pwc-archive/papers-with-abstracts huggingface dataset:
Pointed subspace approach to incomplete data
Authors: Łukasz Struski, Marek Śmieja, Jacek Tabor
Date: 2017-05-02 00:00:00
Tasks: Dimensionality Reduction, General Classification

Incomplete data are often represented as vectors with filled missing
attributes joined with flag vectors indicating missing components. In this
paper we generalize this approach and represent incomplete data as pointed
affine subspaces. This allows to perform various affine transformations of
data, as whitening or dimensionality reduction. We embed such generalized
missing data into a vector space by mapping pointed affine subspace
(generalized missing data point) to a vector containing imputed values joined
with a corresponding projection matrix. Such an operation preserves the scalar
product of the embedding defined for flag vectors and allows to input
transformed incomplete data to typical classification methods.
Generalized RBF kernel for incomplete data
Authors: Łukasz Struski, Marek Śmieja, Jacek Tabor
Date: 2016-12-05 00:00:00

We construct $\bf genRBF$ kernel, which generalizes the classical Gaussian
RBF kernel to the case of incomplete data. We model the uncertainty contained
in missing attributes making use of data distribution and associate every point
with a conditional probability density function. This allows to embed
incomplete data into the function space and to define a kernel between two
missing data points based on scalar product in $L_2$. Experiments show that
introduced kernel applied to SVM classifier gives better results than other
state-of-the-art methods, especially in the case when large number of features
is missing. Moreover, it is easy to implement and can be used together with any
kernel approaches with no additional modifications.
Investigation of Different Skeleton Features for CNN-based 3D Action Recognition
Authors: Zewei Ding, Pichao Wang, Philip O. Ogunbona, Wanqing Li
Date: 2017-05-02 00:00:00
Tasks: 3D Action Recognition, Action Analysis, Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Deep learning techniques are being used in skeleton based action recognition
tasks and outstanding performance has been reported. Compared with RNN based
methods which tend to overemphasize temporal information, CNN-based approaches
can jointly capture spatio-temporal information from texture color images
encoded from skeleton sequences. There are several skeleton-based features that
have proven effective in RNN-based and handcrafted-feature-based methods.
However, it remains unknown whether they are suitable for CNN-based approaches.
This paper proposes to encode five spatial skeleton features into images with
different encoding methods. In addition, the performance implication of
different joints used for feature extraction is studied. The proposed method
achieved state-of-the-art performance on NTU RGB+D dataset for 3D human action
analysis. An accuracy of 75.32\% was achieved in Large Scale 3D Human Activity
Analysis Challenge in Depth Videos.
Multi-view Unsupervised Feature Selection by Cross-diffused Matrix Alignment
Authors: Xiaokai Wei, Bokai Cao, Philip S. Yu
Date: 2017-05-02 00:00:00
Tasks: feature selection, MULTI-VIEW LEARNING

Multi-view high-dimensional data become increasingly popular in the big data
era. Feature selection is a useful technique for alleviating the curse of
dimensionality in multi-view learning. In this paper, we study unsupervised
feature selection for multi-view data, as class labels are usually expensive to
obtain. Traditional feature selection methods are mostly designed for
single-view data and cannot fully exploit the rich information from multi-view
data. Existing multi-view feature selection methods are usually based on noisy
cluster labels which might not preserve sufficient information from multi-view
data. To better utilize multi-view information, we propose a method, CDMA-FS,
to select features for each view by performing alignment on a cross diffused
matrix. We formulate it as a constrained optimization problem and solve it
using Quasi-Newton based method. Experiments results on four real-world
datasets show that the proposed method is more effective than the
state-of-the-art methods in multi-view setting.
STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset
Authors: Yuya Yoshikawa, Yutaro Shigeto, Akikazu Takeuchi
Date: 2017-05-02 00:00:00
Proceeding: ACL 2017 7
Tasks: Image Captioning, Machine Translation, Translation

In recent years, automatic generation of image descriptions (captions), that
is, image captioning, has attracted a great deal of attention. In this paper,
we particularly consider generating Japanese captions for images. Since most
available caption datasets have been constructed for English language, there
are few datasets for Japanese. To tackle this problem, we construct a
large-scale Japanese image caption dataset based on images from MS-COCO, which
is called STAIR Captions. STAIR Captions consists of 820,310 Japanese captions
for 164,062 images. In the experiment, we show that a neural network trained
using STAIR Captions can generate more natural and better Japanese captions,
compared to those generated using English-Japanese machine translation after
generating English captions.
Topically Driven Neural Language Model
Authors: Jey Han Lau, Timothy Baldwin, Trevor Cohn
Date: 2017-04-26 00:00:00
Proceeding: ACL 2017 7
Tasks: Language Modeling, Language Modelling, model, Sentence

Language models are typically applied at the sentence level, without access
to the broader document context. We present a neural language model that
incorporates document context in the form of a topic model-like architecture,
thus providing a succinct representation of the broader document context
outside of the current sentence. Experiments over a range of datasets
demonstrate that our model outperforms a pure sentence-based model in terms of
language model perplexity, and leads to topics that are potentially more
coherent than those produced by a standard LDA topic model. Our model also has
the ability to generate related sentences for a topic, providing another way to
interpret topics.
Statistical learning of rational wavelet transform for natural images
Authors: Naushad Ansari, Anubha Gupta
Date: 2017-05-02 00:00:00
Tasks: compressed sensing

Motivated with the concept of transform learning and the utility of rational
wavelet transform in audio and speech processing, this paper proposes Rational
Wavelet Transform Learning in Statistical sense (RWLS) for natural images. The
proposed RWLS design is carried out via lifting framework and is shown to have
a closed form solution. The efficacy of the learned transform is demonstrated
in the application of compressed sensing (CS) based reconstruction. The learned
RWLS is observed to perform better than the existing standard dyadic wavelet
transforms.
Transforming Bell's Inequalities into State Classifiers with Machine Learning
Authors: Yue-Chi Ma, Man-Hong Yung
Date: 2017-05-02 00:00:00
Tasks: BIG-bench Machine Learning, Quantum State Tomography

Quantum information science has profoundly changed the ways we understand,
store, and process information. A major challenge in this field is to look for
an efficient means for classifying quantum state. For instance, one may want to
determine if a given quantum state is entangled or not. However, the process of
a complete characterization of quantum states, known as quantum state
tomography, is a resource-consuming operation in general. An attractive
proposal would be the use of Bell's inequalities as an entanglement witness,
where only partial information of the quantum state is needed. The problem is
that entanglement is necessary but not sufficient for violating Bell's
inequalities, making it an unreliable state classifier. Here we aim at solving
this problem by the methods of machine learning. More precisely, given a family
of quantum states, we randomly picked a subset of it to construct a
quantum-state classifier, accepting only partial information of each quantum
state. Our results indicated that these transformed Bell-type inequalities can
perform significantly better than the original Bell's inequalities in
classifying entangled states. We further extended our analysis to three-qubit
and four-qubit systems, performing classification of quantum states into
multiple species. These results demonstrate how the tools in machine learning
can be applied to solving problems in quantum information science.
Katyusha: The First Direct Acceleration of Stochastic Gradient Methods
Authors: Zeyuan Allen-Zhu
Date: 2016-03-18 00:00:00
Tasks: Stochastic Optimization

Nesterov's momentum trick is famously known for accelerating gradient
descent, and has been proven useful in building fast iterative algorithms.
However, in the stochastic setting, counterexamples exist and prevent
Nesterov's momentum from providing similar acceleration, even if the underlying
problem is convex and finite-sum.
  We introduce $\mathtt{Katyusha}$, a direct, primal-only stochastic gradient
method to fix this issue. In convex finite-sum stochastic optimization,
$\mathtt{Katyusha}$ has an optimal accelerated convergence rate, and enjoys an
optimal parallel linear speedup in the mini-batch setting.
  The main ingredient is $\textit{Katyusha momentum}$, a novel "negative
momentum" on top of Nesterov's momentum. It can be incorporated into a
variance-reduction based algorithm and speed it up, both in terms of
$\textit{sequential and parallel}$ performance. Since variance reduction has
been successfully applied to a growing list of practical problems, our paper
suggests that in each of such cases, one could potentially try to give Katyusha
a hug.
One-Class Semi-Supervised Learning: Detecting Linearly Separable Class by its Mean
Authors: Evgeny Bauman, Konstantin Bauman
Date: 2017-05-02 00:00:00
Tasks: General Classification, One-Class Classification

In this paper, we presented a novel semi-supervised one-class classification
algorithm which assumes that class is linearly separable from other elements.
We proved theoretically that class is linearly separable if and only if it is
maximal by probability within the sets with the same mean. Furthermore, we
presented an algorithm for identifying such linearly separable class utilizing
linear programming. We described three application cases including an
assumption of linear separability, Gaussian distribution, and the case of
linear separability in transformed space of kernel functions. Finally, we
demonstrated the work of the proposed algorithm on the USPS dataset and
analyzed the relationship of the performance of the algorithm and the size of
the initially labeled sample.
Offline Handwritten Recognition of Malayalam District Name - A Holistic Approach
Authors: Jino P J, Kannan Balakrishnan
Date: 2017-05-02 00:00:00
Tasks: BIG-bench Machine Learning, Dimensionality Reduction

Various machine learning methods for writer independent recognition of
Malayalam handwritten district names are discussed in this paper. Data
collected from 56 different writers are used for the experiments. The proposed
work can be used for the recognition of district in the address written in
Malayalam. Different methods for Dimensionality reduction are discussed.
Features consider for the recognition are Histogram of Oriented Gradient
descriptor, Number of Black Pixels in the upper half and lower half, length of
image. Classifiers used in this work are Neural Network, SVM and RandomForest.
Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation
Authors: Yu Chen, Chunhua Shen, Xiu-Shen Wei, Lingqiao Liu, Jian Yang
Date: 2017-04-30 00:00:00
Proceeding: ICCV 2017 10
Tasks: Pose Estimation

For human pose estimation in monocular images, joint occlusions and
overlapping upon human bodies often result in deviated pose predictions. Under
these circumstances, biologically implausible pose predictions may be produced.
In contrast, human vision is able to predict poses by exploiting geometric
constraints of joint inter-connectivity. To address the problem by
incorporating priors about the structure of human bodies, we propose a novel
structure-aware convolutional network to implicitly take such priors into
account during training of the deep network. Explicit learning of such
constraints is typically challenging. Instead, we design discriminators to
distinguish the real poses from the fake ones (such as biologically implausible
ones). If the pose generator (G) generates results that the discriminator fails
to distinguish from real ones, the network successfully learns the priors.
Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks
Authors: Yehui Yang, Tao Li, Wensi Li, Haishan Wu, Wei Fan, Wensheng Zhang
Date: 2017-05-02 00:00:00
Tasks: Lesion Detection

We propose an automatic diabetic retinopathy (DR) analysis algorithm based on
two-stages deep convolutional neural networks (DCNN). Compared to existing
DCNN-based DR detection methods, the proposed algorithm have the following
advantages: (1) Our method can point out the location and type of lesions in
the fundus images, as well as giving the severity grades of DR. Moreover, since
retina lesions and DR severity appear with different scales in fundus images,
the integration of both local and global networks learn more complete and
specific features for DR analysis. (2) By introducing imbalanced weighting map,
more attentions will be given to lesion patches for DR grading, which
significantly improve the performance of the proposed algorithm. In this study,
we label 12,206 lesion patches and re-annotate the DR grades of 23,595 fundus
images from Kaggle competition dataset. Under the guidance of clinical
ophthalmologists, the experimental results show that our local lesion detection
net achieve comparable performance with trained human observers, and the
proposed imbalanced weighted scheme also be proved to significantly improve the
capability of our DCNN-based DR grading algorithm.
Dense-Captioning Events in Videos
Authors: Ranjay Krishna, Kenji Hata, Frederic Ren, Li Fei-Fei, Juan Carlos Niebles
Date: 2017-05-02 00:00:00
Proceeding: ICCV 2017 10
Tasks: Dense Captioning, Retrieval, Video Retrieval

Most natural videos contain numerous events. For example, in a video of a
"man playing a piano", the video might also contain "another man dancing" or "a
crowd clapping". We introduce the task of dense-captioning events, which
involves both detecting and describing events in a video. We propose a new
model that is able to identify all events in a single pass of the video while
simultaneously describing the detected events with natural language. Our model
introduces a variant of an existing proposal module that is designed to capture
both short as well as long events that span minutes. To capture the
dependencies between the events in a video, our model introduces a new
captioning module that uses contextual information from past and future events
to jointly describe all events. We also introduce ActivityNet Captions, a
large-scale benchmark for dense-captioning events. ActivityNet Captions
contains 20k videos amounting to 849 video hours with 100k total descriptions,
each with it's unique start and end time. Finally, we report performances of
our model for dense-captioning events, video retrieval and localization.
Kiwi - A Minimalist CP Solver
Authors: Renaud Hartert
Date: 2017-04-28 00:00:00

Kiwi is a minimalist and extendable Constraint Programming (CP) solver
specifically designed for education. The particularities of Kiwi stand in its
generic trailing state restoration mechanism and its modulable use of
variables. By developing Kiwi, the author does not aim to provide an
alternative to full featured constraint solvers but rather to provide readers
with a basic architecture that will (hopefully) help them to understand the
core mechanisms hidden under the hood of constraint solvers, to develop their
own extended constraint solver, or to test innovative ideas.
A Teacher-Student Framework for Zero-Resource Neural Machine Translation
Authors: Yun Chen, Yang Liu, Yong Cheng, Victor O. K. Li
Date: 2017-05-02 00:00:00
Proceeding: ACL 2017 7
Tasks: Machine Translation, NMT, Sentence, Translation

While end-to-end neural machine translation (NMT) has made remarkable
progress recently, it still suffers from the data scarcity problem for
low-resource language pairs and domains. In this paper, we propose a method for
zero-resource NMT by assuming that parallel sentences have close probabilities
of generating a sentence in a third language. Based on this assumption, our
method is able to train a source-to-target NMT model ("student") without
parallel corpora available, guided by an existing pivot-to-target NMT model
("teacher") on a source-pivot parallel corpus. Experimental results show that
the proposed method significantly improves over a baseline pivot-based model by
+3.0 BLEU points across various language pairs.
Parseval Networks: Improving Robustness to Adversarial Examples
Authors: Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, Nicolas Usunier
Date: 2017-04-28 00:00:00
Proceeding: ICML 2017 8

We introduce Parseval networks, a form of deep neural networks in which the
Lipschitz constant of linear, convolutional and aggregation layers is
constrained to be smaller than 1. Parseval networks are empirically and
theoretically motivated by an analysis of the robustness of the predictions
made by deep neural networks when their input is subject to an adversarial
perturbation. The most important feature of Parseval networks is to maintain
weight matrices of linear and convolutional layers to be (approximately)
Parseval tight frames, which are extensions of orthogonal matrices to
non-square matrices. We describe how these constraints can be maintained
efficiently during SGD. We show that Parseval networks match the
state-of-the-art in terms of accuracy on CIFAR-10/100 and Street View House
Numbers (SVHN) while being more robust than their vanilla counterpart against
adversarial examples. Incidentally, Parseval networks also tend to train faster
and make a better usage of the full capacity of the networks.
Chat Detection in an Intelligent Assistant: Combining Task-oriented and Non-task-oriented Spoken Dialogue Systems
Authors: Satoshi Akasaki, Nobuhiro Kaji
Date: 2017-05-02 00:00:00
Proceeding: ACL 2017 7
Tasks: Spoken Dialogue Systems

Recently emerged intelligent assistants on smartphones and home electronics
(e.g., Siri and Alexa) can be seen as novel hybrids of domain-specific
task-oriented spoken dialogue systems and open-domain non-task-oriented ones.
To realize such hybrid dialogue systems, this paper investigates determining
whether or not a user is going to have a chat with the system. To address the
lack of benchmark datasets for this task, we construct a new dataset consisting
of 15; 160 utterances collected from the real log data of a commercial
intelligent assistant (and will release the dataset to facilitate future
research activity). In addition, we investigate using tweets and Web search
queries for handling open-domain user utterances, which characterize the task
of chat detection. Experiments demonstrated that, while simple supervised
methods are effective, the use of the tweets and search queries further
improves the F1-score from 86.21 to 87.53.
Regularizing Model Complexity and Label Structure for Multi-Label Text Classification
Authors: Bingyu Wang, Cheng Li, Virgil Pavlu, Javed Aslam
Date: 2017-05-01 00:00:00
Tasks: General Classification, Multi Label Text Classification, Multi-Label Text Classification, Prediction, text-classification, Text Classification

Multi-label text classification is a popular machine learning task where each
document is assigned with multiple relevant labels. This task is challenging
due to high dimensional features and correlated labels. Multi-label text
classifiers need to be carefully regularized to prevent the severe over-fitting
in the high dimensional space, and also need to take into account label
dependencies in order to make accurate predictions under uncertainty. We
demonstrate significant and practical improvement by carefully regularizing the
model complexity during training phase, and also regularizing the label search
space during prediction phase. Specifically, we regularize the classifier
training using Elastic-net (L1+L2) penalty for reducing model complexity/size,
and employ early stopping to prevent overfitting. At prediction time, we apply
support inference to restrict the search space to label sets encountered in the
training set, and F-optimizer GFM to make optimal predictions for the F1
metric. We show that although support inference only provides density
estimations on existing label combinations, when combined with GFM predictor,
the algorithm can output unseen label combinations. Taken collectively, our
experiments show state of the art results on many benchmark datasets. Beyond
performance and practical contributions, we make some interesting observations.
Contrary to the prior belief, which deems support inference as purely an
approximate inference procedure, we show that support inference acts as a
strong regularizer on the label prediction structure. It allows the classifier
to take into account label dependencies during prediction even if the
classifiers had not modeled any label dependencies during training.
Argumentation-based Security for Social Good
Authors: Erisa Karafili, Antonis C. Kakas, Nikolaos I. Spanoudakis, Emil C. Lupu
Date: 2017-05-01 00:00:00
Tasks: Decision Making

The increase of connectivity and the impact it has in every day life is
raising new and existing security problems that are becoming important for
social good. We introduce two particular problems: cyber attack attribution and
regulatory data sharing. For both problems, decisions about which rules to
apply, should be taken under incomplete and context dependent information. The
solution we propose is based on argumentation reasoning, that is a well suited
technique for implementing decision making mechanisms under conflicting and
incomplete information. Our proposal permits us to identify the attacker of a
cyber attack and decide the regulation rule that should be used while using and
sharing data. We illustrate our solution through concrete examples.
Nonlinear Kalman Filtering with Divergence Minimization
Authors: San Gultekin, John Paisley
Date: 2017-05-01 00:00:00

We consider the nonlinear Kalman filtering problem using Kullback-Leibler
(KL) and $\alpha$-divergence measures as optimization criteria. Unlike linear
Kalman filters, nonlinear Kalman filters do not have closed form Gaussian
posteriors because of a lack of conjugacy due to the nonlinearity in the
likelihood. In this paper we propose novel algorithms to optimize the forward
and reverse forms of the KL divergence, as well as the alpha-divergence which
contains these two as limiting cases. Unlike previous approaches, our
algorithms do not make approximations to the divergences being optimized, but
use Monte Carlo integration techniques to derive unbiased algorithms for direct
optimization. We assess performance on radar and sensor tracking, and options
pricing problems, showing general improvement over the UKF and EKF, as well as
competitive performance with particle filtering.