pwc-archive/papers-with-abstracts huggingface dataset:
Saliency Integration: An Arbitrator Model
Authors: Yingyue Xu, Xiaopeng Hong, Fatih Porikli, Xin Liu, Jie Chen, Guoying Zhao
Date: 2016-08-04 00:00:00
Tasks: model

Saliency integration has attracted much attention on unifying saliency maps
from multiple saliency models. Previous offline integration methods usually
face two challenges: 1. if most of the candidate saliency models misjudge the
saliency on an image, the integration result will lean heavily on those
inferior candidate models; 2. an unawareness of the ground truth saliency
labels brings difficulty in estimating the expertise of each candidate model.
To address these problems, in this paper, we propose an arbitrator model (AM)
for saliency integration. Firstly, we incorporate the consensus of multiple
saliency models and the external knowledge into a reference map to effectively
rectify the misleading by candidate models. Secondly, our quest for ways of
estimating the expertise of the saliency models without ground truth labels
gives rise to two distinct online model-expertise estimation methods. Finally,
we derive a Bayesian integration framework to reconcile the saliency models of
varying expertise and the reference map. To extensively evaluate the proposed
AM model, we test twenty-seven state-of-the-art saliency models, covering both
traditional and deep learning ones, on various combinations over four datasets.
The evaluation results show that the AM model improves the performance
substantially compared to the existing state-of-the-art integration methods,
regardless of the chosen candidate saliency models.
Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos
Authors: Suman Saha, Gurkirt Singh, Michael Sapienza, Philip H. S. Torr, Fabio Cuzzolin
Date: 2016-08-04 00:00:00
Tasks: Action Detection, Motion Detection, Optical Flow Estimation

In this work, we propose an approach to the spatiotemporal localisation
(detection) and classification of multiple concurrent actions within temporally
untrimmed videos. Our framework is composed of three stages. In stage 1,
appearance and motion detection networks are employed to localise and score
actions from colour images and optical flow. In stage 2, the appearance network
detections are boosted by combining them with the motion detection scores, in
proportion to their respective spatial overlap. In stage 3, sequences of
detection boxes most likely to be associated with a single action instance,
called action tubes, are constructed by solving two energy maximisation
problems via dynamic programming. While in the first pass, action paths
spanning the whole video are built by linking detection boxes over time using
their class-specific scores and their spatial overlap, in the second pass,
temporal trimming is performed by ensuring label consistency for all
constituting detection boxes. We demonstrate the performance of our algorithm
on the challenging UCF101, J-HMDB-21 and LIRIS-HARL datasets, achieving new
state-of-the-art results across the board and significantly increasing
detection speed at test time. We achieve a huge leap forward in action
detection performance and report a 20% and 11% gain in mAP (mean average
precision) on UCF-101 and J-HMDB-21 datasets respectively when compared to the
state-of-the-art.
Recoding Color Transfer as a Color Homography
Authors: Han Gong, Graham D. Finlayson, Robert B. Fisher
Date: 2016-08-04 00:00:00
Tasks: Image Compression

Color transfer is an image editing process that adjusts the colors of a
picture to match a target picture's color theme. A natural color transfer not
only matches the color styles but also prevents after-transfer artifacts due to
image compression, noise, and gradient smoothness change. The recently
discovered color homography theorem proves that colors across a change in
photometric viewing condition are related by a homography. In this paper, we
propose a color-homography-based color transfer decomposition which encodes
color transfer as a combination of chromaticity shift and shading adjustment. A
powerful form of shading adjustment is shown to be a global shading curve by
which the same shading homography can be applied elsewhere. Our experiments
show that the proposed color transfer decomposition provides a very close
approximation to many popular color transfer methods. The advantage of our
approach is that the learned color transfer can be applied to many other images
(e.g. other frames in a video), instead of a frame-to-frame basis. We
demonstrate two applications for color transfer enhancement and video color
grading re-application. This simple model of color transfer is also important
for future color transfer algorithm design.
Black-Box Policy Search with Probabilistic Programs
Authors: Jan-Willem van de Meent, Brooks Paige, David Tolpin, Frank Wood
Date: 2015-07-16 00:00:00

In this work, we explore how probabilistic programs can be used to represent
policies in sequential decision problems. In this formulation, a probabilistic
program is a black-box stochastic simulator for both the problem domain and the
agent. We relate classic policy gradient techniques to recently introduced
black-box variational methods which generalize to probabilistic program
inference. We present case studies in the Canadian traveler problem, Rock
Sample, and a benchmark for optimal diagnosis inspired by Guess Who. Each study
illustrates how programs can efficiently represent policies using moderate
numbers of parameters.
An improved uncertainty decoding scheme with weighted samples for DNN-HMM hybrid systems
Authors: Christian Huemmer, Ramón Fernández Astudillo, Walter Kellermann
Date: 2016-08-04 00:00:00
Tasks: General Classification

In this paper, we advance a recently-proposed uncertainty decoding scheme for
DNN-HMM (deep neural network - hidden Markov model) hybrid systems. This
numerical sampling concept averages DNN outputs produced by a finite set of
feature samples (drawn from a probabilistic distortion model) to approximate
the posterior likelihoods of the context-dependent HMM states. As main
innovation, we propose a weighted DNN-output averaging based on a minimum
classification error criterion and apply it to a probabilistic distortion model
for spatial diffuseness features. The experimental evaluation is performed on
the 8-channel REVERB Challenge task using a DNN-HMM hybrid system with
multichannel front-end signal enhancement. We show that the recognition
accuracy of the DNN-HMM hybrid system improves by incorporating uncertainty
decoding based on random sampling and that the proposed weighted DNN-output
averaging further reduces the word error rate scores.
UnitBox: An Advanced Object Detection Network
Authors: Jiahui Yu, Yuning Jiang, Zhangyang Wang, Zhimin Cao, Thomas Huang
Date: 2016-08-04 00:00:00
Tasks: Face Detection, Object, object-detection, Object Detection, Region Proposal

In present object detection systems, the deep convolutional neural networks
(CNNs) are utilized to predict bounding boxes of object candidates, and have
gained performance advantages over the traditional region proposal methods.
However, existing deep CNN methods assume the object bounds to be four
independent variables, which could be regressed by the $\ell_2$ loss
separately. Such an oversimplified assumption is contrary to the well-received
observation, that those variables are correlated, resulting to less accurate
localization. To address the issue, we firstly introduce a novel Intersection
over Union ($IoU$) loss function for bounding box prediction, which regresses
the four bounds of a predicted box as a whole unit. By taking the advantages of
$IoU$ loss and deep fully convolutional networks, the UnitBox is introduced,
which performs accurate and efficient localization, shows robust to objects of
varied shapes and scales, and converges fast. We apply UnitBox on face
detection task and achieve the best performance among all published methods on
the FDDB benchmark.
An Evolutionary Strategy based on Partial Imitation for Solving Optimization Problems
Authors: Marco Alberto Javarone
Date: 2016-02-12 00:00:00
Tasks: Combinatorial Optimization, Traveling Salesman Problem

In this work we introduce an evolutionary strategy to solve combinatorial
optimization tasks, i.e. problems characterized by a discrete search space. In
particular, we focus on the Traveling Salesman Problem (TSP), i.e. a famous
problem whose search space grows exponentially, increasing the number of
cities, up to becoming NP-hard. The solutions of the TSP can be codified by
arrays of cities, and can be evaluated by fitness, computed according to a cost
function (e.g. the length of a path). Our method is based on the evolution of
an agent population by means of an imitative mechanism, we define `partial
imitation'. In particular, agents receive a random solution and then,
interacting among themselves, may imitate the solutions of agents with a higher
fitness. Since the imitation mechanism is only partial, agents copy only one
entry (randomly chosen) of another array (i.e. solution). In doing so, the
population converges towards a shared solution, behaving like a spin system
undergoing a cooling process, i.e. driven towards an ordered phase. We
highlight that the adopted `partial imitation' mechanism allows the population
to generate solutions over time, before reaching the final equilibrium. Results
of numerical simulations show that our method is able to find, in a finite
time, both optimal and suboptimal solutions, depending on the size of the
considered search space.
Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations
Authors: Hao Yang, Joey Tianyi Zhou, Jianfei Cai
Date: 2016-08-04 00:00:00
Tasks: Missing Labels, Multi-Label Learning, Object Recognition

Multi-label learning has attracted significant interests in computer vision
recently, finding applications in many vision tasks such as multiple object
recognition and automatic image annotation. Associating multiple labels to a
complex image is very difficult, not only due to the intricacy of describing
the image, but also because of the incompleteness nature of the observed
labels. Existing works on the problem either ignore the label-label and
instance-instance correlations or just assume these correlations are linear and
unstructured. Considering that semantic correlations between images are
actually structured, in this paper we propose to incorporate structured
semantic correlations to solve the missing label problem of multi-label
learning. Specifically, we project images to the semantic space with an
effective semantic descriptor. A semantic graph is then constructed on these
images to capture the structured correlations between them. We utilize the
semantic graph Laplacian as a smooth term in the multi-label learning
formulation to incorporate the structured semantic correlations. Experimental
results demonstrate the effectiveness of the proposed semantic descriptor and
the usefulness of incorporating the structured semantic correlations. We
achieve better results than state-of-the-art multi-label learning methods on
four benchmark datasets.
Discriminative Segmental Cascades for Feature-Rich Phone Recognition
Authors: Hao Tang, Weiran Wang, Kevin Gimpel, Karen Livescu
Date: 2015-07-22 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition, Structured Prediction

Discriminative segmental models, such as segmental conditional random fields
(SCRFs) and segmental structured support vector machines (SSVMs), have had
success in speech recognition via both lattice rescoring and first-pass
decoding. However, such models suffer from slow decoding, hampering the use of
computationally expensive features, such as segment neural networks or other
high-order features. A typical solution is to use approximate decoding, either
by beam pruning in a single pass or by beam pruning to generate a lattice
followed by a second pass. In this work, we study discriminative segmental
models trained with a hinge loss (i.e., segmental structured SVMs). We show
that beam search is not suitable for learning rescoring models in this
approach, though it gives good approximate decoding performance when the model
is already well-trained. Instead, we consider an approach inspired by
structured prediction cascades, which use max-marginal pruning to generate
lattices. We obtain a high-accuracy phonetic recognition system with several
expensive feature types: a segment neural network, a second-order language
model, and second-order phone boundary features.
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks
Authors: Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F. Stewart, Jimeng Sun
Date: 2015-11-18 00:00:00

Leveraging large historical data in electronic health record (EHR), we
developed Doctor AI, a generic predictive model that covers observed medical
conditions and medication uses. Doctor AI is a temporal model using recurrent
neural networks (RNN) and was developed and applied to longitudinal time
stamped EHR data from 260K patients over 8 years. Encounter records (e.g.
diagnosis codes, medication codes or procedure codes) were input to RNN to
predict (all) the diagnosis and medication categories for a subsequent visit.
Doctor AI assesses the history of patients to make multilabel predictions (one
label for each diagnosis or medication category). Based on separate blind test
set evaluation, Doctor AI can perform differential diagnosis with up to 79%
recall@30, significantly higher than several baselines. Moreover, we
demonstrate great generalizability of Doctor AI by adapting the resulting
models from one institution to another without losing substantial accuracy.
Bayesian Kernel and Mutual $k$-Nearest Neighbor Regression
Authors: Hyun-Chul Kim
Date: 2016-08-04 00:00:00
Tasks: regression

We propose Bayesian extensions of two nonparametric regression methods which
are kernel and mutual $k$-nearest neighbor regression methods. Derived based on
Gaussian process models for regression, the extensions provide distributions
for target value estimates and the framework to select the hyperparameters. It
is shown that both the proposed methods asymptotically converge to kernel and
mutual $k$-nearest neighbor regression methods, respectively. The simulation
results show that the proposed methods can select proper hyperparameters and
are better than or comparable to the former methods for an artificial data set
and a real world data set.
Coordination in Categorical Compositional Distributional Semantics
Authors: Dimitri Kartsaklis
Date: 2016-06-05 00:00:00

An open problem with categorical compositional distributional semantics is
the representation of words that are considered semantically vacuous from a
distributional perspective, such as determiners, prepositions, relative
pronouns or coordinators. This paper deals with the topic of coordination
between identical syntactic types, which accounts for the majority of
coordination cases in language. By exploiting the compact closed structure of
the underlying category and Frobenius operators canonically induced over the
fixed basis of finite-dimensional vector spaces, we provide a morphism as
representation of a coordinator tensor, and we show how it lifts from atomic
types to compound types. Linguistic intuitions are provided, and the importance
of the Frobenius operators as an addition to the compact closed setting with
regard to language is discussed.
A Corpus-based Toy Model for DisCoCat
Authors: Stefano Gogioso
Date: 2016-05-13 00:00:00
Tasks: model

The categorical compositional distributional (DisCoCat) model of meaning
rigorously connects distributional semantics and pregroup grammars, and has
found a variety of applications in computational linguistics. From a more
abstract standpoint, the DisCoCat paradigm predicates the construction of a
mapping from syntax to categorical semantics. In this work we present a
concrete construction of one such mapping, from a toy model of syntax for
corpora annotated with constituent structure trees, to categorical semantics
taking place in a category of free R-semimodules over an involutive commutative
semiring R.
Quantum Algorithms for Compositional Natural Language Processing
Authors: William Zeng, Bob Coecke
Date: 2016-08-04 00:00:00
Tasks: Dimensionality Reduction

We propose a new application of quantum computing to the field of natural
language processing. Ongoing work in this field attempts to incorporate
grammatical structure into algorithms that compute meaning. In (Coecke,
Sadrzadeh and Clark, 2010), the authors introduce such a model (the CSC model)
based on tensor product composition. While this algorithm has many advantages,
its implementation is hampered by the large classical computational resources
that it requires. In this work we show how computational shortcomings of the
CSC approach could be resolved using quantum computation (possibly in addition
to existing techniques for dimension reduction). We address the value of
quantum RAM (Giovannetti,2008) for this model and extend an algorithm from
Wiebe, Braun and Lloyd (2012) into a quantum algorithm to categorize sentences
in CSC. Our new algorithm demonstrates a quadratic speedup over classical
methods under certain conditions.
Entailment Relations on Distributions
Authors: John van de Wetering
Date: 2016-08-04 00:00:00

In this paper we give an overview of partial orders on the space of
probability distributions that carry a notion of information content and serve
as a generalisation of the Bayesian order given in (Coecke and Martin, 2011).
We investigate what constraints are necessary in order to get a unique notion
of information content. These partial orders can be used to give an ordering on
words in vector space models of natural language meaning relating to the
contexts in which words are used, which is useful for a notion of entailment
and word disambiguation. The construction used also points towards a way to
create orderings on the space of density operators which allow a more
fine-grained study of entailment. The partial orders in this paper are directed
complete and form domains in the sense of domain theory.
Quantifier Scope in Categorical Compositional Distributional Semantics
Authors: Mehrnoosh Sadrzadeh
Date: 2016-08-04 00:00:00

In previous work with J. Hedges, we formalised a generalised quantifiers
theory of natural language in categorical compositional distributional
semantics with the help of bialgebras. In this paper, we show how quantifier
scope ambiguity can be represented in that setting and how this representation
can be generalised to branching quantifiers.
Words, Concepts, and the Geometry of Analogy
Authors: Stephen McGregor, Matthew Purver, Geraint Wiggins
Date: 2016-08-04 00:00:00

This paper presents a geometric approach to the problem of modelling the
relationship between words and concepts, focusing in particular on analogical
phenomena in language and cognition. Grounded in recent theories regarding
geometric conceptual spaces, we begin with an analysis of existing static
distributional semantic models and move on to an exploration of a dynamic
approach to using high dimensional spaces of word meaning to project subspaces
where analogies can potentially be solved in an online, contextualised way. The
crucial element of this analysis is the positioning of statistics in a
geometric environment replete with opportunities for interpretation.
Interacting Conceptual Spaces
Authors: Josef Bolt, Bob Coecke, Fabrizio Genovese, Martha Lewis, Daniel Marsden, Robin Piedeleu
Date: 2016-08-04 00:00:00

We propose applying the categorical compositional scheme of [6] to conceptual
space models of cognition. In order to do this we introduce the category of
convex relations as a new setting for categorical compositional semantics,
emphasizing the convex structure important to conceptual space applications. We
show how conceptual spaces for composite types such as adjectives and verbs can
be constructed. We illustrate this new model on detailed examples.
Dual Density Operators and Natural Language Meaning
Authors: Daniela Ashoush, Bob Coecke
Date: 2016-08-04 00:00:00
Tasks: Lexical Entailment

Density operators allow for representing ambiguity about a vector
representation, both in quantum theory and in distributional natural language
meaning. Formally equivalently, they allow for discarding part of the
description of a composite system, where we consider the discarded part to be
the context. We introduce dual density operators, which allow for two
independent notions of context. We demonstrate the use of dual density
operators within a grammatical-compositional distributional framework for
natural language meaning. We show that dual density operators can be used to
simultaneously represent: (i) ambiguity about word meanings (e.g. queen as a
person vs. queen as a band), and (ii) lexical entailment (e.g. tiger ->
mammal). We provide a proof-of-concept example.
Language free character recognition using character sketch and center of gravity shifting
Authors: Masoud Nosrati, Fakhereh Rahimi, Ronak Karimi
Date: 2016-08-03 00:00:00

In this research, we present a heuristic method for character recognition.
For this purpose, a sketch is constructed from the image that contains the
character to be recognized. This sketch contains the most important pixels of
image that are representatives of original image. These points are the most
probable points in pixel-by-pixel matching of image that adapt to target image.
Furthermore, a technique called gravity shifting is utilized for taking over
the problem of elongation of characters. The consequence of combining sketch
and gravity techniques leaded to a language free character recognition method.
This method can be implemented independently for real-time uses or in
combination of other classifiers as a feature extraction algorithm. Low
complexity and acceptable performance are the most impressive features of this
method that let it to be simply implemented in mobile and battery-limited
computing devices. Results show that in the best case 86% of accuracy is
obtained and in the worst case 28% of recognized characters are accurate.
Permutation NMF
Authors: Giovanni Barbarino
Date: 2016-08-03 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Position, Translation

Nonnegative Matrix Factorization(NMF) is a common used technique in machine
learning to extract features out of data such as text documents and images
thanks to its natural clustering properties. In particular, it is popular in
image processing since it can decompose several pictures and recognize common
parts if they're located in the same position over the photos. This paper's aim
is to present a way to add the translation invariance to the classical NMF,
that is, the algorithms presented are able to detect common features, even when
they're shifted, in different original images.
Multiple Instance Dictionary Learning using Functions of Multiple Instances
Authors: Changzhe Jiao, Alina Zare
Date: 2015-11-09 00:00:00
Tasks: Dictionary Learning, General Classification, Multiple Instance Learning

A multiple instance dictionary learning method using functions of multiple
instances (DL-FUMI) is proposed to address target detection and two-class
classification problems with inaccurate training labels. Given inaccurate
training labels, DL-FUMI learns a set of target dictionary atoms that describe
the most distinctive and representative features of the true positive class as
well as a set of nontarget dictionary atoms that account for the shared
information found in both the positive and negative instances. Experimental
results show that the estimated target dictionary atoms found by DL-FUMI are
more representative prototypes and identify better discriminative features of
the true positive class than existing methods in the literature. DL-FUMI is
shown to have significantly better performance on several target detection and
classification problems as compared to other multiple instance learning (MIL)
dictionary learning algorithms on a variety of MIL problems.