pwc-archive/papers-with-abstracts huggingface dataset:
Scale-wise Convolution for Image Restoration
Authors: Yuchen Fan, Jiahui Yu, Ding Liu, Thomas S. Huang
Date: 2019-12-19 00:00:00
Tasks: Data Augmentation, Denoising, Image Compression, Image Denoising, Image Restoration, Image Super-Resolution, Super-Resolution

While scale-invariant modeling has substantially boosted the performance of visual recognition tasks, it remains largely under-explored in deep networks based image restoration. Naively applying those scale-invariant techniques (e.g. multi-scale testing, random-scale data augmentation) to image restoration tasks usually leads to inferior performance. In this paper, we show that properly modeling scale-invariance into neural networks can bring significant benefits to image restoration performance. Inspired from spatial-wise convolution for shift-invariance, "scale-wise convolution" is proposed to convolve across multiple scales for scale-invariance. In our scale-wise convolutional network (SCN), we first map the input image to the feature space and then build a feature pyramid representation via bi-linear down-scaling progressively. The feature pyramid is then passed to a residual network with scale-wise convolutions. The proposed scale-wise convolution learns to dynamically activate and aggregate features from different input scales in each residual building block, in order to exploit contextual information on multiple scales. In experiments, we compare the restoration accuracy and parameter efficiency among our model and many different variants of multi-scale neural networks. The proposed network with scale-wise convolution achieves superior performance in multiple image restoration tasks including image super-resolution, image denoising and image compression artifacts removal. Code and models are available at: https://github.com/ychfan/scn_sr
Bounded Manifold Completion
Authors: Kelum Gajamannage, Randy Paffenroth
Date: 2019-12-19 00:00:00
Tasks: Dimensionality Reduction, Image Inpainting, Low-Rank Matrix Completion, Matrix Completion, Recommendation Systems

Nonlinear dimensionality reduction or, equivalently, the approximation of high-dimensional data using a low-dimensional nonlinear manifold is an active area of research. In this paper, we will present a thematically different approach to detect the existence of a low-dimensional manifold of a given dimension that lies within a set of bounds derived from a given point cloud. A matrix representing the appropriately defined distances on a low-dimensional manifold is low-rank, and our method is based on current techniques for recovering a partially observed matrix from a small set of fully observed entries that can be implemented as a low-rank Matrix Completion (MC) problem. MC methods are currently used to solve challenging real-world problems, such as image inpainting and recommender systems, and we leverage extent efficient optimization techniques that use a nuclear norm convex relaxation as a surrogate for non-convex and discontinuous rank minimization. Our proposed method provides several advantages over current nonlinear dimensionality reduction techniques, with the two most important being theoretical guarantees on the detection of low-dimensional embeddings and robustness to non-uniformity in the sampling of the manifold. We validate the performance of this approach using both a theoretical analysis as well as synthetic and real-world benchmark datasets.
Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanations
Authors: Andreas Holzinger, André Carrington, Heimo Müller
Date: 2019-12-19 00:00:00
Tasks: Decision Making, Explainable Artificial Intelligence (XAI)

Recent success in Artificial Intelligence (AI) and Machine Learning (ML) allow problem solving automatically without any human intervention. Autonomous approaches can be very convenient. However, in certain domains, e.g., in the medical domain, it is necessary to enable a domain expert to understand, why an algorithm came up with a certain result. Consequently, the field of Explainable AI (xAI) rapidly gained interest worldwide in various domains, particularly in medicine. Explainable AI studies transparency and traceability of opaque AI/ML and there are already a huge variety of methods. For example with layer-wise relevance propagation relevant parts of inputs to, and representations in, a neural network which caused a result, can be highlighted. This is a first important step to ensure that end users, e.g., medical professionals, assume responsibility for decision making with AI/ML and of interest to professionals and regulators. Interactive ML adds the component of human expertise to AI/ML processes by enabling them to re-enact and retrace AI/ML results, e.g. let them check it for plausibility. This requires new human-AI interfaces for explainable AI. In order to build effective and efficient interactive human-AI interfaces we have to deal with the question of how to evaluate the quality of explanations given by an explainable AI system. In this paper we introduce our System Causability Scale (SCS) to measure the quality of explanations. It is based on our notion of Causability (Holzinger et al., 2019) combined with concepts adapted from a widely accepted usability scale.
Extendable NFV-Integrated Control Method Using Reinforcement Learning
Authors: Akito Suzuki, Ryoichi Kawahara, Masahiro Kobayashi, Shigeaki Harada, Yousuke Takahashi, Keisuke Ishibashi
Date: 2019-12-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Network functions virtualization (NFV) enables telecommunications service providers to realize various network services by flexibly combining multiple virtual network functions (VNFs). To provide such services, an NFV control method should optimally allocate such VNFs into physical networks and servers by taking account of the combination(s) of objective functions and constraints for each metric defined for each VNF type, e.g., VNF placements and routes between the VNFs. The NFV control method should also be extendable for adding new metrics or changing the combination of metrics. One approach for NFV control to optimize allocations is to construct an algorithm that simultaneously solves the combined optimization problem. However, this approach is not extendable because the problem needs to be reformulated every time a new metric is added or a combination of metrics is changed. Another approach involves using an extendable network-control architecture that coordinates multiple control algorithms specified for individual metrics. However, to the best of our knowledge, no method has been developed that can optimize allocations through this kind of coordination. In this paper, we propose an extendable NFV-integrated control method by coordinating multiple control algorithms. We also propose an efficient coordination algorithm based on reinforcement learning. Finally, we evaluate the effectiveness of the proposed method through simulations.
AANet: Attribute Attention Network for Person Re-Identifications
Authors: Chiat-Pin Tay, Sharmili Roy, Kim-Hui Yap
Date: 2019-12-19 00:00:00
Proceeding: CVPR 2019 6
Tasks: Attribute, Person Re-Identification, Re-Ranking

This paper proposes Attribute Attention Network (AANet), a new architecture that integrates person attributes and attribute attention maps into a classification framework to solve the person re-identification (re-ID) problem. Many person re-ID models typically employ semantic cues such as body parts or human pose to improve the re-ID performance. Attribute information, however, is often not utilized. The proposed AANet leverages on a baseline model that uses body parts and integrates the key attribute information in an unified learning framework. The AANet consists of a global person ID task, a part detection task and a crucial attribute detection task. By estimating the class responses of individual attributes and combining them to form the attribute attention map (AAM), a very strong discriminatory representation is constructed. The proposed AANet outperforms the best state-of-the-art method arXiv:1711.09349v3 [cs.CV] using ResNet-50 by 3.36% in mAP and 3.12% in Rank-1 accuracy on DukeMTMC-reID dataset. On Market1501 dataset, AANet achieves 92.38% mAP and 95.10% Rank-1 accuracy with re-ranking, outperforming arXiv:1804.00216v1 [cs.CV], another state of the art method using ResNet-152, by 1.42% in mAP and 0.47% in Rank-1 accuracy. In addition, AANet can perform person attribute prediction (e.g., gender, hair length, clothing length etc.), and localize the attributes in the query image.
Deep Reinforcement Learning Designed Shinnar-Le Roux RF Pulse using Root-Flipping: DeepRF_SLR
Authors: Dongmyung Shin, Sooyeon Ji, Doohee Lee, Jieun Lee, Se-Hong Oh, Jong-Ho Lee
Date: 2019-12-19 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A novel approach of applying deep reinforcement learning to an RF pulse design is introduced. This method, which is referred to as DeepRF_SLR, is designed to minimize the peak amplitude or, equivalently, minimize the pulse duration of a multiband refocusing pulse generated by the Shinar Le-Roux (SLR) algorithm. In the method, the root pattern of SLR polynomial, which determines the RF pulse shape, is optimized by iterative applications of deep reinforcement learning and greedy tree search. When tested for the designs of the multiband factors of three and seven RFs, DeepRF_SLR demonstrated improved performance compared to conventional methods, generating shorter duration RF pulses in shorter computational time. In the experiments, the RF pulse from DeepRF_SLR produced a slice profile similar to the minimum-phase SLR RF pulse and the profiles matched to that of the computer simulation. Our approach suggests a new way of designing an RF by applying a machine learning algorithm, demonstrating a machine-designed MRI sequence.
Adaptive Granularity in Tensors: A Quest for Interpretable Structure
Authors: Ravdeep Pasricha, Ekta Gujral, Evangelos E. Papalexakis
Date: 2019-12-19 00:00:00
Tasks: Point Processes, Tensor Decomposition

Data collected at very frequent intervals is usually extremely sparse and has no structure that is exploitable by modern tensor decomposition algorithms. Thus the utility of such tensors is low, in terms of the amount of interpretable and exploitable structure that one can extract from them. In this paper, we introduce the problem of finding a tensor of adaptive aggregated granularity that can be decomposed to reveal meaningful latent concepts (structures) from datasets that, in their original form, are not amenable to tensor analysis. Such datasets fall under the broad category of sparse point processes that evolve over space and/or time. To the best of our knowledge, this is the first work that explores adaptive granularity aggregation in tensors. Furthermore, we formally define the problem and discuss what different definitions of "good structure" can be in practice, and show that optimal solution is of prohibitive combinatorial complexity. Subsequently, we propose an efficient and effective greedy algorithm called IceBreaker, which follows a number of intuitive decision criteria that locally maximize the "goodness of structure", resulting in high-quality tensors. We evaluate our method on synthetic, semi-synthetic and real datasets. In all the cases, our proposed method constructs tensors that have very high structure quality.
Discriminative Sentence Modeling for Story Ending Prediction
Authors: Yiming Cui, Wanxiang Che, Wei-Nan Zhang, Ting Liu, Shijin Wang, Guoping Hu
Date: 2019-12-19 00:00:00
Tasks: Cloze Test, Prediction, Sentence

Story Ending Prediction is a task that needs to select an appropriate ending for the given story, which requires the machine to understand the story and sometimes needs commonsense knowledge. To tackle this task, we propose a new neural network called Diff-Net for better modeling the differences of each ending in this task. The proposed model could discriminate two endings in three semantic levels: contextual representation, story-aware representation, and discriminative representation. Experimental results on the Story Cloze Test dataset show that the proposed model siginificantly outperforms various systems by a large margin, and detailed ablation studies are given for better understanding our model. We also carefully examine the traditional and BERT-based models on both SCT v1.0 and v1.5 with interesting findings that may potentially help future studies.
Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and Limitations
Authors: Pedro Sequeira, Melinda Gervasio
Date: 2019-12-19 00:00:00
Tasks: Diversity, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose an explainable reinforcement learning (XRL) framework that analyzes an agent's history of interaction with the environment to extract interestingness elements that help explain its behavior. The framework relies on data readily available from standard RL algorithms, augmented with data that can easily be collected by the agent while learning. We describe how to create visual summaries of an agent's behavior in the form of short video-clips highlighting key interaction moments, based on the proposed elements. We also report on a user study where we evaluated the ability of humans to correctly perceive the aptitude of agents with different characteristics, including their capabilities and limitations, given visual summaries automatically generated by our framework. The results show that the diversity of aspects captured by the different interestingness elements is crucial to help humans correctly understand an agent's strengths and limitations in performing a task, and determine when it might need adjustments to improve its performance.
LSTM-TDNN with convolutional front-end for Dialect Identification in the 2019 Multi-Genre Broadcast Challenge
Authors: Xiaoxiao Miao, Ian McLoughlin
Date: 2019-12-19 00:00:00
Tasks: Data Augmentation, Dialect Identification

This paper presents a novel Dialect Identification (DID) system developed for the Fifth Edition of the Multi-Genre Broadcast challenge, the task of Fine-grained Arabic Dialect Identification (MGB-5 ADI Challenge). The system improves upon traditional DNN x-vector performance by employing a Convolutional and Long Short Term Memory-Recurrent (CLSTM) architecture to combine the benefits of a convolutional neural network front-end for feature extraction and a back-end recurrent neural to capture longer temporal dependencies. Furthermore we investigate intensive augmentation of one low resource dialect in the highly unbalanced training set using time-scale modification (TSM). This converts an utterance to several time-stretched or time-compressed versions, subsequently used to train the CLSTM system without using any other corpus. In this paper, we also investigate speech augmentation using MUSAN and the RIR datasets to increase the quantity and diversity of the existing training data in the normal way. Results show firstly that the CLSTM architecture outperforms a traditional DNN x-vector implementation. Secondly, adopting TSM-based speed perturbation yields a small performance improvement for the unbalanced data, finally that traditional data augmentation techniques yield further benefit, in line with evidence from related speaker and language recognition tasks. Our system achieved 2nd place ranking out of 15 entries in the MGB-5 ADI challenge, presented at ASRU 2019.
Regularized Estimation of High-Dimensional Vector AutoRegressions with Weakly Dependent Innovations
Authors: Ricardo P. Masini, Marcelo C. Medeiros, Eduardo F. Mendes
Date: 2019-12-19 00:00:00
Tasks: Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

There has been considerable advance in understanding the properties of sparse regularization procedures in high-dimensional models. In time series context, it is mostly restricted to Gaussian autoregressions or mixing sequences. We study oracle properties of LASSO estimation of weakly sparse vector-autoregressive models with heavy tailed, weakly dependent innovations with virtually no assumption on the conditional heteroskedasticity. In contrast to current literature, our innovation process satisfy an $L^1$ mixingale type condition on the centered conditional covariance matrices. This condition covers $L^1$-NED sequences and strong ($\alpha$-) mixing sequences as particular examples.
Understanding Human Judgments of Causality
Authors: Masahiro Kazama, Yoshihiko Suhara, Andrey Bogomolov, Alex `Sandy' Pentland
Date: 2019-12-19 00:00:00
Tasks: Attribute, BIG-bench Machine Learning

Discriminating between causality and correlation is a major problem in machine learning, and theoretical tools for determining causality are still being developed. However, people commonly make causality judgments and are often correct, even in unfamiliar domains. What are humans doing to make these judgments? This paper examines differences in human experts' and non-experts' ability to attribute causality by comparing their performances to those of machine-learning algorithms. We collected human judgments by using Amazon Mechanical Turk (MTurk) and then divided the human subjects into two groups: experts and non-experts. We also prepared expert and non-expert machine algorithms based on different training of convolutional neural network (CNN) models. The results showed that human experts' judgments were similar to those made by an "expert" CNN model trained on a large number of examples from the target domain. The human non-experts' judgments resembled the prediction outputs of the CNN model that was trained on only the small number of examples used during the MTurk instruction. We also analyzed the differences between the expert and non-expert machine algorithms based on their neural representations to evaluate the performances, providing insight into the human experts' and non-experts' cognitive abilities.
Bayesian high-dimensional linear regression with generic spike-and-slab priors
Authors: Bai Jiang, Qiang Sun
Date: 2019-12-19 00:00:00
Tasks: Model Selection, regression, Vocal Bursts Intensity Prediction

Spike-and-slab priors are popular Bayesian solutions for high-dimensional linear regression problems. Previous theoretical studies on spike-and-slab methods focus on specific prior formulations and use prior-dependent conditions and analyses, and thus can not be generalized directly. In this paper, we propose a class of generic spike-and-slab priors and develop a unified framework to rigorously assess their theoretical properties. Technically, we provide general conditions under which generic spike-and-slab priors can achieve the nearly-optimal posterior contraction rate and the model selection consistency. Our results include those of Narisetty and He (2014) and Castillo et al. (2015) as special cases.
TextTubes for Detecting Curved Text in the Wild
Authors: Joël Seytre, Jon Wu, Alessandro Achille
Date: 2019-12-19 00:00:00

We present a detector for curved text in natural images. We model scene text instances as tubes around their medial axes and introduce a parametrization-invariant loss function. We train a two-stage curved text detector, and evaluate it on the curved text benchmarks CTW-1500 and Total-Text. Our approach achieves state-of-the-art results or improves upon them, notably for CTW-1500 by over 8 percentage points in F-score.
Model Weight Theft With Just Noise Inputs: The Curious Case of the Petulant Attacker
Authors: Nicholas Roberts, Vinay Uday Prabhu, Matthew McAteer
Date: 2019-12-19 00:00:00

This paper explores the scenarios under which an attacker can claim that 'Noise and access to the softmax layer of the model is all you need' to steal the weights of a convolutional neural network whose architecture is already known. We were able to achieve 96% test accuracy using the stolen MNIST model and 82% accuracy using the stolen KMNIST model learned using only i.i.d. Bernoulli noise inputs. We posit that this theft-susceptibility of the weights is indicative of the complexity of the dataset and propose a new metric that captures the same. The goal of this dissemination is to not just showcase how far knowing the architecture can take you in terms of model stealing, but to also draw attention to this rather idiosyncratic weight learnability aspects of CNNs spurred by i.i.d. noise input. We also disseminate some initial results obtained with using the Ising probability distribution in lieu of the i.i.d. Bernoulli distribution.
Deep Connectomics Networks: Neural Network Architectures Inspired by Neuronal Networks
Authors: Nicholas Roberts, Dian Ang Yap, Vinay Uday Prabhu
Date: 2019-12-19 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12

The interplay between inter-neuronal network topology and cognition has been studied deeply by connectomics researchers and network scientists, which is crucial towards understanding the remarkable efficacy of biological neural networks. Curiously, the deep learning revolution that revived neural networks has not paid much attention to topological aspects. The architectures of deep neural networks (DNNs) do not resemble their biological counterparts in the topological sense. We bridge this gap by presenting initial results of Deep Connectomics Networks (DCNs) as DNNs with topologies inspired by real-world neuronal networks. We show high classification accuracy obtained by DCNs whose architecture was inspired by the biological neuronal networks of C. Elegans and the mouse visual cortex.
Identifying Adversarial Sentences by Analyzing Text Complexity
Authors: Hoang-Quoc Nguyen-Son, Tran Phuong Thao, Seira Hidano, Shinsaku Kiyomoto
Date: 2019-12-19 00:00:00
Tasks: Adversarial Text

Attackers create adversarial text to deceive both human perception and the current AI systems to perform malicious purposes such as spam product reviews and fake political posts. We investigate the difference between the adversarial and the original text to prevent the risk. We prove that the text written by a human is more coherent and fluent. Moreover, the human can express the idea through the flexible text with modern words while a machine focuses on optimizing the generated text by the simple and common words. We also suggest a method to identify the adversarial text by extracting the features related to our findings. The proposed method achieves high performance with 82.0% of accuracy and 18.4% of equal error rate, which is better than the existing methods whose the best accuracy is 77.0% corresponding to the error rate 22.8%.
A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation
Authors: Dong Zhang, Shu Zhao, Zhen Duan, Jie Chen, Yangping Zhang, Jie Tang
Date: 2019-12-19 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Paper-reviewer recommendation task is of significant academic importance for conference chairs and journal editors. How to effectively and accurately recommend reviewers for the submitted papers is a meaningful and still tough task. In this paper, we propose a Multi-Label Classification method using a hierarchical and transparent Representation named Hiepar-MLC. Further, we propose a simple multi-label-based reviewer assignment MLBRA strategy to select the appropriate reviewers. It is interesting that we also explore the paper-reviewer recommendation in the coarse-grained granularity.