pwc-archive/papers-with-abstracts huggingface dataset:
Survey on Models and Techniques for Root-Cause Analysis
Authors: Marc Solé, Victor Muntés-Mulero, Annie Ibrahim Rana, Giovani Estrada
Date: 2017-01-30 00:00:00
Tasks: Survey

Automation and computer intelligence to support complex human decisions
becomes essential to manage large and distributed systems in the Cloud and IoT
era. Understanding the root cause of an observed symptom in a complex system
has been a major problem for decades. As industry dives into the IoT world and
the amount of data generated per year grows at an amazing speed, an important
question is how to find appropriate mechanisms to determine root causes that
can handle huge amounts of data or may provide valuable feedback in real-time.
While many survey papers aim at summarizing the landscape of techniques for
modelling system behavior and infering the root cause of a problem based in the
resulting models, none of those focuses on analyzing how the different
techniques in the literature fit growing requirements in terms of performance
and scalability. In this survey, we provide a review of root-cause analysis,
focusing on these particular aspects. We also provide guidance to choose the
best root-cause analysis strategy depending on the requirements of a particular
system and application.
People Mover's Distance: Class level geometry using fast pairwise data adaptive transportation costs
Authors: Alexander Cloninger, Brita Roy, Carley Riley, Harlan M. Krumholz
Date: 2017-07-03 00:00:00
Tasks: Clustering

We address the problem of defining a network graph on a large collection of
classes. Each class is comprised of a collection of data points, sampled in a
non i.i.d. way, from some unknown underlying distribution. The application we
consider in this paper is a large scale high dimensional survey of people
living in the US, and the question of how similar or different are the various
counties in which these people live. We use a co-clustering diffusion metric to
learn the underlying distribution of people, and build an approximate earth
mover's distance algorithm using this data adaptive transportation cost.
Parallel Multi Channel Convolution using General Matrix Multiplication
Authors: Aravind Vasudevan, Andrew Anderson, David Gregg
Date: 2017-04-06 00:00:00
Tasks: CPU

Convolutional neural networks (CNNs) have emerged as one of the most
successful machine learning technologies for image and video processing. The
most computationally intensive parts of CNNs are the convolutional layers,
which convolve multi-channel images with multiple kernels. A common approach to
implementing convolutional layers is to expand the image into a column matrix
(im2col) and perform Multiple Channel Multiple Kernel (MCMK) convolution using
an existing parallel General Matrix Multiplication (GEMM) library. This im2col
conversion greatly increases the memory footprint of the input matrix and
reduces data locality.
  In this paper we propose a new approach to MCMK convolution that is based on
General Matrix Multiplication (GEMM), but not on im2col. Our algorithm
eliminates the need for data replication on the input thereby enabling us to
apply the convolution kernels on the input images directly. We have implemented
several variants of our algorithm on a CPU processor and an embedded ARM
processor. On the CPU, our algorithm is faster than im2col in most cases.
Recommender System for News Articles using Supervised Learning
Authors: Akshay Kumar Chaturvedi, Filipa Peleja, Ana Freire
Date: 2017-07-03 00:00:00
Tasks: Articles, Recommendation Systems, regression

In the last decade we have observed a mass increase of information, in
particular information that is shared through smartphones. Consequently, the
amount of information that is available does not allow the average user to be
aware of all his options. In this context, recommender systems use a number of
techniques to help a user find the desired product. Hence, nowadays recommender
systems play an important role. Recommender Systems' aim to identify products
that best fits user preferences. These techniques are advantageous to both
users and vendors, as it enables the user to rapidly find what he needs and the
vendors to promote their products and sales. As the industry became aware of
the gains that could be accomplished by using these algorithms, also a very
interesting problem for many researchers, recommender systems became a very
active area since the mid 90's. Having in mind that this is an ongoing problem
the present thesis intends to observe the value of using a recommender
algorithm to find users likes by observing her domain preferences. In a
balanced probabilistic method, this thesis will show how news topics can be
used to recommend news articles. In this thesis, we used different machine
learning methods to determine the user ratings for an article. To tackle this
problem, supervised learning methods such as linear regression, Naive Bayes and
logistic regression are used. All the aforementioned models have a different
nature which has an impact on the solution of the given problem. Furthermore,
number of experiments are presented and discussed to identify the feature set
that fits best to the problem.
Decoupled Neural Interfaces using Synthetic Gradients
Authors: Max Jaderberg, Wojciech Marian Czarnecki, Simon Osindero, Oriol Vinyals, Alex Graves, David Silver, Koray Kavukcuoglu
Date: 2016-08-18 00:00:00
Proceeding: ICML 2017 8

Training directed neural networks typically requires forward-propagating data
through a computation graph, followed by backpropagating error signal, to
produce weight updates. All layers, or more generally, modules, of the network
are therefore locked, in the sense that they must wait for the remainder of the
network to execute forwards and propagate error backwards before they can be
updated. In this work we break this constraint by decoupling modules by
introducing a model of the future computation of the network graph. These
models predict what the result of the modelled subgraph will produce using only
local information. In particular we focus on modelling error gradients: by
using the modelled synthetic gradient in place of true backpropagated error
gradients we decouple subgraphs, and can update them independently and
asynchronously i.e. we realise decoupled neural interfaces. We show results for
feed-forward models, where every layer is trained asynchronously, recurrent
neural networks (RNNs) where predicting one's future gradient extends the time
over which the RNN can effectively model, and also a hierarchical RNN system
with ticking at different timescales. Finally, we demonstrate that in addition
to predicting gradients, the same framework can be used to predict inputs,
resulting in models which are decoupled in both the forward and backwards pass
-- amounting to independent networks which co-learn such that they can be
composed into a single functioning corporation.
End-to-End Learning of Video Super-Resolution with Motion Compensation
Authors: Osama Makansi, Eddy Ilg, Thomas Brox
Date: 2017-07-03 00:00:00
Tasks: Motion Compensation, Optical Flow Estimation, Super-Resolution, Video Super-Resolution

Learning approaches have shown great success in the task of super-resolving
an image given a low resolution input. Video super-resolution aims for
exploiting additionally the information from multiple images. Typically, the
images are related via optical flow and consecutive image warping. In this
paper, we provide an end-to-end video super-resolution network that, in
contrast to previous works, includes the estimation of optical flow in the
overall network architecture. We analyze the usage of optical flow for video
super-resolution and find that common off-the-shelf image warping does not
allow video super-resolution to benefit much from optical flow. We rather
propose an operation for motion compensation that performs warping from low to
high resolution directly. We show that with this network configuration, video
super-resolution can benefit from optical flow and we obtain state-of-the-art
results on the popular test sets. We also show that the processing of whole
images rather than independent patches is responsible for a large increase in
accuracy.
A Distance Between Populations for n-Points Crossover in Genetic Algorithms
Authors: Mauro Castelli, Gianpiero Cattaneo, Luca Manzoni, Leonardo Vanneschi
Date: 2017-07-03 00:00:00

Genetic algorithms (GAs) are an optimization technique that has been
successfully used on many real-world problems. There exist different approaches
to their theoretical study. In this paper we complete a recently presented
approach to model one-point crossover using pretopologies (or Cech topologies)
in two ways. First, we extend it to the case of n-points crossover. Then, we
experimentally study how the distance distribution changes when the number of
crossover points increases.
View-invariant Gait Recognition through Genetic Template Segmentation
Authors: Ebenezer Isaac, Susan Elias, Srinivasan Rajagopalan, K. S. Easwarakumar
Date: 2017-05-15 00:00:00
Tasks: Gait Recognition

Template-based model-free approach provides by far the most successful
solution to the gait recognition problem in literature. Recent work discusses
how isolating the head and leg portion of the template increase the performance
of a gait recognition system making it robust against covariates like clothing
and carrying conditions. However, most involve a manual definition of the
boundaries. The method we propose, the genetic template segmentation (GTS),
employs the genetic algorithm to automate the boundary selection process. This
method was tested on the GEI, GEnI and AEI templates. GEI seems to exhibit the
best result when segmented with our approach. Experimental results depict that
our approach significantly outperforms the existing implementations of
view-invariant gait recognition.
Building Emotional Machines: Recognizing Image Emotions through Deep Neural Networks
Authors: Hye-Rin Kim, Yeong-Seok Kim, Seon Joo Kim, In-Kwon Lee
Date: 2017-05-22 00:00:00
Tasks: Object

An image is a very effective tool for conveying emotions. Many researchers
have investigated in computing the image emotions by using various features
extracted from images. In this paper, we focus on two high level features, the
object and the background, and assume that the semantic information of images
is a good cue for predicting emotion. An object is one of the most important
elements that define an image, and we find out through experiments that there
is a high correlation between the object and the emotion in images. Even with
the same object, there may be slight difference in emotion due to different
backgrounds, and we use the semantic information of the background to improve
the prediction performance. By combining the different levels of features, we
build an emotion based feed forward deep neural network which produces the
emotion values of a given image. The output emotion values in our framework are
continuous values in the 2-dimensional space (Valence and Arousal), which are
more effective than using a few number of emotion categories in describing
emotions. Experiments confirm the effectiveness of our network in predicting
the emotion of images.
Detection and Localization of Image Forgeries using Resampling Features and Deep Learning
Authors: Jason Bunk, Jawadul H. Bappy, Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Arjuna Flenner, B. S. Manjunath, Shivkumar Chandrasekaran, Amit K. Roy-Chowdhury, Lawrence Peterson
Date: 2017-07-03 00:00:00
Tasks: Deep Learning, General Classification

Resampling is an important signature of manipulated images. In this paper, we
propose two methods to detect and localize image manipulations based on a
combination of resampling features and deep learning. In the first method, the
Radon transform of resampling features are computed on overlapping image
patches. Deep learning classifiers and a Gaussian conditional random field
model are then used to create a heatmap. Tampered regions are located using a
Random Walker segmentation method. In the second method, resampling features
computed on overlapping image patches are passed through a Long short-term
memory (LSTM) based network for classification and localization. We compare the
performance of detection/localization of both these methods. Our experimental
results show that both techniques are effective in detecting and localizing
digital image forgeries.
Learning Deep Latent Spaces for Multi-Label Classification
Authors: Chih-Kuan Yeh, Wei-Chieh Wu, Wei-Jen Ko, Yu-Chiang Frank Wang
Date: 2017-07-03 00:00:00
Tasks: Classification, General Classification, Missing Labels, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Multi-label classification is a practical yet challenging task in machine
learning related fields, since it requires the prediction of more than one
label category for each input instance. We propose a novel deep neural networks
(DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this
task. Aiming at better relating feature and label domain data for improved
classification, we uniquely perform joint feature and label embedding by
deriving a deep latent space, followed by the introduction of label-correlation
sensitive loss function for recovering the predicted label outputs. Our C2AE is
achieved by integrating the DNN architectures of canonical correlation analysis
and autoencoder, which allows end-to-end learning and prediction with the
ability to exploit label dependency. Moreover, our C2AE can be easily extended
to address the learning problem with missing labels. Our experiments on
multiple datasets with different scales confirm the effectiveness and
robustness of our proposed method, which is shown to perform favorably against
state-of-the-art methods for multi-label classification.
Hierarchical Model for Long-term Video Prediction
Authors: Peter Wang, Zhongxia Yan, Jeff Zhang
Date: 2017-06-27 00:00:00
Tasks: model, Prediction, Video Prediction

Video prediction has been an active topic of research in the past few years.
Many algorithms focus on pixel-level predictions, which generates results that
blur and disintegrate within a few frames. In this project, we use a
hierarchical approach for long-term video prediction. We aim at estimating
high-level structure in the input frame first, then predict how that structure
grows in the future. Finally, we use an image analogy network to recover a
realistic image from the predicted structure. Our method is largely adopted
from the work by Villegas et al. The method is built with a combination of
LSTMs and analogy-based convolutional auto-encoder networks. Additionally, in
order to generate more realistic frame predictions, we also adopt adversarial
loss. We evaluate our method on the Penn Action dataset, and demonstrate good
results on high-level long-term structure prediction.
Dual Supervised Learning
Authors: Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu, Tie-Yan Liu
Date: 2017-07-03 00:00:00
Proceeding: ICML 2017 8
Tasks: General Classification, image-classification, Image Classification, Image Generation, Machine Translation, Sentiment Analysis, speech-recognition, Speech Recognition, text-to-speech, Text to Speech, Translation

Many supervised learning tasks are emerged in dual forms, e.g.,
English-to-French translation vs. French-to-English translation, speech
recognition vs. text to speech, and image classification vs. image generation.
Two dual tasks have intrinsic connections with each other due to the
probabilistic correlation between their models. This connection is, however,
not effectively utilized today, since people usually train the models of two
dual tasks separately and independently. In this work, we propose training the
models of two dual tasks simultaneously, and explicitly exploiting the
probabilistic correlation between them to regularize the training process. For
ease of reference, we call the proposed approach \emph{dual supervised
learning}. We demonstrate that dual supervised learning can improve the
practical performances of both tasks, for various applications including
machine translation, image processing, and sentiment analysis.
Pedestrian Alignment Network for Large-scale Person Re-identification
Authors: Zhedong Zheng, Liang Zheng, Yi Yang
Date: 2017-07-03 00:00:00
Tasks: Image Retrieval, Large-Scale Person Re-Identification, Person Re-Identification, Retrieval

Person re-identification (person re-ID) is mostly viewed as an image
retrieval problem. This task aims to search a query person in a large image
pool. In practice, person re-ID usually adopts automatic detectors to obtain
cropped pedestrian images. However, this process suffers from two types of
detector errors: excessive background and part missing. Both errors deteriorate
the quality of pedestrian alignment and may compromise pedestrian matching due
to the position and scale variances. To address the misalignment problem, we
propose that alignment can be learned from an identification procedure. We
introduce the pedestrian alignment network (PAN) which allows discriminative
embedding learning and pedestrian alignment without extra annotations. Our key
observation is that when the convolutional neural network (CNN) learns to
discriminate between different identities, the learned feature maps usually
exhibit strong activations on the human body rather than the background. The
proposed network thus takes advantage of this attention mechanism to adaptively
locate and align pedestrians within a bounding box. Visual examples show that
pedestrians are better aligned with PAN. Experiments on three large-scale re-ID
datasets confirm that PAN improves the discriminative ability of the feature
embeddings and yields competitive accuracy with the state-of-the-art methods.
Fair Pipelines
Authors: Amanda Bower, Sarah N. Kitchen, Laura Niss, Martin J. Strauss, Alexander Vargas, Suresh Venkatasubramanian
Date: 2017-07-03 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Fairness

This work facilitates ensuring fairness of machine learning in the real world
by decoupling fairness considerations in compound decisions. In particular,
this work studies how fairness propagates through a compound decision-making
processes, which we call a pipeline. Prior work in algorithmic fairness only
focuses on fairness with respect to one decision. However, many decision-making
processes require more than one decision. For instance, hiring is at least a
two stage model: deciding who to interview from the applicant pool and then
deciding who to hire from the interview pool. Perhaps surprisingly, we show
that the composition of fair components may not guarantee a fair pipeline under
a $(1+\varepsilon)$-equal opportunity definition of fair. However, we identify
circumstances that do provide that guarantee. We also propose numerous
directions for future work on more general compound machine learning decisions.
Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation
Authors: Hao Zhao, Ming Lu, Anbang Yao, Yiwen Guo, Yurong Chen, Li Zhang
Date: 2017-07-03 00:00:00
Proceeding: CVPR 2017 7
Tasks: Room Layout Estimation

In this paper, we propose an alternative method to estimate room layouts of
cluttered indoor scenes. This method enjoys the benefits of two novel
techniques. The first one is semantic transfer (ST), which is: (1) a
formulation to integrate the relationship between scene clutter and room layout
into convolutional neural networks; (2) an architecture that can be end-to-end
trained; (3) a practical strategy to initialize weights for very deep networks
under unbalanced training data distribution. ST allows us to extract highly
robust features under various circumstances, and in order to address the
computation redundance hidden in these features we develop a principled and
efficient inference scheme named physics inspired optimization (PIO). PIO's
basic idea is to formulate some phenomena observed in ST features into
mechanics concepts. Evaluations on public datasets LSUN and Hedau show that the
proposed method is more accurate than state-of-the-art methods.
Vectorial Dimension Reduction for Tensors Based on Bayesian Inference
Authors: Fujiao Ju, Yanfeng Sun, Junbin Gao, Yongli Hu, Bao-Cai Yin
Date: 2017-07-03 00:00:00
Tasks: Bayesian Inference, Clustering, Dimensionality Reduction

Dimensionality reduction for high-order tensors is a challenging problem. In
conventional approaches, higher order tensors are `vectorized` via Tucker
decomposition to obtain lower order tensors. This will destroy the inherent
high-order structures or resulting in undesired tensors, respectively. This
paper introduces a probabilistic vectorial dimensionality reduction model for
tensorial data. The model represents a tensor by employing a linear combination
of same order basis tensors, thus it offers a mechanism to directly reduce a
tensor to a vector. Under this expression, the projection base of the model is
based on the tensor CandeComp/PARAFAC (CP) decomposition and the number of free
parameters in the model only grows linearly with the number of modes rather
than exponentially. A Bayesian inference has been established via the
variational EM approach. A criterion to set the parameters (factor number of CP
decomposition and the number of extracted features) is empirically given. The
model outperforms several existing PCA-based methods and CP decomposition on
several publicly available databases in terms of classification and clustering
accuracy.
Active Self-Paced Learning for Cost-Effective and Progressive Face Identification
Authors: Liang Lin, Keze Wang, Deyu Meng, WangMeng Zuo, Lei Zhang
Date: 2017-01-13 00:00:00
Tasks: Active Learning, Face Identification

This paper aims to develop a novel cost-effective framework for face
identification, which progressively maintains a batch of classifiers with the
increasing face images of different individuals. By naturally combining two
recently rising techniques: active learning (AL) and self-paced learning (SPL),
our framework is capable of automatically annotating new instances and
incorporating them into training under weak expert re-certification. We first
initialize the classifier using a few annotated samples for each individual,
and extract image features using the convolutional neural nets. Then, a number
of candidates are selected from the unannotated samples for classifier
updating, in which we apply the current classifiers ranking the samples by the
prediction confidence. In particular, our approach utilizes the high-confidence
and low-confidence samples in the self-paced and the active user-query way,
respectively. The neural nets are later fine-tuned based on the updated
classifiers. Such heuristic implementation is formulated as solving a concise
active SPL optimization problem, which also advances the SPL development by
supplementing a rational dynamic curriculum constraint. The new model finely
accords with the "instructor-student-collaborative" learning mode in human
education. The advantages of this proposed framework are two-folds: i) The
required number of annotated samples is significantly decreased while the
comparable performance is guaranteed. A dramatic reduction of user effort is
also achieved over other state-of-the-art active learning techniques. ii) The
mixture of SPL and AL effectively improves not only the classifier accuracy
compared to existing AL/SPL methods but also the robustness against noisy data.
We evaluate our framework on two challenging datasets, and demonstrate very
promising results. (http://hcp.sysu.edu.cn/projects/aspl/)