pwc-archive/papers-with-abstracts huggingface dataset:
Tensor Sparse and Low-Rank based Submodule Clustering Method for Multi-way Data
Authors: Xinglin Piao, Yongli Hu, Junbin Gao, Yanfeng Sun, Zhouchen Lin, Bao-Cai Yin
Date: 2016-01-02 00:00:00
Tasks: Clustering

A new submodule clustering method via sparse and low-rank representation for
multi-way data is proposed in this paper. Instead of reshaping multi-way data
into vectors, this method maintains their natural orders to preserve data
intrinsic structures, e.g., image data kept as matrices. To implement
clustering, the multi-way data, viewed as tensors, are represented by the
proposed tensor sparse and low-rank model to obtain its submodule
representation, called a free module, which is finally used for spectral
clustering. The proposed method extends the conventional subspace clustering
method based on sparse and low-rank representation to multi-way data submodule
clustering by combining t-product operator. The new method is tested on several
public datasets, including synthetical data, video sequences and toy images.
The experiments show that the new method outperforms the state-of-the-art
methods, such as Sparse Subspace Clustering (SSC), Low-Rank Representation
(LRR), Ordered Subspace Clustering (OSC), Robust Latent Low Rank Representation
(RobustLatLRR) and Sparse Submodule Clustering method (SSmC).
Using Natural Language Processing and Qualitative Analysis to Intervene in Gang Violence: A Collaboration Between Social Work Researchers and Data Scientists
Authors: Desmond Upton Patton, Kathleen McKeown, Owen Rambow, Jamie MacBeth
Date: 2016-09-28 00:00:00

The U.S. has the highest rate of firearm-related deaths when compared to
other industrialized countries. Violence particularly affects low-income, urban
neighborhoods in cities like Chicago, which saw a 40% increase in firearm
violence from 2014 to 2015 to more than 3,000 shooting victims. While recent
studies have found that urban, gang-involved individuals curate a unique and
complex communication style within and between social media platforms,
organizations focused on reducing gang violence are struggling to keep up with
the growing complexity of social media platforms and the sheer volume of data
they present. In this paper, describe the Digital Urban Violence Analysis
Approach (DUVVA), a collaborative qualitative analysis method used in a
collaboration between data scientists and social work researchers to develop a
suite of systems for decoding the high- stress language of urban, gang-involved
youth. Our approach leverages principles of grounded theory when analyzing
approximately 800 tweets posted by Chicago gang members and participation of
youth from Chicago neighborhoods to create a language resource for natural
language processing (NLP) methods. In uncovering the unique language and
communication style, we developed automated tools with the potential to detect
aggressive language on social media and aid individuals and groups in
performing violence prevention and interruption.
The Extended Littlestone's Dimension for Learning with Mistakes and Abstentions
Authors: Chicheng Zhang, Kamalika Chaudhuri
Date: 2016-04-21 00:00:00

This paper studies classification with an abstention option in the online
setting. In this setting, examples arrive sequentially, the learner is given a
hypothesis class $\mathcal H$, and the goal of the learner is to either predict
a label on each example or abstain, while ensuring that it does not make more
than a pre-specified number of mistakes when it does predict a label.
  Previous work on this problem has left open two main challenges. First, not
much is known about the optimality of algorithms, and in particular, about what
an optimal algorithmic strategy is for any individual hypothesis class. Second,
while the realizable case has been studied, the more realistic non-realizable
scenario is not well-understood. In this paper, we address both challenges.
First, we provide a novel measure, called the Extended Littlestone's Dimension,
which captures the number of abstentions needed to ensure a certain number of
mistakes. Second, we explore the non-realizable case, and provide upper and
lower bounds on the number of abstentions required by an algorithm to guarantee
a specified number of mistakes.
Binocular disparity as an explanation for the moon illusion
Authors: Joseph Antonides, Toshiro Kubota
Date: 2013-01-12 00:00:00

We present another explanation for the moon illusion, the phenomenon in which
the moon looks larger near the horizon than near the zenith. In our model of
the moon illusion, the sky is considered a spatially-contiguous and
geometrically-smooth surface. When an object such as the moon breaks the
contiguity of the surface, instead of perceiving the object as appearing
through a hole in the surface, humans perceive an occlusion of the surface.
Binocular vision dictates that the moon is distant, but this perception model
contradicts our binocular vision, dictating that the moon is closer than the
sky. To resolve the contradiction, the brain distorts the projections of the
moon to increase the binocular disparity, which results in an increase in the
perceived size of the moon. The degree of distortion depends upon the apparent
distance to the sky, which is influenced by the surrounding objects and the
condition of the sky. As the apparent distance to the sky decreases, the
illusion becomes stronger. At the horizon, apparent distance to the sky is
minimal, whereas at the zenith, few distance cues are present, causing
difficulty with distance estimation and weakening the illusion.
Video Summarization using Deep Semantic Features
Authors: Mayu Otani, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Naokazu Yokoya
Date: 2016-09-28 00:00:00
Tasks: Clustering, Video Summarization

This paper presents a video summarization technique for an Internet video to
provide a quick way to overview its content. This is a challenging problem
because finding important or informative parts of the original video requires
to understand its content. Furthermore the content of Internet videos is very
diverse, ranging from home videos to documentaries, which makes video
summarization much more tough as prior knowledge is almost not available. To
tackle this problem, we propose to use deep video features that can encode
various levels of content semantics, including objects, actions, and scenes,
improving the efficiency of standard video summarization techniques. For this,
we design a deep neural network that maps videos as well as descriptions to a
common semantic space and jointly trained it with associated pairs of videos
and descriptions. To generate a video summary, we extract the deep features
from each segment of the original video and apply a clustering-based
summarization technique to them. We evaluate our video summaries using the
SumMe dataset as well as baseline approaches. The results demonstrated the
advantages of incorporating our deep semantic features in a video summarization
technique.
Stabilizing Linear Prediction Models using Autoencoder
Authors: Shivapratap Gopakumar, Truyen Tran, Dinh Phung, Svetha Venkatesh
Date: 2016-09-28 00:00:00
Tasks: Prediction

To date, the instability of prognostic predictors in a sparse high
dimensional model, which hinders their clinical adoption, has received little
attention. Stable prediction is often overlooked in favour of performance. Yet,
stability prevails as key when adopting models in critical areas as healthcare.
Our study proposes a stabilization scheme by detecting higher order feature
correlations. Using a linear model as basis for prediction, we achieve feature
stability by regularising latent correlation in features. Latent higher order
correlation among features is modelled using an autoencoder network. Stability
is enhanced by combining a recent technique that uses a feature graph, and
augmenting external unlabelled data for training the autoencoder network. Our
experiments are conducted on a heart failure cohort from an Australian
hospital. Stability was measured using Consistency index for feature subsets
and signal-to-noise ratio for model parameters. Our methods demonstrated
significant improvement in feature stability and model estimation stability
when compared to baselines.
Scalable Discrete Supervised Hash Learning with Asymmetric Matrix Factorization
Authors: Shifeng Zhang, Jianmin Li, Jinma Guo, Bo Zhang
Date: 2016-09-28 00:00:00
Tasks: Clustering, Retrieval, Triplet

Hashing method maps similar data to binary hashcodes with smaller hamming
distance, and it has received a broad attention due to its low storage cost and
fast retrieval speed. However, the existing limitations make the present
algorithms difficult to deal with large-scale datasets: (1) discrete
constraints are involved in the learning of the hash function; (2) pairwise or
triplet similarity is adopted to generate efficient hashcodes, resulting both
time and space complexity are greater than O(n^2). To address these issues, we
propose a novel discrete supervised hash learning framework which can be
scalable to large-scale datasets. First, the discrete learning procedure is
decomposed into a binary classifier learning scheme and binary codes learning
scheme, which makes the learning procedure more efficient. Second, we adopt the
Asymmetric Low-rank Matrix Factorization and propose the Fast Clustering-based
Batch Coordinate Descent method, such that the time and space complexity is
reduced to O(n). The proposed framework also provides a flexible paradigm to
incorporate with arbitrary hash function, including deep neural networks and
kernel methods. Experiments on large-scale datasets demonstrate that the
proposed method is superior or comparable with state-of-the-art hashing
algorithms.
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues
Authors: Nihar B. Shah, Sivaraman Balakrishnan, Adityanand Guntuboyina, Martin J. Wainwright
Date: 2015-10-19 00:00:00

There are various parametric models for analyzing pairwise comparison data,
including the Bradley-Terry-Luce (BTL) and Thurstone models, but their reliance
on strong parametric assumptions is limiting. In this work, we study a flexible
model for pairwise comparisons, under which the probabilities of outcomes are
required only to satisfy a natural form of stochastic transitivity. This class
includes parametric models including the BTL and Thurstone models as special
cases, but is considerably more general. We provide various examples of models
in this broader stochastically transitive class for which classical parametric
models provide poor fits. Despite this greater flexibility, we show that the
matrix of probabilities can be estimated at the same rate as in standard
parametric models. On the other hand, unlike in the BTL and Thurstone models,
computing the minimax-optimal estimator in the stochastically transitive model
is non-trivial, and we explore various computationally tractable alternatives.
We show that a simple singular value thresholding algorithm is statistically
consistent but does not achieve the minimax rate. We then propose and study
algorithms that achieve the minimax rate over interesting sub-classes of the
full stochastically transitive class. We complement our theoretical results
with thorough numerical simulations.
Improved phase-unwrapping method using geometric constraints
Authors: Guangliang Du, Minmin Wang, Canlin Zhou, Shuchun Si, Hui Li, Zhenkun Lei, YanJie Li
Date: 2016-09-28 00:00:00

Conventional dual-frequency fringe projection algorithm often suffers from
phase unwrapping failure when the frequency ratio between the high frequency
and the low one is too large. Zhang et.al. proposed an enhanced two-frequency
phase-shifting method to use geometric constraints of digital fringe
projection(DFP) to reduce the noise impact due to the large frequency ratio.
However, this method needs to calibrate the DFP system and calculate the
minimum phase map at the nearest position from the camera perspective, these
procedures are are relatively complex and more time-cosuming. In this paper, we
proposed an improved method, which eliminates the system calibration and
determination in Zhang's method,meanwhile does not need to use the low
frequency fringe pattern. In the proposed method,we only need a set of high
frequency fringe patterns to measure the object after the high frequency is
directly estimated by the experiment. Thus the proposed method can simplify the
procedure and improve the speed. Finally, the experimental evaluation is
conducted to prove the validity of the proposed method.The results demonstrate
that the proposed method can overcome the main disadvantages encountered by
Zhang's method.
SQUINKY! A Corpus of Sentence-level Formality, Informativeness, and Implicature
Authors: Shibamouli Lahiri
Date: 2015-06-07 00:00:00
Tasks: Informativeness, Sentence

We introduce a corpus of 7,032 sentences rated by human annotators for
formality, informativeness, and implicature on a 1-7 scale. The corpus was
annotated using Amazon Mechanical Turk. Reliability in the obtained judgments
was examined by comparing mean ratings across two MTurk experiments, and
correlation with pilot annotations (on sentence formality) conducted in a more
controlled setting. Despite the subjectivity and inherent difficulty of the
annotation task, correlations between mean ratings were quite encouraging,
especially on formality and informativeness. We further explored correlation
between the three linguistic variables, genre-wise variation of ratings and
correlations within genres, compatibility with automatic stylistic scoring, and
sentential make-up of a document in terms of style. To date, our corpus is the
largest sentence-level annotated corpus released for formality,
informativeness, and implicature.
Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning
Authors: Karthik Narasimhan, Adam Yala, Regina Barzilay
Date: 2016-03-25 00:00:00
Proceeding: EMNLP 2016 11
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Most successful information extraction systems operate with access to a large
collection of documents. In this work, we explore the task of acquiring and
incorporating external evidence to improve extraction accuracy in domains where
the amount of training data is scarce. This process entails issuing search
queries, extraction from new sources and reconciliation of extracted values,
which are repeated until sufficient evidence is collected. We approach the
problem using a reinforcement learning framework where our model learns to
select optimal actions based on contextual information. We employ a deep
Q-network, trained to optimize a reward function that reflects extraction
accuracy while penalizing extra effort. Our experiments on two databases -- of
shooting incidents, and food adulteration cases -- demonstrate that our system
significantly outperforms traditional extractors and a competitive
meta-classifier baseline.
Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act Classification
Authors: Franck Dernoncourt, Ji Young Lee
Date: 2016-09-27 00:00:00
Tasks: Bayesian Optimization, Dialog Act Classification, Gaussian Processes, General Classification

Systems based on artificial neural networks (ANNs) have achieved
state-of-the-art results in many natural language processing tasks. Although
ANNs do not require manually engineered features, ANNs have many
hyperparameters to be optimized. The choice of hyperparameters significantly
impacts models' performances. However, the ANN hyperparameters are typically
chosen by manual, grid, or random search, which either requires expert
experiences or is computationally expensive. Recent approaches based on
Bayesian optimization using Gaussian processes (GPs) is a more systematic way
to automatically pinpoint optimal or near-optimal machine learning
hyperparameters. Using a previously published ANN model yielding
state-of-the-art results for dialog act classification, we demonstrate that
optimizing hyperparameters using GP further improves the results, and reduces
the computational time by a factor of 4 compared to a random search. Therefore
it is a useful technique for tuning ANN models to yield the best performances
for natural language processing tasks.
A Fast Factorization-based Approach to Robust PCA
Authors: Chong Peng, Zhao Kang, Qiang Chen
Date: 2016-09-27 00:00:00

Robust principal component analysis (RPCA) has been widely used for
recovering low-rank matrices in many data mining and machine learning problems.
It separates a data matrix into a low-rank part and a sparse part. The convex
approach has been well studied in the literature. However, state-of-the-art
algorithms for the convex approach usually have relatively high complexity due
to the need of solving (partial) singular value decompositions of large
matrices. A non-convex approach, AltProj, has also been proposed with lighter
complexity and better scalability. Given the true rank $r$ of the underlying
low rank matrix, AltProj has a complexity of $O(r^2dn)$, where $d\times n$ is
the size of data matrix. In this paper, we propose a novel factorization-based
model of RPCA, which has a complexity of $O(kdn)$, where $k$ is an upper bound
of the true rank. Our method does not need the precise value of the true rank.
From extensive experiments, we observe that AltProj can work only when $r$ is
precisely known in advance; however, when the needed rank parameter $r$ is
specified to a value different from the true rank, AltProj cannot fully
separate the two parts while our method succeeds. Even when both work, our
method is about 4 times faster than AltProj. Our method can be used as a
light-weight, scalable tool for RPCA in the absence of the precise value of the
true rank.
MERLiN: Mixture Effect Recovery in Linear Networks
Authors: Sebastian Weichwald, Moritz Grosse-Wentrup, Arthur Gretton
Date: 2015-12-03 00:00:00
Tasks: Causal Inference, EEG, Electroencephalogram (EEG)

Causal inference concerns the identification of cause-effect relationships
between variables, e.g. establishing whether a stimulus affects activity in a
certain brain region. The observed variables themselves often do not constitute
meaningful causal variables, however, and linear combinations need to be
considered. In electroencephalographic studies, for example, one is not
interested in establishing cause-effect relationships between electrode signals
(the observed variables), but rather between cortical signals (the causal
variables) which can be recovered as linear combinations of electrode signals.
  We introduce MERLiN (Mixture Effect Recovery in Linear Networks), a family of
causal inference algorithms that implement a novel means of constructing causal
variables from non-causal variables. We demonstrate through application to EEG
data how the basic MERLiN algorithm can be extended for application to
different (neuroimaging) data modalities. Given an observed linear mixture, the
algorithms can recover a causal variable that is a linear effect of another
given variable. That is, MERLiN allows us to recover a cortical signal that is
affected by activity in a certain brain region, while not being a direct effect
of the stimulus. The Python/Matlab implementation for all presented algorithms
is available on https://github.com/sweichwald/MERLiN
YouTube-8M: A Large-Scale Video Classification Benchmark
Authors: Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, Sudheendra Vijayanarasimhan
Date: 2016-09-27 00:00:00
Tasks: 3D Face Reconstruction, Action Recognition, Action Recognition In Videos, General Classification, Video Classification

Many recent advancements in Computer Vision are attributed to large datasets.
Open-source software packages for Machine Learning and inexpensive commodity
hardware have reduced the barrier of entry for exploring novel approaches at
scale. It is possible to train models over millions of examples within a few
days. Although large-scale datasets exist for image understanding, such as
ImageNet, there are no comparable size video classification datasets.
  In this paper, we introduce YouTube-8M, the largest multi-label video
classification dataset, composed of ~8 million videos (500K hours of video),
annotated with a vocabulary of 4800 visual entities. To get the videos and
their labels, we used a YouTube video annotation system, which labels videos
with their main topics. While the labels are machine-generated, they have
high-precision and are derived from a variety of human-based signals including
metadata and query click signals. We filtered the video labels (Knowledge Graph
entities) using both automated and manual curation strategies, including asking
human raters if the labels are visually recognizable. Then, we decoded each
video at one-frame-per-second, and used a Deep CNN pre-trained on ImageNet to
extract the hidden representation immediately prior to the classification
layer. Finally, we compressed the frame features and make both the features and
video-level labels available for download.
  We trained various (modest) classification models on the dataset, evaluated
them using popular evaluation metrics, and report them as baselines. Despite
the size of the dataset, some of our models train to convergence in less than a
day on a single machine using TensorFlow. We plan to release code for training
a TensorFlow model and for computing metrics.
WordRank: Learning Word Embeddings via Robust Ranking
Authors: Shihao Ji, Hyokun Yun, Pinar Yanardag, Shin Matsushima, S. V. N. Vishwanathan
Date: 2015-06-09 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Learning Word Embeddings, Word Embeddings, Word Similarity

Embedding words in a vector space has gained a lot of attention in recent
years. While state-of-the-art methods provide efficient computation of word
similarities via a low-dimensional matrix embedding, their motivation is often
left unclear. In this paper, we argue that word embedding can be naturally
viewed as a ranking problem due to the ranking nature of the evaluation
metrics. Then, based on this insight, we propose a novel framework WordRank
that efficiently estimates word representations via robust ranking, in which
the attention mechanism and robustness to noise are readily achieved via the
DCG-like ranking losses. The performance of WordRank is measured in word
similarity and word analogy benchmarks, and the results are compared to the
state-of-the-art word embedding techniques. Our algorithm is very competitive
to the state-of-the- arts on large corpora, while outperforms them by a
significant margin when the training set is limited (i.e., sparse and noisy).
With 17 million tokens, WordRank performs almost as well as existing methods
using 7.2 billion tokens on a popular word similarity benchmark. Our multi-node
distributed implementation of WordRank is publicly available for general usage.
A Transportation $L^p$ Distance for Signal Analysis
Authors: Matthew Thorpe, Serim Park, Soheil Kolouri, Gustavo K. Rohde, Dejan Slepčev
Date: 2016-09-27 00:00:00

Transport based distances, such as the Wasserstein distance and earth mover's
distance, have been shown to be an effective tool in signal and image analysis.
The success of transport based distances is in part due to their Lagrangian
nature which allows it to capture the important variations in many signal
classes. However these distances require the signal to be nonnegative and
normalized. Furthermore, the signals are considered as measures and compared by
redistributing (transporting) them, which does not directly take into account
the signal intensity. Here we study a transport-based distance, called the
$TL^p$ distance, that combines Lagrangian and intensity modelling and is
directly applicable to general, non-positive and multi-channelled signals. The
framework allows the application of existing numerical methods. We give an
overview of the basic properties of this distance and applications to
classification, with multi-channelled, non-positive one and two-dimensional
signals, and color transfer.