pwc-archive/papers-with-abstracts huggingface dataset:
Towards Practical Bayesian Parameter and State Estimation
Authors: Yusuf Bugra Erol, Yi Wu, Lei LI, Stuart Russell
Date: 2016-03-29 00:00:00
Tasks: parameter estimation, State Estimation

Joint state and parameter estimation is a core problem for dynamic Bayesian
networks. Although modern probabilistic inference toolkits make it relatively
easy to specify large and practically relevant probabilistic models, the silver
bullet---an efficient and general online inference algorithm for such
problems---remains elusive, forcing users to write special-purpose code for
each application. We propose a novel blackbox algorithm -- a hybrid of particle
filtering for state variables and assumed density filtering for parameter
variables. It has following advantages: (a) it is efficient due to its online
nature, and (b) it is applicable to both discrete and continuous parameter
spaces . On a variety of toy and real models, our system is able to generate
more accurate results within a fixed computation budget. This preliminary
evidence indicates that the proposed approach is likely to be of practical use.
Sweep Distortion Removal from THz Images via Blind Demodulation
Authors: Alireza Aghasi, Barmak Heshmat, Albert Redo-Sanchez, Justin Romberg, Ramesh Raskar
Date: 2016-03-29 00:00:00
Tasks: Denoising

Heavy sweep distortion induced by alignments and inter-reflections of layers
of a sample is a major burden in recovering 2D and 3D information in time
resolved spectral imaging. This problem cannot be addressed by conventional
denoising and signal processing techniques as it heavily depends on the physics
of the acquisition. Here we propose and implement an algorithmic framework
based on low-rank matrix recovery and alternating minimization that exploits
the forward model for THz acquisition. The method allows recovering the
original signal in spite of the presence of temporal-spatial distortions. We
address a blind-demodulation problem, where based on several observations of
the sample texture modulated by an undesired sweep pattern, the two classes of
signals are separated. The performance of the method is examined in both
synthetic and experimental data, and the successful reconstructions are
demonstrated. The proposed general scheme can be implemented to advance
inspection and imaging applications in THz and other time-resolved sensing
modalities.
Cross-modal Supervision for Learning Active Speaker Detection in Video
Authors: Punarjay Chakravarty, Tinne Tuytelaars
Date: 2016-03-29 00:00:00
Tasks: Action Detection, Active Speaker Detection, Activity Detection

In this paper, we show how to use audio to supervise the learning of active
speaker detection in video. Voice Activity Detection (VAD) guides the learning
of the vision-based classifier in a weakly supervised manner. The classifier
uses spatio-temporal features to encode upper body motion - facial expressions
and gesticulations associated with speaking. We further improve a generic model
for active speaker detection by learning person specific models. Finally, we
demonstrate the online adaptation of generic models learnt on one dataset, to
previously unseen people in a new dataset, again using audio (VAD) for weak
supervision. The use of temporal continuity overcomes the lack of clean
training data. We are the first to present an active speaker detection system
that learns on one audio-visual dataset and automatically adapts to speakers in
a new dataset. This work can be seen as an example of how the availability of
multi-modal data allows us to learn a model without the need for supervision,
by transferring knowledge from one modality to another.
Zero Shot Recognition with Unreliable Attributes
Authors: Dinesh Jayaraman, Kristen Grauman
Date: 2014-09-15 00:00:00
Tasks: Attribute, Zero-Shot Learning

In principle, zero-shot learning makes it possible to train a recognition
model simply by specifying the category's attributes. For example, with
classifiers for generic attributes like \emph{striped} and \emph{four-legged},
one can construct a classifier for the zebra category by enumerating which
properties it possesses---even without providing zebra training images. In
practice, however, the standard zero-shot paradigm suffers because attribute
predictions in novel images are hard to get right. We propose a novel random
forest approach to train zero-shot models that explicitly accounts for the
unreliability of attribute predictions. By leveraging statistics about each
attribute's error tendencies, our method obtains more robust discriminative
models for the unseen classes. We further devise extensions to handle the
few-shot scenario and unreliable attribute descriptions. On three datasets, we
demonstrate the benefit for visual category learning with zero or few training
examples, a critical domain for rare categories or categories defined on the
fly.
Learning image representations tied to ego-motion
Authors: Dinesh Jayaraman, Kristen Grauman
Date: 2015-05-08 00:00:00
Proceeding: ICCV 2015 12
Tasks: Autonomous Driving, Scene Recognition

Understanding how images of objects and scenes behave in response to specific
ego-motions is a crucial aspect of proper visual development, yet existing
visual learning methods are conspicuously disconnected from the physical source
of their images. We propose to exploit proprioceptive motor signals to provide
unsupervised regularization in convolutional neural networks to learn visual
representations from egocentric video. Specifically, we enforce that our
learned features exhibit equivariance i.e. they respond predictably to
transformations associated with distinct ego-motions. With three datasets, we
show that our unsupervised feature learning approach significantly outperforms
previous approaches on visual recognition and next-best-view prediction tasks.
In the most challenging test, we show that features learned from video captured
on an autonomous driving platform improve large-scale scene recognition in
static images from a disjoint domain.
Anisotropic Mesh Adaptation for Image Representation
Authors: Xianping Li
Date: 2014-02-20 00:00:00
Tasks: GPR, Image Reconstruction

Triangular meshes have gained much interest in image representation and have
been widely used in image processing. This paper introduces a framework of
anisotropic mesh adaptation (AMA) methods to image representation and proposes
a GPRAMA method that is based on AMA and greedy-point removal (GPR) scheme.
Different than many other methods that triangulate sample points to form the
mesh, the AMA methods start directly with a triangular mesh and then adapt the
mesh based on a user-defined metric tensor to represent the image. The AMA
methods have clear mathematical framework and provides flexibility for both
image representation and image reconstruction. A mesh patching technique is
developed for the implementation of the GPRAMA method, which leads to an
improved version of the popular GPRFS-ED method. The GPRAMA method can achieve
better quality than the GPRFS-ED method but with lower computational cost.
A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data
Authors: Adam Trischler, Zheng Ye, Xingdi Yuan, Jing He, Phillip Bachman, Kaheer Suleman
Date: 2016-03-29 00:00:00
Proceeding: ACL 2016 8
Tasks: Question Answering, Reading Comprehension, Sentence

Understanding unstructured text is a major goal within natural language
processing. Comprehension tests pose questions based on short text passages to
evaluate such understanding. In this work, we investigate machine comprehension
on the challenging {\it MCTest} benchmark. Partly because of its limited size,
prior work on {\it MCTest} has focused mainly on engineering better features.
We tackle the dataset with a neural approach, harnessing simple neural networks
arranged in a parallel hierarchy. The parallel hierarchy enables our model to
compare the passage, question, and answer from a variety of trainable
perspectives, as opposed to using a manually designed, rigid feature set.
Perspectives range from the word level to sentence fragments to sequences of
sentences; the networks operate only on word-embedding representations of text.
When trained with a methodology designed to help cope with limited training
data, our Parallel-Hierarchical model sets a new state of the art for {\it
MCTest}, outperforming previous feature-engineered approaches slightly and
previous neural approaches by a significant margin (over 15\% absolute).
Fusing Face and Periocular biometrics using Canonical correlation analysis
Authors: N. S. Lakshmiprabha
Date: 2016-03-29 00:00:00
Tasks: Face Recognition

This paper presents a novel face and periocular biometric fusion at feature
level using canonical correlation analysis. Face recognition itself has
limitations such as illumination, pose, expression, occlusion etc. Also,
periocular biometrics has spectacles, head angle, hair and expression as its
limitations. Unimodal biometrics cannot surmount all these limitations. The
recognition accuracy can be increased by fusing dual information (face and
periocular) from a single source (face image) using canonical correlation
analysis (CCA). This work also proposes a new wavelet decomposed local binary
pattern (WD-LBP) feature extractor which provides sufficient features for
fusion. A detailed analysis on face and periocular biometrics shows that WD-LBP
features are more accurate and faster than local binary pattern (LBP) and gabor
wavelet. The experimental results using Muct face database reveals that the
proposed multimodal biometrics performs better than the unimodal biometrics.
Algorithms for Batch Hierarchical Reinforcement Learning
Authors: Tiancheng Zhao, Mohammad Gowayyed
Date: 2016-03-29 00:00:00
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), valid

Hierarchical Reinforcement Learning (HRL) exploits temporal abstraction to
solve large Markov Decision Processes (MDP) and provide transferable subtask
policies. In this paper, we introduce an off-policy HRL algorithm: Hierarchical
Q-value Iteration (HQI). We show that it is possible to effectively learn
recursive optimal policies for any valid hierarchical decomposition of the
original MDP, given a fixed dataset collected from a flat stochastic behavioral
policy. We first formally prove the convergence of the algorithm for tabular
MDP. Then our experiments on the Taxi domain show that HQI converges faster
than a flat Q-value Iteration and enjoys easy state abstraction. Also, we
demonstrate that our algorithm is able to learn optimal policies for different
hierarchical structures from the same fixed dataset, which enables model
comparison without recollecting data.
A Readable Read: Automatic Assessment of Language Learning Materials based on Linguistic Complexity
Authors: Ildikó Pilán, Sowmya Vajjala, Elena Volodina
Date: 2016-03-29 00:00:00
Tasks: Sentence

Corpora and web texts can become a rich language learning resource if we have
a means of assessing whether they are linguistically appropriate for learners
at a given proficiency level. In this paper, we aim at addressing this issue by
presenting the first approach for predicting linguistic complexity for Swedish
second language learning material on a 5-point scale. After showing that the
traditional Swedish readability measure, L\"asbarhetsindex (LIX), is not
suitable for this task, we propose a supervised machine learning model, based
on a range of linguistic features, that can reliably classify texts according
to their difficulty level. Our model obtained an accuracy of 81.3% and an
F-score of 0.8, which is comparable to the state of the art in English and is
considerably higher than previously reported results for other languages. We
further studied the utility of our features with single sentences instead of
full texts since sentences are a common linguistic unit in language learning
exercises. We trained a separate model on sentence-level data with five
classes, which yielded 63.4% accuracy. Although this is lower than the document
level performance, we achieved an adjacent accuracy of 92%. Furthermore, we
found that using a combination of different features, compared to using lexical
features alone, resulted in 7% improvement in classification accuracy at the
sentence level, whereas at the document level, lexical features were more
dominant. Our models are intended for use in a freely accessible web-based
language learning platform for the automatic generation of exercises.
Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning
Authors: Tiep Huu Vu, Hojjat Seyed Mousavi, Vishal Monga, Arvind UK Rao, Ganesh Rao
Date: 2015-06-16 00:00:00
Tasks: Classification, Dictionary Learning, General Classification, image-classification, Image Classification, Medical Image Analysis

In histopathological image analysis, feature extraction for classification is
a challenging task due to the diversity of histology features suitable for each
problem as well as presence of rich geometrical structures. In this paper, we
propose an automatic feature discovery framework via learning class-specific
dictionaries and present a low-complexity method for classification and disease
grading in histopathology. Essentially, our Discriminative Feature-oriented
Dictionary Learning (DFDL) method learns class-specific dictionaries such that
under a sparsity constraint, the learned dictionaries allow representing a new
image sample parsimoniously via the dictionary corresponding to the class
identity of the sample. At the same time, the dictionary is designed to be
poorly capable of representing samples from other classes. Experiments on three
challenging real-world image databases: 1) histopathological images of
intraductal breast lesions, 2) mammalian kidney, lung and spleen images
provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University,
and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal
the merits of our proposal over state-of-the-art alternatives. {Moreover, we
demonstrate that DFDL exhibits a more graceful decay in classification accuracy
against the number of training images which is highly desirable in practice
where generous training is often not available
Face Image Analysis using AAM, Gabor, LBP and WD features for Gender, Age, Expression and Ethnicity Classification
Authors: N. S. Lakshmiprabha
Date: 2016-03-29 00:00:00
Tasks: Age Estimation, General Classification, Retrieval

The growth in electronic transactions and human machine interactions rely on
the information such as gender, age, expression and ethnicity provided by the
face image. In order to obtain these information, feature extraction plays a
major role. In this paper, retrieval of age, gender, expression and race
information from an individual face image is analysed using different feature
extraction methods. The performance of four major feature extraction methods
such as Active Appearance Model (AAM), Gabor wavelets, Local Binary Pattern
(LBP) and Wavelet Decomposition (WD) are analyzed for gender recognition, age
estimation, expression recognition and racial recognition in terms of accuracy
(recognition rate), time for feature extraction, neural training and time to
test an image. Each of this recognition system is compared with four feature
extractors on same dataset (training and validation set) to get a better
understanding in its performance. Experiments carried out on FG-NET,
Cohn-Kanade, PAL face database shows that each method has its own merits and
demerits. Hence it is practically impossible to define a method which is best
at all circumstances with less computational complexity. Further, a detailed
comparison of age estimation and age estimation using gender information is
provided along with a solution to overcome aging effect in case of gender
recognition. An attempt has been made in obtaining all (i.e. gender, age range,
expression and ethnicity) information from a test image in a single go.
Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community
Authors: Ethan Fast, Tina Vachovsky, Michael S. Bernstein
Date: 2016-03-29 00:00:00

Imagine a princess asleep in a castle, waiting for her prince to slay the
dragon and rescue her. Tales like the famous Sleeping Beauty clearly divide up
gender roles. But what about more modern stories, borne of a generation
increasingly aware of social constructs like sexism and racism? Do these
stories tend to reinforce gender stereotypes, or counter them? In this paper,
we present a technique that combines natural language processing with a
crowdsourced lexicon of stereotypes to capture gender biases in fiction. We
apply this technique across 1.8 billion words of fiction from the Wattpad
online writing community, investigating gender representation in stories, how
male and female characters behave and are described, and how authors' use of
gender stereotypes is associated with the community's ratings. We find that
male over-representation and traditional gender stereotypes (e.g., dominant men
and submissive women) are common throughout nearly every genre in our corpus.
However, only some of these stereotypes, like sexual or violent men, are
associated with highly rated stories. Finally, despite women often being the
target of negative stereotypes, female authors are equally likely to write such
stereotypes as men.
Spectral M-estimation with Applications to Hidden Markov Models
Authors: Dustin Tran, Minjae Kim, Finale Doshi-Velez
Date: 2016-03-29 00:00:00

Method of moment estimators exhibit appealing statistical properties, such as
asymptotic unbiasedness, for nonconvex problems. However, they typically
require a large number of samples and are extremely sensitive to model
misspecification. In this paper, we apply the framework of M-estimation to
develop both a generalized method of moments procedure and a principled method
for regularization. Our proposed M-estimator obtains optimal sample efficiency
rates (in the class of moment-based estimators) and the same well-known rates
on prediction accuracy as other spectral estimators. It also makes it
straightforward to incorporate regularization into the sample moment
conditions. We demonstrate empirically the gains in sample efficiency from our
approach on hidden Markov models.
Locally Epistatic Models for Genome-wide Prediction and Association by Importance Sampling
Authors: Deniz Akdemir, Jean-Luc Jannink
Date: 2016-03-29 00:00:00
Tasks: Attribute

In statistical genetics an important task involves building predictive models
for the genotype-phenotype relationships and thus attribute a proportion of the
total phenotypic variance to the variation in genotypes. Numerous models have
been proposed to incorporate additive genetic effects into models for
prediction or association. However, there is a scarcity of models that can
adequately account for gene by gene or other forms of genetical interactions.
In addition, there is an increased interest in using marker annotations in
genome-wide prediction and association. In this paper, we discuss an hybrid
modeling methodology which combines the parametric mixed modeling approach and
the non-parametric rule ensembles. This approach gives us a flexible class of
models that can be used to capture additive, locally epistatic genetic effects,
gene x background interactions and allows us to incorporate one or more
annotations into the genomic selection or association models. We use benchmark
data sets covering a range of organisms and traits in addition to simulated
data sets to illustrate the strengths of this approach. The improvement of
model accuracies and association results suggest that a part of the "missing
heritability" in complex traits can be captured by modeling local epistasis.
Scalable Solution for Approximate Nearest Subspace Search
Authors: Masakazu Iwamura, Masataka Konishi, Koichi Kise
Date: 2016-03-29 00:00:00

Finding the nearest subspace is a fundamental problem and influential to many
applications. In particular, a scalable solution that is fast and accurate for
a large problem has a great impact. The existing methods for the problem are,
however, useless in a large-scale problem with a large number of subspaces and
high dimensionality of the feature space. A cause is that they are designed
based on the traditional idea to represent a subspace by a single point. In
this paper, we propose a scalable solution for the approximate nearest subspace
search (ANSS) problem. Intuitively, the proposed method represents a subspace
by multiple points unlike the existing methods. This makes a large-scale ANSS
problem tractable. In the experiment with 3036 subspaces in the
1024-dimensional space, we confirmed that the proposed method was 7.3 times
faster than the previous state-of-the-art without loss of accuracy.
Session-based Recommendations with Recurrent Neural Networks
Authors: Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk
Date: 2015-11-21 00:00:00
Tasks: Recommendation Systems, Session-Based Recommendations

We apply recurrent neural networks (RNN) on a new domain, namely recommender
systems. Real-life recommender systems often face the problem of having to base
recommendations only on short session-based data (e.g. a small sportsware
website) instead of long user histories (as in the case of Netflix). In this
situation the frequently praised matrix factorization approaches are not
accurate. This problem is usually overcome in practice by resorting to
item-to-item recommendations, i.e. recommending similar items. We argue that by
modeling the whole session, more accurate recommendations can be provided. We
therefore propose an RNN-based approach for session-based recommendations. Our
approach also considers practical aspects of the task and introduces several
modifications to classic RNNs such as a ranking loss function that make it more
viable for this specific problem. Experimental results on two data-sets show
marked improvements over widely used approaches.
A Computational Model for Amodal Completion
Authors: Maria Oliver, Gloria Haro, Mariella Dimiccoli, Baptiste Mazin, Coloma Ballester
Date: 2015-11-26 00:00:00
Tasks: model

This paper presents a computational model to recover the most likely
interpretation of the 3D scene structure from a planar image, where some
objects may occlude others. The estimated scene interpretation is obtained by
integrating some global and local cues and provides both the complete
disoccluded objects that form the scene and their ordering according to depth.
Our method first computes several distal scenes which are compatible with the
proximal planar image. To compute these different hypothesized scenes, we
propose a perceptually inspired object disocclusion method, which works by
minimizing the Euler's elastica as well as by incorporating the relatability of
partially occluded contours and the convexity of the disoccluded objects. Then,
to estimate the preferred scene we rely on a Bayesian model and define
probabilities taking into account the global complexity of the objects in the
hypothesized scenes as well as the effort of bringing these objects in their
relative position in the planar image, which is also measured by an Euler's
elastica-based quantity. The model is illustrated with numerical experiments
on, both, synthetic and real images showing the ability of our model to
reconstruct the occluded objects and the preferred perceptual order among them.
We also present results on images of the Berkeley dataset with provided
figure-ground ground-truth labeling.