pwc-archive/papers-with-abstracts huggingface dataset:
Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks
Authors: Mohammad Jubran, Alhabib Abbas, Aaron Chadha, Yiannis Andreopoulos
Date: 2018-09-27 00:00:00
Tasks: Action Recognition, Classification, General Classification, Optical Flow Estimation, Temporal Action Localization, Video Classification

Advanced video classification systems decode video frames to derive the
necessary texture and motion representations for ingestion and analysis by
spatio-temporal deep convolutional neural networks (CNNs). However, when
considering visual Internet-of-Things applications, surveillance systems and
semantic crawlers of large video repositories, the video capture and the
CNN-based semantic analysis parts do not tend to be co-located. This
necessitates the transport of compressed video over networks and incurs
significant overhead in bandwidth and energy consumption, thereby significantly
undermining the deployment potential of such systems. In this paper, we
investigate the trade-off between the encoding bitrate and the achievable
accuracy of CNN-based video classification models that directly ingest
AVC/H.264 and HEVC encoded videos. Instead of retaining entire compressed video
bitstreams and applying complex optical flow calculations prior to CNN
processing, we only retain motion vector and select texture information at
significantly-reduced bitrates and apply no additional processing prior to CNN
ingestion. Based on three CNN architectures and two action recognition
datasets, we achieve 11%-94% saving in bitrate with marginal effect on
classification accuracy. A model-based selection between multiple CNNs
increases these savings further, to the point where, if up to 7% loss of
accuracy can be tolerated, video classification can take place with as little
as 3 kbps for the transport of the required compressed video information to the
system implementing the CNN models.
Kernel Density Estimation Bias under Minimal Assumptions
Authors: Maciej Skorski
Date: 2019-01-02 00:00:00
Tasks: Density Estimation

Kernel Density Estimation is a very popular technique of approximating a
density function from samples. The accuracy is generally well-understood and
depends, roughly speaking, on the kernel decay and local smoothness of the true
density. However concrete statements in the literature are often invoked in
very specific settings (simplified or overly conservative assumptions) or miss
important but subtle points (e.g. it is common to heuristically apply Taylor's
expansion globally without referring to compactness). The contribution of this
paper is twofold (a) we demonstrate that, when the bandwidth is an arbitrary
invertible matrix going to zero, it is necessary to keep a certain balance
between the \emph{kernel decay} and \emph{magnitudes of bandwidth eigenvalues};
in fact, without the sufficient decay the estimates may not be even bounded (b)
we give a rigorous derivation of bounds with explicit constants for the bias,
under possibly minimal assumptions. This connects the kernel decay, bandwidth
norm, bandwidth determinant and density smoothness. It has been folklore that
the issue with Taylor's formula can be fixed with more complicated assumptions
on the density (for example p. 95 of "Kernel Smoothing" by Wand and Jones); we
show that this is actually not necessary and can be handled by the kernel decay
alone.
Plugin Networks for Inference under Partial Evidence
Authors: Michal Koperski, Tomasz Konopczynski, Rafał Nowak, Piotr Semberecki, Tomasz Trzcinski
Date: 2019-01-02 00:00:00
Tasks: Multi-Label Classification, Scene Recognition, Semantic Segmentation

In this paper, we propose a novel method to incorporate partial evidence in the inference of deep convolutional neural networks. Contrary to the existing, top performing methods, which either iteratively modify the input of the network or exploit external label taxonomy to take the partial evidence into account, we add separate network modules ("Plugin Networks") to the intermediate layers of a pre-trained convolutional network. The goal of these modules is to incorporate additional signal, ie information about known labels, into the inference procedure and adjust the predicted output accordingly. Since the attached plugins have a simple structure, consisting of only fully connected layers, we drastically reduced the computational cost of training and inference. At the same time, the proposed architecture allows to propagate information about known labels directly to the intermediate layers to improve the final representation. Extensive evaluation of the proposed method confirms that our Plugin Networks outperform the state-of-the-art in a variety of tasks, including scene categorization, multi-label image annotation, and semantic segmentation.
Cluster analysis of homicide rates in the Brazilian state of Goias from 2002 to 2014
Authors: Samuel Bruno da Silva Sousa, Ronaldo de Castro Del-Fiaco, Lilian Berton
Date: 2018-11-11 00:00:00
Tasks: Clustering

Homicide mortality is a worldwide concern and has occupied the agenda of
researchers and public managers. In Brazil, homicide is the third leading cause
of death in the general population and the first in the 15-39 age group. In
South America, Brazil has the third highest homicide mortality, behind
Venezuela and Colombia. To measure the impacts of violence it is important to
assess health systems and criminal justice, as well as other areas. In this
paper, we analyze the spatial distribution of homicide mortality in the state
of Goias, Center-West of Brazil, since the homicide rate increased from 24.5
per 100,000 in 2002 to 42.6 per 100,000 in 2014 in this location. Moreover,
this state had the fifth position of homicides in Brazil in 2014. We considered
socio-demographic variables for the state, performed analysis about correlation
and employed three clustering algorithms: K-means, Density-based and
Hierarchical. The results indicate the homicide rates are higher in cities
neighbors of large urban centers, although these cities have the best
socioeconomic indicators.
Normal Approximation and Confidence Region of Singular Subspaces
Authors: Dong Xia
Date: 2019-01-02 00:00:00

This paper is on the normal approximation of singular subspaces when the noise matrix has i.i.d. entries. Our contributions are three-fold. First, we derive an explicit representation formula of the empirical spectral projectors. The formula is neat and holds for deterministic matrix perturbations. Second, we calculate the expected projection distance between the empirical singular subspaces and true singular subspaces. Our method allows obtaining arbitrary $k$-th order approximation of the expected projection distance. Third, we prove the non-asymptotical normal approximation of the projection distance with different levels of bias corrections. By the $\lceil \log(d_1+d_2)\rceil$-th order bias corrections, the asymptotical normality holds under optimal signal-to-noise ration (SNR) condition where $d_1$ and $d_2$ denote the matrix sizes. In addition, it shows that higher order approximations are unnecessary when $|d_1-d_2|=O((d_1+d_2)^{1/2})$. Finally, we provide comprehensive simulation results to merit our theoretic discoveries. Unlike the existing results, our approach is non-asymptotical and the convergence rates are established. Our method allows the rank $r$ to diverge as fast as $o((d_1+d_2)^{1/3})$. Moreover, our method requires no eigen-gap condition (except the SNR) and no constraints between $d_1$ and $d_2$.
SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images
Authors: Caijing Miao, Lingxi Xie, Fang Wan, Chi Su, Hongye Liu, Jianbin Jiao, Qixiang Ye
Date: 2019-01-02 00:00:00
Tasks: Object Localization

In this paper, we present a large-scale dataset and establish a baseline for
prohibited item discovery in Security Inspection X-ray images. Our dataset,
named SIXray, consists of 1,059,231 X-ray images, in which 6 classes of 8,929
prohibited items are manually annotated. It raises a brand new challenge of
overlapping image data, meanwhile shares the same properties with existing
datasets, including complex yet meaningless contexts and class imbalance. We
propose an approach named class-balanced hierarchical refinement (CHR) to deal
with these difficulties. CHR assumes that each input image is sampled from a
mixture distribution, and that deep networks require an iterative process to
infer image contents accurately. To accelerate, we insert reversed connections
to different network backbones, delivering high-level visual cues to assist
mid-level features. In addition, a class-balanced loss function is designed to
maximally alleviate the noise introduced by easy negative samples. We evaluate
CHR on SIXray with different ratios of positive/negative samples. Compared to
the baselines, CHR enjoys a better ability of discriminating objects especially
using mid-level features, which offers the possibility of using a
weakly-supervised approach towards accurate object localization. In particular,
the advantage of CHR is more significant in the scenarios with fewer positive
training samples, which demonstrates its potential application in real-world
security inspection.
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
Authors: Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand N. Negahban
Date: 2019-01-02 00:00:00
Tasks: Multi-Armed Bandits

We investigate the feasibility of learning from a mix of both fully-labeled supervised data and contextual bandit data. We specifically consider settings in which the underlying learning signal may be different between these two data sources. Theoretically, we state and prove no-regret algorithms for learning that is robust to misaligned cost distributions between the two sources. Empirically, we evaluate some of these algorithms on a large selection of datasets, showing that our approach is both feasible and helpful in practice.
Ethically Aligned Opportunistic Scheduling for Productive Laziness
Authors: Han Yu, Chunyan Miao, Yongqing Zheng, Lizhen Cui, Simon Fauvel, Cyril Leung
Date: 2019-01-02 00:00:00
Tasks: Management, Scheduling

In artificial intelligence (AI) mediated workforce management systems (e.g.,
crowdsourcing), long-term success depends on workers accomplishing tasks
productively and resting well. This dual objective can be summarized by the
concept of productive laziness. Existing scheduling approaches mostly focus on
efficiency but overlook worker wellbeing through proper rest. In order to
enable workforce management systems to follow the IEEE Ethically Aligned Design
guidelines to prioritize worker wellbeing, we propose a distributed
Computational Productive Laziness (CPL) approach in this paper. It
intelligently recommends personalized work-rest schedules based on local data
concerning a worker's capabilities and situational factors to incorporate
opportunistic resting and achieve superlinear collective productivity without
the need for explicit coordination messages. Extensive experiments based on a
real-world dataset of over 5,000 workers demonstrate that CPL enables workers
to spend 70% of the effort to complete 90% of the tasks on average, providing
more ethically aligned scheduling than existing approaches.
A Deep Learning Approach for Similar Languages, Varieties and Dialects
Authors: Vidya Prasad K, Akarsh S, Vinayakumar R, Soman Kp
Date: 2019-01-02 00:00:00
Tasks: Deep Learning, Dialect Identification, speech-recognition, Speech Recognition

Deep learning mechanisms are prevailing approaches in recent days for the
various tasks in natural language processing, speech recognition, image
processing and many others. To leverage this we use deep learning based
mechanism specifically Bidirectional- Long Short-Term Memory (B-LSTM) for the
task of dialectic identification in Arabic and German broadcast speech and Long
Short-Term Memory (LSTM) for discriminating between similar Languages. Two
unique B-LSTM models are created using the Large-vocabulary Continuous Speech
Recognition (LVCSR) based lexical features and a fixed length of 400 per
utterance bottleneck features generated by i-vector framework. These models
were evaluated on the VarDial 2017 datasets for the tasks Arabic, German
dialect identification with dialects of Egyptian, Gulf, Levantine, North
African, and MSA for Arabic and Basel, Bern, Lucerne, and Zurich for German.
Also for the task of Discriminating between Similar Languages like Bosnian,
Croatian and Serbian. The B-LSTM model showed accuracy of 0.246 on lexical
features and accuracy of 0.577 bottleneck features of i-Vector framework.
End-to-End Model for Speech Enhancement by Consistent Spectrogram Masking
Authors: Xingjian Du, Mengyao Zhu, Xuan Shi, Xinpeng Zhang, Wen Zhang, Jingdong Chen
Date: 2019-01-02 00:00:00
Tasks: Speech Enhancement

Recently, phase processing is attracting increasinginterest in speech
enhancement community. Some researchersintegrate phase estimations module into
speech enhancementmodels by using complex-valued short-time Fourier
transform(STFT) spectrogram based training targets, e.g. Complex RatioMask
(cRM) [1]. However, masking on spectrogram would violentits consistency
constraints. In this work, we prove that theinconsistent problem enlarges the
solution space of the speechenhancement model and causes unintended artifacts.
ConsistencySpectrogram Masking (CSM) is proposed to estimate the
complexspectrogram of a signal with the consistency constraint in asimple but
not trivial way. The experiments comparing ourCSM based end-to-end model with
other methods are conductedto confirm that the CSM accelerate the model
training andhave significant improvements in speech quality. From
ourexperimental results, we assured that our method could enha
On Minimum Discrepancy Estimation for Deep Domain Adaptation
Authors: Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, Sridha Sridharan
Date: 2019-01-02 00:00:00
Tasks: Domain Adaptation, General Classification, image-classification, Image Classification

In the presence of large sets of labeled data, Deep Learning (DL) has
accomplished extraordinary triumphs in the avenue of computer vision,
particularly in object classification and recognition tasks. However, DL cannot
always perform well when the training and testing images come from different
distributions or in the presence of domain shift between training and testing
images. They also suffer in the absence of labeled input data. Domain
adaptation (DA) methods have been proposed to make up the poor performance due
to domain shift. In this paper, we present a new unsupervised deep domain
adaptation method based on the alignment of second order statistics
(covariances) as well as maximum mean discrepancy of the source and target data
with a two stream Convolutional Neural Network (CNN). We demonstrate the
ability of the proposed approach to achieve state-of the-art performance for
image classification on three benchmark domain adaptation datasets: Office-31
[27], Office-Home [37] and Office-Caltech [8].
AIR5: Five Pillars of Artificial Intelligence Research
Authors: Yew-Soon Ong, Abhishek Gupta
Date: 2018-12-30 00:00:00
Tasks: Artificial Life

In this article, we provide and overview of what we consider to be some of
the most pressing research questions facing the fields of artificial
intelligence (AI) and computational intelligence (CI); with the latter focusing
on algorithms that are inspired by various natural phenomena. We demarcate
these questions using five unique Rs - namely, (i) rationalizability, (ii)
resilience, (iii) reproducibility, (iv) realism, and (v) responsibility.
Notably, just as air serves as the basic element of biological life, the term
AIR5 - cumulatively referring to the five aforementioned Rs - is introduced
herein to mark some of the basic elements of artificial life (supporting the
sustained growth of AI and CI). A brief summary of each of the Rs is presented,
highlighting their relevance as pillars of future research in this arena.
Elimination of All Bad Local Minima in Deep Learning
Authors: Kenji Kawaguchi, Leslie Pack Kaelbling
Date: 2019-01-02 00:00:00
Tasks: All, Binary Classification, Deep Learning, General Classification, Multi-class Classification

In this paper, we theoretically prove that adding one special neuron per output unit eliminates all suboptimal local minima of any deep neural network, for multi-class classification, binary classification, and regression with an arbitrary loss function, under practical assumptions. At every local minimum of any deep neural network with these added neurons, the set of parameters of the original neural network (without added neurons) is guaranteed to be a global minimum of the original neural network. The effects of the added neurons are proven to automatically vanish at every local minimum. Moreover, we provide a novel theoretical characterization of a failure mode of eliminating suboptimal local minima via an additional theorem and several examples. This paper also introduces a novel proof technique based on the perturbable gradient basis (PGB) necessary condition of local minima, which provides new insight into the elimination of local minima and is applicable to analyze various models and transformations of objective functions beyond the elimination of local minima.
Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimization
Authors: Miao Zhang, Huiqi Li, Juan Lyu, Sai Ho Ling, Steven Su
Date: 2019-01-02 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes, General Classification, Hyperparameter Optimization, Lung Nodule Classification

This paper investigates lung nodule classification by using deep neural
networks (DNNs). Hyperparameter optimization in DNNs is a computationally
expensive problem, where evaluating a hyperparameter configuration may take
several hours or even days. Bayesian optimization has been recently introduced
for the automatically searching of optimal hyperparameter configurations of
DNNs. It applies probabilistic surrogate models to approximate the validation
error function of hyperparameter configurations, such as Gaussian processes,
and reduce the computational complexity to a large extent. However, most
existing surrogate models adopt stationary covariance functions to measure the
difference between hyperparameter points based on spatial distance without
considering its spatial locations. This distance-based assumption together with
the condition of constant smoothness throughout the whole hyperparameter search
space clearly violates the property that the points far away from optimal
points usually get similarly poor performance even though each two of them have
huge spatial distance between them. In this paper, a non-stationary kernel is
proposed which allows the surrogate model to adapt to functions whose
smoothness varies with the spatial location of inputs, and a multi-level
convolutional neural network (ML-CNN) is built for lung nodule classification
whose hyperparameter configuration is optimized by using the proposed
non-stationary kernel based Gaussian surrogate model. Our algorithm searches
the surrogate for optimal setting via hyperparameter importance based
evolutionary strategy, and the experiments demonstrate our algorithm
outperforms manual tuning and well-established hyperparameter optimization
methods such as Random search, Gaussian processes with stationary kernels, and
recently proposed Hyperparameter Optimization via RBF and Dynamic coordinate
search.
Vector and Line Quantization for Billion-scale Similarity Search on GPUs
Authors: Wei Chen, Jincai Chen, Fuhao Zou, Yuan-Fang Li, Ping Lu, Qiang Wang, Wei Zhao
Date: 2019-01-02 00:00:00
Tasks: CPU, GPU, Quantization

Billion-scale high-dimensional approximate nearest neighbour (ANN) search has
become an important problem for searching similar objects among the vast amount
of images and videos available online. The existing ANN methods are usually
characterized by their specific indexing structures, including the inverted
index and the inverted multi-index structure. The inverted index structure is
amenable to GPU-based implementations, and the state-of-the-art systems such as
Faiss are able to exploit the massive parallelism offered by GPUs. However, the
inverted index requires high memory overhead to index the dataset effectively.
The inverted multi-index structure is difficult to implement for GPUs, and also
ineffective in dealing with database with different data distributions. In this
paper we propose a novel hierarchical inverted index structure generated by
vector and line quantization methods. Our quantization method improves both
search efficiency and accuracy, while maintaining comparable memory
consumption. This is achieved by reducing search space and increasing the
number of indexed regions. We introduce a new ANN search system, VLQ-ADC, that
is based on the proposed inverted index, and perform extensive evaluation on
two public billion-scale benchmark datasets SIFT1B and DEEP1B. Our evaluation
shows that VLQ-ADC significantly outperforms the state-of-the-art GPU- and
CPU-based systems in terms of both accuracy and search speed. The source code
of VLQ-ADC is available at
https://github.com/zjuchenwei/vector-line-quantization.
Learning Humanoid Robot Motions Through Deep Neural Networks
Authors: Luckeciano Carvalho Melo, Marcos Ricardo Omena Albuquerque Maximo, Adilson Marques da Cunha
Date: 2019-01-02 00:00:00

Controlling a high degrees of freedom humanoid robot is acknowledged as one
of the hardest problems in Robotics. Due to the lack of mathematical models, an
approach frequently employed is to rely on human intuition to design keyframe
movements by hand, usually aided by graphical tools. In this paper, we propose
a learning framework based on neural networks in order to mimic humanoid robot
movements. The developed technique does not make any assumption about the
underlying implementation of the movement, therefore both keyframe and
model-based motions may be learned. The framework was applied in the RoboCup 3D
Soccer Simulation domain and promising results were obtained using the same
network architecture for several motions, even when copying motions from
another teams.
General Subpopulation Framework and Taming the Conflict Inside Populations
Authors: Danilo Vasconcellos Vargas, Junichi Murata, Hirotaka Takano, Alexandre Claudio Botazzo Delbem
Date: 2019-01-02 00:00:00
Tasks: Evolutionary Algorithms

Structured evolutionary algorithms have been investigated for some time.
However, they have been under-explored specially in the field of
multi-objective optimization. Despite their good results, the use of complex
dynamics and structures make their understanding and adoption rate low. Here,
we propose the general subpopulation framework that has the capability of
integrating optimization algorithms without restrictions as well as aid the
design of structured algorithms. The proposed framework is capable of
generalizing most of the structured evolutionary algorithms, such as cellular
algorithms, island models, spatial predator-prey and restricted mating based
algorithms under its formalization. Moreover, we propose two algorithms based
on the general subpopulation framework, demonstrating that with the simple
addition of a number of single-objective differential evolution algorithms for
each objective the results improve greatly, even when the combined algorithms
behave poorly when evaluated alone at the tests. Most importantly, the
comparison between the subpopulation algorithms and their related panmictic
algorithms suggests that the competition between different strategies inside
one population can have deleterious consequences for an algorithm and reveal a
strong benefit of using the subpopulation framework.
  The code for SAN, the proposed multi-objective algorithm which has the
current best results in the hardest benchmark, is available at the following
https://github.com/zweifel/zweifel