pwc-archive/papers-with-abstracts huggingface dataset:
Cross-Modal Similarity Learning : A Low Rank Bilinear Formulation
Authors: Cuicui Kang, Shengcai Liao, Yonghao He, Jian Wang, Wenjia Niu, Shiming Xiang, Chunhong Pan
Date: 2014-11-18 00:00:00
Tasks: Metric Learning, Retrieval

The cross-media retrieval problem has received much attention in recent years
due to the rapid increasing of multimedia data on the Internet. A new approach
to the problem has been raised which intends to match features of different
modalities directly. In this research, there are two critical issues: how to
get rid of the heterogeneity between different modalities and how to match the
cross-modal features of different dimensions. Recently metric learning methods
show a good capability in learning a distance metric to explore the
relationship between data points. However, the traditional metric learning
algorithms only focus on single-modal features, which suffer difficulties in
addressing the cross-modal features of different dimensions. In this paper, we
propose a cross-modal similarity learning algorithm for the cross-modal feature
matching. The proposed method takes a bilinear formulation, and with the
nuclear-norm penalization, it achieves low-rank representation. Accordingly,
the accelerated proximal gradient algorithm is successfully imported to find
the optimal solution with a fast convergence rate O(1/t^2). Experiments on
three well known image-text cross-media retrieval databases show that the
proposed method achieves the best performance compared to the state-of-the-art
algorithms.
Numerical Demultiplexing of Color Image Sensor Measurements via Non-linear Random Forest Modeling
Authors: Jason Deglint, Farnoud Kazemzadeh, Daniel Cho, David A. Clausi, Alexander Wong
Date: 2015-12-17 00:00:00

The simultaneous capture of imaging data at multiple wavelengths across the
electromagnetic spectrum is highly challenging, requiring complex and costly
multispectral image sensors. In this study, we introduce a comprehensive
framework for performing simultaneous multispectral imaging using conventional
image sensors with color filter arrays via numerical demultiplexing of the
color image sensor measurements. A numerical forward model characterizing the
formation of sensor measurements from light spectra hitting the sensor is
constructed based on a comprehensive spectral characterization of the sensor. A
numerical demultiplexer is then learned via non-linear random forest modeling
based on the forward model. Given the learned numerical demultiplexer, one can
then demultiplex simultaneously-acquired measurements made by the image sensor
into reflectance intensities at discrete selectable wavelengths, resulting in a
higher resolution reflectance spectrum. Simulation and real-world experimental
results demonstrate the efficacy of such a method for simultaneous
multispectral imaging.
Signal Representations on Graphs: Tools and Applications
Authors: Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovačević
Date: 2015-12-16 00:00:00

We present a framework for representing and modeling data on graphs. Based on
this framework, we study three typical classes of graph signals: smooth graph
signals, piecewise-constant graph signals, and piecewise-smooth graph signals.
For each class, we provide an explicit definition of the graph signals and
construct a corresponding graph dictionary with desirable properties. We then
study how such graph dictionary works in two standard tasks: approximation and
sampling followed with recovery, both from theoretical as well as algorithmic
perspectives. Finally, for each class, we present a case study of a real-world
problem by using the proposed methodology.
Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing
Authors: Nihar B. Shah, Dengyong Zhou
Date: 2014-08-06 00:00:00
Proceeding: NeurIPS 2015 12

Crowdsourcing has gained immense popularity in machine learning applications
for obtaining large amounts of labeled data. Crowdsourcing is cheap and fast,
but suffers from the problem of low-quality data. To address this fundamental
challenge in crowdsourcing, we propose a simple payment mechanism to
incentivize workers to answer only the questions that they are sure of and skip
the rest. We show that surprisingly, under a mild and natural "no-free-lunch"
requirement, this mechanism is the one and only incentive-compatible payment
mechanism possible. We also show that among all possible incentive-compatible
mechanisms (that may or may not satisfy no-free-lunch), our mechanism makes the
smallest possible payment to spammers. We further extend our results to a more
general setting in which workers are required to provide a quantized confidence
for each question. Interestingly, this unique mechanism takes a
"multiplicative" form. The simplicity of the mechanism is an added benefit. In
preliminary experiments involving over 900 worker-task pairs, we observe a
significant drop in the error rates under this unique mechanism for the same or
lower monetary expenditure.
A Restricted Visual Turing Test for Deep Scene and Event Understanding
Authors: Hang Qi, Tianfu Wu, Mun-Wai Lee, Song-Chun Zhu
Date: 2015-12-06 00:00:00
Tasks: Question Answering, Video Captioning, Visual Question Answering, Visual Question Answering (VQA)

This paper presents a restricted visual Turing test (VTT) for story-line
based deep understanding in long-term and multi-camera captured videos. Given a
set of videos of a scene (such as a multi-room office, a garden, and a parking
lot.) and a sequence of story-line based queries, the task is to provide
answers either simply in binary form "true/false" (to a polar query) or in an
accurate natural language description (to a non-polar query). Queries, polar or
non-polar, consist of view-based queries which can be answered from a
particular camera view and scene-centered queries which involves joint
inference across different cameras. The story lines are collected to cover
spatial, temporal and causal understanding of input videos. The data and
queries distinguish our VTT from recently proposed visual question answering in
images and video captioning. A vision system is proposed to perform joint video
and query parsing which integrates different vision modules, a knowledge base
and a query engine. The system provides unified interfaces for different
modules so that individual modules can be reconfigured to test a new method. We
provide a benchmark dataset and a toolkit for ontology guided story-line query
generation which consists of about 93.5 hours videos captured in four different
locations and 3,426 queries split into 127 story lines. We also provide a
baseline implementation and result analyses.
An Event Calculus Production Rule System for Reasoning in Dynamic and Uncertain Domains
Authors: Theodore Patkos, Dimitris Plexousakis, Abdelghani Chibani, Yacine Amirat
Date: 2015-12-14 00:00:00
Tasks: Activity Recognition, Epistemic Reasoning

Action languages have emerged as an important field of Knowledge
Representation for reasoning about change and causality in dynamic domains.
This article presents Cerbere, a production system designed to perform online
causal, temporal and epistemic reasoning based on the Event Calculus. The
framework implements the declarative semantics of the underlying logic theories
in a forward-chaining rule-based reasoning system, coupling the high
expressiveness of its formalisms with the efficiency of rule-based systems. To
illustrate its applicability, we present both the modeling of benchmark
problems in the field, as well as its utilization in the challenging domain of
smart spaces. A hybrid framework that combines logic-based with probabilistic
reasoning has been developed, that aims to accommodate activity recognition and
monitoring tasks in smart spaces. Under consideration in Theory and Practice of
Logic Programming (TPLP)
Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions
Authors: Peter Sunehag, Richard Evans, Gabriel Dulac-Arnold, Yori Zwols, Daniel Visentin, Ben Coppin
Date: 2015-12-03 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, Recommendation Systems, Reinforcement Learning

Many real-world problems come with action spaces represented as feature
vectors. Although high-dimensional control is a largely unsolved problem, there
has recently been progress for modest dimensionalities. Here we report on a
successful attempt at addressing problems of dimensionality as high as $2000$,
of a particular form. Motivated by important applications such as
recommendation systems that do not fit the standard reinforcement learning
frameworks, we introduce Slate Markov Decision Processes (slate-MDPs). A
Slate-MDP is an MDP with a combinatorial action space consisting of slates
(tuples) of primitive actions of which one is executed in an underlying MDP.
The agent does not control the choice of this executed action and the action
might not even be from the slate, e.g., for recommendation systems for which
all recommendations can be ignored. We use deep Q-learning based on feature
representations of both the state and action to learn the value of whole
slates. Unlike existing methods, we optimize for both the combinatorial and
sequential aspects of our tasks. The new agent's superiority over agents that
either ignore the combinatorial or sequential long-term value aspect is
demonstrated on a range of environments with dynamics from a real-world
recommendation system. Further, we use deep deterministic policy gradients to
learn a policy that for each position of the slate, guides attention towards
the part of the action space in which the value is the highest and we only
evaluate actions in this area. The attention is used within a sequentially
greedy procedure leveraging submodularity. Finally, we show how introducing
risk-seeking can dramatically improve the agents performance and ability to
discover more far reaching strategies.
Solving stable matching problems using answer set programming
Authors: Sofie De Clercq, Steven Schockaert, Martine De Cock, Ann Nowé
Date: 2015-12-16 00:00:00

Since the introduction of the stable marriage problem (SMP) by Gale and
Shapley (1962), several variants and extensions have been investigated. While
this variety is useful to widen the application potential, each variant
requires a new algorithm for finding the stable matchings. To address this
issue, we propose an encoding of the SMP using answer set programming (ASP),
which can straightforwardly be adapted and extended to suit the needs of
specific applications. The use of ASP also means that we can take advantage of
highly efficient off-the-shelf solvers. To illustrate the flexibility of our
approach, we show how our ASP encoding naturally allows us to select optimal
stable matchings, i.e. matchings that are optimal according to some
user-specified criterion. To the best of our knowledge, our encoding offers the
first exact implementation to find sex-equal, minimum regret, egalitarian or
maximum cardinality stable matchings for SMP instances in which individuals may
designate unacceptable partners and ties between preferences are allowed.
  This paper is under consideration in Theory and Practice of Logic Programming
(TPLP).
Blockout: Dynamic Model Selection for Hierarchical Deep Networks
Authors: Calvin Murdock, Zhen Li, Howard Zhou, Tom Duerig
Date: 2015-12-16 00:00:00
Proceeding: CVPR 2016 6
Tasks: Clustering, General Classification, image-classification, Image Classification, model, Model Selection

Most deep architectures for image classification--even those that are trained
to classify a large number of diverse categories--learn shared image
representations with a single model. Intuitively, however, categories that are
more similar should share more information than those that are very different.
While hierarchical deep networks address this problem by learning separate
features for subsets of related categories, current implementations require
simplified models using fixed architectures specified via heuristic clustering
methods. Instead, we propose Blockout, a method for regularization and model
selection that simultaneously learns both the model architecture and
parameters. A generalization of Dropout, our approach gives a novel
parametrization of hierarchical architectures that allows for structure
learning via back-propagation. To demonstrate its utility, we evaluate Blockout
on the CIFAR and ImageNet datasets, demonstrating improved classification
accuracy, better regularization performance, faster training, and the clear
emergence of hierarchical network structures.
Symphony from Synapses: Neocortex as a Universal Dynamical Systems Modeller using Hierarchical Temporal Memory
Authors: Fergal Byrne
Date: 2015-12-16 00:00:00

Reverse engineering the brain is proving difficult, perhaps impossible. While
many believe that this is just a matter of time and effort, a different
approach might help. Here, we describe a very simple idea which explains the
power of the brain as well as its structure, exploiting complex dynamics rather
than abstracting it away. Just as a Turing Machine is a Universal Digital
Computer operating in a world of symbols, we propose that the brain is a
Universal Dynamical Systems Modeller, evolved bottom-up (itself using nested
networks of interconnected, self-organised dynamical systems) to prosper in a
world of dynamical systems.
  Recent progress in Applied Mathematics has produced startling evidence of
what happens when abstract Dynamical Systems interact. Key latent information
describing system A can be extracted by system B from very simple signals, and
signals can be used by one system to control and manipulate others. Using these
facts, we show how a region of the neocortex uses its dynamics to intrinsically
"compute" about the external and internal world.
  Building on an existing "static" model of cortical computation (Hawkins'
Hierarchical Temporal Memory - HTM), we describe how a region of neocortex can
be viewed as a network of components which together form a Dynamical Systems
modelling module, connected via sensory and motor pathways to the external
world, and forming part of a larger dynamical network in the brain.
  Empirical modelling and simulations of Dynamical HTM are possible with simple
extensions and combinations of currently existing open source software. We list
a number of relevant projects.
Learning a Hybrid Architecture for Sequence Regression and Annotation
Authors: Yizhe Zhang, Ricardo Henao, Lawrence Carin, Jianling Zhong, Alexander J. Hartemink
Date: 2015-12-16 00:00:00
Tasks: regression

When learning a hidden Markov model (HMM), sequen- tial observations can
often be complemented by real-valued summary response variables generated from
the path of hid- den states. Such settings arise in numerous domains, includ-
ing many applications in biology, like motif discovery and genome annotation.
In this paper, we present a flexible frame- work for jointly modeling both
latent sequence features and the functional mapping that relates the summary
response variables to the hidden state sequence. The algorithm is com- patible
with a rich set of mapping functions. Results show that the availability of
additional continuous response vari- ables can simultaneously improve the
annotation of the se- quential observations and yield good prediction
performance in both synthetic data and real-world datasets.
DNA-Level Splice Junction Prediction using Deep Recurrent Neural Networks
Authors: Byunghan Lee, Taehoon Lee, Byunggook Na, Sungroh Yoon
Date: 2015-12-16 00:00:00

A eukaryotic gene consists of multiple exons (protein coding regions) and
introns (non-coding regions), and a splice junction refers to the boundary
between a pair of exon and intron. Precise identification of spice junctions on
a gene is important for deciphering its primary structure, function, and
interaction. Experimental techniques for determining exon/intron boundaries
include RNA-seq, which is often accompanied by computational approaches.
Canonical splicing signals are known, but computational junction prediction
still remains challenging because of a large number of false positives and
other complications. In this paper, we exploit deep recurrent neural networks
(RNNs) to model DNA sequences and to detect splice junctions thereon. We test
various RNN units and architectures including long short-term memory units,
gated recurrent units, and recently proposed iRNN for in-depth design space
exploration. According to our experimental results, the proposed approach
significantly outperforms not only conventional machine learning-based methods
but also a recent state-of-the-art deep belief network-based technique in terms
of prediction accuracy.
Effects of GIMP Retinex Filtering Evaluated by the Image Entropy
Authors: A. C. Sparavigna, R. Marazzato
Date: 2015-12-16 00:00:00

A GIMP Retinex filtering can be used for enhancing images, with good results
on foggy images, as recently discussed. Since this filter has some parameters
that can be adjusted to optimize the output image, several approaches can be
decided according to desired results. Here, as a criterion for optimizing the
filtering parameters, we consider the maximization of the image entropy. We
use, besides the Shannon entropy, also a generalized entropy.
A Novel Minimum Divergence Approach to Robust Speaker Identification
Authors: Ayanendranath Basu, Smarajit Bose, Amita Pal, Anish Mukherjee, Debasmita Das
Date: 2015-12-16 00:00:00
Tasks: General Classification, Speaker Identification

In this work, a novel solution to the speaker identification problem is
proposed through minimization of statistical divergences between the
probability distribution (g). of feature vectors from the test utterance and
the probability distributions of the feature vector corresponding to the
speaker classes. This approach is made more robust to the presence of outliers,
through the use of suitably modified versions of the standard divergence
measures. The relevant solutions to the minimum distance methods are referred
to as the minimum rescaled modified distance estimators (MRMDEs). Three
measures were considered - the likelihood disparity, the Hellinger distance and
Pearson's chi-square distance. The proposed approach is motivated by the
observation that, in the case of the likelihood disparity, when the empirical
distribution function is used to estimate g, it becomes equivalent to maximum
likelihood classification with Gaussian Mixture Models (GMMs) for speaker
classes, a highly effective approach used, for example, by Reynolds [22] based
on Mel Frequency Cepstral Coefficients (MFCCs) as features. Significant
improvement in classification accuracy is observed under this approach on the
benchmark speech corpus NTIMIT and a new bilingual speech corpus NISIS, with
MFCC features, both in isolation and in combination with delta MFCC features.
Moreover, the ubiquitous principal component transformation, by itself and in
conjunction with the principle of classifier combination, is found to further
enhance the performance.
Streaming Kernel Principal Component Analysis
Authors: Mina Ghashami, Daniel Perry, Jeff M. Phillips
Date: 2015-12-16 00:00:00

Kernel principal component analysis (KPCA) provides a concise set of basis
vectors which capture non-linear structures within large data sets, and is a
central tool in data analysis and learning. To allow for non-linear relations,
typically a full $n \times n$ kernel matrix is constructed over $n$ data
points, but this requires too much space and time for large values of $n$.
Techniques such as the Nystr\"om method and random feature maps can help
towards this goal, but they do not explicitly maintain the basis vectors in a
stream and take more space than desired. We propose a new approach for
streaming KPCA which maintains a small set of basis elements in a stream,
requiring space only logarithmic in $n$, and also improves the dependence on
the error parameter. Our technique combines together random feature maps with
recent advances in matrix sketching, it has guaranteed spectral norm error
bounds with respect to the original kernel matrix, and it compares favorably in
practice to state-of-the-art approaches.
Inferring Gene Regulatory Network Using An Evolutionary Multi-Objective Method
Authors: Yu Chen, Xiufen Zou
Date: 2015-12-16 00:00:00
Tasks: parameter estimation

Inference of gene regulatory networks (GRNs) based on experimental data is a
challenging task in bioinformatics. In this paper, we present a bi-objective
minimization model (BoMM) for inference of GRNs, where one objective is the
fitting error of derivatives, and the other is the number of connections in the
network. To solve the BoMM efficiently, we propose a multi-objective
evolutionary algorithm (MOEA), and utilize the separable parameter estimation
method (SPEM) decoupling the ordinary differential equation (ODE) system. Then,
the Akaike Information Criterion (AIC) is employed to select one inference
result from the obtained Pareto set. Taking the S-system as the investigated
GRN model, our method can properly identify the topologies and parameter values
of benchmark systems. There is no need to preset problem-dependent parameter
values to obtain appropriate results, and thus, our method could be applicable
to inference of various GRNs models.
Wood Species Recognition Based on SIFT Keypoint Histogram
Authors: Shuaiqi Hu, Ke Li, Xudong Bao
Date: 2015-11-05 00:00:00
Tasks: Clustering

Traditionally, only experts who are equipped with professional knowledge and
rich experience are able to recognize different species of wood. Applying image
processing techniques for wood species recognition can not only reduce the
expense to train qualified identifiers, but also increase the recognition
accuracy. In this paper, a wood species recognition technique base on Scale
Invariant Feature Transformation (SIFT) keypoint histogram is proposed. We use
first the SIFT algorithm to extract keypoints from wood cross section images,
and then k-means and k-means++ algorithms are used for clustering. Using the
clustering results, an SIFT keypoints histogram is calculated for each wood
image. Furthermore, several classification models, including Artificial Neural
Networks (ANN), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) are
used to verify the performance of the method. Finally, through comparing with
other prevalent wood recognition methods such as GLCM and LBP, results show
that our scheme achieves higher accuracy.
Zero-Shot Event Detection by Multimodal Distributional Semantic Embedding of Videos
Authors: Mohamed Elhoseiny, Jingen Liu, Hui Cheng, Harpreet Sawhney, Ahmed Elgammal
Date: 2015-12-02 00:00:00
Tasks: Event Detection

We propose a new zero-shot Event Detection method by Multi-modal
Distributional Semantic embedding of videos. Our model embeds object and action
concepts as well as other available modalities from videos into a
distributional semantic space. To our knowledge, this is the first Zero-Shot
event detection model that is built on top of distributional semantics and
extends it in the following directions: (a) semantic embedding of multimodal
information in videos (with focus on the visual modalities), (b) automatically
determining relevance of concepts/attributes to a free text query, which could
be useful for other applications, and (c) retrieving videos by free text event
query (e.g., "changing a vehicle tire") based on their content. We embed videos
into a distributional semantic space and then measure the similarity between
videos and the event query in a free text form. We validated our method on the
large TRECVID MED (Multimedia Event Detection) challenge. Using only the event
title as a query, our method outperformed the state-of-the-art that uses big
descriptions from 12.6% to 13.5% with MAP metric and 0.73 to 0.83 with ROC-AUC
metric. It is also an order of magnitude faster.