours and the instance. This paper explores a methodology on providing explanations for a neural network's decisions, in a local scope, through a process that actively takes into consideration the neural network's architecture on creating an instance's neighbourhood, that assures the adjacency among the generated neighbours and the instance.
Deep Set Prediction Networks
Authors: Yan Zhang, Jonathon Hare, Adam Prügel-Bennett
Date: 2019-06-15 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Prediction

Current approaches for predicting sets from feature vectors ignore the unordered nature of sets and suffer from discontinuity issues as a result. We propose a general model for predicting sets that properly respects the structure of sets and avoids this problem. With a single feature vector as input, we show that our model is able to auto-encode point sets, predict the set of bounding boxes of objects in an image, and predict the set of attributes of these objects.
Mask Based Unsupervised Content Transfer
Authors: Ron Mokady, Sagie Benaim, Lior Wolf, Amit Bermano
Date: 2019-06-15 00:00:00
Tasks: Semantic Segmentation, Translation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

We consider the problem of translating, in an unsupervised manner, between two domains where one contains some additional information compared to the other. The proposed method disentangles the common and separate parts of these domains and, through the generation of a mask, focuses the attention of the underlying network to the desired augmentation alone, without wastefully reconstructing the entire target. This enables state-of-the-art quality and variety of content translation, as demonstrated through extensive quantitative and qualitative evaluation. Our method is also capable of adding the separate content of different guide images and domains as well as remove existing separate content. Furthermore, our method enables weakly-supervised semantic segmentation of the separate part of each domain, where only class labels are provided. Our code is publicly available at https://github.com/rmokady/mbu-content-tansfer.
Towards Integration of Statistical Hypothesis Tests into Deep Neural Networks
Authors: Ahmad Aghaebrahimian, Mark Cieliebak
Date: 2019-06-15 00:00:00
Proceeding: ACL 2019 7
Tasks: Classification, General Classification, Multi-class Classification, text-classification, Text Classification, Two-sample testing

We report our ongoing work about a new deep architecture working in tandem with a statistical test procedure for jointly training texts and their label descriptions for multi-label and multi-class classification tasks. A statistical hypothesis testing method is used to extract the most informative words for each given class. These words are used as a class description for more label-aware text classification. Intuition is to help the model to concentrate on more informative words rather than more frequent ones. The model leverages the use of label descriptions in addition to the input text to enhance text classification performance. Our method is entirely data-driven, has no dependency on other sources of information than the training data, and is adaptable to different classification problems by providing appropriate training data without major hyper-parameter tuning. We trained and tested our system on several publicly available datasets, where we managed to improve the state-of-the-art on one set with a high margin, and to obtain competitive results on all other ones.
Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era
Authors: Xian-Feng Han, Hamid Laga, Mohammed Bennamoun
Date: 2019-06-15 00:00:00
Tasks: 3D Object Reconstruction, 3D Reconstruction, Object Reconstruction, Survey

3D reconstruction is a longstanding ill-posed problem, which has been explored for decades by the computer vision, computer graphics, and machine learning communities. Since 2015, image-based 3D reconstruction using convolutional neural networks (CNN) has attracted increasing interest and demonstrated an impressive performance. Given this new era of rapid evolution, this article provides a comprehensive survey of the recent developments in this field. We focus on the works which use deep learning techniques to estimate the 3D shape of generic objects either from a single or multiple RGB images. We organize the literature based on the shape representations, the network architectures, and the training mechanisms they use. While this survey is intended for methods which reconstruct generic objects, we also review some of the recent works which focus on specific object classes such as human body shapes and faces. We provide an analysis and comparison of the performance of some key papers, summarize some of the open problems in this field, and discuss promising directions for future research.
MV-C3D: A Spatial Correlated Multi-View 3D Convolutional Neural Networks
Authors: Qi Xuan, Fuxian Li, Yi Liu, Yun Xiang
Date: 2019-06-15 00:00:00
Tasks: 3D Object Recognition, Object Recognition

As the development of deep neural networks, 3D object recognition is becoming increasingly popular in computer vision community. Many multi-view based methods are proposed to improve the category recognition accuracy. These approaches mainly rely on multi-view images which are rendered with the whole circumference. In real-world applications, however, 3D objects are mostly observed from partial viewpoints in a less range. Therefore, we propose a multi-view based 3D convolutional neural network, which takes only part of contiguous multi-view images as input and can still maintain high accuracy. Moreover, our model takes these view images as a joint variable to better learn spatially correlated features using 3D convolution and 3D max-pooling layers. Experimental results on ModelNet10 and ModelNet40 datasets show that our MV-C3D technique can achieve outstanding performance with multi-view images which are captured from partial angles with less range. The results on 3D rotated real image dataset MIRO further demonstrate that MV-C3D is more adaptable in real-world scenarios. The classification accuracy can be further improved with the increasing number of view images.
Attributed Graph Clustering: A Deep Attentional Embedding Approach
Authors: Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Chengqi Zhang
Date: 2019-06-15 00:00:00
Tasks: Clustering, Decoder, Graph Clustering, Graph Embedding, Node Clustering

Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on developing deep learning approaches to learn a compact graph embedding, upon which classic clustering methods like k-means or spectral clustering algorithms are applied. These two-step frameworks are difficult to manipulate and usually lead to suboptimal performance, mainly because the graph embedding is not goal-directed, i.e., designed for the specific clustering task. In this paper, we propose a goal-directed deep learning approach, Deep Attentional Embedded Graph Clustering (DAEGC for short). Our method focuses on attributed graphs to sufficiently explore the two sides of information in graphs. By employing an attention network to capture the importance of the neighboring nodes to a target node, our DAEGC algorithm encodes the topological structure and node content in a graph to a compact representation, on which an inner product decoder is trained to reconstruct the graph structure. Furthermore, soft labels from the graph embedding itself are generated to supervise a self-training graph clustering process, which iteratively refines the clustering results. The self-training process is jointly learned and optimized with the graph embedding in a unified framework, to mutually benefit both components. Experimental results compared with state-of-the-art algorithms demonstrate the superiority of our method.
Relevance Feedback with Latent Variables in Riemann spaces
Authors: Simone Santini
Date: 2019-06-15 00:00:00

In this paper we develop and evaluate two methods for relevance feedback based on endowing a suitable "semantic query space" with a Riemann metric derived from the probability distribution of the positive samples of the feedback. The first method uses a Gaussian distribution to model the data, while the second uses a more complex Latent Semantic variable model. A mixed (discrete-continuous) version of the Expectation-Maximization algorithm is developed for this model. We motivate the need for the semantic query space by analyzing in some depth three well-known relevance feedback methods, and we develop a new experimental methodology to evaluate these methods and compare their performance in a neutral way, that is, without making assumptions on the system in which they will be embedded.
Delving into 3D Action Anticipation from Streaming Videos
Authors: Hongsong Wang, Jiashi Feng
Date: 2019-06-15 00:00:00
Tasks: Action Anticipation, Action Classification, Multi-Task Learning

Action anticipation, which aims to recognize the action with a partial observation, becomes increasingly popular due to a wide range of applications. In this paper, we investigate the problem of 3D action anticipation from streaming videos with the target of understanding best practices for solving this problem. We first introduce several complementary evaluation metrics and present a basic model based on frame-wise action classification. To achieve better performance, we then investigate two important factors, i.e., the length of the training clip and clip sampling method. We also explore multi-task learning strategies by incorporating auxiliary information from two aspects: the full action representation and the class-agnostic action label. Our comprehensive experiments uncover the best practices for 3D action anticipation, and accordingly we propose a novel method with a multi-task loss. The proposed method considerably outperforms the recent methods and exhibits the state-of-the-art performance on standard benchmarks.
Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolution
Authors: Sebastian Weiss, Mengyu Chu, Nils Thuerey, Rüdiger Westermann
Date: 2019-06-15 00:00:00
Tasks: Deep Learning, Image Super-Resolution, Super-Resolution

Rendering an accurate image of an isosurface in a volumetric field typically requires large numbers of data samples. Reducing the number of required samples lies at the core of research in volume rendering. With the advent of deep learning networks, a number of architectures have been proposed recently to infer missing samples in multi-dimensional fields, for applications such as image super-resolution and scan completion. In this paper, we investigate the use of such architectures for learning the upscaling of a low-resolution sampling of an isosurface to a higher resolution, with high fidelity reconstruction of spatial detail and shading. We introduce a fully convolutional neural network, to learn a latent representation generating a smooth, edge-aware normal field and ambient occlusions from a low-resolution normal and depth field. By adding a frame-to-frame motion loss into the learning stage, the upscaling can consider temporal variations and achieves improved frame-to-frame coherence. We demonstrate the quality of the network for isosurfaces which were never seen during training, and discuss remote and in-situ visualization as well as focus+context visualization as potential applications