pwc-archive/papers-with-abstracts huggingface dataset:
Radon-Gabor Barcodes for Medical Image Retrieval
Authors: Mina Nouredanesh, H. R. Tizhoosh, Ershad Banijamali, James Tung
Date: 2016-09-16 00:00:00
Tasks: Image Retrieval, Medical Image Retrieval, Retrieval

In recent years, with the explosion of digital images on the Web,
content-based retrieval has emerged as a significant research area. Shapes,
textures, edges and segments may play a key role in describing the content of
an image. Radon and Gabor transforms are both powerful techniques that have
been widely studied to extract shape-texture-based information. The combined
Radon-Gabor features may be more robust against scale/rotation variations,
presence of noise, and illumination changes. The objective of this paper is to
harness the potentials of both Gabor and Radon transforms in order to introduce
expressive binary features, called barcodes, for image annotation/tagging
tasks. We propose two different techniques: Gabor-of-Radon-Image Barcodes
(GRIBCs), and Guided-Radon-of-Gabor Barcodes (GRGBCs). For validation, we
employ the IRMA x-ray dataset with 193 classes, containing 12,677 training
images and 1,733 test images. A total error score as low as 322 and 330 were
achieved for GRGBCs and GRIBCs, respectively. This corresponds to $\approx
81\%$ retrieval accuracy for the first hit.
Dense Wide-Baseline Scene Flow From Two Handheld Video Cameras
Authors: Christian Richardt, Hyeongwoo Kim, Levi Valgaerts, Christian Theobalt
Date: 2016-09-16 00:00:00
Tasks: Vocal Bursts Valence Prediction

We propose a new technique for computing dense scene flow from two handheld
videos with wide camera baselines and different photometric properties due to
different sensors or camera settings like exposure and white balance. Our
technique innovates in two ways over existing methods: (1) it supports
independently moving cameras, and (2) it computes dense scene flow for
wide-baseline scenarios.We achieve this by combining state-of-the-art
wide-baseline correspondence finding with a variational scene flow formulation.
First, we compute dense, wide-baseline correspondences using DAISY descriptors
for matching between cameras and over time. We then detect and replace occluded
pixels in the correspondence fields using a novel edge-preserving Laplacian
correspondence completion technique. We finally refine the computed
correspondence fields in a variational scene flow formulation. We show dense
scene flow results computed from challenging datasets with independently
moving, handheld cameras of varying camera settings.
Barcodes for Medical Image Retrieval Using Autoencoded Radon Transform
Authors: Hamid. R. Tizhoosh, Christopher Mitcheltree, Shujin Zhu, Shamak Dutta
Date: 2016-09-16 00:00:00
Tasks: Binarization, Image Retrieval, Medical Image Retrieval, Retrieval, TAG

Using content-based binary codes to tag digital images has emerged as a
promising retrieval technology. Recently, Radon barcodes (RBCs) have been
introduced as a new binary descriptor for image search. RBCs are generated by
binarization of Radon projections and by assembling them into a vector, namely
the barcode. A simple local thresholding has been suggested for binarization.
In this paper, we put forward the idea of "autoencoded Radon barcodes". Using
images in a training dataset, we autoencode Radon projections to perform
binarization on outputs of hidden layers. We employed the mini-batch stochastic
gradient descent approach for the training. Each hidden layer of the
autoencoder can produce a barcode using a threshold determined based on the
range of the logistic function used. The compressing capability of autoencoders
apparently reduces the redundancies inherent in Radon projections leading to
more accurate retrieval results. The IRMA dataset with 14,410 x-ray images is
used to validate the performance of the proposed method. The experimental
results, containing comparison with RBCs, SURF and BRISK, show that autoencoded
Radon barcode (ARBC) has the capacity to capture important information and to
learn richer representations resulting in lower retrieval errors for image
retrieval measured with the accuracy of the first hit only.
A Curriculum Learning Method for Improved Noise Robustness in Automatic Speech Recognition
Authors: Stefan Braun, Daniel Neil, Shih-Chii Liu
Date: 2016-06-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

The performance of automatic speech recognition systems under noisy
environments still leaves room for improvement. Speech enhancement or feature
enhancement techniques for increasing noise robustness of these systems usually
add components to the recognition system that need careful optimization. In
this work, we propose the use of a relatively simple curriculum training
strategy called accordion annealing (ACCAN). It uses a multi-stage training
schedule where samples at signal-to-noise ratio (SNR) values as low as 0dB are
first added and samples at increasing higher SNR values are gradually added up
to an SNR value of 50dB. We also use a method called per-epoch noise mixing
(PEM) that generates noisy training samples online during training and thus
enables dynamically changing the SNR of our training data. Both the ACCAN and
the PEM methods are evaluated on a end-to-end speech recognition pipeline on
the Wall Street Journal corpus. ACCAN decreases the average word error rate
(WER) on the 20dB to -10dB SNR range by up to 31.4% when compared to a
conventional multi-condition training method.
A Formal Solution to the Grain of Truth Problem
Authors: Jan Leike, Jessica Taylor, Benya Fallenstein
Date: 2016-09-16 00:00:00
Tasks: Thompson Sampling

A Bayesian agent acting in a multi-agent environment learns to predict the
other agents' policies if its prior assigns positive probability to them (in
other words, its prior contains a \emph{grain of truth}). Finding a reasonably
large class of policies that contains the Bayes-optimal policies with respect
to this class is known as the \emph{grain of truth problem}. Only small classes
are known to have a grain of truth and the literature contains several related
impossibility results. In this paper we present a formal and general solution
to the full grain of truth problem: we construct a class of policies that
contains all computable policies as well as Bayes-optimal policies for every
lower semicomputable prior over the class. When the environment is unknown,
Bayes-optimal agents may fail to act optimally even asymptotically. However,
agents based on Thompson sampling converge to play {\epsilon}-Nash equilibria
in arbitrary unknown computable multi-agent environments. While these results
are purely theoretical, we show that they can be computationally approximated
arbitrarily closely.
A Holistic Approach for Data-Driven Object Cutout
Authors: Huayong Xu, Yangyan Li, Wenzheng Chen, Dani Lischinski, Daniel Cohen-Or, Baoquan Chen
Date: 2016-08-18 00:00:00
Tasks: Object

Object cutout is a fundamental operation for image editing and manipulation,
yet it is extremely challenging to automate it in real-world images, which
typically contain considerable background clutter. In contrast to existing
cutout methods, which are based mainly on low-level image analysis, we propose
a more holistic approach, which considers the entire shape of the object of
interest by leveraging higher-level image analysis and learnt global shape
priors. Specifically, we leverage a deep neural network (DNN) trained for
objects of a particular class (chairs) for realizing this mechanism. Given a
rectangular image region, the DNN outputs a probability map (P-map) that
indicates for each pixel inside the rectangle how likely it is to be contained
inside an object from the class of interest. We show that the resulting P-maps
may be used to evaluate how likely a rectangle proposal is to contain an
instance of the class, and further process good proposals to produce an
accurate object cutout mask. This amounts to an automatic end-to-end pipeline
for catergory-specific object cutout. We evaluate our approach on segmentation
benchmark datasets, and show that it significantly outperforms the
state-of-the-art on them.
Stamp processing with examplar features
Authors: Yash Bhalgat, Mandar Kulkarni, Shirish Karande, Sachin Lodha
Date: 2016-09-16 00:00:00
Tasks: Clustering

Document digitization is becoming increasingly crucial. In this work, we
propose a shape based approach for automatic stamp verification/detection in
document images using an unsupervised feature learning. Given a small set of
training images, our algorithm learns an appropriate shape representation using
an unsupervised clustering. Experimental results demonstrate the effectiveness
of our framework in challenging scenarios.
Improved Recurrent Neural Networks for Session-based Recommendations
Authors: Yong Kiam Tan, Xinxing Xu, Yong liu
Date: 2016-06-27 00:00:00
Tasks: Data Augmentation, Session-Based Recommendations

Recurrent neural networks (RNNs) were recently proposed for the session-based
recommendation task. The models showed promising improvements over traditional
recommendation approaches. In this work, we further study RNN-based models for
session-based recommendations. We propose the application of two techniques to
improve model performance, namely, data augmentation, and a method to account
for shifts in the input data distribution. We also empirically study the use of
generalised distillation, and a novel alternative model that directly predicts
item embeddings. Experiments on the RecSys Challenge 2015 dataset demonstrate
relative improvements of 12.8% and 14.8% over previously reported results on
the Recall@20 and Mean Reciprocal Rank@20 metrics respectively.
Likelihood-free Model Choice
Authors: Jean-Michel Marin, Pierre Pudlo, Arnaud Estoup, Christian P. Robert
Date: 2015-03-26 00:00:00
Tasks: model

This document is an invited chapter covering the specificities of ABC model
choice, intended for the incoming Handbook of ABC by Sisson, Fan, and Beaumont
(2017). Beyond exposing the potential pitfalls of ABC based posterior
probabilities, the review emphasizes mostly the solution proposed by Pudlo et
al. (2016) on the use of random forests for aggregating summary statistics and
and for estimating the posterior probability of the most likely model via a
secondary random fores.
SynsetRank: Degree-adjusted Random Walk for Relation Identification
Authors: Shinichi Nakajima, Sebastian Krause, Dirk Weissenborn, Sven Schmeier, Nico Goernitz, Feiyu Xu
Date: 2016-09-02 00:00:00
Tasks: Relation, Relation Extraction

In relation extraction, a key process is to obtain good detectors that find
relevant sentences describing the target relation. To minimize the necessity of
labeled data for refining detectors, previous work successfully made use of
BabelNet, a semantic graph structure expressing relationships between synsets,
as side information or prior knowledge. The goal of this paper is to enhance
the use of graph structure in the framework of random walk with a few
adjustable parameters. Actually, a straightforward application of random walk
degrades the performance even after parameter optimization. With the insight
from this unsuccessful trial, we propose SynsetRank, which adjusts the initial
probability so that high degree nodes influence the neighbors as strong as low
degree nodes. In our experiment on 13 relations in the FB15K-237 dataset,
SynsetRank significantly outperforms baselines and the plain random walk
approach.
NPCs as People, Too: The Extreme AI Personality Engine
Authors: Jeffrey Georgeson, Christopher Child
Date: 2016-09-15 00:00:00

PK Dick once asked "Do Androids Dream of Electric Sheep?" In video games, a
similar question could be asked of non-player characters: Do NPCs have dreams?
Can they live and change as humans do? Can NPCs have personalities, and can
these develop through interactions with players, other NPCs, and the world
around them? Despite advances in personality AI for games, most NPCs are still
undeveloped and undeveloping, reacting with flat affect and predictable
routines that make them far less than human--in fact, they become little more
than bits of the scenery that give out parcels of information. This need not be
the case. Extreme AI, a psychology-based personality engine, creates adaptive
NPC personalities. Originally developed as part of the thesis "NPCs as People:
Using Databases and Behaviour Trees to Give Non-Player Characters Personality,"
Extreme AI is now a fully functioning personality engine using all thirty
facets of the Five Factor model of personality and an AI system that is live
throughout gameplay. This paper discusses the research leading to Extreme AI;
develops the ideas found in that thesis; discusses the development of other
personality engines; and provides examples of Extreme AI's use in two game
demos.
Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning
Authors: Tiancheng Zhao, Maxine Eskenazi
Date: 2016-06-08 00:00:00
Proceeding: WS 2016 9
Tasks: Deep Reinforcement Learning, dialog state tracking, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents an end-to-end framework for task-oriented dialog systems
using a variant of Deep Recurrent Q-Networks (DRQN). The model is able to
interface with a relational database and jointly learn policies for both
language understanding and dialog strategy. Moreover, we propose a hybrid
algorithm that combines the strength of reinforcement learning and supervised
learning to achieve faster learning speed. We evaluated the proposed model on a
20 Question Game conversational game simulator. Results show that the proposed
method outperforms the modular-based baseline and learns a distributed
representation of the latent dialog state.
A Tutorial about Random Neural Networks in Supervised Learning
Authors: Sebastián Basterrech, Gerardo Rubino
Date: 2016-09-15 00:00:00
Tasks: Combinatorial Optimization

Random Neural Networks (RNNs) are a class of Neural Networks (NNs) that can
also be seen as a specific type of queuing network. They have been successfully
used in several domains during the last 25 years, as queuing networks to
analyze the performance of resource sharing in many engineering areas, as
learning tools and in combinatorial optimization, where they are seen as neural
systems, and also as models of neurological aspects of living beings. In this
article we focus on their learning capabilities, and more specifically, we
present a practical guide for using the RNN to solve supervised learning
problems. We give a general description of these models using almost
indistinctly the terminology of Queuing Theory and the neural one. We present
the standard learning procedures used by RNNs, adapted from similar
well-established improvements in the standard NN field. We describe in
particular a set of learning algorithms covering techniques based on the use of
first order and, then, of second order derivatives. We also discuss some issues
related to these objects and present new perspectives about their use in
supervised learning problems. The tutorial describes their most relevant
applications, and also provides a large bibliography.
Semi-Supervised Representation Learning based on Probabilistic Labeling
Authors: Ershad Banijamali, Ali Ghodsi
Date: 2016-05-10 00:00:00
Tasks: Representation Learning

In this paper, we present a new algorithm for semi-supervised representation learning. In this algorithm, we first find a vector representation for the labels of the data points based on their local positions in the space. Then, we map the data to lower-dimensional space using a linear transformation such that the dependency between the transformed data and the assigned labels is maximized. In fact, we try to find a mapping that is as discriminative as possible. The approach will use Hilber-Schmidt Independence Criterion (HSIC) as the dependence measure. We also present a kernelized version of the algorithm, which allows non-linear transformations and provides more flexibility in finding the appropriate mapping. Use of unlabeled data for learning new representation is not always beneficial and there is no algorithm that can deterministically guarantee the improvement of the performance by exploiting unlabeled data. Therefore, we also propose a bound on the performance of the algorithm, which can be used to determine the effectiveness of using the unlabeled data in the algorithm. We demonstrate the ability of the algorithm in finding the transformation using both toy examples and real-world datasets.
Characterizing the Language of Online Communities and its Relation to Community Reception
Authors: Trang Tran, Mari Ostendorf
Date: 2016-09-15 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Language Modeling, Language Modelling, Relation, TAG

This work investigates style and topic aspects of language in online
communities: looking at both utility as an identifier of the community and
correlation with community reception of content. Style is characterized using a
hybrid word and part-of-speech tag n-gram language model, while topic is
represented using Latent Dirichlet Allocation. Experiments with several Reddit
forums show that style is a better indicator of community identity than topic,
even for communities organized around specific topics. Further, there is a
positive correlation between the community reception to a contribution and the
style similarity to that community, but not so for topic similarity.
Transport-based analysis, modeling, and learning from signal and data distributions
Authors: Soheil Kolouri, Serim Park, Matthew Thorpe, Dejan Slepčev, Gustavo K. Rohde
Date: 2016-09-15 00:00:00
Tasks: Image Super-Resolution, Retrieval, Super-Resolution

Transport-based techniques for signal and data analysis have received
increased attention recently. Given their abilities to provide accurate
generative models for signal intensities and other data distributions, they
have been used in a variety of applications including content-based retrieval,
cancer detection, image super-resolution, and statistical machine learning, to
name a few, and shown to produce state of the art in several applications.
Moreover, the geometric characteristics of transport-related metrics have
inspired new kinds of algorithms for interpreting the meaning of data
distributions. Here we provide an overview of the mathematical underpinnings of
mass transport-related methods, including numerical implementation, as well as
a review, with demonstrations, of several applications.
Perceptual Quality Prediction on Authentically Distorted Images Using a Bag of Features Approach
Authors: Deepti Ghadiyaram, Alan C. Bovik
Date: 2016-09-15 00:00:00
Tasks: Prediction

Current top-performing blind perceptual image quality prediction models are
generally trained on legacy databases of human quality opinion scores on
synthetically distorted images. Therefore they learn image features that
effectively predict human visual quality judgments of inauthentic, and usually
isolated (single) distortions. However, real-world images usually contain
complex, composite mixtures of multiple distortions. We study the perceptually
relevant natural scene statistics of such authentically distorted images, in
different color spaces and transform domains. We propose a bag of feature-maps
approach which avoids assumptions about the type of distortion(s) contained in
an image, focusing instead on capturing consistencies, or departures therefrom,
of the statistics of real world images. Using a large database of authentically
distorted images, human opinions of them, and bags of features computed on
them, we train a regressor to conduct image quality prediction. We demonstrate
the competence of the features towards improving automatic perceptual quality
prediction by testing a learned algorithm using them on a benchmark legacy
database as well as on a newly introduced distortion-realistic resource called
the LIVE In the Wild Image Quality Challenge Database. We extensively evaluate
the perceptual quality prediction model and algorithm and show that it is able
to achieve good quality prediction power that is better than other leading
models.
Information Utilization Ratio in Heuristic Optimization Algorithms
Authors: Junzhi Li, Ying Tan
Date: 2016-04-06 00:00:00

Heuristic algorithms are able to optimize objective functions efficiently
because they use intelligently the information about the objective functions.
Thus, information utilization is critical to the performance of heuristics.
However, the concept of information utilization has remained vague and abstract
because there is no reliable metric to reflect the extent to which the
information about the objective function is utilized by heuristic algorithms.
In this paper, the metric of information utilization ratio (IUR) is defined,
which is the ratio of the utilized information quantity over the acquired
information quantity in the search process. The IUR proves to be well-defined.
Several examples of typical heuristic algorithms are given to demonstrate the
procedure of calculating the IUR. Empirical evidences on the correlation
between the IUR and the performance of a heuristic are also provided. The IUR
can be an index of how finely an algorithm is designed and guide the invention
of new heuristics and the improvement of existing ones.
Detecting phase transitions in collective behavior using manifold's curvature
Authors: Kelum Gajamannage, Erik M. Bollt
Date: 2015-09-23 00:00:00

If a given behavior of a multi-agent system restricts the phase variable to a
invariant manifold, then we define a phase transition as change of physical
characteristics such as speed, coordination, and structure. We define such a
phase transition as splitting an underlying manifold into two sub-manifolds
with distinct dimensionalities around the singularity where the phase
transition physically exists. Here, we propose a method of detecting phase
transitions and splitting the manifold into phase transitions free
sub-manifolds. Therein, we utilize a relationship between curvature and
singular value ratio of points sampled in a curve, and then extend the
assertion into higher-dimensions using the shape operator. Then we attest that
the same phase transition can also be approximated by singular value ratios
computed locally over the data in a neighborhood on the manifold. We validate
the phase transitions detection method using one particle simulation and three
real world examples.
Mixture model modal clustering
Authors: José E. Chacón
Date: 2016-09-15 00:00:00
Tasks: Clustering, model

The two most extended density-based approaches to clustering are surely
mixture model clustering and modal clustering. In the mixture model approach,
the density is represented as a mixture and clusters are associated to the
different mixture components. In modal clustering, clusters are understood as
regions of high density separated from each other by zones of lower density, so
that they are closely related to certain regions around the density modes. If
the true density is indeed in the assumed class of mixture densities, then
mixture model clustering allows to scrutinize more subtle situations than modal
clustering. However, when mixture modeling is used in a nonparametric way,
taking advantage of the denseness of the sieve of mixture densities to
approximate any density, then the correspondence between clusters and mixture
components may become questionable. In this paper we introduce two methods to
adopt a modal clustering point of view after a mixture model fit. Numerous
examples are provided to illustrate that mixture modeling can also be used for
clustering in a nonparametric sense, as long as clusters are understood as the
domains of attraction of the density modes.