pwc-archive/papers-with-abstracts huggingface dataset:
aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model
Authors: Liu Yang, Qingyao Ai, Jiafeng Guo, W. Bruce Croft
Date: 2018-01-05 00:00:00
Tasks: Feature Engineering, Question Answering

As an alternative to question answering methods based on feature engineering, deep learning approaches such as convolutional neural networks (CNNs) and Long Short-Term Memory Models (LSTMs) have recently been proposed for semantic matching of questions and answers. To achieve good results, however, these models have been combined with additional features such as word overlap or BM25 scores. Without this combination, these models perform significantly worse than methods based on linguistic feature engineering. In this paper, we propose an attention based neural matching model for ranking short answer text. We adopt value-shared weighting scheme instead of position-shared weighting scheme for combining different matching signals and incorporate question term importance learning using question attention network. Using the popular benchmark TREC QA data, we show that the relatively simple aNMM model can significantly outperform other neural network models that have been used for the question answering task, and is competitive with models that are combined with additional features. When aNMM is combined with additional features, it outperforms all baselines.
Social Media Analysis based on Semanticity of Streaming and Batch Data
Authors: Barathi Ganesh HB
Date: 2018-01-03 00:00:00
Tasks: Author Profiling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Languages shared by people differ in different regions based on their
accents, pronunciation and word usages. In this era sharing of language takes
place mainly through social media and blogs. Every second swing of such a micro
posts exist which induces the need of processing those micro posts, in-order to
extract knowledge out of it. Knowledge extraction differs with respect to the
application in which the research on cognitive science fed the necessities for
the same. This work further moves forward such a research by extracting
semantic information of streaming and batch data in applications like Named
Entity Recognition and Author Profiling. In the case of Named Entity
Recognition context of a single micro post has been utilized and context that
lies in the pool of micro posts were utilized to identify the sociolect aspects
of the author of those micro posts. In this work Conditional Random Field has
been utilized to do the entity recognition and a novel approach has been
proposed to find the sociolect aspects of the author (Gender, Age group).
VSE-ens: Visual-Semantic Embeddings with Efficient Negative Sampling
Authors: Guibing Guo, Songlin Zhai, Fajie Yuan, Yu-An Liu, Xingwei Wang
Date: 2018-01-05 00:00:00

Jointing visual-semantic embeddings (VSE) have become a research hotpot for
the task of image annotation, which suffers from the issue of semantic gap,
i.e., the gap between images' visual features (low-level) and labels' semantic
features (high-level). This issue will be even more challenging if visual
features cannot be retrieved from images, that is, when images are only denoted
by numerical IDs as given in some real datasets. The typical way of existing
VSE methods is to perform a uniform sampling method for negative examples that
violate the ranking order against positive examples, which requires a
time-consuming search in the whole label space. In this paper, we propose a
fast adaptive negative sampler that can work well in the settings of no figure
pixels available. Our sampling strategy is to choose the negative examples that
are most likely to meet the requirements of violation according to the latent
factors of images. In this way, our approach can linearly scale up to large
datasets. The experiments demonstrate that our approach converges 5.02x faster
than the state-of-the-art approaches on OpenImages, 2.5x on IAPR-TCI2 and 2.06x
on NUS-WIDE datasets, as well as better ranking accuracy across datasets.
Introspective Classification with Convolutional Nets
Authors: Long Jin, Justin Lazarow, Zhuowen Tu
Date: 2017-04-25 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Classification, General Classification

We propose introspective convolutional networks (ICN) that emphasize the
importance of having convolutional neural networks empowered with generative
capabilities. We employ a reclassification-by-synthesis algorithm to perform
training using a formulation stemmed from the Bayes theory. Our ICN tries to
iteratively: (1) synthesize pseudo-negative samples; and (2) enhance itself by
improving the classification. The single CNN classifier learned is at the same
time generative --- being able to directly synthesize new samples within its
own discriminative model. We conduct experiments on benchmark datasets
including MNIST, CIFAR-10, and SVHN using state-of-the-art CNN architectures,
and observe improved classification results.
Deep learning for word-level handwritten Indic script identification
Authors: Soumya Ukil, Swarnendu Ghosh, Sk Md Obaidullah, K. C. Santosh, Kaushik Roy, Nibaran Das
Date: 2018-01-05 00:00:00
Tasks: Deep Learning

We propose a novel method that uses convolutional neural networks (CNNs) for
feature extraction. Not just limited to conventional spatial domain
representation, we use multilevel 2D discrete Haar wavelet transform, where
image representations are scaled to a variety of different sizes. These are
then used to train different CNNs to select features. To be precise, we use 10
different CNNs that select a set of 10240 features, i.e. 1024/CNN. With this,
11 different handwritten scripts are identified, where 1K words per script are
used. In our test, we have achieved the maximum script identification rate of
94.73% using multi-layer perceptron (MLP). Our results outperform the
state-of-the-art techniques.
Residual Features and Unified Prediction Network for Single Stage Detection
Authors: Kyoungmin Lee, Jae-Seok Choi, Jisoo Jeong, Nojun Kwak
Date: 2017-07-17 00:00:00
Tasks: Prediction, Region Proposal

Recently, a lot of single stage detectors using multi-scale features have
been actively proposed. They are much faster than two stage detectors that use
region proposal networks (RPN) without much degradation in the detection
performances. However, the feature maps in the lower layers close to the input
which are responsible for detecting small objects in a single stage detector
have a problem of insufficient representation power because they are too
shallow. There is also a structural contradiction that the feature maps have to
deliver low-level information to next layers as well as contain high-level
abstraction for prediction. In this paper, we propose a method to enrich the
representation power of feature maps using Resblock and deconvolution layers.
In addition, a unified prediction module is applied to generalize output
results and boost earlier layers' representation power for prediction. The
proposed method enables more precise prediction, which achieved higher score
than SSD on PASCAL VOC and MS COCO. In addition, it maintains the advantage of
fast computation of a single stage detector, which requires much less
computation than other detectors with similar performance. Code is available at
https://github.com/kmlee-snu/run
Higher-order clustering in networks
Authors: Hao Yin, Austin R. Benson, Jure Leskovec
Date: 2017-04-12 00:00:00
Tasks: Clustering

A fundamental property of complex networks is the tendency for edges to
cluster. The extent of the clustering is typically quantified by the clustering
coefficient, which is the probability that a length-2 path is closed, i.e.,
induces a triangle in the network. However, higher-order cliques beyond
triangles are crucial to understanding complex networks, and the clustering
behavior with respect to such higher-order network structures is not well
understood. Here we introduce higher-order clustering coefficients that measure
the closure probability of higher-order network cliques and provide a more
comprehensive view of how the edges of complex networks cluster. Our
higher-order clustering coefficients are a natural generalization of the
traditional clustering coefficient. We derive several properties about
higher-order clustering coefficients and analyze them under common random graph
models. Finally, we use higher-order clustering coefficients to gain new
insights into the structure of real-world networks from several domains.
Dynamic Island Model based on Spectral Clustering in Genetic Algorithm
Authors: Qinxue Meng, Jia Wu, John Ellisy, Paul J. Kennedy
Date: 2018-01-05 00:00:00
Tasks: Clustering, Diversity

How to maintain relative high diversity is important to avoid premature
convergence in population-based optimization methods. Island model is widely
considered as a major approach to achieve this because of its flexibility and
high efficiency. The model maintains a group of sub-populations on different
islands and allows sub-populations to interact with each other via predefined
migration policies. However, current island model has some drawbacks. One is
that after a certain number of generations, different islands may retain quite
similar, converged sub-populations thereby losing diversity and decreasing
efficiency. Another drawback is that determining the number of islands to
maintain is also very challenging. Meanwhile initializing many sub-populations
increases the randomness of island model. To address these issues, we proposed
a dynamic island model~(DIM-SP) which can force each island to maintain
different sub-populations, control the number of islands dynamically and starts
with one sub-population. The proposed island model outperforms the other three
state-of-the-art island models in three baseline optimization problems
including job shop scheduler problem, travelling salesmen problem and quadratic
multiple knapsack problem.
Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies
Authors: Hanbyul Joo, Tomas Simon, Yaser Sheikh
Date: 2018-01-05 00:00:00
Proceeding: CVPR 2018 6

We present a unified deformation model for the markerless capture of multiple
scales of human movement, including facial expressions, body motion, and hand
gestures. An initial model is generated by locally stitching together models of
the individual parts of the human body, which we refer to as the "Frankenstein"
model. This model enables the full expression of part movements, including face
and hands by a single seamless model. Using a large-scale capture of people
wearing everyday clothes, we optimize the Frankenstein model to create "Adam".
Adam is a calibrated model that shares the same skeleton hierarchy as the
initial model but can express hair and clothing geometry, making it directly
usable for fitting people as they normally appear in everyday life. Finally, we
demonstrate the use of these models for total motion tracking, simultaneously
capturing the large-scale body movements and the subtle face and hand motion of
a social group of people.
Learning from Pseudo-Randomness With an Artificial Neural Network - Does God Play Pseudo-Dice?
Authors: Fenglei Fan, Ge Wang
Date: 2018-01-05 00:00:00
Tasks: BIG-bench Machine Learning, Sensitivity

Inspired by the fact that the neural network, as the mainstream for machine
learning, has brought successes in many application areas, here we propose to
use this approach for decoding hidden correlation among pseudo-random data and
predicting events accordingly. With a simple neural network structure and a
typical training procedure, we demonstrate the learning and prediction power of
the neural network in extremely random environment. Finally, we postulate that
the high sensitivity and efficiency of the neural network may allow to
critically test if there could be any fundamental difference between quantum
randomness and pseudo randomness, which is equivalent to the question: Does God
play dice?
Learning $3$D-FilterMap for Deep Convolutional Neural Networks
Authors: Yingzhen Yang, Jianchao Yang, Ning Xu, Wei Han
Date: 2018-01-05 00:00:00

We present a novel and compact architecture for deep Convolutional Neural
Networks (CNNs) in this paper, termed $3$D-FilterMap Convolutional Neural
Networks ($3$D-FM-CNNs). The convolution layer of $3$D-FM-CNN learns a compact
representation of the filters, named $3$D-FilterMap, instead of a set of
independent filters in the conventional convolution layer. The filters are
extracted from the $3$D-FilterMap as overlapping $3$D submatrics with weight
sharing among nearby filters, and these filters are convolved with the input to
generate the output of the convolution layer for $3$D-FM-CNN. Due to the weight
sharing scheme, the parameter size of the $3$D-FilterMap is much smaller than
that of the filters to be learned in the conventional convolution layer when
$3$D-FilterMap generates the same number of filters. Our work is fundamentally
different from the network compression literature that reduces the size of a
learned large network in the sense that a small network is directly learned
from scratch. Experimental results demonstrate that $3$D-FM-CNN enjoys a small
parameter space by learning compact $3$D-FilterMaps, while achieving
performance compared to that of the baseline CNNs which learn the same number
of filters as that generated by the corresponding $3$D-FilterMap.
Intelligence Graph
Authors: Han Xiao
Date: 2018-01-05 00:00:00
Tasks: Diversity

In fact, there exist three genres of intelligence architectures: logics (e.g.
\textit{Random Forest, A$^*$ Searching}), neurons (e.g. \textit{CNN, LSTM}) and
probabilities (e.g. \textit{Naive Bayes, HMM}), all of which are incompatible
to each other. However, to construct powerful intelligence systems with various
methods, we propose the intelligence graph (short as \textbf{\textit{iGraph}}),
which is composed by both of neural and probabilistic graph, under the
framework of forward-backward propagation. By the paradigm of iGraph, we design
a recommendation model with semantic principle. First, the probabilistic
distributions of categories are generated from the embedding representations of
users/items, in the manner of neurons. Second, the probabilistic graph infers
the distributions of features, in the manner of probabilities. Last, for the
recommendation diversity, we perform an expectation computation then conduct a
logic judgment, in the manner of logics. Experimentally, we beat the
state-of-the-art baselines and verify our conclusions.
Combination of Hyperband and Bayesian Optimization for Hyperparameter Optimization in Deep Learning
Authors: Jiazhuo Wang, Jason Xu, Xuejun Wang
Date: 2018-01-05 00:00:00
Tasks: Bayesian Optimization, Deep Learning, Hyperparameter Optimization

Deep learning has achieved impressive results on many problems. However, it
requires high degree of expertise or a lot of experience to tune well the
hyperparameters, and such manual tuning process is likely to be biased.
Moreover, it is not practical to try out as many different hyperparameter
configurations in deep learning as in other machine learning scenarios, because
evaluating each single hyperparameter configuration in deep learning would mean
training a deep neural network, which usually takes quite long time. Hyperband
algorithm achieves state-of-the-art performance on various hyperparameter
optimization problems in the field of deep learning. However, Hyperband
algorithm does not utilize history information of previous explored
hyperparameter configurations, thus the solution found is suboptimal. We
propose to combine Hyperband algorithm with Bayesian optimization (which does
not ignore history when sampling next trial configuration). Experimental
results show that our combination approach is superior to other hyperparameter
optimization approaches including Hyperband algorithm.
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines
Authors: David Charte, Francisco Charte, Salvador García, María J. del Jesus, Francisco Herrera
Date: 2018-01-04 00:00:00

Many of the existing machine learning algorithms, both supervised and
unsupervised, depend on the quality of the input characteristics to generate a
good model. The amount of these variables is also important, since performance
tends to decline as the input dimensionality increases, hence the interest in
using feature fusion techniques, able to produce feature sets that are more
compact and higher level. A plethora of procedures to fuse original variables
for producing new ones has been developed in the past decades. The most basic
ones use linear combinations of the original variables, such as PCA (Principal
Component Analysis) and LDA (Linear Discriminant Analysis), while others find
manifold embeddings of lower dimensionality based on non-linear combinations,
such as Isomap or LLE (Linear Locally Embedding) techniques.
  More recently, autoencoders (AEs) have emerged as an alternative to manifold
learning for conducting nonlinear feature fusion. Dozens of AE models have been
proposed lately, each with its own specific traits. Although many of them can
be used to generate reduced feature sets through the fusion of the original
ones, there also AEs designed with other applications in mind.
  The goal of this paper is to provide the reader with a broad view of what an
AE is, how they are used for feature fusion, a taxonomy gathering a broad range
of models, and how they relate to other classical techniques. In addition, a
set of didactic guidelines on how to choose the proper AE for a given task is
supplied, together with a discussion of the software tools available. Finally,
two case studies illustrate the usage of AEs with datasets of handwritten
digits and breast cancer.
Selective-Candidate Framework with Similarity Selection Rule for Evolutionary Optimization
Authors: Sheng Xin Zhang, Wing Shing Chan, Zi Kang Peng, Shao Yong Zheng, Kit Sang Tang
Date: 2017-12-18 00:00:00

Achieving better exploitation and exploration capabilities (EEC) have always been an important yet challenging issue in the design of evolutionary optimization algorithm (EOA). The difficulties lie in obtaining a good balance in EEC, which is determined cooperatively by operations and parameters in an EOA. When deficiencies in exploitation or exploration are observed, most existing works consider a piecemeal approach, either by designing new operations or by altering the parameters. Unfortunately, when different situations are encountered, these proposals may fail to be the winner. To address these problems, this paper proposes an explicit EEC control method named selective-candidate framework with similarity selection rule (SCSS). M (M > 1) candidates are first generated from each current solution with independent operations and parameters to enrich the search. Then, a similarity selection rule is designed to determine the final candidate by considering the fitness ranking of the current solution and its Euclidian distance to each of these M candidates. Superior current solutions will prefer the closest candidates for efficient local exploitation while inferior ones will favor the farthest for exploration purpose. In this way, the rule could synthesize exploitation and exploration, making the evolution more effective. When applied to three classic, four state-of-the-art and four up-to-date EOAs from branches of differential evolution, evolution strategy and particle swarm optimization, significant enhancement in performance is achieved.
Testing Optimality of Sequential Decision-Making
Authors: Meik Dörpinghaus, Izaak Neri, Édgar Roldán, Heinrich Meyr, Frank Jülicher
Date: 2018-01-04 00:00:00
Tasks: Decision Making, Sequential Decision Making

This paper provides a statistical method to test whether a system that
performs a binary sequential hypothesis test is optimal in the sense of
minimizing the average decision times while taking decisions with given
reliabilities. The proposed method requires samples of the decision times, the
decision outcomes, and the true hypotheses, but does not require knowledge on
the statistics of the observations or the properties of the decision-making
system. The method is based on fluctuation relations for decision time
distributions which are proved for sequential probability ratio tests. These
relations follow from the martingale property of probability ratios and hold
under fairly general conditions. We illustrate these tests with numerical
experiments and discuss potential applications.
LoopSmart: Smart Visual SLAM Through Surface Loop Closure
Authors: Guoxiang Zhang, YangQuan Chen
Date: 2018-01-04 00:00:00
Tasks: Point Cloud Registration, Simultaneous Localization and Mapping, Surface Reconstruction, Visual Odometry

We present a visual simultaneous localization and mapping (SLAM) framework of
closing surface loops. It combines both sparse feature matching and dense
surface alignment. Sparse feature matching is used for visual odometry and
globally camera pose fine-tuning when dense loops are detected, while dense
surface alignment is the way of closing large loops and solving surface
mismatching problem. To achieve smart dense surface loop closure, a highly
efficient CUDA-based global point cloud registration method and a map content
dependent loop verification method are proposed. We run extensive experiments
on different datasets, our method outperforms state-of-the-art ones in terms of
both camera trajectory and surface reconstruction accuracy.
Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation
Authors: Hao-Shu Fang, Yuanlu Xu, Wenguan Wang, Xiaobai Liu, Song-Chun Zhu
Date: 2017-10-17 00:00:00
Tasks: 3D Absolute Human Pose Estimation, 3D Human Pose Estimation, 3D Pose Estimation, Pose Estimation

In this paper, we propose a pose grammar to tackle the problem of 3D human
pose estimation. Our model directly takes 2D pose as input and learns a
generalized 2D-3D mapping function. The proposed model consists of a base
network which efficiently captures pose-aligned features and a hierarchy of
Bi-directional RNNs (BRNN) on the top to explicitly incorporate a set of
knowledge regarding human body configuration (i.e., kinematics, symmetry, motor
coordination). The proposed model thus enforces high-level constraints over
human poses. In learning, we develop a pose sample simulator to augment
training samples in virtual camera views, which further improves our model
generalizability. We validate our method on public 3D human pose benchmarks and
propose a new evaluation protocol working on cross-view setting to verify the
generalization capability of different methods. We empirically observe that
most state-of-the-art methods encounter difficulty under such setting while our
method can well handle such challenges.