pwc-archive/papers-with-abstracts huggingface dataset:
The Impact of Random Models on Clustering Similarity
Authors: Alexander J. Gates, Yong-Yeol Ahn
Date: 2017-01-23 00:00:00
Tasks: Clustering, Clustering Ensemble, Handwriting Recognition

Clustering is a central approach for unsupervised learning. After clustering
is applied, the most fundamental analysis is to quantitatively compare
clusterings. Such comparisons are crucial for the evaluation of clustering
methods as well as other tasks such as consensus clustering. It is often argued
that, in order to establish a baseline, clustering similarity should be
assessed in the context of a random ensemble of clusterings. The prevailing
assumption for the random clustering ensemble is the permutation model in which
the number and sizes of clusters are fixed. However, this assumption does not
necessarily hold in practice; for example, multiple runs of K-means clustering
returns clusterings with a fixed number of clusters, while the cluster size
distribution varies greatly. Here, we derive corrected variants of two
clustering similarity measures (the Rand index and Mutual Information) in the
context of two random clustering ensembles in which the number and sizes of
clusters vary. In addition, we study the impact of one-sided comparisons in the
scenario with a reference clustering. The consequences of different random
models are illustrated using synthetic examples, handwriting recognition, and
gene expression data. We demonstrate that the choice of random model can have a
drastic impact on the ranking of similar clustering pairs, and the evaluation
of a clustering method with respect to a random baseline; thus, the choice of
random clustering model should be carefully justified.
Robust Tuning Datasets for Statistical Machine Translation
Authors: Preslav Nakov, Stephan Vogel
Date: 2017-10-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Machine Translation, Sentence, Translation

We explore the idea of automatically crafting a tuning dataset for
Statistical Machine Translation (SMT) that makes the hyper-parameters of the
SMT system more robust with respect to some specific deficiencies of the
parameter tuning algorithms. This is an under-explored research direction,
which can allow better parameter tuning. In this paper, we achieve this goal by
selecting a subset of the available sentence pairs, which are more suitable for
specific combinations of optimizers, objective functions, and evaluation
measures. We demonstrate the potential of the idea with the pairwise ranking
optimization (PRO) optimizer, which is known to yield too short translations.
We show that the learning problem can be alleviated by tuning on a subset of
the development set, selected based on sentence length. In particular, using
the longest 50% of the tuning sentences, we achieve two-fold tuning speedup,
and improvements in BLEU score that rival those of alternatives, which fix
BLEU+1's smoothing instead.
Fully Automated Fact Checking Using External Sources
Authors: Georgi Karadzhov, Preslav Nakov, Lluis Marquez, Alberto Barron-Cedeno, Ivan Koychev
Date: 2017-10-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Community Question Answering, Fact Checking, Question Answering

Given the constantly growing proliferation of false claims online in recent
years, there has been also a growing research interest in automatically
distinguishing false rumors from factually true claims. Here, we propose a
general-purpose framework for fully-automatic fact checking using external
sources, tapping the potential of the entire Web as a knowledge source to
confirm or reject a claim. Our framework uses a deep neural network with LSTM
text encoding to combine semantic kernels with task-specific embeddings that
encode a claim together with pieces of potentially-relevant text fragments from
the Web, taking the source reliability into account. The evaluation results
show good performance on two different tasks and datasets: (i) rumor detection
and (ii) fact checking of the answers to a question in community question
answering forums.
Personalized Recommender System for Children's Book Recommendation with A Realtime Interactive Robot
Authors: Yun Liu, Tianmeng Gao, Baolin Song, Chengwei Huang
Date: 2017-10-01 00:00:00
Tasks: Recommendation Systems

In this paper we study the personalized book recommender system in a child-robot interactive environment. Firstly, we propose a novel text search algorithm using an inverse filtering mechanism that improves the efficiency. Secondly, we propose a user interest prediction method based on the Bayesian network and a novel feedback mechanism. According to children's fuzzy language input, the proposed method gives the predicted interests. Thirdly, the domain specific synonym association is proposed based on word vectorization, in order to improve the understanding of user intention. Experimental results show that the proposed recommender system has an improved performance and it can operate on embedded consumer devices with limited computational resources.
Pyramidal RoR for Image Classification
Authors: Ke Zhang, Liru Guo, Ce Gao, Zhenbing Zhao
Date: 2017-10-01 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

The Residual Networks of Residual Networks (RoR) exhibits excellent
performance in the image classification task, but sharply increasing the number
of feature map channels makes the characteristic information transmission
incoherent, which losses a certain of information related to classification
prediction, limiting the classification performance. In this paper, a Pyramidal
RoR network model is proposed by analysing the performance characteristics of
RoR and combining with the PyramidNet. Firstly, based on RoR, the Pyramidal RoR
network model with channels gradually increasing is designed. Secondly, we
analysed the effect of different residual block structures on performance, and
chosen the residual block structure which best favoured the classification
performance. Finally, we add an important principle to further optimize
Pyramidal RoR networks, drop-path is used to avoid over-fitting and save
training time. In this paper, image classification experiments were performed
on CIFAR-10/100 and SVHN datasets, and we achieved the current lowest
classification error rates were 2.96%, 16.40% and 1.59%, respectively.
Experiments show that the Pyramidal RoR network optimization method can improve
the network performance for different data sets and effectively suppress the
gradient disappearance problem in DCNN training.
Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks
Authors: Anh Nguyen, Dimitrios Kanoulas, Luca Muratore, Darwin G. Caldwell, Nikos G. Tsagarakis
Date: 2017-10-01 00:00:00
Tasks: Decoder, Translation

We present a new method to translate videos to commands for robotic
manipulation using Deep Recurrent Neural Networks (RNN). Our framework first
extracts deep features from the input video frames with a deep Convolutional
Neural Networks (CNN). Two RNN layers with an encoder-decoder architecture are
then used to encode the visual features and sequentially generate the output
words as the command. We demonstrate that the translation accuracy can be
improved by allowing a smooth transaction between two RNN layers and using the
state-of-the-art feature extractor. The experimental results on our new
challenging dataset show that our approach outperforms recent methods by a fair
margin. Furthermore, we combine the proposed translation module with the vision
and planning system to let a robot perform various manipulation tasks. Finally,
we demonstrate the effectiveness of our framework on a full-size humanoid robot
WALK-MAN.
DTATG: An Automatic Title Generator based on Dependency Trees
Authors: Liqun Shao, Jie Wang
Date: 2017-10-01 00:00:00
Tasks: Sentence

We study automatic title generation for a given block of text and present a
method called DTATG to generate titles. DTATG first extracts a small number of
central sentences that convey the main meanings of the text and are in a
suitable structure for conversion into a title. DTATG then constructs a
dependency tree for each of these sentences and removes certain branches using
a Dependency Tree Compression Model we devise. We also devise a title test to
determine if a sentence can be used as a title. If a trimmed sentence passes
the title test, then it becomes a title candidate. DTATG selects the title
candidate with the highest ranking score as the final title. Our experiments
showed that DTATG can generate adequate titles. We also showed that
DTATG-generated titles have higher F1 scores than those generated by the
previous methods.
Upper Bound of Bayesian Generalization Error in Non-negative Matrix Factorization
Authors: Naoki Hayashi, Sumio Watanabe
Date: 2016-12-13 00:00:00

Non-negative matrix factorization (NMF) is a new knowledge discovery method
that is used for text mining, signal processing, bioinformatics, and consumer
analysis. However, its basic property as a learning machine is not yet
clarified, as it is not a regular statistical model, resulting that theoretical
optimization method of NMF has not yet established. In this paper, we study the
real log canonical threshold of NMF and give an upper bound of the
generalization error in Bayesian learning. The results show that the
generalization error of the matrix factorization can be made smaller than
regular statistical models if Bayesian learning is applied.
Efficient and Effective Single-Document Summarizations and A Word-Embedding Measurement of Quality
Authors: Liqun Shao, Hao Zhang, Ming Jia, Jie Wang
Date: 2017-10-01 00:00:00
Tasks: Clustering, Keyword Extraction

Our task is to generate an effective summary for a given document with
specific realtime requirements. We use the softplus function to enhance keyword
rankings to favor important sentences, based on which we present a number of
summarization algorithms using various keyword extraction and topic clustering
methods. We show that our algorithms meet the realtime requirements and yield
the best ROUGE recall scores on DUC-02 over all previously-known algorithms. We
show that our algorithms meet the realtime requirements and yield the best
ROUGE recall scores on DUC-02 over all previously-known algorithms. To evaluate
the quality of summaries without human-generated benchmarks, we define a
measure called WESM based on word-embedding using Word Mover's Distance. We
show that the orderings of the ROUGE and WESM scores of our algorithms are
highly comparable, suggesting that WESM may serve as a viable alternative for
measuring the quality of a summary.
A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods
Authors: Zhiyuan Huang, Yaohui Guo, Henry Lam, Ding Zhao
Date: 2017-10-01 00:00:00

Evaluation and validation of complicated control systems are crucial to
guarantee usability and safety. Usually, failure happens in some very rarely
encountered situations, but once triggered, the consequence is disastrous.
Accelerated Evaluation is a methodology that efficiently tests those
rarely-occurring yet critical failures via smartly-sampled test cases. The
distribution used in sampling is pivotal to the performance of the method, but
building a suitable distribution requires case-by-case analysis. This paper
proposes a versatile approach for constructing sampling distribution using
kernel method. The approach uses statistical learning tools to approximate the
critical event sets and constructs distributions based on the unique properties
of Gaussian distributions. We applied the method to evaluate the automated
vehicles. Numerical experiments show proposed approach can robustly identify
the rare failures and significantly reduce the evaluation time.
Image Dehazing using Bilinear Composition Loss Function
Authors: Hui Yang, Jinshan Pan, Qiong Yan, Wenxiu Sun, Jimmy Ren, Yu-Wing Tai
Date: 2017-10-01 00:00:00
Tasks: Blocking, Image Dehazing

In this paper, we introduce a bilinear composition loss function to address
the problem of image dehazing. Previous methods in image dehazing use a
two-stage approach which first estimate the transmission map followed by clear
image estimation. The drawback of a two-stage method is that it tends to boost
local image artifacts such as noise, aliasing and blocking. This is especially
the case for heavy haze images captured with a low quality device. Our method
is based on convolutional neural networks. Unique in our method is the bilinear
composition loss function which directly model the correlations between
transmission map, clear image, and atmospheric light. This allows errors to be
back-propagated to each sub-network concurrently, while maintaining the
composition constraint to avoid overfitting of each sub-network. We evaluate
the effectiveness of our proposed method using both synthetic and real world
examples. Extensive experiments show that our method outperfoms
state-of-the-art methods especially for haze images with severe noise level and
compressions.
Backprop KF: Learning Discriminative Deterministic State Estimators
Authors: Tuomas Haarnoja, Anurag Ajay, Sergey Levine, Pieter Abbeel
Date: 2016-05-23 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Autonomous Vehicles, State Estimation, Visual Odometry

Generative state estimators based on probabilistic filters and smoothers are
one of the most popular classes of state estimators for robots and autonomous
vehicles. However, generative models have limited capacity to handle rich
sensory observations, such as camera images, since they must model the entire
distribution over sensor readings. Discriminative models do not suffer from
this limitation, but are typically more complex to train as latent variable
models for state estimation. We present an alternative approach where the
parameters of the latent state distribution are directly optimized as a
deterministic computation graph, resulting in a simple and effective gradient
descent algorithm for training discriminative state estimators. We show that
this procedure can be used to train state estimators that use complex input,
such as raw camera images, which must be processed using expressive nonlinear
function approximators such as convolutional neural networks. Our model can be
viewed as a type of recurrent neural network, and the connection to
probabilistic filtering allows us to design a network architecture that is
particularly well suited for state estimation. We evaluate our approach on
synthetic tracking task with raw image inputs and on the visual odometry task
in the KITTI dataset. The results show significant improvement over both
standard generative approaches and regular recurrent neural networks.
What Words Do We Use to Lie?: Word Choice in Deceptive Messages
Authors: Jason Xiaotian Dou, Michelle Liu, Haaris Muneer, Adam Schlussel
Date: 2017-10-01 00:00:00

Text messaging is the most widely used form of computer-mediated communication (CMC). Previous findings have shown that linguistic factors can reliably indicate messages as deceptive. For example, users take longer and use more words to craft deceptive messages than they do truthful messages. Existing research has also examined how factors, such as student status and gender, affect rates of deception and word choice in deceptive messages. However, this research has been limited by small sample sizes and has returned contradicting findings. This paper aims to address these issues by using a dataset of text messages collected from a large and varied set of participants using an Android messaging application. The results of this paper show significant differences in word choice and frequency of deceptive messages between male and female participants, as well as between students and non-students.
Bayesian estimation from few samples: community detection and related problems
Authors: Samuel B. Hopkins, David Steurer
Date: 2017-09-30 00:00:00
Tasks: Community Detection, Stochastic Block Model, Tensor Decomposition

We propose an efficient meta-algorithm for Bayesian estimation problems that
is based on low-degree polynomials, semidefinite programming, and tensor
decomposition. The algorithm is inspired by recent lower bound constructions
for sum-of-squares and related to the method of moments. Our focus is on sample
complexity bounds that are as tight as possible (up to additive lower-order
terms) and often achieve statistical thresholds or conjectured computational
thresholds.
  Our algorithm recovers the best known bounds for community detection in the
sparse stochastic block model, a widely-studied class of estimation problems
for community detection in graphs. We obtain the first recovery guarantees for
the mixed-membership stochastic block model (Airoldi et el.) in constant
average degree graphs---up to what we conjecture to be the computational
threshold for this model. We show that our algorithm exhibits a sharp
computational threshold for the stochastic block model with multiple
communities beyond the Kesten--Stigum bound---giving evidence that this task
may require exponential time.
  The basic strategy of our algorithm is strikingly simple: we compute the
best-possible low-degree approximation for the moments of the posterior
distribution of the parameters and use a robust tensor decomposition algorithm
to recover the parameters from these approximate posterior moments.
Fine-grained Event Learning of Human-Object Interaction with LSTM-CRF
Authors: Tuan Do, James Pustejovsky
Date: 2017-09-30 00:00:00
Tasks: General Classification, Human-Object Interaction Detection, Object

Event learning is one of the most important problems in AI. However,
notwithstanding significant research efforts, it is still a very complex task,
especially when the events involve the interaction of humans or agents with
other objects, as it requires modeling human kinematics and object movements.
This study proposes a methodology for learning complex human-object interaction
(HOI) events, involving the recording, annotation and classification of event
interactions. For annotation, we allow multiple interpretations of a motion
capture by slicing over its temporal span, for classification, we use
Long-Short Term Memory (LSTM) sequential models with Conditional Randon Field
(CRF) for constraints of outputs. Using a setup involving captures of
human-object interaction as three dimensional inputs, we argue that this
approach could be used for event types involving complex spatio-temporal
dynamics.
Gaussian Three-Dimensional kernel SVM for Edge Detection Applications
Authors: Safar Irandoust-Pakchin, Aydin Ayanzadeh, Siamak Beikzadeh
Date: 2017-09-30 00:00:00
Tasks: Edge Detection

This paper presents a novel and uniform algorithm for edge detection based on
SVM (support vector machine) with Three-dimensional Gaussian radial basis
function with kernel. Because of disadvantages in traditional edge detection
such as inaccurate edge location, rough edge and careless on detect soft edge.
The experimental results indicate how the SVM can detect edge in efficient way.
The performance of the proposed algorithm is compared with existing methods,
including Sobel and canny detectors. The results show that this method is
better than classical algorithm such as canny and Sobel detector.
Fast Fine-grained Image Classification via Weakly Supervised Discriminative Localization
Authors: Xiangteng He, Yuxin Peng, Junjie Zhao
Date: 2017-09-30 00:00:00
Tasks: Classification, Fine-Grained Image Classification, General Classification, image-classification, Image Classification, Region Proposal

Fine-grained image classification is to recognize hundreds of subcategories
in each basic-level category. Existing methods employ discriminative
localization to find the key distinctions among subcategories. However, they
generally have two limitations: (1) Discriminative localization relies on
region proposal methods to hypothesize the locations of discriminative regions,
which are time-consuming. (2) The training of discriminative localization
depends on object or part annotations, which are heavily labor-consuming. It is
highly challenging to address the two key limitations simultaneously, and
existing methods only focus on one of them. Therefore, we propose a weakly
supervised discriminative localization approach (WSDL) for fast fine-grained
image classification to address the two limitations at the same time, and its
main advantages are: (1) n-pathway end-to-end discriminative localization
network is designed to improve classification speed, which simultaneously
localizes multiple different discriminative regions for one image to boost
classification accuracy, and shares full-image convolutional features generated
by region proposal network to accelerate the process of generating region
proposals as well as reduce the computation of convolutional operation. (2)
Multi-level attention guided localization learning is proposed to localize
discriminative regions with different focuses automatically, without using
object and part annotations, avoiding the labor consumption. Different level
attentions focus on different characteristics of the image, which are
complementary and boost the classification accuracy. Both are jointly employed
to simultaneously improve classification speed and eliminate dependence on
object and part annotations. Compared with state-of-the-art methods on 2
widely-used fine-grained image classification datasets, our WSDL approach
achieves the best performance.
Robust Surface Reconstruction from Gradients via Adaptive Dictionary Regularization
Authors: Andrew J. Wagenmaker, Brian E. Moore, Raj Rao Nadakuditi
Date: 2017-09-30 00:00:00
Tasks: Dictionary Learning, Surface Reconstruction

This paper introduces a novel approach to robust surface reconstruction from
photometric stereo normal vector maps that is particularly well-suited for
reconstructing surfaces from noisy gradients. Specifically, we propose an
adaptive dictionary learning based approach that attempts to simultaneously
integrate the gradient fields while sparsely representing the spatial patches
of the reconstructed surface in an adaptive dictionary domain. We show that our
formulation learns the underlying structure of the surface, effectively acting
as an adaptive regularizer that enforces a smoothness constraint on the
reconstructed surface. Our method is general and may be coupled with many
existing approaches in the literature to improve the integrity of the
reconstructed surfaces. We demonstrate the performance of our method on
synthetic data as well as real photometric stereo data and evaluate its
robustness to noise.
Robust Photometric Stereo Using Learned Image and Gradient Dictionaries
Authors: Andrew J. Wagenmaker, Brian E. Moore, Raj Rao Nadakuditi
Date: 2017-09-30 00:00:00
Tasks: Dictionary Learning

Photometric stereo is a method for estimating the normal vectors of an object
from images of the object under varying lighting conditions. Motivated by
several recent works that extend photometric stereo to more general objects and
lighting conditions, we study a new robust approach to photometric stereo that
utilizes dictionary learning. Specifically, we propose and analyze two
approaches to adaptive dictionary regularization for the photometric stereo
problem. First, we propose an image preprocessing step that utilizes an
adaptive dictionary learning model to remove noise and other non-idealities
from the image dataset before estimating the normal vectors. We also propose an
alternative model where we directly apply the adaptive dictionary
regularization to the normal vectors themselves during estimation. We study the
practical performance of both methods through extensive simulations, which
demonstrate the state-of-the-art performance of both methods in the presence of
noise.