pwc-archive/papers-with-abstracts huggingface dataset:
Fast online low-rank tensor subspace tracking by CP decomposition using recursive least squares from incomplete observations
Authors: Hiroyuki Kasai
Date: 2017-09-29 00:00:00

We consider the problem of online subspace tracking of a partially observed
high-dimensional data stream corrupted by noise, where we assume that the data
lie in a low-dimensional linear subspace. This problem is cast as an online
low-rank tensor completion problem. We propose a novel online tensor subspace
tracking algorithm based on the CANDECOMP/PARAFAC (CP) decomposition, dubbed
OnLine Low-rank Subspace tracking by TEnsor CP Decomposition (OLSTEC). The
proposed algorithm especially addresses the case in which the subspace of
interest is dynamically time-varying. To this end, we build up our proposed
algorithm exploiting the recursive least squares (RLS), which is the
second-order gradient algorithm. Numerical evaluations on synthetic datasets
and real-world datasets such as communication network traffic, environmental
data, and surveillance videos, show that the proposed OLSTEC algorithm
outperforms state-of-the-art online algorithms in terms of the convergence rate
per iteration.
Demystifying Relational Latent Representations
Authors: Sebastijan Dumančić, Hendrik Blockeel
Date: 2017-05-16 00:00:00
Tasks: Clustering, Relational Reasoning

Latent features learned by deep learning approaches have proven to be a
powerful tool for machine learning. They serve as a data abstraction that makes
learning easier by capturing regularities in data explicitly. Their benefits
motivated their adaptation to relational learning context. In our previous
work, we introduce an approach that learns relational latent features by means
of clustering instances and their relations. The major drawback of latent
representations is that they are often black-box and difficult to interpret.
This work addresses these issues and shows that (1) latent features created by
clustering are interpretable and capture interesting properties of data; (2)
they identify local regions of instances that match well with the label, which
partially explains their benefit; and (3) although the number of latent
features generated by this approach is large, often many of them are highly
redundant and can be removed without hurting performance much.
Exploring Automated Essay Scoring for Nonnative English Speakers
Authors: Amber Nigam
Date: 2017-06-11 00:00:00
Tasks: Automated Essay Scoring

Automated Essay Scoring (AES) has been quite popular and is being widely
used. However, lack of appropriate methodology for rating nonnative English
speakers' essays has meant a lopsided advancement in this field. In this paper,
we report initial results of our experiments with nonnative AES that learns
from manual evaluation of nonnative essays. For this purpose, we conducted an
exercise in which essays written by nonnative English speakers in test
environment were rated both manually and by the automated system designed for
the experiment. In the process, we experimented with a few features to learn
about nuances linked to nonnative evaluation. The proposed methodology of
automated essay evaluation has yielded a correlation coefficient of 0.750 with
the manual evaluation.
Explainable Planning
Authors: Maria Fox, Derek Long, Daniele Magazzeni
Date: 2017-09-29 00:00:00

As AI is increasingly being adopted into application solutions, the challenge
of supporting interaction with humans is becoming more apparent. Partly this is
to support integrated working styles, in which humans and intelligent systems
cooperate in problem-solving, but also it is a necessary step in the process of
building trust as humans migrate greater responsibility to such systems. The
challenge is to find effective ways to communicate the foundations of AI-driven
behaviour, when the algorithms that drive it are far from transparent to
humans. In this paper we consider the opportunities that arise in AI planning,
exploiting the model-based representations that form a familiar and common
basis for communication with users, while acknowledging the gap between
planning algorithms and human problem-solving.
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
Authors: Zhaoliang Lun, Matheus Gadelha, Evangelos Kalogerakis, Subhransu Maji, Rui Wang
Date: 2017-07-20 00:00:00
Tasks: 3D Reconstruction, 3D Shape Reconstruction, Decoder

We propose a method for reconstructing 3D shapes from 2D sketches in the form
of line drawings. Our method takes as input a single sketch, or multiple
sketches, and outputs a dense point cloud representing a 3D reconstruction of
the input sketch(es). The point cloud is then converted into a polygon mesh. At
the heart of our method lies a deep, encoder-decoder network. The encoder
converts the sketch into a compact representation encoding shape information.
The decoder converts this representation into depth and normal maps capturing
the underlying surface from several output viewpoints. The multi-view maps are
then consolidated into a 3D point cloud by solving an optimization problem that
fuses depth and normals across all viewpoints. Based on our experiments,
compared to other methods, such as volumetric networks, our architecture offers
several advantages, including more faithful reconstruction, higher output
surface resolution, better preservation of topology and shape structure.
Light Cascaded Convolutional Neural Networks for Accurate Player Detection
Authors: Keyu Lu, Jianhui Chen, James J. Little, Hangen He
Date: 2017-09-29 00:00:00
Tasks: General Classification

Vision based player detection is important in sports applications. Accuracy,
efficiency, and low memory consumption are desirable for real-time tasks such
as intelligent broadcasting and automatic event classification. In this paper,
we present a cascaded convolutional neural network (CNN) that satisfies all
three of these requirements. Our method first trains a binary
(player/non-player) classification network from labeled image patches. Then,
our method efficiently applies the network to a whole image in testing. We
conducted experiments on basketball and soccer games. Experimental results
demonstrate that our method can accurately detect players under challenging
conditions such as varying illumination, highly dynamic camera movements and
motion blur. Comparing with conventional CNNs, our approach achieves
state-of-the-art accuracy on both games with 1000x fewer parameters (i.e., it
is light}.
Comparison of PCA with ICA from data distribution perspective
Authors: Miron Ivanov
Date: 2017-09-29 00:00:00
Tasks: Time Series, Time Series Analysis

We performed an empirical comparison of ICA and PCA algorithms by applying
them on two simulated noisy time series with varying distribution parameters
and level of noise. In general, ICA shows better results than PCA because it
takes into account higher moments of data distribution. On the other hand, PCA
remains quite sensitive to the level of correlations among signals.
The First Evaluation of Chinese Human-Computer Dialogue Technology
Authors: Wei-Nan Zhang, Zhigang Chen, Wanxiang Che, Guoping Hu, Ting Liu
Date: 2017-09-29 00:00:00
Tasks: intent-classification, Intent Classification

In this paper, we introduce the first evaluation of Chinese human-computer dialogue technology. We detail the evaluation scheme, tasks, metrics and how to collect and annotate the data for training, developing and test. The evaluation includes two tasks, namely user intent classification and online testing of task-oriented dialogue. To consider the different sources of the data for training and developing, the first task can also be divided into two sub tasks. Both the two tasks are coming from the real problems when using the applications developed by industry. The evaluation data is provided by the iFLYTEK Corporation. Meanwhile, in this paper, we publish the evaluation results to present the current performance of the participants in the two tasks of Chinese human-computer dialogue technology. Moreover, we analyze the existing problems of human-computer dialogue as well as the evaluation scheme itself.
Sentiment Classification with Word Attention based on Weakly Supervised Learning with a Convolutional Neural Network
Authors: Gichang Lee, Jaeyun Jeong, Seungwan Seo, CzangYeob Kim, Pilsung Kang
Date: 2017-09-28 00:00:00
Tasks: Classification, General Classification, Sentence, Sentiment Analysis, Sentiment Classification, Weakly-supervised Learning

In order to maximize the applicability of sentiment analysis results, it is
necessary to not only classify the overall sentiment (positive/negative) of a
given document but also to identify the main words that contribute to the
classification. However, most datasets for sentiment analysis only have the
sentiment label for each document or sentence. In other words, there is no
information about which words play an important role in sentiment
classification. In this paper, we propose a method for identifying key words
discriminating positive and negative sentences by using a weakly supervised
learning method based on a convolutional neural network (CNN). In our model,
each word is represented as a continuous-valued vector and each sentence is
represented as a matrix whose rows correspond to the word vector used in the
sentence. Then, the CNN model is trained using these sentence matrices as
inputs and the sentiment labels as the output. Once the CNN model is trained,
we implement the word attention mechanism that identifies high-contributing
words to classification results with a class activation map, using the weights
from the fully connected layer at the end of the learned CNN model. In order to
verify the proposed methodology, we evaluated the classification accuracy and
inclusion rate of polarity words using two movie review datasets. Experimental
result show that the proposed model can not only correctly classify the
sentence polarity but also successfully identify the corresponding words with
high polarity scores.
Reservoir Computing using Stochastic p-Bits
Authors: Samiran Ganguly, Kerem Y. Camsari, Avik W. Ghosh
Date: 2017-09-29 00:00:00

We present a general hardware framework for building networks that directly
implement Reservoir Computing, a popular software method for implementing and
training Recurrent Neural Networks and are particularly suited for temporal
inferencing and pattern recognition. We provide a specific example of a
candidate hardware unit based on a combination of soft-magnets, spin-orbit
materials and CMOS transistors that can implement these networks. Efficient non
von-Neumann hardware implementation of reservoir computers can open up a
pathway for integration of temporal Neural Networks in a wide variety of
emerging systems such as Internet of Things (IoTs), industrial controls, bio-
and photo-sensors, and self-driving automotives.
Fast Barcode Retrieval for Consensus Contouring
Authors: H. R. Tizhoosh, G. J. Czarnota
Date: 2017-09-28 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Retrieval, Semantic Segmentation

Marking tumors and organs is a challenging task suffering from both inter-
and intra-observer variability. The literature quantifies observer variability
by generating consensus among multiple experts when they mark the same image.
Automatically building consensus contours to establish quality assurance for
image segmentation is presently absent in the clinical practice. As the
\emph{big data} becomes more and more available, techniques to access a large
number of existing segments of multiple experts becomes possible. Fast
algorithms are, hence, required to facilitate the search for similar cases. The
present work puts forward a potential framework that tested with small datasets
(both synthetic and real images) displays the reliability of finding similar
images. In this paper, the idea of content-based barcodes is used to retrieve
similar cases in order to build consensus contours in medical image
segmentation. This approach may be regarded as an extension of the conventional
atlas-based segmentation that generally works with rather small atlases due to
required computational expenses. The fast segment-retrieval process via
barcodes makes it possible to create and use large atlases, something that
directly contributes to the quality of the consensus building. Because the
accuracy of experts' contours must be measured, we first used 500 synthetic
prostate images with their gold markers and delineations by 20 simulated users.
The fast barcode-guided computed consensus delivered an average error of
$8\%\!\pm\!5\%$ compared against the gold standard segments. Furthermore, we
used magnetic resonance images of prostates from 15 patients delineated by 5
oncologists and selected the best delineations to serve as the gold-standard
segments. The proposed barcode atlas achieved a Jaccard overlap of
$87\%\!\pm\!9\%$ with the contours of the gold-standard segments.
Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks
Authors: Mingbo Ma, Kai Zhao, Liang Huang, Bing Xiang, Bo-Wen Zhou
Date: 2017-09-28 00:00:00
Tasks: Classification, General Classification, intent-classification, Intent Classification, Intent Classification and Slot Filling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Sentence, slot-filling, Slot Filling, Topic Classification

Sentence-level classification and sequential labeling are two fundamental
tasks in language understanding. While these two tasks are usually modeled
separately, in reality, they are often correlated, for example in intent
classification and slot filling, or in topic classification and named-entity
recognition. In order to utilize the potential benefits from their
correlations, we propose a jointly trained model for learning the two tasks
simultaneously via Long Short-Term Memory (LSTM) networks. This model predicts
the sentence-level category and the word-level label sequence from the stepwise
output hidden representations of LSTM. We also introduce a novel mechanism of
"sparse attention" to weigh words differently based on their semantic relevance
to sentence-level classification. The proposed method outperforms baseline
models on ATIS and TREC datasets.
Unified Deep Supervised Domain Adaptation and Generalization
Authors: Saeid Motiian, Marco Piccirilli, Donald A. Adjeroh, Gianfranco Doretto
Date: 2017-09-28 00:00:00
Proceeding: ICCV 2017 10
Tasks: Domain Adaptation, Domain Generalization

This work provides a unified framework for addressing the problem of visual
supervised domain adaptation and generalization with deep models. The main idea
is to exploit the Siamese architecture to learn an embedding subspace that is
discriminative, and where mapped visual domains are semantically aligned and
yet maximally separated. The supervised setting becomes attractive especially
when only few target data samples need to be labeled. In this scenario,
alignment and separation of semantic probability distributions is difficult
because of the lack of data. We found that by reverting to point-wise
surrogates of distribution distances and similarities provides an effective
solution. In addition, the approach has a high speed of adaptation, which
requires an extremely low number of labeled target training samples, even one
per category can be effective. The approach is extended to domain
generalization. For both applications the experiments show very promising
results.
A Brief Survey of Deep Reinforcement Learning
Authors: Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath
Date: 2017-08-19 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Survey

Deep reinforcement learning is poised to revolutionise the field of AI and
represents a step towards building autonomous systems with a higher level
understanding of the visual world. Currently, deep learning is enabling
reinforcement learning to scale to problems that were previously intractable,
such as learning to play video games directly from pixels. Deep reinforcement
learning algorithms are also applied to robotics, allowing control policies for
robots to be learned directly from camera inputs in the real world. In this
survey, we begin with an introduction to the general field of reinforcement
learning, then progress to the main streams of value-based and policy-based
methods. Our survey will cover central algorithms in deep reinforcement
learning, including the deep $Q$-network, trust region policy optimisation, and
asynchronous advantage actor-critic. In parallel, we highlight the unique
advantages of deep neural networks, focusing on visual understanding via
reinforcement learning. To conclude, we describe several current areas of
research within the field.
Possibilistic Fuzzy Local Information C-Means for Sonar Image Segmentation
Authors: Alina Zare, Nicholas Young, Daniel Suen, Thomas Nabelek, Aquila Galusha, James Keller
Date: 2017-09-28 00:00:00
Tasks: Clustering, Image Segmentation, Sand, Segmentation, Semantic Segmentation

Side-look synthetic aperture sonar (SAS) can produce very high quality images
of the sea-floor. When viewing this imagery, a human observer can often easily
identify various sea-floor textures such as sand ripple, hard-packed sand, sea
grass and rock. In this paper, we present the Possibilistic Fuzzy Local
Information C-Means (PFLICM) approach to segment SAS imagery into sea-floor
regions that exhibit these various natural textures. The proposed PFLICM method
incorporates fuzzy and possibilistic clustering methods and leverages (local)
spatial information to perform soft segmentation. Results are shown on several
SAS scenes and compared to alternative segmentation approaches.
Recognition of feature curves on 3D shapes using an algebraic approach to Hough transforms
Authors: Maria-Laura Torrente, Silvia Biasotti, Bianca Falcidieno
Date: 2017-09-28 00:00:00

Feature curves are largely adopted to highlight shape features, such as sharp
lines, or to divide surfaces into meaningful segments, like convex or concave
regions. Extracting these curves is not sufficient to convey prominent and
meaningful information about a shape. We have first to separate the curves
belonging to features from those caused by noise and then to select the lines,
which describe non-trivial portions of a surface. The automatic detection of
such features is crucial for the identification and/or annotation of relevant
parts of a given shape. To do this, the Hough transform (HT) is a feature
extraction technique widely used in image analysis, computer vision and digital
image processing, while, for 3D shapes, the extraction of salient feature
curves is still an open problem.
  Thanks to algebraic geometry concepts, the HT technique has been recently
extended to include a vast class of algebraic curves, thus proving to be a
competitive tool for yielding an explicit representation of the diverse feature
lines equations. In the paper, for the first time we apply this novel extension
of the HT technique to the realm of 3D shapes in order to identify and localize
semantic features like patterns, decorations or anatomical details on 3D
objects (both complete and fragments), even in the case of features partially
damaged or incomplete. The method recognizes various features, possibly
compound, and it selects the most suitable feature profiles among families of
algebraic curves.
Fundamental Limits of Weak Recovery with Applications to Phase Retrieval
Authors: Marco Mondelli, Andrea Montanari
Date: 2017-08-20 00:00:00
Tasks: Retrieval

In phase retrieval we want to recover an unknown signal $\boldsymbol
x\in\mathbb C^d$ from $n$ quadratic measurements of the form $y_i =
|\langle{\boldsymbol a}_i,{\boldsymbol x}\rangle|^2+w_i$ where $\boldsymbol
a_i\in \mathbb C^d$ are known sensing vectors and $w_i$ is measurement noise.
We ask the following weak recovery question: what is the minimum number of
measurements $n$ needed to produce an estimator $\hat{\boldsymbol
x}(\boldsymbol y)$ that is positively correlated with the signal $\boldsymbol
x$? We consider the case of Gaussian vectors $\boldsymbol a_i$. We prove that -
in the high-dimensional limit - a sharp phase transition takes place, and we
locate the threshold in the regime of vanishingly small noise. For $n\le
d-o(d)$ no estimator can do significantly better than random and achieve a
strictly positive correlation. For $n\ge d+o(d)$ a simple spectral estimator
achieves a positive correlation. Surprisingly, numerical simulations with the
same spectral estimator demonstrate promising performance with realistic
sensing matrices. Spectral methods are used to initialize non-convex
optimization algorithms in phase retrieval, and our approach can boost the
performance in this setting as well.
  Our impossibility result is based on classical information-theory arguments.
The spectral algorithm computes the leading eigenvector of a weighted empirical
covariance matrix. We obtain a sharp characterization of the spectral
properties of this random matrix using tools from free probability and
generalizing a recent result by Lu and Li. Both the upper and lower bound
generalize beyond phase retrieval to measurements $y_i$ produced according to a
generalized linear model. As a byproduct of our analysis, we compare the
threshold of the proposed spectral method with that of a message passing
algorithm.
A Web of Hate: Tackling Hateful Speech in Online Social Spaces
Authors: Haji Mohammad Saleem, Kelly P Dillon, Susan Benesch, Derek Ruths
Date: 2017-09-28 00:00:00

Online social platforms are beset with hateful speech - content that
expresses hatred for a person or group of people. Such content can frighten,
intimidate, or silence platform users, and some of it can inspire other users
to commit violence. Despite widespread recognition of the problems posed by
such content, reliable solutions even for detecting hateful speech are lacking.
In the present work, we establish why keyword-based methods are insufficient
for detection. We then propose an approach to detecting hateful speech that
uses content produced by self-identifying hateful communities as training data.
Our approach bypasses the expensive annotation process often required to train
keyword systems and performs well across several established platforms, making
substantial improvements over current state-of-the-art approaches.
A Simple and Fast Algorithm for L1-norm Kernel PCA
Authors: Cheolmin Kim, Diego Klabjan
Date: 2017-09-28 00:00:00
Tasks: Outlier Detection

We present an algorithm for L1-norm kernel PCA and provide a convergence analysis for it. While an optimal solution of L2-norm kernel PCA can be obtained through matrix decomposition, finding that of L1-norm kernel PCA is not trivial due to its non-convexity and non-smoothness. We provide a novel reformulation through which an equivalent, geometrically interpretable problem is obtained. Based on the geometric interpretation of the reformulated problem, we present a fixed-point type algorithm that iteratively computes a binary weight for each observation. As the algorithm requires only inner products of data vectors, it is computationally efficient and the kernel trick is applicable. In the convergence analysis, we show that the algorithm converges to a local optimal solution in a finite number of steps. Moreover, we provide a rate of convergence analysis, which has been never done for any L1-norm PCA algorithm, proving that the sequence of objective values converges at a linear rate. In numerical experiments, we show that the algorithm is robust in the presence of entry-wise perturbations and computationally scalable, especially in a large-scale setting. Lastly, we introduce an application to outlier detection where the model based on the proposed algorithm outperforms the benchmark algorithms.