pwc-archive/papers-with-abstracts huggingface dataset:
Weighted Nonlocal Total Variation in Image Processing
Authors: Haohan Li, Zuoqiang Shi, Xiao-Ping Wang
Date: 2018-01-31 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Colorization, Image Colorization, Image Inpainting

In this paper, a novel weighted nonlocal total variation (WNTV) method is
proposed. Compared to the classical nonlocal total variation methods, our
method modifies the energy functional to introduce a weight to balance between
the labeled sets and unlabeled sets. With extensive numerical examples in
semi-supervised clustering, image inpainting and image colorization, we
demonstrate that WNTV provides an effective and efficient method in many image
processing and machine learning problems.
Deep Learning Works in Practice. But Does it Work in Theory?
Authors: Lê Nguyên Hoang, Rachid Guerraoui
Date: 2018-01-31 00:00:00
Tasks: Deep Learning, speech-recognition, Speech Recognition

Deep learning relies on a very specific kind of neural networks: those
superposing several neural layers. In the last few years, deep learning
achieved major breakthroughs in many tasks such as image analysis, speech
recognition, natural language processing, and so on. Yet, there is no
theoretical explanation of this success. In particular, it is not clear why the
deeper the network, the better it actually performs.
  We argue that the explanation is intimately connected to a key feature of the
data collected from our surrounding universe to feed the machine learning
algorithms: large non-parallelizable logical depth. Roughly speaking, we
conjecture that the shortest computational descriptions of the universe are
algorithms with inherently large computation times, even when a large number of
computers are available for parallelization. Interestingly, this conjecture,
combined with the folklore conjecture in theoretical computer science that $ P
\neq NC$, explains the success of deep learning.
Robust 3D Human Motion Reconstruction Via Dynamic Template Construction
Authors: Zhong Li, Yu Ji, Wei Yang, Jinwei Ye, Jingyi Yu
Date: 2018-01-31 00:00:00
Tasks: 3D geometry

In multi-view human body capture systems, the recovered 3D geometry or even
the acquired imagery data can be heavily corrupted due to occlusions, noise,
limited field of- view, etc. Direct estimation of 3D pose, body shape or motion
on these low-quality data has been traditionally challenging.In this paper, we
present a graph-based non-rigid shape registration framework that can
simultaneously recover 3D human body geometry and estimate pose/motion at high
fidelity.Our approach first generates a global full-body template by
registering all poses in the acquired motion sequence.We then construct a
deformable graph by utilizing the rigid components in the global template. We
directly warp the global template graph back to each motion frame in order to
fill in missing geometry. Specifically, we combine local rigidity and temporal
coherence constraints to maintain geometry and motion consistencies.
Comprehensive experiments on various scenes show that our method is accurate
and robust even in the presence of drastic motions.
Deep Multi-view Learning to Rank
Authors: Guanqun Cao, Alexandros Iosifidis, Moncef Gabbouj, Vijay Raghavan, Raju Gottumukkala
Date: 2018-01-31 00:00:00
Tasks: Learning-To-Rank, MULTI-VIEW LEARNING

We study the problem of learning to rank from multiple information sources. Though multi-view learning and learning to rank have been studied extensively leading to a wide range of applications, multi-view learning to rank as a synergy of both topics has received little attention. The aim of the paper is to propose a composite ranking method while keeping a close correlation with the individual rankings simultaneously. We present a generic framework for multi-view subspace learning to rank (MvSL2R), and two novel solutions are introduced under the framework. The first solution captures information of feature mappings from within each view as well as across views using autoencoder-like networks. Novel feature embedding methods are formulated in the optimization of multi-view unsupervised and discriminant autoencoders. Moreover, we introduce an end-to-end solution to learning towards both the joint ranking objective and the individual rankings. The proposed solution enhances the joint ranking with minimum view-specific ranking loss, so that it can achieve the maximum global view agreements in a single optimization process. The proposed method is evaluated on three different ranking problems, i.e. university ranking, multi-view lingual text ranking and image data ranking, providing superior results compared to related methods.
AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
Authors: Xuan Zhang, Hao Luo, Xing Fan, Weilai Xiang, Yixiao Sun, Qiqi Xiao, Wei Jiang, Chi Zhang, Jian Sun
Date: 2017-11-22 00:00:00
Tasks: Person Re-Identification

In this paper, we propose a novel method called AlignedReID that extracts a
global feature which is jointly learned with local features. Global feature
learning benefits greatly from local feature learning, which performs an
alignment/matching by calculating the shortest path between two sets of local
features, without requiring extra supervision. After the joint learning, we
only keep the global feature to compute the similarities between images. Our
method achieves rank-1 accuracy of 94.4% on Market1501 and 97.8% on CUHK03,
outperforming state-of-the-art methods by a large margin. We also evaluate
human-level performance and demonstrate that our method is the first to surpass
human-level performance on Market1501 and CUHK03, two widely used Person ReID
datasets.
One-class Collective Anomaly Detection based on Long Short-Term Memory Recurrent Neural Networks
Authors: Nga Nguyen Thi, Van Loi Cao, Nhien-An Le-Khac
Date: 2018-01-31 00:00:00
Tasks: Anomaly Detection, Intrusion Detection, Time Series, Time Series Analysis

Intrusion detection for computer network systems has been becoming one of the
most critical tasks for network administrators today. It has an important role
for organizations, governments and our society due to the valuable resources
hosted on computer networks. Traditional misuse detection strategies are unable
to detect new and unknown intrusion types. In contrast, anomaly detection in
network security aims to distinguish between illegal or malicious events and
normal behavior of network systems. Anomaly detection can be considered as a
classification problem where it builds models of normal network behavior, of
which it uses to detect new patterns that significantly deviate from the model.
Most of the current approaches on anomaly detection is based on the learning of
normal behavior and anomalous actions. They do not include memory that is they
do not take into account previous events classify new ones. In this paper, we
propose a one class collective anomaly detection model based on neural network
learning. Normally a Long Short Term Memory Recurrent Neural Network (LSTM RNN)
is trained only on normal data, and it is capable of predicting several time
steps ahead of an input. In our approach, a LSTM RNN is trained on normal time
series data before performing a prediction for each time step. Instead of
considering each time-step separately, the observation of prediction errors
from a certain number of time-steps is now proposed as a new idea for detecting
collective anomalies. The prediction errors of a certain number of the latest
time-steps above a threshold will indicate a collective anomaly. The model is
evaluated on a time series version of the KDD 1999 dataset. The experiments
demonstrate that the proposed model is capable to detect collective anomaly
efficiently
Parameter Space Noise for Exploration
Authors: Matthias Plappert, Rein Houthooft, Prafulla Dhariwal, Szymon Sidor, Richard Y. Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, Marcin Andrychowicz
Date: 2017-06-06 00:00:00
Proceeding: ICLR 2018 1
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning (RL) methods generally engage in exploratory
behavior through noise injection in the action space. An alternative is to add
noise directly to the agent's parameters, which can lead to more consistent
exploration and a richer set of behaviors. Methods such as evolutionary
strategies use parameter perturbations, but discard all temporal structure in
the process and require significantly more samples. Combining parameter noise
with traditional RL methods allows to combine the best of both worlds. We
demonstrate that both off- and on-policy methods benefit from this approach
through experimental comparison of DQN, DDPG, and TRPO on high-dimensional
discrete action environments as well as continuous control tasks. Our results
show that RL with parameter noise learns more efficiently than traditional RL
with action space noise and evolutionary strategies individually.
A CNN-based Spatial Feature Fusion Algorithm for Hyperspectral Imagery Classification
Authors: Alan J. X. Guo, Fei Zhu
Date: 2018-01-31 00:00:00
Tasks: General Classification

The shortage of training samples remains one of the main obstacles in
applying the artificial neural networks (ANN) to the hyperspectral images
classification. To fuse the spatial and spectral information, pixel patches are
often utilized to train a model, which may further aggregate this problem. In
the existing works, an ANN model supervised by center-loss (ANNC) was
introduced. Training merely with spectral information, the ANNC yields
discriminative spectral features suitable for the subsequent classification
tasks. In this paper, a CNN-based spatial feature fusion (CSFF) algorithm is
proposed, which allows a smart fusion of the spatial information to the
spectral features extracted by ANNC. As a critical part of CSFF, a CNN-based
discriminant model is introduced to estimate whether two paring pixels belong
to the same class. At the testing stage, by applying the discriminant model to
the pixel-pairs generated by the test pixel and its neighbors, the local
structure is estimated and represented as a customized convolutional kernel.
The spectral-spatial feature is obtained by a convolutional operation between
the estimated kernel and the corresponding spectral features within a
neighborhood. At last, the label of the test pixel is predicted by classifying
the resulting spectral-spatial feature. Without increasing the number of
training samples or involving pixel patches at the training stage, the CSFF
framework achieves the state-of-the-art by declining $20\%-50\%$ classification
failures in experiments on three well-known hyperspectral images.
Performance Evaluation of Channel Decoding With Deep Neural Networks
Authors: Wei Lyu, Zhaoyang Zhang, Chunxu Jiao, Kangjian Qin, Huazi Zhang
Date: 2017-11-01 00:00:00
Tasks: Decoder

With the demand of high data rate and low latency in fifth generation (5G),
deep neural network decoder (NND) has become a promising candidate due to its
capability of one-shot decoding and parallel computing. In this paper, three
types of NND, i.e., multi-layer perceptron (MLP), convolution neural network
(CNN) and recurrent neural network (RNN), are proposed with the same parameter
magnitude. The performance of these deep neural networks are evaluated through
extensive simulation. Numerical results show that RNN has the best decoding
performance, yet at the price of the highest computational overhead. Moreover,
we find there exists a saturation length for each type of neural network, which
is caused by their restricted learning abilities.
Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling
Authors: Christophe Dupuy, Francis Bach
Date: 2016-03-08 00:00:00
Tasks: Bayesian Inference, Variational Inference

We study parameter inference in large-scale latent variable models. We first
propose an unified treatment of online inference for latent variable models
from a non-canonical exponential family, and draw explicit links between
several previously proposed frequentist or Bayesian methods. We then propose a
novel inference method for the frequentist estimation of parameters, that
adapts MCMC methods to online inference of latent variable models with the
proper use of local Gibbs sampling. Then, for latent Dirich-let allocation,we
provide an extensive set of experiments and comparisons with existing work,
where our new approach outperforms all previously proposed methods. In
particular, using Gibbs sampling for latent variable inference is superior to
variational inference in terms of test log-likelihoods. Moreover, Bayesian
inference through variational methods perform poorly, sometimes leading to
worse fits with latent variables of higher dimensionality.
Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations
Authors: Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf
Date: 2018-01-31 00:00:00
Tasks: Clustering, General Classification

We propose the Onto2Vec method, an approach to learn feature vectors for
biological entities based on their annotations to biomedical ontologies. Our
method can be applied to a wide range of bioinformatics research problems such
as similarity-based prediction of interactions between proteins, classification
of interaction types using supervised learning, or clustering.
ConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep Learning
Authors: Xiaotong Lu, Weisheng Dong, Peiyao Wang, Guangming Shi, Xuemei Xie
Date: 2018-01-31 00:00:00
Tasks: Blocking, Compressive Sensing, Deep Learning

Compressive sensing (CS), aiming to reconstruct an image/signal from a small
set of random measurements has attracted considerable attentions in recent
years. Due to the high dimensionality of images, previous CS methods mainly
work on image blocks to avoid the huge requirements of memory and computation,
i.e., image blocks are measured with Gaussian random matrices, and the whole
images are recovered from the reconstructed image blocks. Though efficient,
such methods suffer from serious blocking artifacts. In this paper, we propose
a convolutional CS framework that senses the whole image using a set of
convolutional filters. Instead of reconstructing individual blocks, the whole
image is reconstructed from the linear convolutional measurements.
Specifically, the convolutional CS is implemented based on a convolutional
neural network (CNN), which performs both the convolutional CS and nonlinear
reconstruction. Through end-to-end training, the sensing filters and the
reconstruction network can be jointly optimized. To facilitate the design of
the CS reconstruction network, a novel two-branch CNN inspired from a
sparsity-based CS reconstruction model is developed. Experimental results show
that the proposed method substantially outperforms previous state-of-the-art CS
methods in term of both PSNR and visual quality.
An Infinitesimal Probabilistic Model for Principal Component Analysis of Manifold Valued Data
Authors: Stefan Sommer
Date: 2018-01-31 00:00:00

We provide a probabilistic and infinitesimal view of how the principal
component analysis procedure (PCA) can be generalized to analysis of nonlinear
manifold valued data. Starting with the probabilistic PCA interpretation of the
Euclidean PCA procedure, we show how PCA can be generalized to manifolds in an
intrinsic way that does not resort to linearization of the data space. The
underlying probability model is constructed by mapping a Euclidean stochastic
process to the manifold using stochastic development of Euclidean
semimartingales. The construction uses a connection and bundles of covariant
tensors to allow global transport of principal eigenvectors, and the model is
thereby an example of how principal fiber bundles can be used to handle the
lack of global coordinate system and orientations that characterizes manifold
valued statistics. We show how curvature implies non-integrability of the
equivalent of Euclidean principal subspaces, and how the stochastic flows
provide an alternative to explicit construction of such subspaces. We describe
estimation procedures for inference of parameters and prediction of principal
components, and we give examples of properties of the model on embedded
surfaces.
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
Authors: Li Liu, Jie Chen, Paul Fieguth, Guoying Zhao, Rama Chellappa, Matti Pietikainen
Date: 2018-01-31 00:00:00
Tasks: Attribute, General Classification, Survey, Texture Classification

Texture is a fundamental characteristic of many types of images, and texture
representation is one of the essential and challenging problems in computer
vision and pattern recognition which has attracted extensive research
attention. Since 2000, texture representations based on Bag of Words (BoW) and
on Convolutional Neural Networks (CNNs) have been extensively studied with
impressive performance. Given this period of remarkable evolution, this paper
aims to present a comprehensive survey of advances in texture representation
over the last two decades. More than 200 major publications are cited in this
survey covering different aspects of the research, which includes (i) problem
description; (ii) recent advances in the broad categories of BoW-based,
CNN-based and attribute-based methods; and (iii) evaluation issues,
specifically benchmark datasets and state of the art results. In retrospect of
what has been achieved so far, the survey discusses open challenges and
directions for future research.
SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks
Authors: Xi Cheng, Xiang Li, Ying Tai, Jian Yang
Date: 2018-01-31 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Single image super resolution is a very important computer vision task, with
a wide range of applications. In recent years, the depth of the
super-resolution model has been constantly increasing, but with a small
increase in performance, it has brought a huge amount of computation and memory
consumption. In this work, in order to make the super resolution models more
effective, we proposed a novel single image super resolution method via
recursive squeeze and excitation networks (SESR). By introducing the squeeze
and excitation module, our SESR can model the interdependencies and
relationships between channels and that makes our model more efficiency. In
addition, the recursive structure and progressive reconstruction method in our
model minimized the layers and parameters and enabled SESR to simultaneously
train multi-scale super resolution in a single model. After evaluating on four
benchmark test sets, our model is proved to be above the state-of-the-art
methods in terms of speed and accuracy.
Towards Building Large Scale Multimodal Domain-Aware Conversation Systems
Authors: Amrita Saha, Mitesh Khapra, Karthik Sankaranarayanan
Date: 2017-04-01 00:00:00
Tasks: Response Generation

While multimodal conversation agents are gaining importance in several
domains such as retail, travel etc., deep learning research in this area has
been limited primarily due to the lack of availability of large-scale, open
chatlogs. To overcome this bottleneck, in this paper we introduce the task of
multimodal, domain-aware conversations, and propose the MMD benchmark dataset.
This dataset was gathered by working in close coordination with large number of
domain experts in the retail domain. These experts suggested various
conversations flows and dialog states which are typically seen in multimodal
conversations in the fashion domain. Keeping these flows and states in mind, we
created a dataset consisting of over 150K conversation sessions between
shoppers and sales agents, with the help of in-house annotators using a
semi-automated manually intense iterative process. With this dataset, we
propose 5 new sub-tasks for multimodal conversations along with their
evaluation methodology. We also propose two multimodal neural models in the
encode-attend-decode paradigm and demonstrate their performance on two of the
sub-tasks, namely text response generation and best image response selection.
These experiments serve to establish baseline performance and open new research
directions for each of these sub-tasks. Further, for each of the sub-tasks, we
present a `per-state evaluation' of 9 most significant dialog states, which
would enable more focused research into understanding the challenges and
complexities involved in each of these states.
Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph
Authors: Amrita Saha, Vardaan Pahuja, Mitesh M. Khapra, Karthik Sankaranarayanan, Sarath Chandar
Date: 2018-01-31 00:00:00
Tasks: Knowledge Graphs, Question Answering

While conversing with chatbots, humans typically tend to ask many questions,
a significant portion of which can be answered by referring to large-scale
knowledge graphs (KG). While Question Answering (QA) and dialog systems have
been studied independently, there is a need to study them closely to evaluate
such real-world scenarios faced by bots involving both these tasks. Towards
this end, we introduce the task of Complex Sequential QA which combines the two
tasks of (i) answering factual questions through complex inferencing over a
realistic-sized KG of millions of entities, and (ii) learning to converse
through a series of coherently linked QA pairs. Through a labor intensive
semi-automatic process, involving in-house and crowdsourced workers, we created
a dataset containing around 200K dialogs with a total of 1.6M turns. Further,
unlike existing large scale QA datasets which contain simple questions that can
be answered from a single tuple, the questions in our dialogs require a larger
subgraph of the KG. Specifically, our dataset has questions which require
logical, quantitative, and comparative reasoning as well as their combinations.
This calls for models which can: (i) parse complex natural language questions,
(ii) use conversation context to resolve coreferences and ellipsis in
utterances, (iii) ask for clarifications for ambiguous queries, and finally
(iv) retrieve relevant subgraphs of the KG to answer such questions. However,
our experiments with a combination of state of the art dialog and QA models
show that they clearly do not achieve the above objectives and are inadequate
for dealing with such complex real world settings. We believe that this new
dataset coupled with the limitations of existing models as reported in this
paper should encourage further research in Complex Sequential QA.
A Deep Ranking Model for Spatio-Temporal Highlight Detection from a 360 Video
Authors: Youngjae Yu, Sang-ho Lee, Joonil Na, Jaeyun Kang, Gunhee Kim
Date: 2018-01-31 00:00:00
Tasks: Highlight Detection

We address the problem of highlight detection from a 360 degree video by
summarizing it both spatially and temporally. Given a long 360 degree video, we
spatially select pleasantly-looking normal field-of-view (NFOV) segments from
unlimited field of views (FOV) of the 360 degree video, and temporally
summarize it into a concise and informative highlight as a selected subset of
subshots. We propose a novel deep ranking model named as Composition View Score
(CVS) model, which produces a spherical score map of composition per video
segment, and determines which view is suitable for highlight via a sliding
window kernel at inference. To evaluate the proposed framework, we perform
experiments on the Pano2Vid benchmark dataset and our newly collected 360
degree video highlight dataset from YouTube and Vimeo. Through evaluation using
both quantitative summarization metrics and user studies via Amazon Mechanical
Turk, we demonstrate that our approach outperforms several state-of-the-art
highlight detection methods. We also show that our model is 16 times faster at
inference than AutoCam, which is one of the first summarization algorithms of
360 degree videos