pwc-archive/papers-with-abstracts huggingface dataset:
Window-of-interest based Multi-objective Evolutionary Search for Satisficing Concepts
Authors: Eliran Farhi, Amiram Moshaiov
Date: 2017-07-04 00:00:00
Tasks: Benchmarking

The set-based concept approach has been suggested as a means to
simultaneously explore different design concepts, which are meaningful sub-sets
of the entire set of solutions. Previous efforts concerning the suggested
approach focused on either revealing the global front (s-Pareto front), of all
the concepts, or on finding the concepts' fronts, within a relaxation zone. In
contrast, here the aim is to reveal which of the concepts have at least one
solution with a performance vector within a pre-defined window-of-interest
(WOI). This paper provides the rational for this new concept-based exploration
problem, and suggests a WOI-based rather than Pareto-based multi-objective
evolutionary algorithm. The proposed algorithm, which simultaneously explores
different concepts, is tested using a recently suggested concept-based
benchmarking approach. The numerical study of this paper shows that the
algorithm can cope with various numerical difficulties in a simultaneous way,
which outperforms a sequential exploration approach.
The Candidate Multi-Cut for Cell Segmentation
Authors: Jan Funke, Chong Zhang, Tobias Pietzsch, Stephan Saalfeld
Date: 2017-07-04 00:00:00
Tasks: Cell Segmentation, Clustering, Superpixels

Two successful approaches for the segmentation of biomedical images are (1)
the selection of segment candidates from a merge-tree, and (2) the clustering
of small superpixels by solving a Multi-Cut problem. In this paper, we
introduce a model that unifies both approaches. Our model, the Candidate
Multi-Cut (CMC), allows joint selection and clustering of segment candidates
from a merge-tree. This way, we overcome the respective limitations of the
individual methods: (1) the space of possible segmentations is not constrained
to candidates of a merge-tree, and (2) the decision for clustering can be made
on candidates larger than superpixels, using features over larger contexts. We
solve the optimization problem of selecting and clustering of candidates using
an integer linear program. On datasets of 2D light microscopy of cell
populations and 3D electron microscopy of neurons, we show that our method
generalizes well and generates more accurate segmentations than merge-tree or
Multi-Cut methods alone.
Hidden-Markov-Model Based Speech Enhancement
Authors: Daniel Dzibela, Armin Sehr
Date: 2017-07-04 00:00:00
Tasks: model, Speech Enhancement, Speech Synthesis

The goal of this contribution is to use a parametric speech synthesis system
for reducing background noise and other interferences from recorded speech
signals. In a first step, Hidden Markov Models of the synthesis system are
trained.
  Two adequate training corpora consisting of text and corresponding speech
files have been set up and cleared of various faults, including inaudible
utterances or incorrect assignments between audio and text data. Those are
tested and compared against each other regarding e.g. flaws in the synthesized
speech, it's naturalness and intelligibility. Thus different voices have been
synthesized, whose quality depends less on the number of training samples used,
but much more on the cleanliness and signal-to-noise ratio of those.
Generalized voice models have been used for synthesis and the results greatly
differ between the two speech corpora.
  Tests regarding the adaptation to different speakers show that a resemblance
to the original speaker is audible throughout all recordings, yet the
synthesized voices sound robotic and unnatural in smaller parts. The spoken
text, however, is usually intelligible, which shows that the models are working
well.
  In a novel approach, speech is synthesized using side information of the
original audio signal, particularly the pitch frequency. Results show an
increase of speech quality and intelligibility in comparison to speech
synthesized solely from text, up to the point of being nearly indistinguishable
from the original.
Identification of non-linear behavior models with restricted or redundant data
Authors: S. Carbillet, V. Guicheret-Retel, F. Trivaudey, F. Richard, M. L. Boubakar
Date: 2017-07-04 00:00:00

This study presents a new strategy for the identification of material
parameters in the case of restricted or redundant data, based on a hybrid
approach combining a genetic algorithm and the Levenberg-Marquardt method. The
proposed methodology consists essentially in a statistically based topological
analysis of the search domain, after this one has been reduced by the analysis
of the parameters ranges. This is used to identify the parameters of a model
representing the behavior of damaged elastic, visco-elastic, plastic and
visco-plastic composite laminates. Optimization of the experimental tests on
tubular samples leads to the selective identification of these parameters.
Weighted Low Rank Approximation for Background Estimation Problems
Authors: Aritra Dutta, Xin Li
Date: 2017-07-04 00:00:00

Classical principal component analysis (PCA) is not robust to the presence of
sparse outliers in the data. The use of the $\ell_1$ norm in the Robust PCA
(RPCA) method successfully eliminates the weakness of PCA in separating the
sparse outliers. In this paper, by sticking a simple weight to the Frobenius
norm, we propose a weighted low rank (WLR) method to avoid the often
computationally expensive algorithms relying on the $\ell_1$ norm. As a proof
of concept, a background estimation model has been presented and compared with
two $\ell_1$ norm minimization algorithms. We illustrate that as long as a
simple weight matrix is inferred from the data, one can use the weighted
Frobenius norm and achieve the same or better performance.
DeepStory: Video Story QA by Deep Embedded Memory Networks
Authors: Kyung-Min Kim, Min-Oh Heo, Seong-Ho Choi, Byoung-Tak Zhang
Date: 2017-07-04 00:00:00
Tasks: AI Agent, Question Answering, Triplet, Video Story QA

Question-answering (QA) on video contents is a significant challenge for
achieving human-level intelligence as it involves both vision and language in
real-world settings. Here we demonstrate the possibility of an AI agent
performing video story QA by learning from a large amount of cartoon videos. We
develop a video-story learning model, i.e. Deep Embedded Memory Networks
(DEMN), to reconstruct stories from a joint scene-dialogue video stream using a
latent embedding space of observed data. The video stories are stored in a
long-term memory component. For a given question, an LSTM-based attention model
uses the long-term memory to recall the best question-story-answer triplet by
focusing on specific words containing key information. We trained the DEMN on a
novel QA dataset of children's cartoon video series, Pororo. The dataset
contains 16,066 scene-dialogue pairs of 20.5-hour videos, 27,328 fine-grained
sentences for scene description, and 8,913 story-related QA pairs. Our
experimental results show that the DEMN outperforms other QA models. This is
mainly due to 1) the reconstruction of video stories in a scene-dialogue
combined form that utilize the latent embedding and 2) attention. DEMN also
achieved state-of-the-art results on the MovieQA benchmark.
Face Recognition with Machine Learning in OpenCV_ Fusion of the results with the Localization Data of an Acoustic Camera for Speaker Identification
Authors: Johannes Reschke, Armin Sehr
Date: 2017-07-04 00:00:00
Tasks: Face Model, Face Recognition, Position, Speaker Identification, Speech Enhancement

This contribution gives an overview of face recogni-tion algorithms, their
implementation and practical uses. First, a training set of different persons'
faces has to be collected and used to train a face recognizer. The resulting
face model can be utilized to classify people in specific individuals or
unknowns. After tracking the recognized face and estimating the acoustic sound
source's position, both can be combined to give detailed information about
possible speakers and if they are talking or not. This leads to a precise
real-time description of the situation, which can be used for further
applications, e.g. for multi-channel speech enhancement by adaptive
beamformers.
Automatic Trimap Generation for Image Matting
Authors: Vikas Gupta, Shanmuganathan Raman
Date: 2017-07-02 00:00:00
Tasks: Image Matting

Image matting is a longstanding problem in computational photography.
Although, it has been studied for more than two decades, yet there is a
challenge of developing an automatic matting algorithm which does not require
any human efforts. Most of the state-of-the-art matting algorithms require
human intervention in the form of trimap or scribbles to generate the alpha
matte form the input image. In this paper, we present a simple and efficient
approach to automatically generate the trimap from the input image and make the
whole matting process free from human-in-the-loop. We use learning based
matting method to generate the matte from the automatically generated trimap.
Experimental results demonstrate that our method produces good quality trimap
which results into accurate matte estimation. We validate our results by
replacing the automatically generated trimap by manually created trimap while
using the same image matting algorithm.
The Maximum Cosine Framework for Deriving Perceptron Based Linear Classifiers
Authors: Nader H. Bshouty, Catherine A. Haddad-Zaknoon
Date: 2017-07-04 00:00:00

In this work, we introduce a mathematical framework, called the Maximum
Cosine Framework or MCF, for deriving new linear classifiers. The method is
based on selecting an appropriate bound on the cosine of the angle between the
target function and the algorithm's. To justify its correctness, we use the MCF
to show how to regenerate the update rule of Aggressive ROMMA. Moreover, we
construct a cosine bound from which we build the Maximum Cosine Perceptron
algorithm or, for short, the MCP algorithm. We prove that the MCP shares the
same mistake bound like the Perceptron. In addition, we demonstrate the
promising performance of the MCP on a real dataset. Our experiments show that,
under the restriction of single pass learning, the MCP algorithm outperforms PA
and Aggressive ROMMA.
Causal Consistency of Structural Equation Models
Authors: Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf
Date: 2017-07-04 00:00:00
Tasks: Time Series, Time Series Analysis

Complex systems can be modelled at various levels of detail. Ideally, causal
models of the same system should be consistent with one another in the sense
that they agree in their predictions of the effects of interventions. We
formalise this notion of consistency in the case of Structural Equation Models
(SEMs) by introducing exact transformations between SEMs. This provides a
general language to consider, for instance, the different levels of description
in the following three scenarios: (a) models with large numbers of variables
versus models in which the `irrelevant' or unobservable variables have been
marginalised out; (b) micro-level models versus macro-level models in which the
macro-variables are aggregate features of the micro-variables; (c) dynamical
time series models versus models of their stationary behaviour. Our analysis
stresses the importance of well specified interventions in the causal modelling
process and sheds light on the interpretation of cyclic SEMs.
One-Shot Fine-Grained Instance Retrieval
Authors: Hantao Yao, Shiliang Zhang, Yongdong Zhang, Jintao Li, Qi Tian
Date: 2017-07-04 00:00:00
Tasks: Fine-Grained Visual Categorization, Image Retrieval, Retrieval

Fine-Grained Visual Categorization (FGVC) has achieved significant progress
recently. However, the number of fine-grained species could be huge and
dynamically increasing in real scenarios, making it difficult to recognize
unseen objects under the current FGVC framework. This raises an open issue to
perform large-scale fine-grained identification without a complete training
set. Aiming to conquer this issue, we propose a retrieval task named One-Shot
Fine-Grained Instance Retrieval (OSFGIR). "One-Shot" denotes the ability of
identifying unseen objects through a fine-grained retrieval task assisted with
an incomplete auxiliary training set. This paper first presents the detailed
description to OSFGIR task and our collected OSFGIR-378K dataset. Next, we
propose the Convolutional and Normalization Networks (CN-Nets) learned on the
auxiliary dataset to generate a concise and discriminative representation.
Finally, we present a coarse-to-fine retrieval framework consisting of three
components, i.e., coarse retrieval, fine-grained retrieval, and query
expansion, respectively. The framework progressively retrieves images with
similar semantics, and performs fine-grained identification. Experiments show
our OSFGIR framework achieves significantly better accuracy and efficiency than
existing FGVC and image retrieval methods, thus could be a better solution for
large-scale fine-grained object identification.
High-Quality Face Image SR Using Conditional Generative Adversarial Networks
Authors: Huang Bin, Chen Weihai, Wu Xingming, Lin Chun-Liang
Date: 2017-07-04 00:00:00
Tasks: Generative Adversarial Network, Image Super-Resolution, Super-Resolution, Vocal Bursts Intensity Prediction

We propose a novel single face image super-resolution method, which named
Face Conditional Generative Adversarial Network(FCGAN), based on boundary
equilibrium generative adversarial networks. Without taking any facial prior
information, our method can generate a high-resolution face image from a
low-resolution one. Compared with existing studies, both our training and
testing phases are end-to-end pipeline with little pre/post-processing. To
enhance the convergence speed and strengthen feature propagation, skip-layer
connection is further employed in the generative and discriminative networks.
Extensive experiments demonstrate that our model achieves competitive
performance compared with state-of-the-art models.
Enhance Feature Discrimination for Unsupervised Hashing
Authors: Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung
Date: 2017-04-06 00:00:00
Tasks: Quantization

We introduce a novel approach to improve unsupervised hashing. Specifically,
we propose a very efficient embedding method: Gaussian Mixture Model embedding
(Gemb). The proposed method, using Gaussian Mixture Model, embeds feature
vector into a low-dimensional vector and, simultaneously, enhances the
discriminative property of features before passing them into hashing. Our
experiment shows that the proposed method boosts the hashing performance of
many state-of-the-art, e.g. Binary Autoencoder (BA) [1], Iterative Quantization
(ITQ) [2], in standard evaluation metrics for the three main benchmark
datasets.
Aggregating Frame-level Features for Large-Scale Video Classification
Authors: Shaoxiang Chen, Xi Wang, Yongyi Tang, Xinpeng Chen, Zuxuan Wu, Yu-Gang Jiang
Date: 2017-07-04 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Video Classification, Video Understanding

This paper introduces the system we developed for the Google Cloud &
YouTube-8M Video Understanding Challenge, which can be considered as a
multi-label classification problem defined on top of the large scale YouTube-8M
Dataset. We employ a large set of techniques to aggregate the provided
frame-level feature representations and generate video-level predictions,
including several variants of recurrent neural networks (RNN) and generalized
VLAD. We also adopt several fusion strategies to explore the complementarity
among the models. In terms of the official metric GAP@20 (global average
precision at 20), our best fusion model attains 0.84198 on the public 50\% of
test data and 0.84193 on the private 50\% of test data, ranking 4th out of 650
teams worldwide in the competition.
Arabic Character Segmentation Using Projection Based Approach with Profile's Amplitude Filter
Authors: Mahmoud A. A. Mousa, Mohammed S. Sayed, Mahmoud I. Abdalla
Date: 2017-07-04 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Segmentation

Arabic is one of the languages that present special challenges to Optical
character recognition (OCR). The main challenge in Arabic is that it is mostly
cursive. Therefore, a segmentation process must be carried out to determine
where the character begins and where it ends. This step is essential for
character recognition. This paper presents Arabic character segmentation
algorithm. The proposed algorithm uses the projection-based approach concepts
to separate lines, words, and characters. This is done using profile's
amplitude filter and simple edge tool to find characters separations. Our
algorithm shows promising performance when applied on different machine printed
documents with different Arabic fonts.
Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE
Authors: Qiang Liu, Dilin Wang
Date: 2017-07-04 00:00:00

We propose a number of new algorithms for learning deep energy models and
demonstrate their properties. We show that our SteinCD performs well in term of
test likelihood, while SteinGAN performs well in terms of generating realistic
looking images. Our results suggest promising directions for learning better
models by combining GAN-style methods with traditional energy-based learning.
A Minimax Approach to Supervised Learning
Authors: Farzan Farnia, David Tse
Date: 2016-06-07 00:00:00
Proceeding: NeurIPS 2016 12

Given a task of predicting $Y$ from $X$, a loss function $L$, and a set of
probability distributions $\Gamma$ on $(X,Y)$, what is the optimal decision
rule minimizing the worst-case expected loss over $\Gamma$? In this paper, we
address this question by introducing a generalization of the principle of
maximum entropy. Applying this principle to sets of distributions with marginal
on $X$ constrained to be the empirical marginal from the data, we develop a
general minimax approach for supervised learning problems. While for some loss
functions such as squared-error and log loss, the minimax approach rederives
well-knwon regression models, for the 0-1 loss it results in a new linear
classifier which we call the maximum entropy machine. The maximum entropy
machine minimizes the worst-case 0-1 loss over the structured set of
distribution, and by our numerical experiments can outperform other well-known
linear classifiers such as SVM. We also prove a bound on the generalization
worst-case error in the minimax approach.
Visualizing the Consequences of Evidence in Bayesian Networks
Authors: Clifford Champion, Charles Elkan
Date: 2017-07-04 00:00:00

This paper addresses the challenge of viewing and navigating Bayesian
networks as their structural size and complexity grow. Starting with a review
of the state of the art of visualizing Bayesian networks, an area which has
largely been passed over, we improve upon existing visualizations in three
ways. First, we apply a disciplined approach to the graphic design of the basic
elements of the Bayesian network. Second, we propose a technique for direct,
visual comparison of posterior distributions resulting from alternative
evidence sets. Third, we leverage a central mathematical tool in information
theory, to assist the user in finding variables of interest in the network, and
to reduce visual complexity where unimportant. We present our methods applied
to two modestly large Bayesian networks constructed from real-world data sets.
Results suggest the new techniques can be a useful tool for discovering
information flow phenomena, and also for qualitative comparisons of different
evidence configurations, especially in large probabilistic networks.
OPEB: Open Physical Environment Benchmark for Artificial Intelligence
Authors: Hamid Mirzaei, Mona Fathollahi, Tony Givargis
Date: 2017-07-04 00:00:00
Tasks: continuous-control, Continuous Control, Reinforcement Learning, Reinforcement Learning (RL)

Artificial Intelligence methods to solve continuous- control tasks have made
significant progress in recent years. However, these algorithms have important
limitations and still need significant improvement to be used in industry and
real- world applications. This means that this area is still in an active
research phase. To involve a large number of research groups, standard
benchmarks are needed to evaluate and compare proposed algorithms. In this
paper, we propose a physical environment benchmark framework to facilitate
collaborative research in this area by enabling different research groups to
integrate their designed benchmarks in a unified cloud-based repository and
also share their actual implemented benchmarks via the cloud. We demonstrate
the proposed framework using an actual implementation of the classical
mountain-car example and present the results obtained using a Reinforcement
Learning algorithm.
Zero-Shot Fine-Grained Classification by Deep Feature Learning with Semantics
Authors: Aoxue Li, Zhiwu Lu, Li-Wei Wang, Tao Xiang, Xinqi Li, Ji-Rong Wen
Date: 2017-07-04 00:00:00
Tasks: Classification, Domain Adaptation, Fine-Grained Image Classification, General Classification, image-classification, Image Classification, Zero-Shot Learning

Fine-grained image classification, which aims to distinguish images with
subtle distinctions, is a challenging task due to two main issues: lack of
sufficient training data for every class and difficulty in learning
discriminative features for representation. In this paper, to address the two
issues, we propose a two-phase framework for recognizing images from unseen
fine-grained classes, i.e. zero-shot fine-grained classification. In the first
feature learning phase, we finetune deep convolutional neural networks using
hierarchical semantic structure among fine-grained classes to extract
discriminative deep visual features. Meanwhile, a domain adaptation structure
is induced into deep convolutional neural networks to avoid domain shift from
training data to test data. In the second label inference phase, a semantic
directed graph is constructed over attributes of fine-grained classes. Based on
this graph, we develop a label propagation algorithm to infer the labels of
images in the unseen classes. Experimental results on two benchmark datasets
demonstrate that our model outperforms the state-of-the-art zero-shot learning
models. In addition, the features obtained by our feature learning model also
yield significant gains when they are used by other zero-shot learning models,
which shows the flexility of our model in zero-shot fine-grained
classification.
Deep neural network initialization with decision trees
Authors: K. D. Humbird, J. L. Peterson, R. G. McClarren
Date: 2017-07-03 00:00:00

In this work a novel, automated process for constructing and initializing
deep feed-forward neural networks based on decision trees is presented. The
proposed algorithm maps a collection of decision trees trained on the data into
a collection of initialized neural networks, with the structures of the
networks determined by the structures of the trees. The tree-informed
initialization acts as a warm-start to the neural network training process,
resulting in efficiently trained, accurate networks. These models, referred to
as "deep jointly-informed neural networks" (DJINN), demonstrate high predictive
performance for a variety of regression and classification datasets, and
display comparable performance to Bayesian hyper-parameter optimization at a
lower computational cost. By combining the user-friendly features of decision
tree models with the flexibility and scalability of deep neural networks, DJINN
is an attractive algorithm for training predictive models on a wide range of
complex datasets.