pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Accuracy Prediction for AMR Parsing
Authors: Juri Opitz, Anette Frank
Date: 2019-04-17 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Abstract Meaning Representation, AMR Parsing, Prediction

Abstract Meaning Representation (AMR) represents sentences as directed,
acyclic and rooted graphs, aiming at capturing their meaning in a machine
readable format. AMR parsing converts natural language sentences into such
graphs. However, evaluating a parser on new data by means of comparison to
manually created AMR graphs is very costly. Also, we would like to be able to
detect parses of questionable quality, or preferring results of alternative
systems by selecting the ones for which we can assess good quality. We propose
AMR accuracy prediction as the task of predicting several metrics of
correctness for an automatically generated AMR parse - in absence of the
corresponding gold parse. We develop a neural end-to-end multi-output
regression model and perform three case studies: firstly, we evaluate the
model's capacity of predicting AMR parse accuracies and test whether it can
reliably assign high scores to gold parses. Secondly, we perform parse
selection based on predicted parse accuracies of candidate parses from
alternative systems, with the aim of improving overall results. Finally, we
predict system ranks for submissions from two AMR shared tasks on the basis of
their predicted parse accuracy averages. All experiments are carried out across
two different domains and show that our method is effective.
Amobee at SemEval-2019 Tasks 5 and 6: Multiple Choice CNN Over Contextual Embedding
Authors: Alon Rozental, Dadi Biton
Date: 2019-04-17 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Multiple-choice

This article describes Amobee's participation in "HatEval: Multilingual
detection of hate speech against immigrants and women in Twitter" (task 5) and
"OffensEval: Identifying and Categorizing Offensive Language in Social Media"
(task 6). The goal of task 5 was to detect hate speech targeted to women and
immigrants. The goal of task 6 was to identify and categorized offensive
language in social media, and identify offense target. We present a novel type
of convolutional neural network called "Multiple Choice CNN" (MC-CNN) that we
used over our newly developed contextual embedding, Rozental et al. (2019). For
both tasks we used this architecture and achieved 4th place out of 69
participants with an F1 score of 0.53 in task 5, in task 6 achieved 2nd place
(out of 75) in Sub-task B - automatic categorization of offense types (our
model reached places 18/2/7 out of 103/75/65 for sub-tasks A, B and C
respectively in task 6).
Interpreting Adversarial Examples with Attributes
Authors: Sadaf Gulshad, Jan Hendrik Metzen, Arnold Smeulders, Zeynep Akata
Date: 2019-04-17 00:00:00
Tasks: Attribute, General Classification

Deep computer vision systems being vulnerable to imperceptible and carefully
crafted noise have raised questions regarding the robustness of their
decisions. We take a step back and approach this problem from an orthogonal
direction. We propose to enable black-box neural networks to justify their
reasoning both for clean and for adversarial examples by leveraging attributes,
i.e. visually discriminative properties of objects. We rank attributes based on
their class relevance, i.e. how the classification decision changes when the
input is visually slightly perturbed, as well as image relevance, i.e. how well
the attributes can be localized on both clean and perturbed images. We present
comprehensive experiments for attribute prediction, adversarial example
generation, adversarially robust learning, and their qualitative and
quantitative analysis using predicted attributes on three benchmark datasets.
Attentive Aspect Modeling for Review-aware Recommendation
Authors: Xinyu Guan, Zhiyong Cheng, Xiangnan He, Yongfeng Zhang, Zhibo Zhu, Qinke Peng, Tat-Seng Chua
Date: 2018-11-11 00:00:00
Tasks: Diversity

In recent years, many studies extract aspects from user reviews and integrate
them with ratings for improving the recommendation performance. The common
aspects mentioned in a user's reviews and a product's reviews indicate indirect
connections between the user and product. However, these aspect-based methods
suffer from two problems. First, the common aspects are usually very sparse,
which is caused by the sparsity of user-product interactions and the diversity
of individual users' vocabularies. Second, a user's interests on aspects could
be different with respect to different products, which are usually assumed to
be static in existing methods. In this paper, we propose an Attentive
Aspect-based Recommendation Model (AARM) to tackle these challenges. For the
first problem, to enrich the aspect connections between user and product,
besides common aspects, AARM also models the interactions between synonymous
and similar aspects. For the second problem, a neural attention network which
simultaneously considers user, product and aspect information is constructed to
capture a user's attention towards aspects when examining different products.
Extensive quantitative and qualitative experiments show that AARM can
effectively alleviate the two aforementioned problems and significantly
outperforms several state-of-the-art recommendation methods on top-N
recommendation task.
BS-Nets: An End-to-End Framework For Band Selection of Hyperspectral Image
Authors: Yaoming Cai, Xiaobo Liu, Zhihua Cai
Date: 2019-04-17 00:00:00
Tasks: Hyperspectral Image Classification

Hyperspectral image (HSI) consists of hundreds of continuous narrow bands
with high spectral correlation, which would lead to the so-called Hughes
phenomenon and the high computational cost in processing. Band selection has
been proven effective in avoiding such problems by removing the redundant
bands. However, many of existing band selection methods separately estimate the
significance for every single band and cannot fully consider the nonlinear and
global interaction between spectral bands. In this paper, by assuming that a
complete HSI can be reconstructed from its few informative bands, we propose a
general band selection framework, Band Selection Network (termed as BS-Net).
The framework consists of a band attention module (BAM), which aims to
explicitly model the nonlinear inter-dependencies between spectral bands, and a
reconstruction network (RecNet), which is used to restore the original HSI cube
from the learned informative bands, resulting in a flexible architecture. The
resulting framework is end-to-end trainable, making it easier to train from
scratch and to combine with existing networks. We implement two BS-Nets
respectively using fully connected networks (BS-Net-FC) and convolutional
neural networks (BS-Net-Conv), and compare the results with many existing band
selection approaches for three real hyperspectral images, demonstrating that
the proposed BS-Nets can accurately select informative band subset with less
redundancy and achieve significantly better classification performance with an
acceptable time cost.
Cycle-SUM: Cycle-consistent Adversarial LSTM Networks for Unsupervised Video Summarization
Authors: Li Yuan, Francis EH Tay, Ping Li, Li Zhou, Jiashi Feng
Date: 2019-04-17 00:00:00
Tasks: Unsupervised Video Summarization, Video Summarization

In this paper, we present a novel unsupervised video summarization model that
requires no manual annotation. The proposed model termed Cycle-SUM adopts a new
cycle-consistent adversarial LSTM architecture that can effectively maximize
the information preserving and compactness of the summary video. It consists of
a frame selector and a cycle-consistent learning based evaluator. The selector
is a bi-direction LSTM network that learns video representations that embed the
long-range relationships among video frames. The evaluator defines a learnable
information preserving metric between original video and summary video and
"supervises" the selector to identify the most informative frames to form the
summary video. In particular, the evaluator is composed of two generative
adversarial networks (GANs), in which the forward GAN is learned to reconstruct
original video from summary video while the backward GAN learns to invert the
processing. The consistency between the output of such cycle learning is
adopted as the information preserving metric for video summarization. We
demonstrate the close relation between mutual information maximization and such
cycle learning procedure. Experiments on two video summarization benchmark
datasets validate the state-of-the-art performance and superiority of the
Cycle-SUM model over previous baselines.
USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets
Authors: Leonardo Rundo, Changhee Han, Yudai Nagano, Jin Zhang, Ryuichiro Hataya, Carmelo Militello, Andrea Tangherloni, Marco S. Nobile, Claudio Ferretti, Daniela Besozzi, Maria Carla Gilardi, Salvatore Vitabile, Giancarlo Mauri, Hideki Nakayama, Paolo Cazzaniga
Date: 2019-04-17 00:00:00

Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to differential diagnosis, since tumor's frequency and severity differ in these regions. To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net. Especially, the SE blocks are added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec USE-Net). This study evaluates the generalization ability of CNN-based architectures on three T2-weighted MRI datasets, each one consisting of a different number of patients and heterogeneous image characteristics, collected by different institutions. The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations. USE-Net is compared against three state-of-the-art CNN-based architectures (i.e., U-Net, pix2pix, and Mixed-Scale Dense Network), along with a semi-automatic continuous max-flow model. The results show that training on the union of the datasets generally outperforms training on each dataset separately, allowing for both intra-/cross-dataset generalization. Enc USE-Net shows good overall generalization under any training condition, while Enc-Dec USE-Net remarkably outperforms the other methods when trained on all datasets. These findings reveal that the SE blocks' adaptive feature recalibration provides excellent cross-dataset generalization when testing is performed on samples of the datasets used during training.
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
Authors: Sujay Khandagale, Han Xiao, Rohit Babbar
Date: 2019-04-17 00:00:00
Tasks: Classification, Extreme Multi-Label Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Learning

Extreme multi-label classification (XMC) refers to supervised multi-label learning involving hundreds of thousand or even millions of labels. In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees. We show three concrete realizations of this label representation space including : (i) the input space which is spanned by the input features, (ii) the output space spanned by label vectors based on their co-occurrence with other labels, and (iii) the joint space by combining the input and output representations. Furthermore, the constraint-free multi-way partitions learnt iteratively in these spaces lead to shallow trees. By combining the effect of shallow trees and generalized label representation, Bonsai achieves the best of both worlds - fast training which is comparable to state-of-the-art tree-based methods in XMC, and much better prediction accuracy, particularly on tail-labels. On a benchmark Amazon-3M dataset with 3 million labels, \bonsai outperforms a state-of-the-art one-vs-rest method in terms of prediction accuracy, while being approximately 200 times faster to train. The code for Bonsai is available at \url{https://github.com/xmc-aalto/bonsai}
End-to-End Learning of Representations for Asynchronous Event-Based Data
Authors: Daniel Gehrig, Antonio Loquercio, Konstantinos G. Derpanis, Davide Scaramuzza
Date: 2019-04-17 00:00:00
Proceeding: ICCV 2019 10
Tasks: Classification, Object Recognition, Optical Flow Estimation, Robust classification

Event cameras are vision sensors that record asynchronous streams of per-pixel brightness changes, referred to as "events". They have appealing advantages over frame-based cameras for computer vision, including high temporal resolution, high dynamic range, and no motion blur. Due to the sparse, non-uniform spatiotemporal layout of the event signal, pattern recognition algorithms typically aggregate events into a grid-based representation and subsequently process it by a standard vision pipeline, e.g., Convolutional Neural Network (CNN). In this work, we introduce a general framework to convert event streams into grid-based representations through a sequence of differentiable operations. Our framework comes with two main advantages: (i) allows learning the input event representation together with the task dedicated network in an end to end manner, and (ii) lays out a taxonomy that unifies the majority of extant event representations in the literature and identifies novel ones. Empirically, we show that our approach to learning the event representation end-to-end yields an improvement of approximately 12% on optical flow estimation and object recognition over state-of-the-art methods.
X-Armed Bandits: Optimizing Quantiles, CVaR and Other Risks
Authors: Léonard Torossian, Aurélien Garivier, Victor Picheny
Date: 2019-04-17 00:00:00
Tasks: Decision Making

We propose and analyze StoROO, an algorithm for risk optimization on stochastic black-box functions derived from StoOO. Motivated by risk-averse decision making fields like agriculture, medicine, biology or finance, we do not focus on the mean payoff but on generic functionals of the return distribution. We provide a generic regret analysis of StoROO and illustrate its applicability with two examples: the optimization of quantiles and CVaR. Inspired by the bandit literature and black-box mean optimizers, StoROO relies on the possibility to construct confidence intervals for the targeted functional based on random-size samples. We detail their construction in the case of quantiles, providing tight bounds based on Kullback-Leibler divergence. We finally present numerical experiments that show a dramatic impact of tight bounds for the optimization of quantiles and CVaR.
Effective Estimation of Deep Generative Language Models
Authors: Tom Pelsmaeker, Wilker Aziz
Date: 2019-04-17 00:00:00
Proceeding: ACL 2020 6
Tasks: Bayesian Optimisation, Language Modelling, Variational Inference

Advances in variational inference enable parameterisation of probabilistic models by deep neural networks. This combines the statistical transparency of the probabilistic modelling framework with the representational power of deep learning. Yet, due to a problem known as posterior collapse, it is difficult to estimate such models in the context of language modelling effectively. We concentrate on one such model, the variational auto-encoder, which we argue is an important building block in hierarchical probabilistic models of language. This paper contributes a sober view of the problem, a survey of techniques to address it, novel techniques, and extensions to the model. To establish a ranking of techniques, we perform a systematic comparison using Bayesian optimisation and find that many techniques perform reasonably similar, given enough resources. Still, a favourite can be named based on convenience. We also make several empirical observations and recommendations of best practices that should help researchers interested in this exciting field.
CenterNet: Keypoint Triplets for Object Detection
Authors: Kaiwen Duan, Song Bai, Lingxi Xie, Honggang Qi, Qingming Huang, Qi Tian
Date: 2019-04-17 00:00:00
Proceeding: ICCV 2019 10
Tasks: Object, object-detection, Object Detection, Triplet

In object detection, keypoint-based approaches often suffer a large number of
incorrect object bounding boxes, arguably due to the lack of an additional look
into the cropped regions. This paper presents an efficient solution which
explores the visual patterns within each cropped region with minimal costs. We
build our framework upon a representative one-stage keypoint-based detector
named CornerNet. Our approach, named CenterNet, detects each object as a
triplet, rather than a pair, of keypoints, which improves both precision and
recall. Accordingly, we design two customized modules named cascade corner
pooling and center pooling, which play the roles of enriching information
collected by both top-left and bottom-right corners and providing more
recognizable information at the central regions, respectively. On the MS-COCO
dataset, CenterNet achieves an AP of 47.0%, which outperforms all existing
one-stage detectors by at least 4.9%. Meanwhile, with a faster inference speed,
CenterNet demonstrates quite comparable performance to the top-ranked two-stage
detectors. Code is available at https://github.com/Duankaiwen/CenterNet.
Downhole Track Detection via Multiscale Conditional Generative Adversarial Nets
Authors: Jia Li, Xing Wei, Guoqiang Yang, Xiao Sun, Changliang Li
Date: 2019-04-17 00:00:00
Tasks: Autonomous Driving, Generative Adversarial Network

Frequent mine disasters cause a large number of casualties and property
losses. Autonomous driving is a fundamental measure for solving this problem,
and track detection is one of the key technologies for computer vision to
achieve downhole automatic driving. The track detection result based on the
traditional convolutional neural network (CNN) algorithm lacks the detailed and
unique description of the object and relies too much on visual postprocessing
technology. Therefore, this paper proposes a track detection algorithm based on
a multiscale conditional generative adversarial network (CGAN). The generator
is decomposed into global and local parts using a multigranularity structure in
the generator network. A multiscale shared convolution structure is adopted in
the discriminator network to further supervise training the generator. Finally,
the Monte Carlo search technique is introduced to search the intermediate state
of the generator, and the result is sent to the discriminator for comparison.
Compared with the existing work, our model achieved 82.43\% pixel accuracy and
an average intersection-over-union (IOU) of 0.6218, and the detection of the
track reached 95.01\% accuracy in the downhole roadway scene test set.
CaseNet: Content-Adaptive Scale Interaction Networks for Scene Parsing
Authors: Xin Jin, Cuiling Lan, Wen-Jun Zeng, Zhizheng Zhang, Zhibo Chen
Date: 2019-04-17 00:00:00
Tasks: Position, Scene Parsing

Objects at different spatial positions in an image exhibit different scales. Adaptive receptive fields are expected to capture suitable ranges of context for accurate pixel level semantic prediction. Recently, atrous convolution with different dilation rates has been used to generate features of multi-scales through several branches which are then fused for prediction. However, there is a lack of explicit interaction among the branches of different scales to adaptively make full use of the contexts. In this paper, we propose a Content-Adaptive Scale Interaction Network (CASINet) to exploit the multi-scale features for scene parsing. We build CASINet based on the classic Atrous Spatial Pyramid Pooling (ASPP) module, followed by a proposed contextual scale interaction (CSI) module, and a scale adaptation (SA) module. Specifically, in the CSI module, for each spatial position of some scale, instead of being limited by a fixed set of convolutional filters that are shared across different spatial positions for feature learning, we promote the adaptivity of the convolutional filters to spatial positions. We achieve this by the context interaction among the features of different scales. The SA module explicitly and softly selects the suitable scale for each spatial position and each channel. Ablation studies demonstrate the effectiveness of the proposed modules. We achieve state-of-the-art performance on three scene parsing benchmarks Cityscapes, ADE20K and LIP.
Analysing Neural Network Topologies: a Game Theoretic Approach
Authors: Julian Stier, Gabriele Gianini, Michael Granitzer, Konstantin Ziegler
Date: 2019-04-17 00:00:00
Tasks: Network Pruning

Artificial Neural Networks have shown impressive success in very different
application cases. Choosing a proper network architecture is a critical
decision for a network's success, usually done in a manual manner. As a
straightforward strategy, large, mostly fully connected architectures are
selected, thereby relying on a good optimization strategy to find proper
weights while at the same time avoiding overfitting. However, large parts of
the final network are redundant. In the best case, large parts of the network
become simply irrelevant for later inferencing. In the worst case, highly
parameterized architectures hinder proper optimization and allow the easy
creation of adverserial examples fooling the network. A first step in removing
irrelevant architectural parts lies in identifying those parts, which requires
measuring the contribution of individual components such as neurons. In
previous work, heuristics based on using the weight distribution of a neuron as
contribution measure have shown some success, but do not provide a proper
theoretical understanding. Therefore, in our work we investigate game theoretic
measures, namely the Shapley value (SV), in order to separate relevant from
irrelevant parts of an artificial neural network. We begin by designing a
coalitional game for an artificial neural network, where neurons form
coalitions and the average contributions of neurons to coalitions yield to the
Shapley value. In order to measure how well the Shapley value measures the
contribution of individual neurons, we remove low-contributing neurons and
measure its impact on the network performance. In our experiments we show that
the Shapley value outperforms other heuristics for measuring the contribution
of neurons.