pwc-archive/papers-with-abstracts huggingface dataset:
Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks
Authors: Pichao Wang, Wanqing Li, Song Liu, Zhimin Gao, Chang Tang, Philip Ogunbona
Date: 2017-01-07 00:00:00
Tasks: General Classification, Gesture Recognition

This paper proposes three simple, compact yet effective representations of
depth sequences, referred to respectively as Dynamic Depth Images (DDI),
Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images
(DDMNI). These dynamic images are constructed from a sequence of depth maps
using bidirectional rank pooling to effectively capture the spatial-temporal
information. Such image-based representations enable us to fine-tune the
existing ConvNets models trained on image data for classification of depth
sequences, without introducing large parameters to learn. Upon the proposed
representations, a convolutional Neural networks (ConvNets) based method is
developed for gesture recognition and evaluated on the Large-scale Isolated
Gesture Recognition at the ChaLearn Looking at People (LAP) challenge 2016. The
method achieved 55.57\% classification accuracy and ranked $2^{nd}$ place in
this challenge but was very close to the best performance even though we only
used depth data.
Structural Attention Neural Networks for improved sentiment analysis
Authors: Filippos Kokkinos, Alexandros Potamianos
Date: 2017-01-07 00:00:00
Proceeding: EACL 2017 4
Tasks: General Classification, Sentiment Analysis, Sentiment Classification

We introduce a tree-structured attention neural network for sentences and
small phrases and apply it to the problem of sentiment classification. Our
model expands the current recursive models by incorporating structural
information around a node of a syntactic tree using both bottom-up and top-down
information propagation. Also, the model utilizes structural attention to
identify the most salient representations during the construction of the
syntactic tree. To our knowledge, the proposed models achieve state of the art
performance on the Stanford Sentiment Treebank dataset.
Classification Accuracy Improvement for Neuromorphic Computing Systems with One-level Precision Synapses
Authors: Yandan Wang, Wei Wen, Linghao Song, Hai Li
Date: 2017-01-07 00:00:00
Tasks: General Classification, image-classification, Image Classification, Quantization

Brain inspired neuromorphic computing has demonstrated remarkable advantages
over traditional von Neumann architecture for its high energy efficiency and
parallel data processing. However, the limited resolution of synaptic weights
degrades system accuracy and thus impedes the use of neuromorphic systems. In
this work, we propose three orthogonal methods to learn synapses with one-level
precision, namely, distribution-aware quantization, quantization regularization
and bias tuning, to make image classification accuracy comparable to the
state-of-the-art. Experiments on both multi-layer perception and convolutional
neural networks show that the accuracy drop can be well controlled within 0.19%
(5.53%) for MNIST (CIFAR-10) database, compared to an ideal system without
quantization.
Similarity Function Tracking using Pairwise Comparisons
Authors: Kristjan Greenewald, Stephen Kelley, Brandon Oselio, Alfred O. Hero III
Date: 2017-01-07 00:00:00
Tasks: Clustering, Metric Learning, Retrieval

Recent work in distance metric learning has focused on learning
transformations of data that best align with specified pairwise similarity and
dissimilarity constraints, often supplied by a human observer. The learned
transformations lead to improved retrieval, classification, and clustering
algorithms due to the better adapted distance or similarity measures. Here, we
address the problem of learning these transformations when the underlying
constraint generation process is nonstationary. This nonstationarity can be due
to changes in either the ground-truth clustering used to generate constraints
or changes in the feature subspaces in which the class structure is apparent.
We propose Online Convex Ensemble StrongLy Adaptive Dynamic Learning (OCELAD),
a general adaptive, online approach for learning and tracking optimal metrics
as they change over time that is highly robust to a variety of nonstationary
behaviors in the changing metric. We apply the OCELAD framework to an ensemble
of online learners. Specifically, we create a retro-initialized composite
objective mirror descent (COMID) ensemble (RICE) consisting of a set of
parallel COMID learners with different learning rates, and demonstrate
parameter-free RICE-OCELAD metric learning on both synthetic data and a highly
nonstationary Twitter dataset. We show significant performance improvements and
increased robustness to nonstationary effects relative to previously proposed
batch and online distance metric learning algorithms.
Stock price direction prediction by directly using prices data: an empirical study on the KOSPI and HSI
Authors: Yanshan Wang
Date: 2013-09-27 00:00:00
Tasks: Prediction, Stock Prediction

The prediction of a stock market direction may serve as an early
recommendation system for short-term investors and as an early financial
distress warning system for long-term shareholders. Many stock prediction
studies focus on using macroeconomic indicators, such as CPI and GDP, to train
the prediction model. However, daily data of the macroeconomic indicators are
almost impossible to obtain. Thus, those methods are difficult to be employed
in practice. In this paper, we propose a method that directly uses prices data
to predict market index direction and stock price direction. An extensive
empirical study of the proposed method is presented on the Korean Composite
Stock Price Index (KOSPI) and Hang Seng Index (HSI), as well as the individual
constituents included in the indices. The experimental results show notably
high hit ratios in predicting the movements of the individual constituents in
the KOSPI and HIS.
AdaCluster : Adaptive Clustering for Heterogeneous Data
Authors: Mehmet Emin Basbug, Barbara Engelhardt
Date: 2015-10-19 00:00:00
Tasks: Clustering

Clustering algorithms start with a fixed divergence, which captures the
possibly asymmetric distance between a sample and a centroid. In the mixture
model setting, the sample distribution plays the same role. When all attributes
have the same topology and dispersion, the data are said to be homogeneous. If
the prior knowledge of the distribution is inaccurate or the set of plausible
distributions is large, an adaptive approach is essential. The motivation is
more compelling for heterogeneous data, where the dispersion or the topology
differs among attributes. We propose an adaptive approach to clustering using
classes of parametrized Bregman divergences. We first show that the density of
a steep exponential dispersion model (EDM) can be represented with a Bregman
divergence. We then propose AdaCluster, an expectation-maximization (EM)
algorithm to cluster heterogeneous data using classes of steep EDMs. We compare
AdaCluster with EM for a Gaussian mixture model on synthetic data and nine UCI
data sets. We also propose an adaptive hard clustering algorithm based on
Generalized Method of Moments. We compare the hard clustering algorithm with
k-means on the UCI data sets. We empirically verified that adaptively learning
the underlying topology yields better clustering of heterogeneous data.
Cross Euclidean-to-Riemannian Metric Learning with Application to Face Recognition from Video
Authors: Zhiwu Huang, Ruiping Wang, Shiguang Shan, Luc van Gool, Xilin Chen
Date: 2016-08-15 00:00:00
Tasks: Face Recognition, Metric Learning

Riemannian manifolds have been widely employed for video representations in
visual classification tasks including video-based face recognition. The success
mainly derives from learning a discriminant Riemannian metric which encodes the
non-linear geometry of the underlying Riemannian manifolds. In this paper, we
propose a novel metric learning framework to learn a distance metric across a
Euclidean space and a Riemannian manifold to fuse the average appearance and
pattern variation of faces within one video. The proposed metric learning
framework can handle three typical tasks of video-based face recognition:
Video-to-Still, Still-to-Video and Video-to-Video settings. To accomplish this
new framework, by exploiting typical Riemannian geometries for kernel
embedding, we map the source Euclidean space and Riemannian manifold into a
common Euclidean subspace, each through a corresponding high-dimensional
Reproducing Kernel Hilbert Space (RKHS). With this mapping, the problem of
learning a cross-view metric between the two source heterogeneous spaces can be
expressed as learning a single-view Euclidean distance metric in the target
common Euclidean space. By learning information on heterogeneous data with the
shared label, the discriminant metric in the common space improves face
recognition from videos. Extensive experiments on four challenging video face
databases demonstrate that the proposed framework has a clear advantage over
the state-of-the-art methods in the three classical video-based face
recognition tasks.
Wavelet Scattering Regression of Quantum Chemical Energies
Authors: Matthew Hirn, Stéphane Mallat, Nicolas Poilvert
Date: 2016-05-16 00:00:00
Tasks: regression

We introduce multiscale invariant dictionaries to estimate quantum chemical
energies of organic molecules, from training databases. Molecular energies are
invariant to isometric atomic displacements, and are Lipschitz continuous to
molecular deformations. Similarly to density functional theory (DFT), the
molecule is represented by an electronic density function. A multiscale
invariant dictionary is calculated with wavelet scattering invariants. It
cascades a first wavelet transform which separates scales, with a second
wavelet transform which computes interactions across scales. Sparse scattering
regressions give state of the art results over two databases of organic planar
molecules. On these databases, the regression error is of the order of the
error produced by DFT codes, but at a fraction of the computational cost.
Map-guided Hyperspectral Image Superpixel Segmentation Using Proportion Maps
Authors: Hao Sun, Alina Zare
Date: 2017-01-06 00:00:00
Tasks: Segmentation

A map-guided superpixel segmentation method for hyperspectral imagery is
developed and introduced. The proposed approach develops a
hyperspectral-appropriate version of the SLIC superpixel segmentation
algorithm, leverages map information to guide segmentation, and incorporates
the semi-supervised Partial Membership Latent Dirichlet Allocation (sPM-LDA) to
obtain a final superpixel segmentation. The proposed method is applied to two
real hyperspectral data sets and quantitative cluster validity metrics indicate
that the proposed approach outperforms existing hyperspectral superpixel
segmentation methods.
Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces
Authors: Silvio Amir, Rámon Astudillo, Wang Ling, Paula C. Carvalho, Mário J. Silva
Date: 2016-12-31 00:00:00
Tasks: Word Embeddings

Recent approaches for sentiment lexicon induction have capitalized on
pre-trained word embeddings that capture latent semantic properties. However,
embeddings obtained by optimizing performance of a given task (e.g. predicting
contextual words) are sub-optimal for other applications. In this paper, we
address this problem by exploiting task-specific representations, induced via
embedding sub-space projection. This allows us to expand lexicons describing
multiple semantic properties. For each property, our model jointly learns
suitable representations and the concomitant predictor. Experiments conducted
over multiple subjective lexicons, show that our model outperforms previous
work and other baselines; even in low training data regimes. Furthermore,
lexicon-based sentiment classifiers built on top of our lexicons outperform
similar resources and yield performances comparable to those of supervised
models.
To Boost or Not to Boost? On the Limits of Boosted Trees for Object Detection
Authors: Eshed Ohn-Bar, Mohan M. Trivedi
Date: 2017-01-06 00:00:00
Tasks: Face Detection, object-detection, Object Detection, Pedestrian Detection

We aim to study the modeling limitations of the commonly employed boosted
decision trees classifier. Inspired by the success of large, data-hungry visual
recognition models (e.g. deep convolutional neural networks), this paper
focuses on the relationship between modeling capacity of the weak learners,
dataset size, and dataset properties. A set of novel experiments on the Caltech
Pedestrian Detection benchmark results in the best known performance among
non-CNN techniques while operating at fast run-time speed. Furthermore, the
performance is on par with deep architectures (9.71% log-average miss rate),
while using only HOG+LUV channels as features. The conclusions from this study
are shown to generalize over different object detection domains as demonstrated
on the FDDB face detection benchmark (93.37% accuracy). Despite the impressive
performance, this study reveals the limited modeling capacity of the common
boosted trees model, motivating a need for architectural changes in order to
compete with multi-level and very deep architectures.
Canonical Correlation Analysis for Analyzing Sequences of Medical Billing Codes
Authors: Corinne L. Jones, Sham M. Kakade, Lucas W. Thornblade, David R. Flum, Abraham D. Flaxman
Date: 2016-12-01 00:00:00

We propose using canonical correlation analysis (CCA) to generate features
from sequences of medical billing codes. Applying this novel use of CCA to a
database of medical billing codes for patients with diverticulitis, we first
demonstrate that the CCA embeddings capture meaningful relationships among the
codes. We then generate features from these embeddings and establish their
usefulness in predicting future elective surgery for diverticulitis, an
important marker in efforts for reducing costs in healthcare.
Deep Convolutional Denoising of Low-Light Images
Authors: Tal Remez, Or Litany, Raja Giryes, Alex M. Bronstein
Date: 2017-01-06 00:00:00
Tasks: Astronomy, Denoising

Poisson distribution is used for modeling noise in photon-limited imaging.
While canonical examples include relatively exotic types of sensing like
spectral imaging or astronomy, the problem is relevant to regular photography
now more than ever due to the booming market for mobile cameras. Restricted
form factor limits the amount of absorbed light, thus computational
post-processing is called for. In this paper, we make use of the powerful
framework of deep convolutional neural networks for Poisson denoising. We
demonstrate how by training the same network with images having a specific peak
value, our denoiser outperforms previous state-of-the-art by a large margin
both visually and quantitatively. Being flexible and data-driven, our solution
resolves the heavy ad hoc engineering used in previous methods and is an order
of magnitude faster. We further show that by adding a reasonable prior on the
class of the image being processed, another significant boost in performance is
achieved.
Scalable inference for a full multivariate stochastic volatility model
Authors: P. Dellaportas, A. Plataniotis, M. K. Titsias
Date: 2015-10-18 00:00:00

We introduce a multivariate stochastic volatility model for asset returns
that imposes no restrictions to the structure of the volatility matrix and
treats all its elements as functions of latent stochastic processes. When the
number of assets is prohibitively large, we propose a factor multivariate
stochastic volatility model in which the variances and correlations of the
factors evolve stochastically over time. Inference is achieved via a carefully
designed feasible and scalable Markov chain Monte Carlo algorithm that combines
two computationally important ingredients: it utilizes invariant to the prior
Metropolis proposal densities for simultaneously updating all latent paths and
has quadratic, rather than cubic, computational complexity when evaluating the
multivariate normal densities required. We apply our modelling and
computational methodology to $571$ stock daily returns of Euro STOXX index for
data over a period of $10$ years. MATLAB software for this paper is available
at http://www.aueb.gr/users/mtitsias/code/msv.zip.
Single-Image Depth Perception in the Wild
Authors: Weifeng Chen, Zhao Fu, Dawei Yang, Jia Deng
Date: 2016-04-13 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Depth Estimation

This paper studies single-image depth perception in the wild, i.e.,
recovering depth from a single image taken in unconstrained settings. We
introduce a new dataset "Depth in the Wild" consisting of images in the wild
annotated with relative depth between pairs of random points. We also propose a
new algorithm that learns to estimate metric depth using annotations of
relative depth. Compared to the state of the art, our algorithm is simpler and
performs better. Experiments show that our algorithm, combined with existing
RGB-D data and our new relative depth annotations, significantly improves
single-image depth perception in the wild.
A scalable convolutional neural network for task-specified scenarios via knowledge distillation
Authors: Mengnan Shi, Fei Qin, Qixiang Ye, Zhenjun Han, Jianbin Jiao
Date: 2016-09-19 00:00:00
Tasks: Knowledge Distillation

In this paper, we explore the redundancy in convolutional neural network,
which scales with the complexity of vision tasks. Considering that many
front-end visual systems are interested in only a limited range of visual
targets, the removing of task-specified network redundancy can promote a wide
range of potential applications. We propose a task-specified knowledge
distillation algorithm to derive a simplified model with pre-set computation
cost and minimized accuracy loss, which suits the resource constraint front-end
systems well. Experiments on the MNIST and CIFAR10 datasets demonstrate the
feasibility of the proposed approach as well as the existence of task-specified
redundancy.
The Complexity of Bayesian Networks Specified by Propositional and Relational Languages
Authors: Fabio Gagliardi Cozman, Denis Deratani Mauá
Date: 2016-12-04 00:00:00

We examine the complexity of inference in Bayesian networks specified by
logical languages. We consider representations that range from fragments of
propositional logic to function-free first-order logic with equality; in doing
so we cover a variety of plate models and of probabilistic relational models.
We study the complexity of inferences when network, query and domain are the
input (the inferential and the combined complexity), when the network is fixed
and query and domain are the input (the query/data complexity), and when the
network and query are fixed and the domain is the input (the domain
complexity). We draw connections with probabilistic databases and liftability
results, and obtain complexity classes that range from polynomial to
exponential levels.
Cross-Lingual Dependency Parsing with Late Decoding for Truly Low-Resource Languages
Authors: Michael Sejr Schlichtkrull, Anders Søgaard
Date: 2017-01-06 00:00:00
Tasks: Dependency Parsing

In cross-lingual dependency annotation projection, information is often lost
during transfer because of early decoding. We present an end-to-end graph-based
neural network dependency parser that can be trained to reproduce matrices of
edge scores, which can be directly projected across word alignments. We show
that our approach to cross-lingual dependency parsing is not only simpler, but
also achieves an absolute improvement of 2.25% averaged across 10 languages
compared to the previous state of the art.
Enumeration of Extractive Oracle Summaries
Authors: Tsutomu Hirao, Masaaki Nishino, Jun Suzuki, Masaaki Nagata
Date: 2017-01-06 00:00:00
Proceeding: EACL 2017 4
Tasks: document understanding, Extractive Summarization

To analyze the limitations and the future directions of the extractive
summarization paradigm, this paper proposes an Integer Linear Programming (ILP)
formulation to obtain extractive oracle summaries in terms of ROUGE-N. We also
propose an algorithm that enumerates all of the oracle summaries for a set of
reference summaries to exploit F-measures that evaluate which system summaries
contain how many sentences that are extracted as an oracle summary. Our
experimental results obtained from Document Understanding Conference (DUC)
corpora demonstrated the following: (1) room still exists to improve the
performance of extractive summarization; (2) the F-measures derived from the
enumerated oracle summaries have significantly stronger correlations with human
judgment than those derived from single oracle summaries.
Real Multi-Sense or Pseudo Multi-Sense: An Approach to Improve Word Representation
Authors: Haoyue Shi, Caihua Li, Junfeng Hu
Date: 2017-01-06 00:00:00
Proceeding: WS 2016 12
Tasks: Word Embeddings, Word Similarity

Previous researches have shown that learning multiple representations for
polysemous words can improve the performance of word embeddings on many tasks.
However, this leads to another problem. Several vectors of a word may actually
point to the same meaning, namely pseudo multi-sense. In this paper, we
introduce the concept of pseudo multi-sense, and then propose an algorithm to
detect such cases. With the consideration of the detected pseudo multi-sense
cases, we try to refine the existing word embeddings to eliminate the influence
of pseudo multi-sense. Moreover, we apply our algorithm on previous released
multi-sense word embeddings and tested it on artificial word similarity tasks
and the analogy task. The result of the experiments shows that diminishing
pseudo multi-sense can improve the quality of word representations. Thus, our
method is actually an efficient way to reduce linguistic complexity.
Replication issues in syntax-based aspect extraction for opinion mining
Authors: Edison Marrese-Taylor, Yutaka Matsuo
Date: 2017-01-06 00:00:00
Proceeding: EACL 2017 4
Tasks: Aspect Extraction, Opinion Mining

Reproducing experiments is an important instrument to validate previous work
and build upon existing approaches. It has been tackled numerous times in
different areas of science. In this paper, we introduce an empirical
replicability study of three well-known algorithms for syntactic centric
aspect-based opinion mining. We show that reproducing results continues to be a
difficult endeavor, mainly due to the lack of details regarding preprocessing
and parameter setting, as well as due to the absence of available
implementations that clarify these details. We consider these are important
threats to validity of the research on the field, specifically when compared to
other problems in NLP where public datasets and code availability are critical
validity components. We conclude by encouraging code-based research, which we
think has a key role in helping researchers to understand the meaning of the
state-of-the-art better and to generate continuous advances.
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
Authors: Yong Shean Chong, Yong Haur Tay
Date: 2017-01-06 00:00:00
Tasks: Abnormal Event Detection In Video, Anomaly Detection, Event Detection, object-detection, Object Detection, Semi-supervised Anomaly Detection

We present an efficient method for detecting anomalies in videos. Recent
applications of convolutional neural networks have shown promises of
convolutional layers for object detection and recognition, especially in
images. However, convolutional neural networks are supervised and require
labels as learning signals. We propose a spatiotemporal architecture for
anomaly detection in videos including crowded scenes. Our architecture includes
two main components, one for spatial feature representation, and one for
learning the temporal evolution of the spatial features. Experimental results
on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of
our method is comparable to state-of-the-art methods at a considerable speed of
up to 140 fps.