pwc-archive/papers-with-abstracts huggingface dataset:
Images Don't Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank
Authors: Corey Lynch, Kamelia Aryafar, Josh Attenberg
Date: 2015-11-20 00:00:00
Tasks: Learning-To-Rank

Search is at the heart of modern e-commerce. As a result, the task of ranking
search results automatically (learning to rank) is a multibillion dollar
machine learning problem. Traditional models optimize over a few
hand-constructed features based on the item's text. In this paper, we introduce
a multimodal learning to rank model that combines these traditional features
with visual semantic features transferred from a deep convolutional neural
network. In a large scale experiment using data from the online marketplace
Etsy, we verify that moving to a multimodal representation significantly
improves ranking quality. We show how image features can capture fine-grained
style information not available in a text-only representation. In addition, we
show concrete examples of how image information can successfully disentangle
pairs of highly different items that are ranked similarly by a text-only model.
Efficient Learning of Ensembles with QuadBoost
Authors: Louis Fortier-Dubois, François Laviolette, Mario Marchand, Louis-Emile Robitaille, Jean-Francis Roy
Date: 2015-06-08 00:00:00

We first present a general risk bound for ensembles that depends on the Lp
norm of the weighted combination of voters which can be selected from a
continuous set. We then propose a boosting method, called QuadBoost, which is
strongly supported by the general risk bound and has very simple rules for
assigning the voters' weights. Moreover, QuadBoost exhibits a rate of decrease
of its empirical error which is slightly faster than the one achieved by
AdaBoost. The experimental results confirm the expectation of the theory that
QuadBoost is a very efficient method for learning ensembles.
Machine Learning Classification of SDSS Transient Survey Images
Authors: L. du Buisson, N. Sivanandam, B. A. Bassett, M. Smith
Date: 2014-07-15 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Survey

We show that multiple machine learning algorithms can match human performance
in classifying transient imaging data from the Sloan Digital Sky Survey (SDSS)
supernova survey into real objects and artefacts. This is a first step in any
transient science pipeline and is currently still done by humans, but future
surveys such as the Large Synoptic Survey Telescope (LSST) will necessitate
fully machine-enabled solutions. Using features trained from eigenimage
analysis (principal component analysis, PCA) of single-epoch g, r and
i-difference images, we can reach a completeness (recall) of 96 per cent, while
only incorrectly classifying at most 18 per cent of artefacts as real objects,
corresponding to a precision (purity) of 84 per cent. In general, random
forests performed best, followed by the k-nearest neighbour and the SkyNet
artificial neural net algorithms, compared to other methods such as na\"ive
Bayes and kernel support vector machine. Our results show that PCA-based
machine learning can match human success levels and can naturally be extended
by including multiple epochs of data, transient colours and host galaxy
information which should allow for significant further improvements, especially
at low signal-to-noise.
Top-N recommendations from expressive recommender systems
Authors: Cyril Stark
Date: 2015-11-20 00:00:00
Tasks: Recommendation Systems

Normalized nonnegative models assign probability distributions to users and
random variables to items; see [Stark, 2015]. Rating an item is regarded as
sampling the random variable assigned to the item with respect to the
distribution assigned to the user who rates the item. Models of that kind are
highly expressive. For instance, using normalized nonnegative models we can
understand users' preferences as mixtures of interpretable user stereotypes,
and we can arrange properties of users and items in a hierarchical manner.
These features would not be useful if the predictive power of normalized
nonnegative models was poor. Thus, we analyze here the performance of
normalized nonnegative models for top-N recommendation and observe that their
performance matches the performance of methods like PureSVD which was
introduced in [Cremonesi et al., 2010]. We conclude that normalized nonnegative
models not only provide accurate recommendations but they also deliver (for
free) representations that are interpretable. We deepen the discussion of
normalized nonnegative models by providing further theoretical insights. In
particular, we introduce total variational distance as an operational
similarity measure, we discover scenarios where normalized nonnegative models
yield unique representations of users and items, we prove that the inference of
optimal normalized nonnegative models is NP-hard and finally, we discuss the
relationship between normalized nonnegative models and nonnegative matrix
factorization.
Semantic Diversity versus Visual Diversity in Visual Dictionaries
Authors: Otávio A. B. Penatti, Sandra Avila, Eduardo Valle, Ricardo da S. Torres
Date: 2015-11-20 00:00:00
Tasks: Diversity, General Classification, image-classification, Image Classification, Retrieval

Visual dictionaries are a critical component for image
classification/retrieval systems based on the bag-of-visual-words (BoVW) model.
Dictionaries are usually learned without supervision from a training set of
images sampled from the collection of interest. However, for large,
general-purpose, dynamic image collections (e.g., the Web), obtaining a
representative sample in terms of semantic concepts is not straightforward. In
this paper, we evaluate the impact of semantics in the dictionary quality,
aiming at verifying the importance of semantic diversity in relation visual
diversity for visual dictionaries. In the experiments, we vary the amount of
classes used for creating the dictionary and then compute different BoVW
descriptors, using multiple codebook sizes and different coding and pooling
methods (standard BoVW and Fisher Vectors). Results for image classification
show that as visual dictionaries are based on low-level visual appearances,
visual diversity is more important than semantic diversity. Our conclusions
open the opportunity to alleviate the burden in generating visual dictionaries
as we need only a visually diverse set of images instead of the whole
collection to create a good dictionary.
Contamination-Free Measures and Algebraic Operations
Authors: A. Mani
Date: 2015-11-20 00:00:00

An open concept of rough evolution and an axiomatic approach to granules was
also developed recently by the present author. Subsequently the concepts were
used in the formal framework of rough Y-systems (RYS) for developing on
granular correspondences by her. These have since been used for a new approach
towards comparison of rough algebraic semantics across different semantic
domains by way of correspondences that preserve rough evolution and try to
avoid contamination. In this research paper, new methods are proposed and a
semantics for handling possibly contaminated operations and structured bigness
is developed. These would also be of natural interest for relative consistency
of one collection of knowledge relative other.
Top-k Multiclass SVM
Authors: Maksim Lapin, Matthias Hein, Bernt Schiele
Date: 2015-11-20 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: General Classification, image-classification, Image Classification

Class ambiguity is typical in image classification problems with a large
number of classes. When classes are difficult to discriminate, it makes sense
to allow k guesses and evaluate classifiers based on the top-k error instead of
the standard zero-one loss. We propose top-k multiclass SVM as a direct method
to optimize for top-k performance. Our generalization of the well-known
multiclass SVM is based on a tight convex upper bound of the top-k error. We
propose a fast optimization scheme based on an efficient projection onto the
top-k simplex, which is of its own interest. Experiments on five datasets show
consistent improvements in top-k accuracy compared to various baselines.
Deep End2End Voxel2Voxel Prediction
Authors: Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri
Date: 2015-11-20 00:00:00
Tasks: Neural Architecture Search, Optical Flow Estimation, Prediction, Semantic Segmentation, Video Classification, Video Semantic Segmentation

Over the last few years deep learning methods have emerged as one of the most
prominent approaches for video analysis. However, so far their most successful
applications have been in the area of video classification and detection, i.e.,
problems involving the prediction of a single class label or a handful of
output variables per video. Furthermore, while deep networks are commonly
recognized as the best models to use in these domains, there is a widespread
perception that in order to yield successful results they often require
time-consuming architecture search, manual tweaking of parameters and
computationally intensive pre-processing or post-processing methods.
  In this paper we challenge these views by presenting a deep 3D convolutional
architecture trained end to end to perform voxel-level prediction, i.e., to
output a variable at every voxel of the video. Most importantly, we show that
the same exact architecture can be used to achieve competitive results on three
widely different voxel-prediction tasks: video semantic segmentation, optical
flow estimation, and video coloring. The three networks learned on these
problems are trained from raw video without any form of preprocessing and their
outputs do not require post-processing to achieve outstanding performance.
Thus, they offer an efficient alternative to traditional and much more
computationally expensive methods in these video domains.
Stories in the Eye: Contextual Visual Interactions for Efficient Video to Language Translation
Authors: Anirudh Goyal, Marius Leordeanu
Date: 2015-11-20 00:00:00
Tasks: Language Modeling, Language Modelling, Translation

Integrating higher level visual and linguistic interpretations is at the
heart of human intelligence. As automatic visual category recognition in images
is approaching human performance, the high level understanding in the dynamic
spatiotemporal domain of videos and its translation into natural language is
still far from being solved. While most works on vision-to-text translations
use pre-learned or pre-established computational linguistic models, in this
paper we present an approach that uses vision alone to efficiently learn how to
translate into language the video content. We discover, in simple form, the
story played by main actors, while using only visual cues for representing
objects and their interactions. Our method learns in a hierarchical manner
higher level representations for recognizing subjects, actions and objects
involved, their relevant contextual background and their interaction to one
another over time. We have a three stage approach: first we take in
consideration features of the individual entities at the local level of
appearance, then we consider the relationship between these objects and actions
and their video background, and third, we consider their spatiotemporal
relations as inputs to classifiers at the highest level of interpretation.
Thus, our approach finds a coherent linguistic description of videos in the
form of a subject, verb and object based on their role played in the overall
visual story learned directly from training data, without using a known
language model. We test the efficiency of our approach on a large scale dataset
containing YouTube clips taken in the wild and demonstrate state-of-the-art
performance, often superior to current approaches that use more complex,
pre-learned linguistic knowledge.
L1 logistic regression as a feature selection step for training stable classification trees for the prediction of severity criteria in imported malaria
Authors: Luca Talenti, Margaux Luck, Anastasia Yartseva, Nicolas Argy, Sandrine Houzé, Cecilia Damon
Date: 2015-11-20 00:00:00
Tasks: Classification, feature selection, General Classification, regression, Small Data Image Classification

Multivariate classification methods using explanatory and predictive models
are necessary for characterizing subgroups of patients according to their risk
profiles. Popular methods include logistic regression and classification trees
with performances that vary according to the nature and the characteristics of
the dataset. In the context of imported malaria, we aimed at classifying
severity criteria based on a heterogeneous patient population. We investigated
these approaches by implementing two different strategies: L1 logistic
regression (L1LR) that models a single global solution and classification trees
that model multiple local solutions corresponding to discriminant subregions of
the feature space. For each strategy, we built a standard model, and a sparser
version of it. As an alternative to pruning, we explore a promising approach
that first constrains the tree model with an L1LR-based feature selection, an
approach we called L1LR-Tree. The objective is to decrease its vulnerability to
small data variations by removing variables corresponding to unstable local
phenomena. Our study is twofold: i) from a methodological perspective comparing
the performances and the stability of the three previous methods, i.e L1LR,
classification trees and L1LR-Tree, for the classification of severe forms of
imported malaria, and ii) from an applied perspective improving the actual
classification of severe forms of imported malaria by identifying more
personalized profiles predictive of several clinical criteria based on
variables dismissed for the clinical definition of the disease. The main
methodological results show that the combined method L1LR-Tree builds sparse
and stable models that significantly predicts the different severity criteria
and outperforms all the other methods in terms of accuracy.
Empirical Study on Deep Learning Models for Question Answering
Authors: Yang Yu, Wei zhang, Chung-Wei Hang, Bing Xiang, Bo-Wen Zhou
Date: 2015-10-26 00:00:00
Tasks: Deep Learning, Machine Translation, Question Answering, Translation

In this paper we explore deep learning models with memory component or
attention mechanism for question answering task. We combine and compare three
models, Neural Machine Translation, Neural Turing Machine, and Memory Networks
for a simulated QA data set. This paper is the first one that uses Neural
Machine Translation and Neural Turing Machines for solving QA tasks. Our
results suggest that the combination of attention and memory have potential to
solve certain QA problem.
A Model for Safety Case Confidence Assessment
Authors: Jérémie Guiochet, Quynh Anh Do Hoang, Mohamed Kaaniche
Date: 2015-11-20 00:00:00
Tasks: model

Building a safety case is a common approach to make expert judgement explicit
about safety of a system. The issue of confidence in such argumentation is
still an open research field. Providing quantitative estimation of confidence
is an interesting approach to manage complexity of arguments. This paper
explores the main current approaches, and proposes a new model for quantitative
confidence estimation based on Belief Theory for its definition, and on
Bayesian Belief Networks for its propagation in safety case networks.
Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation
Authors: Matthias J. Ehrhardt, Marta M. Betcke
Date: 2015-11-20 00:00:00
Tasks: Anatomy, MRI Reconstruction

Magnetic resonance imaging (MRI) is a versatile imaging technique that allows
different contrasts depending on the acquisition parameters. Many clinical
imaging studies acquire MRI data for more than one of these contrasts---such as
for instance T1 and T2 weighted images---which makes the overall scanning
procedure very time consuming. As all of these images show the same underlying
anatomy one can try to omit unnecessary measurements by taking the similarity
into account during reconstruction. We will discuss two modifications of total
variation---based on i) location and ii) direction---that take structural a
priori knowledge into account and reduce to total variation in the degenerate
case when no structural knowledge is available. We solve the resulting convex
minimization problem with the alternating direction method of multipliers that
separates the forward operator from the prior. For both priors the
corresponding proximal operator can be implemented as an extension of the fast
gradient projection method on the dual problem for total variation. We tested
the priors on six data sets that are based on phantoms and real MRI images. In
all test cases exploiting the structural information from the other contrast
yields better results than separate reconstruction with total variation in
terms of standard metrics like peak signal-to-noise ratio and structural
similarity index. Furthermore, we found that exploiting the two dimensional
directional information results in images with well defined edges, superior to
those reconstructed solely using a priori information about the edge location.
Towards Arbitrary-View Face Alignment by Recommendation Trees
Authors: Shizhan Zhu, Cheng Li, Chen Change Loy, Xiaoou Tang
Date: 2015-11-20 00:00:00
Tasks: Face Alignment, Head Pose Estimation, Model Selection, Pose Estimation

Learning to simultaneously handle face alignment of arbitrary views, e.g.
frontal and profile views, appears to be more challenging than we thought. The
difficulties lay in i) accommodating the complex appearance-shape relations
exhibited in different views, and ii) encompassing the varying landmark point
sets due to self-occlusion and different landmark protocols. Most existing
studies approach this problem via training multiple viewpoint-specific models,
and conduct head pose estimation for model selection. This solution is
intuitive but the performance is highly susceptible to inaccurate head pose
estimation. In this study, we address this shortcoming through learning an
Ensemble of Model Recommendation Trees (EMRT), which is capable of selecting
optimal model configuration without prior head pose estimation. The unified
framework seamlessly handles different viewpoints and landmark protocols, and
it is trained by optimising directly on landmark locations, thus yielding
superior results on arbitrary-view face alignment. This is the first study that
performs face alignment on the full AFLWdataset with faces of different views
including profile view. State-of-the-art performances are also reported on
MultiPIE and AFW datasets containing both frontaland profile-view faces.
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
Authors: Manuel Watter, Jost Tobias Springenberg, Joschka Boedecker, Martin Riedmiller
Date: 2015-06-24 00:00:00
Proceeding: NeurIPS 2015 12

We introduce Embed to Control (E2C), a method for model learning and control
of non-linear dynamical systems from raw pixel images. E2C consists of a deep
generative model, belonging to the family of variational autoencoders, that
learns to generate image trajectories from a latent space in which the dynamics
is constrained to be locally linear. Our model is derived directly from an
optimal control formulation in latent space, supports long-term prediction of
image sequences and exhibits strong performance on a variety of complex control
problems.
Identifying the Absorption Bump with Deep Learning
Authors: Min Li, Sudeep Gaddam, Xiaolin Li, Yinan Zhao, Jingzhe Ma, Jian Ge
Date: 2015-11-17 00:00:00
Tasks: Deep Learning

The pervasive interstellar dust grains provide significant insights to
understand the formation and evolution of the stars, planetary systems, and the
galaxies, and may harbor the building blocks of life. One of the most effective
way to analyze the dust is via their interaction with the light from background
sources. The observed extinction curves and spectral features carry the size
and composition information of dust. The broad absorption bump at 2175 Angstrom
is the most prominent feature in the extinction curves. Traditionally,
statistical methods are applied to detect the existence of the absorption bump.
These methods require heavy preprocessing and the co-existence of other
reference features to alleviate the influence from the noises. In this paper,
we apply Deep Learning techniques to detect the broad absorption bump. We
demonstrate the key steps for training the selected models and their results.
The success of Deep Learning based method inspires us to generalize a common
methodology for broader science discovery problems. We present our on-going
work to build the DeepDis system for such kind of applications.
Exponential Natural Particle Filter
Authors: Ghazal Zand, Mojtaba Taherkhani, Reza Safabakhsh
Date: 2015-11-20 00:00:00
Tasks: Diversity

Particle Filter algorithm (PF) suffers from some problems such as the loss of
particle diversity, the need for large number of particles, and the costly
selection of the importance density functions. In this paper, a novel
Exponential Natural Particle Filter (xNPF) is introduced to solve the above
problems. In this approach, a state transitional probability with the use of
natural gradient learning is proposed which balances exploration and
exploitation more robustly. The results show that xNPF converges much closer to
the true target states than the other state of the art particle filter.
Polysemy in Controlled Natural Language Texts
Authors: Normunds Gruzitis, Guntis Barzdins
Date: 2015-11-20 00:00:00
Tasks: Word Sense Disambiguation

Computational semantics and logic-based controlled natural languages (CNL) do
not address systematically the word sense disambiguation problem of content
words, i.e., they tend to interpret only some functional words that are crucial
for construction of discourse representation structures. We show that
micro-ontologies and multi-word units allow integration of the rich and
polysemous multi-domain background knowledge into CNL thus providing
interpretation for the content words. The proposed approach is demonstrated by
extending the Attempto Controlled English (ACE) with polysemous and procedural
constructs resulting in a more natural CNL named PAO covering narrative
multi-domain texts.
Crowd Behavior Analysis: A Review where Physics meets Biology
Authors: Ven Jyn Kok, Mei Kuan Lim, Chee Seng Chan
Date: 2015-11-20 00:00:00

Although the traits emerged in a mass gathering are often non-deliberative,
the act of mass impulse may lead to irre- vocable crowd disasters. The two-fold
increase of carnage in crowd since the past two decades has spurred significant
advances in the field of computer vision, towards effective and proactive crowd
surveillance. Computer vision stud- ies related to crowd are observed to
resonate with the understanding of the emergent behavior in physics (complex
systems) and biology (animal swarm). These studies, which are inspired by
biology and physics, share surprisingly common insights, and interesting
contradictions. However, this aspect of discussion has not been fully explored.
Therefore, this survey provides the readers with a review of the
state-of-the-art methods in crowd behavior analysis from the physics and
biologically inspired perspectives. We provide insights and comprehensive
discussions for a broader understanding of the underlying prospect of blending
physics and biology studies in computer vision.