pwc-archive/papers-with-abstracts huggingface dataset:
Synapse at CAp 2017 NER challenge: Fasttext CRF
Authors: Damien Sileo, Camille Pradel, Philippe Muller, Tim Van De Cruys
Date: 2017-09-14 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence

We present our system for the CAp 2017 NER challenge which is about named
entity recognition on French tweets. Our system leverages unsupervised learning
on a larger dataset of French tweets to learn features feeding a CRF model. It
was ranked first without using any gazetteer or structured external data, with
an F-measure of 58.89\%. To the best of our knowledge, it is the first system
to use fasttext embeddings (which include subword representations) and an
embedding-based sentence representation for NER.
On Multi-Relational Link Prediction with Bilinear Models
Authors: Yanjie Wang, Rainer Gemulla, Hui Li
Date: 2017-09-14 00:00:00
Tasks: Knowledge Graph Completion, Link Prediction, Prediction

We study bilinear embedding models for the task of multi-relational link
prediction and knowledge graph completion. Bilinear models belong to the most
basic models for this task, they are comparably efficient to train and use, and
they can provide good prediction performance. The main goal of this paper is to
explore the expressiveness of and the connections between various bilinear
models proposed in the literature. In particular, a substantial number of
models can be represented as bilinear models with certain additional
constraints enforced on the embeddings. We explore whether or not these
constraints lead to universal models, which can in principle represent every
set of relations, and whether or not there are subsumption relationships
between various models. We report results of an independent experimental study
that evaluates recent bilinear models in a common experimental setup. Finally,
we provide evidence that relation-level ensembles of multiple bilinear models
can achieve state-of-the art prediction performance.
Exploring Food Detection using CNNs
Authors: Eduardo Aguilar, Marc Bolaños, Petia Radeva
Date: 2017-09-14 00:00:00
Tasks: Food Recognition

One of the most common critical factors directly related to the cause of a
chronic disease is unhealthy diet consumption. In this sense, building an
automatic system for food analysis could allow a better understanding of the
nutritional information with respect to the food eaten and thus it could help
in taking corrective actions in order to consume a better diet. The Computer
Vision community has focused its efforts on several areas involved in the
visual food analysis such as: food detection, food recognition, food
localization, portion estimation, among others. For food detection, the best
results evidenced in the state of the art were obtained using Convolutional
Neural Network. However, the results of all these different approaches were
gotten on different datasets and therefore are not directly comparable. This
article proposes an overview of the last advances on food detection and an
optimal model based on GoogLeNet Convolutional Neural Network method, principal
component analysis, and a support vector machine that outperforms the state of
the art on two public food/non-food datasets.
Dialogue Act Sequence Labeling using Hierarchical encoder with CRF
Authors: Harshit Kumar, Arvind Agarwal, Riddhiman Dasgupta, Sachindra Joshi, Arun Kumar
Date: 2017-09-13 00:00:00
Tasks: Dialogue Act Classification

Dialogue Act recognition associate dialogue acts (i.e., semantic labels) to
utterances in a conversation. The problem of associating semantic labels to
utterances can be treated as a sequence labeling problem. In this work, we
build a hierarchical recurrent neural network using bidirectional LSTM as a
base unit and the conditional random field (CRF) as the top layer to classify
each utterance into its corresponding dialogue act. The hierarchical network
learns representations at multiple levels, i.e., word level, utterance level,
and conversation level. The conversation level representations are input to the
CRF layer, which takes into account not only all previous utterances but also
their dialogue acts, thus modeling the dependency among both, labels and
utterances, an important consideration of natural dialogue. We validate our
approach on two different benchmark data sets, Switchboard and Meeting Recorder
Dialogue Act, and show performance improvement over the state-of-the-art
methods by $2.2\%$ and $4.1\%$ absolute points, respectively. It is worth
noting that the inter-annotator agreement on Switchboard data set is $84\%$,
and our method is able to achieve the accuracy of about $79\%$ despite being
trained on the noisy data.
Simple Open Stance Classification for Rumour Analysis
Authors: Ahmet Aker, Leon Derczynski, Kalina Bontcheva
Date: 2017-08-17 00:00:00
Proceeding: RANLP 2017 9
Tasks: Classification, General Classification, Stance Classification, Veracity Classification

Stance classification determines the attitude, or stance, in a (typically
short) text. The task has powerful applications, such as the detection of fake
news or the automatic extraction of attitudes toward entities or events in the
media. This paper describes a surprisingly simple and efficient classification
approach to open stance classification in Twitter, for rumour and veracity
classification. The approach profits from a novel set of automatically
identifiable problem-specific features, which significantly boost classifier
accuracy and achieve above state-of-the-art results on recent benchmark
datasets. This calls into question the value of using complex sophisticated
models for stance classification without first doing informed feature
extraction.
SORT: Second-Order Response Transform for Visual Recognition
Authors: Yan Wang, Lingxi Xie, Chenxi Liu, Ya zhang, Wenjun Zhang, Alan Yuille
Date: 2017-03-20 00:00:00
Proceeding: ICCV 2017 10

In this paper, we reveal the importance and benefits of introducing
second-order operations into deep neural networks. We propose a novel approach
named Second-Order Response Transform (SORT), which appends element-wise
product transform to the linear sum of a two-branch network module. A direct
advantage of SORT is to facilitate cross-branch response propagation, so that
each branch can update its weights based on the current status of the other
branch. Moreover, SORT augments the family of transform operations and
increases the nonlinearity of the network, making it possible to learn flexible
functions to fit the complicated distribution of feature space. SORT can be
applied to a wide range of network architectures, including a branched variant
of a chain-styled network and a residual network, with very light-weighted
modifications. We observe consistent accuracy gain on both small (CIFAR10,
CIFAR100 and SVHN) and big (ILSVRC2012) datasets. In addition, SORT is very
efficient, as the extra computation overhead is less than 5%.
Interpretable Graph-Based Semi-Supervised Learning via Flows
Authors: Raif M. Rustamov, James T. Klosowski
Date: 2017-09-14 00:00:00

In this paper, we consider the interpretability of the foundational
Laplacian-based semi-supervised learning approaches on graphs. We introduce a
novel flow-based learning framework that subsumes the foundational approaches
and additionally provides a detailed, transparent, and easily understood
expression of the learning process in terms of graph flows. As a result, one
can visualize and interactively explore the precise subgraph along which the
information from labeled nodes flows to an unlabeled node of interest.
Surprisingly, the proposed framework avoids trading accuracy for
interpretability, but in fact leads to improved prediction accuracy, which is
supported both by theoretical considerations and empirical results. The
flow-based framework guarantees the maximum principle by construction and can
handle directed graphs in an out-of-the-box manner.
Denoising Autoencoders for Overgeneralization in Neural Networks
Authors: Giacomo Spigler
Date: 2017-09-14 00:00:00
Tasks: Denoising, Open Set Learning

Despite the recent developments that allowed neural networks to achieve
impressive performance on a variety of applications, these models are
intrinsically affected by the problem of overgeneralization, due to their
partitioning of the full input space into the fixed set of target classes used
during training. Thus it is possible for novel inputs belonging to categories
unknown during training or even completely unrecognizable to humans to fool the
system into classifying them as one of the known classes, even with a high
degree of confidence. Solving this problem may help improve the security of
such systems in critical applications, and may further lead to applications in
the context of open set recognition and 1-class recognition. This paper
presents a novel way to compute a confidence score using denoising autoencoders
and shows that such confidence score can correctly identify the regions of the
input space close to the training distribution by approximately identifying its
local maxima.
From Plants to Landmarks: Time-invariant Plant Localization that uses Deep Pose Regression in Agricultural Fields
Authors: Florian Kraemer, Alexander Schaefer, Andreas Eitel, Johan Vertens, Wolfram Burgard
Date: 2017-09-14 00:00:00
Tasks: regression

Agricultural robots are expected to increase yields in a sustainable way and
automate precision tasks, such as weeding and plant monitoring. At the same
time, they move in a continuously changing, semi-structured field environment,
in which features can hardly be found and reproduced at a later time.
Challenges for Lidar and visual detection systems stem from the fact that
plants can be very small, overlapping and have a steadily changing appearance.
Therefore, a popular way to localize vehicles with high accuracy is based on
ex- pensive global navigation satellite systems and not on natural landmarks.
The contribution of this work is a novel image- based plant localization
technique that uses the time-invariant stem emerging point as a reference. Our
approach is based on a fully convolutional neural network that learns landmark
localization from RGB and NIR image input in an end-to-end manner. The network
performs pose regression to generate a plant location likelihood map. Our
approach allows us to cope with visual variances of plants both for different
species and different growth stages. We achieve high localization accuracies as
shown in detailed evaluations of a sugar beet cultivation phase. In experiments
with our BoniRob we demonstrate that detections can be robustly reproduced with
centimeter accuracy.
Subspace Clustering using Ensembles of $K$-Subspaces
Authors: John Lipor, David Hong, Yan Shuo Tan, Laura Balzano
Date: 2017-09-14 00:00:00
Tasks: Clustering

Subspace clustering is the unsupervised grouping of points lying near a union of low-dimensional linear subspaces. Algorithms based directly on geometric properties of such data tend to either provide poor empirical performance, lack theoretical guarantees, or depend heavily on their initialization. We present a novel geometric approach to the subspace clustering problem that leverages ensembles of the K-subspaces (KSS) algorithm via the evidence accumulation clustering framework. Our algorithm, referred to as ensemble K-subspaces (EKSS), forms a co-association matrix whose (i,j)th entry is the number of times points i and j are clustered together by several runs of KSS with random initializations. We prove general recovery guarantees for any algorithm that forms an affinity matrix with entries close to a monotonic transformation of pairwise absolute inner products. We then show that a specific instance of EKSS results in an affinity matrix with entries of this form, and hence our proposed algorithm can provably recover subspaces under similar conditions to state-of-the-art algorithms. The finding is, to the best of our knowledge, the first recovery guarantee for evidence accumulation clustering and for KSS variants. We show on synthetic data that our method performs well in the traditionally challenging settings of subspaces with large intersection, subspaces with small principal angles, and noisy data. Finally, we evaluate our algorithm on six common benchmark datasets and show that unlike existing methods, EKSS achieves excellent empirical performance when there are both a small and large number of points per subspace.
Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games
Authors: Peng Peng, Ying Wen, Yaodong Yang, Quan Yuan, Zhenkun Tang, Haitao Long, Jun Wang
Date: 2017-03-29 00:00:00
Tasks: Starcraft

Many artificial intelligence (AI) applications often require multiple
intelligent agents to work in a collaborative effort. Efficient learning for
intra-agent communication and coordination is an indispensable step towards
general AI. In this paper, we take StarCraft combat game as a case study, where
the task is to coordinate multiple agents as a team to defeat their enemies. To
maintain a scalable yet effective communication protocol, we introduce a
Multiagent Bidirectionally-Coordinated Network (BiCNet ['bIknet]) with a
vectorised extension of actor-critic formulation. We show that BiCNet can
handle different types of combats with arbitrary numbers of AI agents for both
sides. Our analysis demonstrates that without any supervisions such as human
demonstrations or labelled data, BiCNet could learn various types of advanced
coordination strategies that have been commonly used by experienced game
players. In our experiments, we evaluate our approach against multiple
baselines under different scenarios; it shows state-of-the-art performance, and
possesses potential values for large-scale real-world applications.
Perspectives for Evaluating Conversational AI
Authors: Mahipal Jadeja, Neelanshi Varia
Date: 2017-09-14 00:00:00
Tasks: Chatbot, Information Retrieval, Retrieval

Conversational AI systems are becoming famous in day to day lives. In this
paper, we are trying to address the following key question: To identify whether
design, as well as development efforts for search oriented conversational AI
are successful or not.It is tricky to define 'success' in the case of
conversational AI and equally tricky part is to use appropriate metrics for the
evaluation of conversational AI. We propose four different perspectives namely
user experience, information retrieval, linguistic and artificial intelligence
for the evaluation of conversational AI systems. Additionally, background
details of conversational AI systems are provided including desirable
characteristics of personal assistants, differences between chatbot and an AI
based personal assistant. An importance of personalization and how it can be
achieved is explained in detail. Current challenges in the development of an
ideal conversational AI (personal assistant) are also highlighted along with
guidelines for achieving personalized experience for users.
Completion of High Order Tensor Data with Missing Entries via Tensor-train Decomposition
Authors: Longhao Yuan, Qibin Zhao, Jianting Cao
Date: 2017-09-08 00:00:00

In this paper, we aim at the completion problem of high order tensor data
with missing entries. The existing tensor factorization and completion methods
suffer from the curse of dimensionality when the order of tensor N>>3. To
overcome this problem, we propose an efficient algorithm called TT-WOPT
(Tensor-train Weighted OPTimization) to find the latent core tensors of tensor
data and recover the missing entries. Tensor-train decomposition, which has the
powerful representation ability with linear scalability to tensor order, is
employed in our algorithm. The experimental results on synthetic data and
natural image completion demonstrate that our method significantly outperforms
the other related methods. Especially when the missing rate of data is very
high, e.g., 85% to 99%, our algorithm can achieve much better performance than
other state-of-the-art algorithms.
Binary-decomposed DCNN for accelerating computation and compressing model without retraining
Authors: Ryuji Kamiya, Takayoshi Yamashita, Mitsuru Ambai, Ikuro Sato, Yuji Yamauchi, Hironobu Fujiyoshi
Date: 2017-09-14 00:00:00

Recent trends show recognition accuracy increasing even more profoundly.
Inference process of Deep Convolutional Neural Networks (DCNN) has a large
number of parameters, requires a large amount of computation, and can be very
slow. The large number of parameters also require large amounts of memory. This
is resulting in increasingly long computation times and large model sizes. To
implement mobile and other low performance devices incorporating DCNN, model
sizes must be compressed and computation must be accelerated. To that end, this
paper proposes Binary-decomposed DCNN, which resolves these issues without the
need for retraining. Our method replaces real-valued inner-product computations
with binary inner-product computations in existing network models to accelerate
computation of inference and decrease model size without the need for
retraining. Binary computations can be done at high speed using logical
operators such as XOR and AND, together with bit counting. In tests using
AlexNet with the ImageNet classification task, speed increased by a factor of
1.79, models were compressed by approximately 80%, and increase in error rate
was limited to 1.20%. With VGG-16, speed increased by a factor of 2.07, model
sizes decreased by 81%, and error increased by only 2.16%.
Convolution with Logarithmic Filter Groups for Efficient Shallow CNN
Authors: Tae Kwan Lee, Wissam J. Baddar, Seong Tae Kim, Yong Man Ro
Date: 2017-07-31 00:00:00
Tasks: Classification, Facial Expression Recognition, Facial Expression Recognition (FER), General Classification

In convolutional neural networks (CNNs), the filter grouping in convolution
layers is known to be useful to reduce the network parameter size. In this
paper, we propose a new logarithmic filter grouping which can capture the
nonlinearity of filter distribution in CNNs. The proposed logarithmic filter
grouping is installed in shallow CNNs applicable in a mobile application.
Experiments were performed with the shallow CNNs for classification tasks. Our
classification results on Multi-PIE dataset for facial expression recognition
and CIFAR-10 dataset for object classification reveal that the compact CNN with
the proposed logarithmic filter grouping scheme outperforms the same network
with the uniform filter grouping in terms of accuracy and parameter efficiency.
Our results indicate that the efficiency of shallow CNNs can be improved by the
proposed logarithmic filter grouping.
Embedded-Graph Theory
Authors: Atsushi Yokoyama
Date: 2017-09-14 00:00:00
Tasks: Graph Similarity, Translation

In this paper, we propose a new type of graph, denoted as "embedded-graph",
and its theory, which employs a distributed representation to describe the
relations on the graph edges. Embedded-graphs can express linguistic and
complicated relations, which cannot be expressed by the existing edge-graphs or
weighted-graphs. We introduce the mathematical definition of embedded-graph,
translation, edge distance, and graph similarity. We can transform an
embedded-graph into a weighted-graph and a weighted-graph into an edge-graph by
the translation method and by threshold calculation, respectively. The edge
distance of an embedded-graph is a distance based on the components of a target
vector, and it is calculated through cosine similarity with the target vector.
The graph similarity is obtained considering the relations with linguistic
complexity. In addition, we provide some examples and data structures for
embedded-graphs in this paper.
Towards a Crowd Analytic Framework For Crowd Management in Majid-al-Haram
Authors: Sultan Daud Khan, Muhammad Tayyab, Muhammad Khurram Amin, Akram Nour, Anas Basalamah, Saleh Basalamah, Sohaib Ahmad Khan
Date: 2017-09-14 00:00:00
Tasks: Management

The scared cities of Makkah Al Mukarramah and Madina Al Munawarah host
millions of pilgrims every year. During Hajj, the movement of large number of
people has a unique spatial and temporal constraints, which makes Hajj one of
toughest challenges for crowd management. In this paper, we propose a computer
vision based framework that automatically analyses video sequence and computes
important measurements which include estimation of crowd density,
identification of dominant patterns, detection and localization of congestion.
In addition, we analyze helpful statistics of the crowd like speed, and
direction, that could provide support to crowd management personnel. The
framework presented in this paper indicate that new advances in computer vision
and machine learning can be leveraged effectively for challenging and high
density crowd management applications. However, significant customization of
existing approaches is required to apply them to the challenging crowd
management situations in Masjid Al Haram. Our results paint a promising picture
for deployment of computer vision technologies to assist in quantitative
measurement of crowd size, density and congestion.
Transfer Deep Learning for Low-Resource Chinese Word Segmentation with a Novel Neural Network
Authors: Jingjing Xu, Xu sun
Date: 2017-02-15 00:00:00
Tasks: Chinese Word Segmentation, Segmentation, Transfer Learning

Recent studies have shown effectiveness in using neural networks for Chinese
word segmentation. However, these models rely on large-scale data and are less
effective for low-resource datasets because of insufficient training data. We
propose a transfer learning method to improve low-resource word segmentation by
leveraging high-resource corpora. First, we train a teacher model on
high-resource corpora and then use the learned knowledge to initialize a
student model. Second, a weighted data similarity method is proposed to train
the student model on low-resource data. Experiment results show that our work
significantly improves the performance on low-resource datasets: 2.3% and 1.5%
F-score on PKU and CTB datasets. Furthermore, this paper achieves
state-of-the-art results: 96.1%, and 96.2% F-score on PKU and CTB datasets.
The Conditional Analogy GAN: Swapping Fashion Articles on People Images
Authors: Nikolay Jetchev, Urs Bergmann
Date: 2017-09-14 00:00:00
Tasks: Articles, Generative Adversarial Network, Relation, Segmentation

We present a novel method to solve image analogy problems : it allows to
learn the relation between paired images present in training data, and then
generalize and generate images that correspond to the relation, but were never
seen in the training set. Therefore, we call the method Conditional Analogy
Generative Adversarial Network (CAGAN), as it is based on adversarial training
and employs deep convolutional neural networks. An especially interesting
application of that technique is automatic swapping of clothing on fashion
model photos. Our work has the following contributions. First, the definition
of the end-to-end trainable CAGAN architecture, which implicitly learns
segmentation masks without expensive supervised labeling data. Second,
experimental results show plausible segmentation masks and often convincing
swapped images, given the target article. Finally, we discuss the next steps
for that technique: neural network architecture improvements and more advanced
applications.