pwc-archive/papers-with-abstracts huggingface dataset:
Multi-modality Sensor Data Classification with Selective Attention
Authors: Xiang Zhang, Lina Yao, Chaoran Huang, Sen Wang, Mingkui Tan, Guodong Long, Can Wang
Date: 2018-04-16 00:00:00
Tasks: Classification, Deep Reinforcement Learning, General Classification, Reinforcement Learning

Multimodal wearable sensor data classification plays an important role in
ubiquitous computing and has a wide range of applications in scenarios from
healthcare to entertainment. However, most existing work in this field employs
domain-specific approaches and is thus ineffective in complex sit- uations
where multi-modality sensor data are col- lected. Moreover, the wearable sensor
data are less informative than the conventional data such as texts or images.
In this paper, to improve the adapt- ability of such classification methods
across differ- ent application domains, we turn this classification task into a
game and apply a deep reinforcement learning scheme to deal with complex
situations dynamically. Additionally, we introduce a selective attention
mechanism into the reinforcement learn- ing scheme to focus on the crucial
dimensions of the data. This mechanism helps to capture extra information from
the signal and thus it is able to significantly improve the discriminative
power of the classifier. We carry out several experiments on three wearable
sensor datasets and demonstrate the competitive performance of the proposed
approach compared to several state-of-the-art baselines.
Word2Vec and Doc2Vec in Unsupervised Sentiment Analysis of Clinical Discharge Summaries
Authors: Qufei Chen, Marina Sokolova
Date: 2018-05-01 00:00:00
Tasks: Sentiment Analysis

In this study, we explored application of Word2Vec and Doc2Vec for sentiment
analysis of clinical discharge summaries. We applied unsupervised learning
since the data sets did not have sentiment annotations. Note that unsupervised
learning is a more realistic scenario than supervised learning which requires
an access to a training set of sentiment-annotated data. We aim to detect if
there exists any underlying bias towards or against a certain disease. We used
SentiWordNet to establish a gold sentiment standard for the data sets and
evaluate performance of Word2Vec and Doc2Vec methods. We have shown that the
Word2vec and Doc2Vec methods complement each other results in sentiment
analysis of the data sets.
A Feedback Neural Network for Small Target Motion Detection in Cluttered Backgrounds
Authors: Hongxin Wang, Jigen Peng, Shigang Yue
Date: 2018-05-01 00:00:00
Tasks: Motion Detection

Small target motion detection is critical for insects to search for and track
mates or prey which always appear as small dim speckles in the visual field. A
class of specific neurons, called small target motion detectors (STMDs), has
been characterized by exquisite sensitivity for small target motion.
Understanding and analyzing visual pathway of STMD neurons are beneficial to
design artificial visual systems for small target motion detection. Feedback
loops have been widely identified in visual neural circuits and play an
important role in target detection. However, if there exists a feedback loop in
the STMD visual pathway or if a feedback loop could significantly improve the
detection performance of STMD neurons, is unclear. In this paper, we propose a
feedback neural network for small target motion detection against naturally
cluttered backgrounds. In order to form a feedback loop, model output is
temporally delayed and relayed to previous neural layer as feedback signal.
Extensive experiments showed that the significant improvement of the proposed
feedback neural network over the existing STMD-based models for small target
motion detection.
A Taxonomy for Neural Memory Networks
Authors: Ying Ma, Jose Principe
Date: 2018-05-01 00:00:00

In this paper, a taxonomy for memory networks is proposed based on their
memory organization. The taxonomy includes all the popular memory networks:
vanilla recurrent neural network (RNN), long short term memory (LSTM ), neural
stack and neural Turing machine and their variants. The taxonomy puts all these
networks under a single umbrella and shows their relative expressive power ,
i.e. vanilla RNN <=LSTM<=neural stack<=neural RAM. The differences and
commonality between these networks are analyzed. These differences are also
connected to the requirements of different tasks which can give the user
instructions of how to choose or design an appropriate memory network for a
specific task. As a conceptual simplified class of problems, four tasks of
synthetic symbol sequences: counting, counting with interference, reversing and
repeat counting are developed and tested to verify our arguments. And we use
two natural language processing problems to discuss how this taxonomy helps
choosing the appropriate neural memory networks for real world problem.
Multi-Cohort Intelligence Algorithm: An Intra- and Inter-group Learning Behavior based Socio-inspired Optimization Methodology
Authors: Apoorva S Shastri, Anand J Kulkarni
Date: 2018-05-01 00:00:00

A Multi-Cohort Intelligence (Multi-CI) metaheuristic algorithm in emerging
socio-inspired optimization domain is proposed. The algorithm implements
intra-group and inter-group learning mechanisms. It focusses on the interaction
amongst different cohorts. The performance of the algorithm is validated by
solving 75 unconstrained test problems with dimensions up to 30. The solutions
were comparing with several recent algorithms such as Particle Swarm
Optimization, Covariance Matrix Adaptation Evolution Strategy, Artificial Bee
Colony, Self-adaptive differential evolution algorithm, Comprehensive Learning
Particle Swarm Optimization, Backtracking Search Optimization Algorithm and
Ideology Algorithm. The Wilcoxon signed rank test was carried out for the
statistical analysis and verification of the performance. The proposed Multi-CI
outperformed these algorithms in terms of the solution quality including
objective function value and computational cost, i.e. computational time and
functional evaluations. The prominent feature of the Multi-CI algorithm along
with the limitations are discussed as well. In addition, an illustrative
example is also solved and every detail is provided.
Multitask Parsing Across Semantic Representations
Authors: Daniel Hershcovich, Omri Abend, Ari Rappoport
Date: 2018-05-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Semantic Parsing, UCCA Parsing

The ability to consolidate information of different types is at the core of
intelligence, and has tremendous practical value in allowing learning for one
task to benefit from generalizations learned for others. In this paper we
tackle the challenging task of improving semantic parsing performance, taking
UCCA parsing as a test case, and AMR, SDP and Universal Dependencies (UD)
parsing as auxiliary tasks. We experiment on three languages, using a uniform
transition-based system and learning architecture for all parsing tasks.
Despite notable conceptual, formal and domain differences, we show that
multitask learning significantly improves UCCA parsing in both in-domain and
out-of-domain settings.
Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags
Authors: Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama
Date: 2017-04-22 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Multiple Instance Learning, Retrieval, Text Categorization

Multiple instance learning (MIL) is a variation of traditional supervised
learning problems where data (referred to as bags) are composed of sub-elements
(referred to as instances) and only bag labels are available. MIL has a variety
of applications such as content-based image retrieval, text categorization and
medical diagnosis. Most of the previous work for MIL assume that the training
bags are fully labeled. However, it is often difficult to obtain an enough
number of labeled bags in practical situations, while many unlabeled bags are
available. A learning framework called PU learning (positive and unlabeled
learning) can address this problem. In this paper, we propose a convex PU
learning method to solve an MIL problem. We experimentally show that the
proposed method achieves better performance with significantly lower
computational costs than an existing method for PU-MIL.
Multiobjective Optimization Differential Evolution Enhanced with Principle Component Analysis for Constrained Optimization
Authors: Wei Huang, Tao Xu, Kangshun Li, Jun He
Date: 2018-05-01 00:00:00
Tasks: Evolutionary Algorithms, Multiobjective Optimization

Multiobjective evolutionary algorithms (MOEAs) have been successfully applied to a number of constrained optimization problems. Many of them adopt mutation and crossover operators from differential evolution. However, these operators do not explicitly utilise features of fitness landscapes. To improve the performance of algorithms, this paper aims at designing a search operator adapting to fitness landscapes. Through an observation, we find that principle component analysis (PCA) can be used to characterise fitness landscapes. Based on this finding, a new search operator, called PCA-projection, is proposed. In order to verify the effectiveness of PCA-projection, we design two algorithms enhanced with PCA-projection for solving constrained optimization problems, called PMODE and HECO-PDE, respectively. Experiments have been conducted on the IEEE CEC 2017 competition benchmark suite in constrained optimisation. PMODE and HECO-PDE are compared with the algorithms from the IEEE CEC 2018 competition and another recent MOEA for constrained optimisation. Experimental results show that an algorithm enhanced with PCA-projection performs better than its corresponding opponent without this operator. Furthermore, HECO-PDE is ranked first on all dimensions according to the competition rules. This study reveals that decomposition-based MOEAs, such as HECO-PDE, are competitive with best single-objective and multiobjective evolutionary algorithms for constrained optimisation, but MOEAs based on non-dominance, such as PMODE, may not.
Capturing Ambiguity in Crowdsourcing Frame Disambiguation
Authors: Anca Dumitrache, Lora Aroyo, Chris Welty
Date: 2018-05-01 00:00:00
Tasks: Sentence

FrameNet is a computational linguistics resource composed of semantic frames,
high-level concepts that represent the meanings of words. In this paper, we
present an approach to gather frame disambiguation annotations in sentences
using a crowdsourcing approach with multiple workers per sentence to capture
inter-annotator disagreement. We perform an experiment over a set of 433
sentences annotated with frames from the FrameNet corpus, and show that the
aggregated crowd annotations achieve an F1 score greater than 0.67 as compared
to expert linguists. We highlight cases where the crowd annotation was correct
even though the expert is in disagreement, arguing for the need to have
multiple annotators per sentence. Most importantly, we examine cases in which
crowd workers could not agree, and demonstrate that these cases exhibit
ambiguity, either in the sentence, frame, or the task itself, and argue that
collapsing such cases to a single, discrete truth value (i.e. correct or
incorrect) is inappropriate, creating arbitrary targets for machine learning.
Non-Gaussian information from weak lensing data via deep learning
Authors: Arushi Gupta, José Manuel Zorrilla Matilla, Daniel Hsu, Zoltán Haiman
Date: 2018-02-04 00:00:00
Tasks: Deep Learning

Weak lensing maps contain information beyond two-point statistics on small
scales. Much recent work has tried to extract this information through a range
of different observables or via nonlinear transformations of the lensing field.
Here we train and apply a 2D convolutional neural network to simulated
noiseless lensing maps covering 96 different cosmological models over a range
of {$\Omega_m,\sigma_8$}. Using the area of the confidence contour in the
{$\Omega_m,\sigma_8$} plane as a figure-of-merit, derived from simulated
convergence maps smoothed on a scale of 1.0 arcmin, we show that the neural
network yields $\approx 5 \times$ tighter constraints than the power spectrum,
and $\approx 4 \times$ tighter than the lensing peaks. Such gains illustrate
the extent to which weak lensing data encode cosmological information not
accessible to the power spectrum or even other, non-Gaussian statistics such as
lensing peaks.
Fast and Efficient Depth Map Estimation from Light Fields
Authors: Yuriy Anisimov, Didier Stricker
Date: 2018-05-01 00:00:00
Tasks: CPU, Density Estimation

The paper presents an algorithm for depth map estimation from the light field
images in relatively small amount of time, using only single thread on CPU. The
proposed method improves existing principle of line fitting in 4-dimensional
light field space. Line fitting is based on color values comparison using
kernel density estimation. Our method utilizes result of Semi-Global Matching
(SGM) with Census transform-based matching cost as a border initialization for
line fitting. It provides a significant reduction of computations needed to
find the best depth match. With the suggested evaluation metric we show that
proposed method is applicable for efficient depth map estimation while
preserving low computational time compared to others.
Object Activity Scene Description, Construction and Recognition
Authors: Hui Feng, Shan-Shan Wang, Shuzhi Sam Ge
Date: 2018-05-01 00:00:00
Tasks: Action Recognition, General Classification, Object, Skeleton Based Action Recognition, Temporal Action Localization, text-classification, Text Classification

Action recognition is a critical task for social robots to meaningfully
engage with their environment. 3D human skeleton-based action recognition is an
attractive research area in recent years. Although, the existing approaches are
good at action recognition, it is a great challenge to recognize a group of
actions in an activity scene. To tackle this problem, at first, we partition
the scene into several primitive actions (PAs) based upon motion attention
mechanism. Then, the primitive actions are described by the trajectory vectors
of corresponding joints. After that, motivated by text classification based on
word embedding, we employ convolution neural network (CNN) to recognize
activity scenes by considering motion of joints as "word" of activity. The
experimental results on the scenes of human activity dataset show the
efficiency of the proposed approach.
Joint Bootstrapping Machines for High Confidence Relation Extraction
Authors: Pankaj Gupta, Benjamin Roth, Hinrich Schütze
Date: 2018-05-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Relation, Relation Extraction, Relationship Extraction (Distant Supervised), Vocal Bursts Intensity Prediction

Semi-supervised bootstrapping techniques for relationship extraction from
text iteratively expand a set of initial seed instances. Due to the lack of
labeled data, a key challenge in bootstrapping is semantic drift: if a false
positive instance is added during an iteration, then all following iterations
are contaminated. We introduce BREX, a new bootstrapping method that protects
against such contamination by highly effective confidence assessment. This is
achieved by using entity and template seeds jointly (as opposed to just one as
in previous work), by expanding entities and templates in parallel and in a
mutually constraining fashion in each iteration and by introducing
higherquality similarity measures for templates. Experimental results show that
BREX achieves an F1 that is 0.13 (0.87 vs. 0.74) better than the state of the
art for four relationships.
Conditional Image-to-Image Translation
Authors: Jianxin Lin, Yingce Xia, Tao Qin, Zhibo Chen, Tie-Yan Liu
Date: 2018-05-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image-to-Image Translation, Translation

Image-to-image translation tasks have been widely investigated with
Generative Adversarial Networks (GANs) and dual learning. However, existing
models lack the ability to control the translated results in the target domain
and their results usually lack of diversity in the sense that a fixed image
usually leads to (almost) deterministic translation result. In this paper, we
study a new problem, conditional image-to-image translation, which is to
translate an image from the source domain to the target domain conditioned on a
given image in the target domain. It requires that the generated image should
inherit some domain-specific features of the conditional image from the target
domain. Therefore, changing the conditional image in the target domain will
lead to diverse translation results for a fixed input image from the source
domain, and therefore the conditional input image helps to control the
translation results. We tackle this problem with unpaired data based on GANs
and dual learning. We twist two conditional translation models (one translation
from A domain to B domain, and the other one from B domain to A domain)
together for inputs combination and reconstruction while preserving domain
independent features. We carry out experiments on men's faces from-to women's
faces translation and edges to shoes&bags translations. The results demonstrate
the effectiveness of our proposed method.
Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time
Authors: Pankaj Gupta, Subburam Rajaram, Hinrich Schütze, Bernt Andrassy
Date: 2017-11-15 00:00:00
Proceeding: NAACL 2018 6
Tasks: Articles, Dynamic Topic Modeling, Topic Models

Dynamic topic modeling facilitates the identification of topical trends over
time in temporal collections of unstructured documents. We introduce a novel
unsupervised neural dynamic topic model named as Recurrent Neural
Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each
time influence the topic discovery in the subsequent time steps. We account for
the temporal ordering of documents by explicitly modeling a joint distribution
of latent topical dependencies over time, using distributional estimators with
temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP
research, we demonstrate that compared to state-of-the art topic models, RNNRSM
shows better generalization, topic interpretation, evolution and trends. We
also introduce a metric (named as SPAN) to quantify the capability of dynamic
topic model to capture word evolution in topics over time.
Nugget Proposal Networks for Chinese Event Detection
Authors: Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
Date: 2018-05-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Event Detection, General Classification

Neural network based models commonly regard event detection as a word-wise
classification task, which suffer from the mismatch problem between words and
event triggers, especially in languages without natural word delimiters such as
Chinese. In this paper, we propose Nugget Proposal Networks (NPNs), which can
solve the word-trigger mismatch problem by directly proposing entire trigger
nuggets centered at each character regardless of word boundaries. Specifically,
NPNs perform event detection in a character-wise paradigm, where a hybrid
representation for each character is first learned to capture both structural
and semantic information from both characters and words. Then based on learned
representations, trigger nuggets are proposed and categorized by exploiting
character compositional structures of Chinese event triggers. Experiments on
both ACE2005 and TAC KBP 2017 datasets show that NPNs significantly outperform
the state-of-the-art methods.
Learning to Sketch with Shortcut Cycle Consistency
Authors: Jifei Song, Kaiyue Pang, Yi-Zhe Song, Tao Xiang, Timothy Hospedales
Date: 2018-05-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Decoder, Image Retrieval, Multi-Task Learning, Retrieval, Sketch-Based Image Retrieval

To see is to sketch -- free-hand sketching naturally builds ties between
human and machine vision. In this paper, we present a novel approach for
translating an object photo to a sketch, mimicking the human sketching process.
This is an extremely challenging task because the photo and sketch domains
differ significantly. Furthermore, human sketches exhibit various levels of
sophistication and abstraction even when depicting the same object instance in
a reference photo. This means that even if photo-sketch pairs are available,
they only provide weak supervision signal to learn a translation model.
Compared with existing supervised approaches that solve the problem of
D(E(photo)) -> sketch, where E($\cdot$) and D($\cdot$) denote encoder and
decoder respectively, we take advantage of the inverse problem (e.g.,
D(E(sketch)) -> photo), and combine with the unsupervised learning tasks of
within-domain reconstruction, all within a multi-task learning framework.
Compared with existing unsupervised approaches based on cycle consistency
(i.e., D(E(D(E(photo)))) -> photo), we introduce a shortcut consistency
enforced at the encoder bottleneck (e.g., D(E(photo)) -> photo) to exploit the
additional self-supervision. Both qualitative and quantitative results show
that the proposed model is superior to a number of state-of-the-art
alternatives. We also show that the synthetic sketches can be used to train a
better fine-grained sketch-based image retrieval (FG-SBIR) model, effectively
alleviating the problem of sketch data scarcity.
Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation
Authors: Yen-Cheng Liu, Yu-Ying Yeh, Tzu-Chien Fu, Sheng-De Wang, Wei-Chen Chiu, Yu-Chiang Frank Wang
Date: 2017-05-03 00:00:00
Proceeding: CVPR 2018 6
Tasks: Attribute, Disentanglement, Domain Adaptation, Representation Learning, Translation, Unsupervised Domain Adaptation

While representation learning aims to derive interpretable features for
describing visual data, representation disentanglement further results in such
features so that particular image attributes can be identified and manipulated.
However, one cannot easily address this task without observing ground truth
annotation for the training data. To address this problem, we propose a novel
deep learning model of Cross-Domain Representation Disentangler (CDRD). By
observing fully annotated source-domain data and unlabeled target-domain data
of interest, our model bridges the information across data domains and
transfers the attribute information accordingly. Thus, cross-domain joint
feature disentanglement and adaptation can be jointly performed. In the
experiments, we provide qualitative results to verify our disentanglement
capability. Moreover, we further confirm that our model can be applied for
solving classification tasks of unsupervised domain adaptation, and performs
favorably against state-of-the-art image disentanglement and translation
methods.
Multi-Image Semantic Matching by Mining Consistent Features
Authors: Qianqian Wang, Xiaowei Zhou, Kostas Daniilidis
Date: 2017-11-21 00:00:00
Proceeding: CVPR 2018 6
Tasks: Graph Matching, Object

This work proposes a multi-image matching method to estimate semantic
correspondences across multiple images. In contrast to the previous methods
that optimize all pairwise correspondences, the proposed method identifies and
matches only a sparse set of reliable features in the image collection. In this
way, the proposed method is able to prune nonrepeatable features and also
highly scalable to handle thousands of images. We additionally propose a
low-rank constraint to ensure the geometric consistency of feature
correspondences over the whole image collection. Besides the competitive
performance on multi-graph matching and semantic flow benchmarks, we also
demonstrate the applicability of the proposed method for reconstructing
object-class models and discovering object-class landmarks from images without
using any annotation.