pwc-archive/papers-with-abstracts huggingface dataset:
Learning Discriminative 3D Shape Representations by View Discerning Networks
Authors: Biao Leng, Cheng Zhang, Xiaocheng Zhou, Cheng Xu, Kai Xu
Date: 2018-08-11 00:00:00
Tasks: 3D Shape Recognition, 3D Shape Representation, Retrieval

In view-based 3D shape recognition, extracting discriminative visual
representation of 3D shapes from projected images is considered the core
problem. Projections with low discriminative ability can adversely influence
the final 3D shape representation. Especially under the real situations with
background clutter and object occlusion, the adverse effect is even more
severe. To resolve this problem, we propose a novel deep neural network, View
Discerning Network, which learns to judge the quality of views and adjust their
contributions to the representation of shapes. In this network, a Score
Generation Unit is devised to evaluate the quality of each projected image with
score vectors. These score vectors are used to weight the image features and
the weighted features perform much better than original features in 3D shape
recognition task. In particular, we introduce two structures of Score
Generation Unit, Channel-wise Score Unit and Part-wise Score Unit, to assess
the quality of feature maps from different perspectives. Our network aggregates
features and scores in an end-to-end framework, so that final shape descriptors
are directly obtained from its output. Our experiments on ModelNet and ShapeNet
Core55 show that View Discerning Network outperforms the state-of-the-arts in
terms of the retrieval task, with excellent robustness against background
clutter and object occlusion.
Automatically designing CNN architectures using genetic algorithm for image classification
Authors: Yanan Sun, Bing Xue, Mengjie Zhang, Gary G. Yen
Date: 2018-08-11 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Convolutional Neural Networks (CNNs) have gained a remarkable success on many image classification tasks in recent years. However, the performance of CNNs highly relies upon their architectures. For most state-of-the-art CNNs, their architectures are often manually-designed with expertise in both CNNs and the investigated problems. Therefore, it is difficult for users, who have no extended expertise in CNNs, to design optimal CNN architectures for their own image classification problems of interest. In this paper, we propose an automatic CNN architecture design method by using genetic algorithms, to effectively address the image classification tasks. The most merit of the proposed algorithm remains in its "automatic" characteristic that users do not need domain knowledge of CNNs when using the proposed algorithm, while they can still obtain a promising CNN architecture for the given images. The proposed algorithm is validated on widely used benchmark image classification datasets, by comparing to the state-of-the-art peer competitors covering eight manually-designed CNNs, seven automatic+manually tuning and five automatic CNN architecture design algorithms. The experimental results indicate the proposed algorithm outperforms the existing automatic CNN architecture design algorithms in terms of classification accuracy, parameter numbers and consumed computational resources. The proposed algorithm also shows the very comparable classification accuracy to the best one from manually-designed and automatic+manually tuning CNNs, while consumes much less of computational resource.
A Full End-to-End Semantic Role Labeler, Syntax-agnostic Over Syntax-aware?
Authors: Jiaxun Cai, Shexia He, Zuchao Li, Hai Zhao
Date: 2018-08-11 00:00:00
Tasks: Decoder, Semantic Role Labeling, Sentence

Semantic role labeling (SRL) is to recognize the predicate-argument structure
of a sentence, including subtasks of predicate disambiguation and argument
labeling. Previous studies usually formulate the entire SRL problem into two or
more subtasks. For the first time, this paper introduces an end-to-end neural
model which unifiedly tackles the predicate disambiguation and the argument
labeling in one shot. Using a biaffine scorer, our model directly predicts all
semantic role labels for all given word pairs in the sentence without relying
on any syntactic parse information. Specifically, we augment the BiLSTM encoder
with a non-linear transformation to further distinguish the predicate and the
argument in a given sentence, and model the semantic role labeling process as a
word pair classification task by employing the biaffine attentional mechanism.
Though the proposed model is syntax-agnostic with local decoder, it outperforms
the state-of-the-art syntax-aware SRL systems on the CoNLL-2008, 2009
benchmarks for both English and Chinese. To our best knowledge, we report the
first syntax-agnostic SRL model that surpasses all known syntax-aware models.
The Impact of Automatic Pre-annotation in Clinical Note Data Element Extraction - the CLEAN Tool
Authors: Tsung-Ting Kuo, Jina Huh, Ji-Hoon Kim, Robert El-Kareh, Siddharth Singh, Stephanie Feudjio Feupe, Vincent Kuri, Gordon Lin, Michele E. Day, Lucila Ohno-Machado, Chun-Nan Hsu
Date: 2018-08-11 00:00:00
Tasks: Open-Ended Question Answering

Objective. Annotation is expensive but essential for clinical note review and
clinical natural language processing (cNLP). However, the extent to which
computer-generated pre-annotation is beneficial to human annotation is still an
open question. Our study introduces CLEAN (CLinical note rEview and
ANnotation), a pre-annotation-based cNLP annotation system to improve clinical
note annotation of data elements, and comprehensively compares CLEAN with the
widely-used annotation system Brat Rapid Annotation Tool (BRAT).
  Materials and Methods. CLEAN includes an ensemble pipeline (CLEAN-EP) with a
newly developed annotation tool (CLEAN-AT). A domain expert and a novice
user/annotator participated in a comparative usability test by tagging 87 data
elements related to Congestive Heart Failure (CHF) and Kawasaki Disease (KD)
cohorts in 84 public notes.
  Results. CLEAN achieved higher note-level F1-score (0.896) over BRAT (0.820),
with significant difference in correctness (P-value < 0.001), and the mostly
related factor being system/software (P-value < 0.001). No significant
difference (P-value 0.188) in annotation time was observed between CLEAN (7.262
minutes/note) and BRAT (8.286 minutes/note). The difference was mostly
associated with note length (P-value < 0.001) and system/software (P-value
0.013). The expert reported CLEAN to be useful/satisfactory, while the novice
reported slight improvements.
  Discussion. CLEAN improves the correctness of annotation and increases
usefulness/satisfaction with the same level of efficiency. Limitations include
untested impact of pre-annotation correctness rate, small sample size, small
user size, and restrictedly validated gold standard.
  Conclusion. CLEAN with pre-annotation can be beneficial for an expert to deal
with complex annotation tasks involving numerous and diverse target data
elements.
Document Informed Neural Autoregressive Topic Models
Authors: Pankaj Gupta, Florian Buettner, Hinrich Schütze
Date: 2018-08-11 00:00:00
Tasks: Language Modeling, Language Modelling, Retrieval, Text Categorization, Topic Models

Context information around words helps in determining their actual meaning,
for example "networks" used in contexts of artificial neural networks or
biological neuron networks. Generative topic models infer topic-word
distributions, taking no or only little context into account. Here, we extend a
neural autoregressive topic model to exploit the full context information
around words in a document in a language modeling fashion. This results in an
improved performance in terms of generalization, interpretability and
applicability. We apply our modeling approach to seven data sets from various
domains and demonstrate that our approach consistently outperforms
stateof-the-art generative topic models. With the learned representations, we
show on an average a gain of 9.6% (0.57 Vs 0.52) in precision at retrieval
fraction 0.02 and 7.2% (0.582 Vs 0.543) in F1 for text categorization.
The ActivityNet Large-Scale Activity Recognition Challenge 2018 Summary
Authors: Bernard Ghanem, Juan Carlos Niebles, Cees Snoek, Fabian Caba Heilbron, Humam Alwassel, Victor Escorcia, Ranjay Krishna, Shyamal Buch, Cuong Duc Dao
Date: 2018-08-11 00:00:00
Tasks: Activity Recognition

The 3rd annual installment of the ActivityNet Large- Scale Activity
Recognition Challenge, held as a full-day workshop in CVPR 2018, focused on the
recognition of daily life, high-level, goal-oriented activities from
user-generated videos as those found in internet video portals. The 2018
challenge hosted six diverse tasks which aimed to push the limits of semantic
visual understanding of videos as well as bridge visual content with human
captions. Three out of the six tasks were based on the ActivityNet dataset,
which was introduced in CVPR 2015 and organized hierarchically in a semantic
taxonomy. These tasks focused on tracing evidence of activities in time in the
form of proposals, class labels, and captions. In this installment of the
challenge, we hosted three guest tasks to enrich the understanding of visual
information in videos. The guest tasks focused on complementary aspects of the
activity recognition problem at large scale and involved three challenging and
recently compiled datasets: the Kinetics-600 dataset from Google DeepMind, the
AVA dataset from Berkeley and Google, and the Moments in Time dataset from MIT
and IBM Research.
MARVIN: An Open Machine Learning Corpus and Environment for Automated Machine Learning Primitive Annotation and Execution
Authors: Chris A. Mattmann, Sujen Shah, Brian Wilson
Date: 2018-08-11 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, image-classification, Image Classification, regression

In this demo paper, we introduce the DARPA D3M program for automatic machine
learning (ML) and JPL's MARVIN tool that provides an environment to locate,
annotate, and execute machine learning primitives for use in ML pipelines.
MARVIN is a web-based application and associated back-end interface written in
Python that enables composition of ML pipelines from hundreds of primitives
from the world of Scikit-Learn, Keras, DL4J and other widely used libraries.
MARVIN allows for the creation of Docker containers that run on Kubernetes
clusters within DARPA to provide an execution environment for automated machine
learning. MARVIN currently contains over 400 datasets and challenge problems
from a wide array of ML domains including routine classification and regression
to advanced video/image classification and remote sensing.
Knowledge Graph Embedding with Entity Neighbors and Deep Memory Network
Authors: Kai Wang, Yu Liu, Xiujuan Xu, Dan Lin
Date: 2018-08-11 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding

Knowledge Graph Embedding (KGE) aims to represent entities and relations of
knowledge graph in a low-dimensional continuous vector space. Recent works
focus on incorporating structural knowledge with additional information, such
as entity descriptions, relation paths and so on. However, common used
additional information usually contains plenty of noise, which makes it hard to
learn valuable representation. In this paper, we propose a new kind of
additional information, called entity neighbors, which contain both semantic
and topological features about given entity. We then develop a deep memory
network model to encode information from neighbors. Employing a gating
mechanism, representations of structure and neighbors are integrated into a
joint representation. The experimental results show that our model outperforms
existing KGE methods utilizing entity descriptions and achieves
state-of-the-art metrics on 4 datasets.
Neural Network Encapsulation
Authors: Hongyang Li, Xiaoyang Guo, Bo Dai, Wanli Ouyang, Xiaogang Wang
Date: 2018-08-11 00:00:00
Proceeding: ECCV 2018 9

A capsule is a collection of neurons which represents different variants of a
pattern in the network. The routing scheme ensures only certain capsules which
resemble lower counterparts in the higher layer should be activated. However,
the computational complexity becomes a bottleneck for scaling up to larger
networks, as lower capsules need to correspond to each and every higher
capsule. To resolve this limitation, we approximate the routing process with
two branches: a master branch which collects primary information from its
direct contact in the lower layer and an aide branch that replenishes master
based on pattern variants encoded in other lower capsules. Compared with
previous iterative and unsupervised routing scheme, these two branches are
communicated in a fast, supervised and one-time pass fashion. The complexity
and runtime of the model are therefore decreased by a large margin. Motivated
by the routing to make higher capsule have agreement with lower capsule, we
extend the mechanism as a compensation for the rapid loss of information in
nearby layers. We devise a feedback agreement unit to send back higher capsules
as feedback. It could be regarded as an additional regularization to the
network. The feedback agreement is achieved by comparing the optimal transport
divergence between two distributions (lower and higher capsules). Such an
add-on witnesses a unanimous gain in both capsule and vanilla networks. Our
proposed EncapNet performs favorably better against previous state-of-the-arts
on CIFAR10/100, SVHN and a subset of ImageNet.
Dropout during inference as a model for neurological degeneration in an image captioning network
Authors: Bai Li, Ran Zhang, Frank Rudzicz
Date: 2018-08-11 00:00:00
Tasks: Image Captioning

We replicate a variation of the image captioning architecture by Vinyals et
al. (2015), then introduce dropout during inference mode to simulate the
effects of neurodegenerative diseases like Alzheimer's disease (AD) and
Wernicke's aphasia (WA). We evaluate the effects of dropout on language
production by measuring the KL-divergence of word frequency distributions and
other linguistic metrics as dropout is added. We find that the generated
sentences most closely approximate the word frequency distribution of the
training corpus when using a moderate dropout of 0.4 during inference.
Ancient-Modern Chinese Translation with a Large Training Dataset
Authors: Dayiheng Liu, Jiancheng Lv, Kexin Yang, Qian Qu
Date: 2018-08-11 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Machine Translation, NMT, Translation

Ancient Chinese brings the wisdom and spirit culture of the Chinese nation. Automatic translation from ancient Chinese to modern Chinese helps to inherit and carry forward the quintessence of the ancients. However, the lack of large-scale parallel corpus limits the study of machine translation in Ancient-Modern Chinese. In this paper, we propose an Ancient-Modern Chinese clause alignment approach based on the characteristics of these two languages. This method combines both lexical-based information and statistical-based information, which achieves 94.2 F1-score on our manual annotation Test set. We use this method to create a new large-scale Ancient-Modern Chinese parallel corpus which contains 1.24M bilingual pairs. To our best knowledge, this is the first large high-quality Ancient-Modern Chinese dataset. Furthermore, we analyzed and compared the performance of the SMT and various NMT models on this dataset and provided a strong baseline for this task.
Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising
Authors: Kan Ren, Yuchen Fang, Wei-Nan Zhang, Shuhao Liu, Jiajun Li, Ya zhang, Yong Yu, Jun Wang
Date: 2018-08-11 00:00:00

In online advertising, the Internet users may be exposed to a sequence of
different ad campaigns, i.e., display ads, search, or referrals from multiple
channels, before led up to any final sales conversion and transaction. For both
campaigners and publishers, it is fundamentally critical to estimate the
contribution from ad campaign touch-points during the customer journey
(conversion funnel) and assign the right credit to the right ad exposure
accordingly. However, the existing research on the multi-touch attribution
problem lacks a principled way of utilizing the users' pre-conversion actions
(i.e., clicks), and quite often fails to model the sequential patterns among
the touch points from a user's behavior data. To make it worse, the current
industry practice is merely employing a set of arbitrary rules as the
attribution model, e.g., the popular last-touch model assigns 100% credit to
the final touch-point regardless of actual attributions. In this paper, we
propose a Dual-attention Recurrent Neural Network (DARNN) for the multi-touch
attribution problem. It learns the attribution values through an attention
mechanism directly from the conversion estimation objective. To achieve this,
we utilize sequence-to-sequence prediction for user clicks, and combine both
post-view and post-click attribution patterns together for the final conversion
estimation. To quantitatively benchmark attribution models, we also propose a
novel yet practical attribution evaluation scheme through the proxy of budget
allocation (under the estimated attributions) over ad channels. The
experimental results on two real datasets demonstrate the significant
performance gains of our attribution model against the state of the art.
An Implementation, Empirical Evaluation and Proposed Improvement for Bidirectional Splitting Method for Argumentation Frameworks under Stable Semantics
Authors: Renata Wong
Date: 2018-08-11 00:00:00
Tasks: Abstract Argumentation

Abstract argumentation frameworks are formal systems that facilitate
obtaining conclusions from non-monotonic knowledge systems. Within such a
system, an argumentation semantics is defined as a set of arguments with some
desired qualities, for example, that the elements are not in conflict with each
other. Splitting an argumentation framework can efficiently speed up the
computation of argumentation semantics. With respect to stable semantics, two
methods have been proposed to split an argumentation framework either in a
unidirectional or bidirectional fashion. The advantage of bidirectional
splitting is that it is not structure-dependent and, unlike unidirectional
splitting, it can be used for frameworks consisting of a single strongly
connected component. Bidirectional splitting makes use of a minimum cut. In
this paper, we implement and test the performance of the bidirectional
splitting method, along with two types of graph cut algorithms. Experimental
data suggest that using a minimum cut will not improve the performance of
computing stable semantics in most cases. Hence, instead of a minimum cut, we
propose to use a balanced cut, where the framework is split into two
sub-frameworks of equal size. Experimental results conducted on bidirectional
splitting using the balanced cut show a significant improvement in the
performance of computing semantics.
Familia: A Configurable Topic Modeling Framework for Industrial Text Engineering
Authors: Di Jiang, Yuanfeng Song, Rongzhong Lian, Siqi Bao, Jinhua Peng, Huang He, Hua Wu
Date: 2018-08-11 00:00:00
Tasks: Topic Models

In the last decade, a variety of topic models have been proposed for text
engineering. However, except Probabilistic Latent Semantic Analysis (PLSA) and
Latent Dirichlet Allocation (LDA), most of existing topic models are seldom
applied or considered in industrial scenarios. This phenomenon is caused by the
fact that there are very few convenient tools to support these topic models so
far. Intimidated by the demanding expertise and labor of designing and
implementing parameter inference algorithms, software engineers are prone to
simply resort to PLSA/LDA, without considering whether it is proper for their
problem at hand or not. In this paper, we propose a configurable topic modeling
framework named Familia, in order to bridge the huge gap between academic
research fruits and current industrial practice. Familia supports an important
line of topic models that are widely applicable in text engineering scenarios.
In order to relieve burdens of software engineers without knowledge of Bayesian
networks, Familia is able to conduct automatic parameter inference for a
variety of topic models. Simply through changing the data organization of
Familia, software engineers are able to easily explore a broad spectrum of
existing topic models or even design their own topic models, and find the one
that best suits the problem at hand. With its superior extendability, Familia
has a novel sampling mechanism that strikes balance between effectiveness and
efficiency of parameter inference. Furthermore, Familia is essentially a big
topic modeling framework that supports parallel parameter inference and
distributed parameter storage. The utilities and necessity of Familia are
demonstrated in real-life industrial applications. Familia would significantly
enlarge software engineers' arsenal of topic models and pave the way for
utilizing highly customized topic models in real-life problems.
From POS tagging to dependency parsing for biomedical event extraction
Authors: Dat Quoc Nguyen, Karin Verspoor
Date: 2018-08-11 00:00:00
Tasks: Dependency Parsing, Event Extraction, Part-Of-Speech Tagging, POS, POS Tagging

Background: Given the importance of relation or event extraction from
biomedical research publications to support knowledge capture and synthesis,
and the strong dependency of approaches to this information extraction task on
syntactic information, it is valuable to understand which approaches to
syntactic processing of biomedical text have the highest performance. Results:
We perform an empirical study comparing state-of-the-art traditional
feature-based and neural network-based models for two core natural language
processing tasks of part-of-speech (POS) tagging and dependency parsing on two
benchmark biomedical corpora, GENIA and CRAFT. To the best of our knowledge,
there is no recent work making such comparisons in the biomedical context;
specifically no detailed analysis of neural models on this data is available.
Experimental results show that in general, the neural models outperform the
feature-based models on two benchmark biomedical corpora GENIA and CRAFT. We
also perform a task-oriented evaluation to investigate the influences of these
models in a downstream application on biomedical event extraction, and show
that better intrinsic parsing performance does not always imply better
extrinsic event extraction performance. Conclusion: We have presented a
detailed empirical study comparing traditional feature-based and neural
network-based models for POS tagging and dependency parsing in the biomedical
context, and also investigated the influence of parser selection for a
biomedical event extraction downstream task. Availability of data and material:
We make the retrained models available at
https://github.com/datquocnguyen/BioPosDep
Building Safer AGI by introducing Artificial Stupidity
Authors: Michaël Trazzi, Roman V. Yampolskiy
Date: 2018-08-11 00:00:00

Artificial Intelligence (AI) achieved super-human performance in a broad
variety of domains. We say that an AI is made Artificially Stupid on a task
when some limitations are deliberately introduced to match a human's ability to
do the task. An Artificial General Intelligence (AGI) can be made safer by
limiting its computing power and memory, or by introducing Artificial Stupidity
on certain tasks. We survey human intellectual limits and give recommendations
for which limits to implement in order to build a safe AGI.