pwc-archive/papers-with-abstracts huggingface dataset:
AID++: An Updated Version of AID on Scene Classification
Authors: Pu Jin, Gui-Song Xia, Fan Hu, Qikai Lu, Liangpei Zhang
Date: 2018-06-03 00:00:00
Tasks: Aerial Scene Classification, Classification, Diversity, General Classification, Scene Classification

Aerial image scene classification is a fundamental problem for understanding
high-resolution remote sensing images and has become an active research task in
the field of remote sensing due to its important role in a wide range of
applications. However, the limitations of existing datasets for scene
classification, such as the small scale and low-diversity, severely hamper the
potential usage of the new generation deep convolutional neural networks
(CNNs). Although huge efforts have been made in building large-scale datasets
very recently, e.g., the Aerial Image Dataset (AID) which contains 10,000 image
samples, they are still far from sufficient to fully train a high-capacity deep
CNN model. To this end, we present a larger-scale dataset in this paper, named
as AID++, for aerial scene classification based on the AID dataset. The
proposed AID++ consists of more than 400,000 image samples that are
semi-automatically annotated by using the existing the geographical data. We
evaluate several prevalent CNN models on the proposed dataset, and the results
show that our dataset can be used as a promising benchmark for scene
classification.
ProFlow: Learning to Predict Optical Flow
Authors: Daniel Maurer, Andrés Bruhn
Date: 2018-06-03 00:00:00
Tasks: Optical Flow Estimation

Temporal coherence is a valuable source of information in the context of
optical flow estimation. However, finding a suitable motion model to leverage
this information is a non-trivial task. In this paper we propose an
unsupervised online learning approach based on a convolutional neural network
(CNN) that estimates such a motion model individually for each frame. By
relating forward and backward motion these learned models not only allow to
infer valuable motion information based on the backward flow, they also help to
improve the performance at occlusions, where a reliable prediction is
particularly useful. Moreover, our learned models are spatially variant and
hence allow to estimate non-rigid motion per construction. This, in turns,
allows to overcome the major limitation of recent rigidity-based approaches
that seek to improve the estimation by incorporating additional stereo/SfM
constraints. Experiments demonstrate the usefulness of our new approach. They
not only show a consistent improvement of up to 27% for all major benchmarks
(KITTI 2012, KITTI 2015, MPI Sintel) compared to a baseline without prediction,
they also show top results for the MPI Sintel benchmark -- the one of the three
benchmarks that contains the largest amount of non-rigid motion.
Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network
Authors: Ayan Kumar Bhunia, Abir Bhowmick, Ankan Kumar Bhunia, Aishik Konwer, Prithaj Banerjee, Partha Pratim Roy, Umapada Pal
Date: 2018-01-22 00:00:00
Tasks: Decoder, Retrieval, Trajectory Recovery

In this paper, we introduce a novel technique to recover the pen trajectory
of offline characters which is a crucial step for handwritten character
recognition. Generally, online acquisition approach has more advantage than its
offline counterpart as the online technique keeps track of the pen movement.
Hence, pen tip trajectory retrieval from offline text can bridge the gap
between online and offline methods. Our proposed framework employs sequence to
sequence model which consists of an encoder-decoder LSTM module. Our encoder
module consists of Convolutional LSTM network, which takes an offline character
image as the input and encodes the feature sequence to a hidden representation.
The output of the encoder is fed to a decoder LSTM and we get the successive
coordinate points from every time step of the decoder LSTM. Although the
sequence to sequence model is a popular paradigm in various computer vision and
language translation tasks, the main contribution of our work lies in designing
an end-to-end network for a decade old popular problem in Document Image
Analysis community. Tamil, Telugu and Devanagari characters of LIPI Toolkit
dataset are used for our experiments. Our proposed method has achieved superior
performance compared to the other conventional approaches.
Echo state networks are universal
Authors: Lyudmila Grigoryeva, Juan-Pablo Ortega
Date: 2018-06-03 00:00:00
Tasks: valid

This paper shows that echo state networks are universal uniform approximants
in the context of discrete-time fading memory filters with uniformly bounded
inputs defined on negative infinite times. This result guarantees that any
fading memory input/output system in discrete time can be realized as a simple
finite-dimensional neural network-type state-space model with a static linear
readout map. This approximation is valid for infinite time intervals. The proof
of this statement is based on fundamental results, also presented in this work,
about the topological nature of the fading memory property and about reservoir
computing systems generated by continuous reservoir maps.
Transfer Topic Labeling with Domain-Specific Knowledge Base: An Analysis of UK House of Commons Speeches 1935-2014
Authors: Alexander Herzog, Peter John, Slava Jankin Mikhaylov
Date: 2018-06-03 00:00:00
Tasks: Topic Models

Topic models are widely used in natural language processing, allowing
researchers to estimate the underlying themes in a collection of documents.
Most topic models use unsupervised methods and hence require the additional
step of attaching meaningful labels to estimated topics. This process of manual
labeling is not scalable and suffers from human bias. We present a
semi-automatic transfer topic labeling method that seeks to remedy these
problems. Domain-specific codebooks form the knowledge-base for automated topic
labeling. We demonstrate our approach with a dynamic topic model analysis of
the complete corpus of UK House of Commons speeches 1935-2014, using the coding
instructions of the Comparative Agendas Project to label topics. We show that
our method works well for a majority of the topics we estimate; but we also
find that institution-specific topics, in particular on subnational governance,
require manual input. We validate our results using human expert coding.
A Comparison of Audio Signal Preprocessing Methods for Deep Neural Networks on Music Tagging
Authors: Keunwoo Choi, György Fazekas, Kyunghyun Cho, Mark Sandler
Date: 2017-09-06 00:00:00
Tasks: Music Tagging

In this paper, we empirically investigate the effect of audio preprocessing on music tagging with deep neural networks. We perform comprehensive experiments involving audio preprocessing using different time-frequency representations, logarithmic magnitude compression, frequency weighting, and scaling. We show that many commonly used input preprocessing techniques are redundant except magnitude compression.
Building Advanced Dialogue Managers for Goal-Oriented Dialogue Systems
Authors: Vladimir Ilievski
Date: 2018-06-03 00:00:00
Tasks: Deep Reinforcement Learning, Goal-Oriented Dialogue Systems, Natural Language Understanding, Reinforcement Learning, Specificity, Transfer Learning

Goal-Oriented (GO) Dialogue Systems, colloquially known as goal oriented
chatbots, help users achieve a predefined goal (e.g. book a movie ticket)
within a closed domain. A first step is to understand the user's goal by using
natural language understanding techniques. Once the goal is known, the bot must
manage a dialogue to achieve that goal, which is conducted with respect to a
learnt policy. The success of the dialogue system depends on the quality of the
policy, which is in turn reliant on the availability of high-quality training
data for the policy learning method, for instance Deep Reinforcement Learning.
  Due to the domain specificity, the amount of available data is typically too
low to allow the training of good dialogue policies. In this master thesis we
introduce a transfer learning method to mitigate the effects of the low
in-domain data availability. Our transfer learning based approach improves the
bot's success rate by $20\%$ in relative terms for distant domains and we more
than double it for close domains, compared to the model without transfer
learning. Moreover, the transfer learning chatbots learn the policy up to 5 to
10 times faster. Finally, as the transfer learning approach is complementary to
additional processing such as warm-starting, we show that their joint
application gives the best outcomes.
Multi-Cast Attention Networks for Retrieval-based Question Answering and Response Prediction
Authors: Yi Tay, Luu Anh Tuan, Siu Cheung Hui
Date: 2018-06-03 00:00:00
Tasks: Question Answering, Representation Learning, Retrieval, Word Embeddings

Attention is typically used to select informative sub-phrases that are used
for prediction. This paper investigates the novel use of attention as a form of
feature augmentation, i.e, casted attention. We propose Multi-Cast Attention
Networks (MCAN), a new attention mechanism and general model architecture for a
potpourri of ranking tasks in the conversational modeling and question
answering domains. Our approach performs a series of soft attention operations,
each time casting a scalar feature upon the inner word embeddings. The key idea
is to provide a real-valued hint (feature) to a subsequent encoder layer and is
targeted at improving the representation learning process. There are several
advantages to this design, e.g., it allows an arbitrary number of attention
mechanisms to be casted, allowing for multiple attention types (e.g.,
co-attention, intra-attention) and attention variants (e.g., alignment-pooling,
max-pooling, mean-pooling) to be executed simultaneously. This not only
eliminates the costly need to tune the nature of the co-attention layer, but
also provides greater extents of explainability to practitioners. Via extensive
experiments on four well-known benchmark datasets, we show that MCAN achieves
state-of-the-art performance. On the Ubuntu Dialogue Corpus, MCAN outperforms
existing state-of-the-art models by $9\%$. MCAN also achieves the best
performing score to date on the well-studied TrecQA dataset.
Exploration in Structured Reinforcement Learning
Authors: Jungseul Ok, Alexandre Proutiere, Damianos Tranos
Date: 2018-06-03 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We address reinforcement learning problems with finite state and action
spaces where the underlying MDP has some known structure that could be
potentially exploited to minimize the exploration rates of suboptimal (state,
action) pairs. For any arbitrary structure, we derive problem-specific regret
lower bounds satisfied by any learning algorithm. These lower bounds are made
explicit for unstructured MDPs and for those whose transition probabilities and
average reward functions are Lipschitz continuous w.r.t. the state and action.
For Lipschitz MDPs, the bounds are shown not to scale with the sizes $S$ and
$A$ of the state and action spaces, i.e., they are smaller than $c\log T$ where
$T$ is the time horizon and the constant $c$ only depends on the Lipschitz
structure, the span of the bias function, and the minimal action sub-optimality
gap. This contrasts with unstructured MDPs where the regret lower bound
typically scales as $SA\log T$. We devise DEL (Directed Exploration Learning),
an algorithm that matches our regret lower bounds. We further simplify the
algorithm for Lipschitz MDPs, and show that the simplified version is still
able to efficiently exploit the structure.
Dual-Primal Graph Convolutional Networks
Authors: Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski, Or Litany, Stephan Günnemann, Michael M. Bronstein
Date: 2018-06-03 00:00:00
Tasks: Graph Attention, Recommendation Systems

In recent years, there has been a surge of interest in developing deep
learning methods for non-Euclidean structured data such as graphs. In this
paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture
that alternates convolution-like operations on the graph and its dual. Our
approach allows to learn both vertex- and edge features and generalizes the
previous graph attention (GAT) model. We provide extensive experimental
validation showing state-of-the-art results on a variety of tasks tested on
established graph benchmarks, including CORA and Citeseer citation networks as
well as MovieLens, Flixter, Douban and Yahoo Music graph-guided recommender
systems.
Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise Reduction
Authors: Bin Liang, Hongcheng Li, Miaoqiang Su, Xirong Li, Wenchang Shi, Xiao-Feng Wang
Date: 2017-05-23 00:00:00
Tasks: Quantization

Recently, many studies have demonstrated deep neural network (DNN)
classifiers can be fooled by the adversarial example, which is crafted via
introducing some perturbations into an original sample. Accordingly, some
powerful defense techniques were proposed. However, existing defense techniques
often require modifying the target model or depend on the prior knowledge of
attacks. In this paper, we propose a straightforward method for detecting
adversarial image examples, which can be directly deployed into unmodified
off-the-shelf DNN models. We consider the perturbation to images as a kind of
noise and introduce two classic image processing techniques, scalar
quantization and smoothing spatial filter, to reduce its effect. The image
entropy is employed as a metric to implement an adaptive noise reduction for
different kinds of images. Consequently, the adversarial example can be
effectively detected by comparing the classification results of a given sample
and its denoised version, without referring to any prior knowledge of attacks.
More than 20,000 adversarial examples against some state-of-the-art DNN models
are used to evaluate the proposed method, which are crafted with different
attack techniques. The experiments show that our detection method can achieve a
high overall F1 score of 96.39% and certainly raises the bar for defense-aware
attacks.
What we really want to find by Sentiment Analysis: The Relationship between Computational Models and Psychological State
Authors: Hwiyeol Jo, Soo-Min Kim, Jeong Ryu
Date: 2017-04-11 00:00:00
Tasks: Sentiment Analysis, Transfer Learning

As the first step to model emotional state of a person, we build sentiment
analysis models with existing deep neural network algorithms and compare the
models with psychological measurements to enlighten the relationship. In the
experiments, we first examined psychological state of 64 participants and asked
them to summarize the story of a book, Chronicle of a Death Foretold (Marquez,
1981). Secondly, we trained models using crawled 365,802 movie review data;
then we evaluated participants' summaries using the pretrained model as a
concept of transfer learning. With the background that emotion affects on
memories, we investigated the relationship between the evaluation score of the
summaries from computational models and the examined psychological
measurements. The result shows that although CNN performed the best among other
deep neural network algorithms (LSTM, GRU), its results are not related to the
psychological state. Rather, GRU shows more explainable results depending on
the psychological state. The contribution of this paper can be summarized as
follows: (1) we enlighten the relationship between computational models and
psychological measurements. (2) we suggest this framework as objective methods
to evaluate the emotion; the real sentiment analysis of a person.
Psychological State in Text: A Limitation of Sentiment Analysis
Authors: Hwiyeol Jo, Jeong Ryu
Date: 2018-06-03 00:00:00
Tasks: Sentiment Analysis, Transfer Learning

Starting with the idea that sentiment analysis models should be able to
predict not only positive or negative but also other psychological states of a
person, we implement a sentiment analysis model to investigate the relationship
between the model and emotional state. We first examine psychological
measurements of 64 participants and ask them to write a book report about a
story. After that, we train our sentiment analysis model using crawled movie
review data. We finally evaluate participants' writings, using the pretrained
model as a concept of transfer learning. The result shows that sentiment
analysis model performs good at predicting a score, but the score does not have
any correlation with human's self-checked sentiment.
Multiple Models for Recommending Temporal Aspects of Entities
Authors: Tu Nguyen, Nattiya Kanhabua, Wolfgang Nejdl
Date: 2018-03-21 00:00:00

Entity aspect recommendation is an emerging task in semantic search that helps users discover serendipitous and prominent information with respect to an entity, of which salience (e.g., popularity) is the most important factor in previous work. However, entity aspects are temporally dynamic and often driven by events happening over time. For such cases, aspect suggestion based solely on salience features can give unsatisfactory results, for two reasons. First, salience is often accumulated over a long time period and does not account for recency. Second, many aspects related to an event entity are strongly time-dependent. In this paper, we study the task of temporal aspect recommendation for a given entity, which aims at recommending the most relevant aspects and takes into account time in order to improve search experience. We propose a novel event-centric ensemble ranking method that learns from multiple time and type-dependent models and dynamically trades off salience and recency characteristics. Through extensive experiments on real-world query logs, we demonstrate that our method is robust and achieves better effectiveness than competitive baselines.
Extrofitting: Enriching Word Representation and its Vector Space with Semantic Lexicons
Authors: Hwiyeol Jo, Stanley Jungkyu Choi
Date: 2018-04-21 00:00:00
Proceeding: WS 2018 7
Tasks: Word Similarity

We propose post-processing method for enriching not only word representation
but also its vector space using semantic lexicons, which we call extrofitting.
The method consists of 3 steps as follows: (i) Expanding 1 or more dimension(s)
on all the word vectors, filling with their representative value. (ii)
Transferring semantic knowledge by averaging each representative values of
synonyms and filling them in the expanded dimension(s). These two steps make
representations of the synonyms close together. (iii) Projecting the vector
space using Linear Discriminant Analysis, which eliminates the expanded
dimension(s) with semantic knowledge. When experimenting with GloVe, we find
that our method outperforms Faruqui's retrofitting on some of word similarity
task. We also report further analysis on our method in respect to word vector
dimensions, vocabulary size as well as other well-known pretrained word vectors
(e.g., Word2Vec, Fasttext).
TI-CNN: Convolutional Neural Networks for Fake News Detection
Authors: Yang Yang, Lei Zheng, Jiawei Zhang, Qingcai Cui, Zhoujun Li, Philip S. Yu
Date: 2018-06-03 00:00:00
Tasks: Fact Checking, Fake News Detection

With the development of social networks, fake news for various commercial and political purposes has been appearing in large numbers and gotten widespread in the online world. With deceptive words, people can get infected by the fake news very easily and will share them without any fact-checking. For instance, during the 2016 US president election, various kinds of fake news about the candidates widely spread through both official news media and the online social networks. These fake news is usually released to either smear the opponents or support the candidate on their side. The erroneous information in the fake news is usually written to motivate the voters' irrational emotion and enthusiasm. Such kinds of fake news sometimes can bring about devastating effects, and an important goal in improving the credibility of online social networks is to identify the fake news timely. In this paper, we propose to study the fake news detection problem. Automatic fake news identification is extremely hard, since pure model based fact-checking for news is still an open problem, and few existing models can be applied to solve the problem. With a thorough investigation of a fake news data, lots of useful explicit features are identified from both the text words and images used in the fake news. Besides the explicit features, there also exist some hidden patterns in the words and images used in fake news, which can be captured with a set of latent features extracted via the multiple convolutional layers in our model. A model named as TI-CNN (Text and Image information based Convolutinal Neural Network) is proposed in this paper. By projecting the explicit and latent features into a unified feature space, TI-CNN is trained with both the text and image information simultaneously. Extensive experiments carried on the real-world fake news datasets have demonstrate the effectiveness of TI-CNN.
Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network
Authors: Amarjot Singh, Devendra Patil, SN Omkar
Date: 2018-06-03 00:00:00
Tasks: Pose Estimation

Drone systems have been deployed by various law enforcement agencies to
monitor hostiles, spy on foreign drug cartels, conduct border control
operations, etc. This paper introduces a real-time drone surveillance system to
identify violent individuals in public areas. The system first uses the Feature
Pyramid Network to detect humans from aerial images. The image region with the
human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network
for human pose estimation. The orientations between the limbs of the estimated
pose are next used to identify the violent individuals. The proposed deep
network can learn meaningful representations quickly using ScatterNet and
structural priors with relatively fewer labeled examples. The system detects
the violent individuals in real-time by processing the drone images in the
cloud. This research also introduces the aerial violent individual dataset used
for training the deep network which hopefully may encourage researchers
interested in using deep learning for aerial surveillance. The pose estimation
and violent individuals identification performance is compared with the
state-of-the-art techniques.
Clustering-driven Deep Embedding with Pairwise Constraints
Authors: Sharon Fogel, Hadar Averbuch-Elor, Jacov Goldberger, Daniel Cohen-Or
Date: 2018-03-22 00:00:00
Tasks: Clustering

Recently, there has been increasing interest to leverage the competence of
neural networks to analyze data. In particular, new clustering methods that
employ deep embeddings have been presented. In this paper, we depart from
centroid-based models and suggest a new framework, called Clustering-driven
deep embedding with PAirwise Constraints (CPAC), for non-parametric clustering
using a neural network. We present a clustering-driven embedding based on a
Siamese network that encourages pairs of data points to output similar
representations in the latent space. Our pair-based model allows augmenting the
information with labeled pairs to constitute a semi-supervised framework. Our
approach is based on analyzing the losses associated with each pair to refine
the set of constraints. We show that clustering performance increases when
using this scheme, even with a limited amount of user queries. We demonstrate
how our architecture is adapted for various types of data and present the first
deep framework to cluster 3D shapes.