pwc-archive/papers-with-abstracts huggingface dataset:
Object Recognition by Using Multi-level Feature Point Extraction
Authors: Yang Cheng, Timeo Dubois
Date: 2017-10-28 00:00:00
Tasks: General Classification, Object Recognition

In this paper, we present a novel approach for object recognition in
real-time by employing multilevel feature analysis and demonstrate the
practicality of adapting feature extraction into a Naive Bayesian
classification framework that enables simple, efficient, and robust
performance. We also show the proposed method scales well as the number of
level-classes grows. To effectively understand the patches surrounding a
keypoint, the trained classifier uses hundreds of simple binary features and
models class posterior probabilities. In addition, the classification process
is computationally cheap under the assumed independence between arbitrary sets
of features. Even though for some particular scenarios, this assumption can be
invalid. We demonstrate that the efficient classifier nevertheless performs
remarkably well on image datasets with a large variation in the illumination
environment and image capture perspectives. The experiment results show
consistent accuracy can be achieved on many challenging dataset while offer
interactive speed for large resolution images. The method demonstrates
promising results that outperform the state-of-the-art methods on pattern
recognition.
A Dual Encoder Sequence to Sequence Model for Open-Domain Dialogue Modeling
Authors: Sharath T. S., Shubhangi Tandon, Ryan Bauer
Date: 2017-10-28 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Text Generation, Translation

Ever since the successful application of sequence to sequence learning for
neural machine translation systems, interest has surged in its applicability
towards language generation in other problem domains. Recent work has
investigated the use of these neural architectures towards modeling open-domain
conversational dialogue, where it has been found that although these models are
capable of learning a good distributional language model, dialogue coherence is
still of concern. Unlike translation, conversation is much more a one-to-many
mapping from utterance to a response, and it is even more pressing that the
model be aware of the preceding flow of conversation. In this paper we propose
to tackle this problem by introducing previous conversational context in terms
of latent representations of dialogue acts over time. We inject the latent
context representations into a sequence to sequence neural network in the form
of dialog acts using a second encoder to enhance the quality and the coherence
of the conversations generated. The main task of this research work is to show
that adding latent variables that capture discourse relations does indeed
result in more coherent responses when compared to conventional sequence to
sequence models.
Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization
Authors: Lili Meng, Frederick Tung, James J. Little, Julien Valentin, Clarence de Silva
Date: 2017-10-28 00:00:00
Tasks: Camera Relocalization, Loop Closure Detection, regression

Camera relocalization plays a vital role in many robotics and computer vision
tasks, such as global localization, recovery from tracking failure and loop
closure detection. Recent random forests based methods exploit randomly sampled
pixel comparison features to predict 3D world locations for 2D image locations
to guide the camera pose optimization. However, these image features are only
sampled randomly in the images, without considering the spatial structures or
geometric information, leading to large errors or failure cases with the
existence of poorly textured areas or in motion blur. Line segment features are
more robust in these environments. In this work, we propose to jointly exploit
points and lines within the framework of uncertainty driven regression forests.
The proposed approach is thoroughly evaluated on three publicly available
datasets against several strong state-of-the-art baselines in terms of several
different error metrics. Experimental results prove the efficacy of our method,
showing superior or on-par state-of-the-art performance.
BodyDigitizer: An Open Source Photogrammetry-based 3D Body Scanner
Authors: Travis Gesslein, Daniel Scherer, Jens Grubert
Date: 2017-10-03 00:00:00
Tasks: Mixed Reality

With the rising popularity of Augmented and Virtual Reality, there is a need
for representing humans as virtual avatars in various application domains
ranging from remote telepresence, games to medical applications. Besides
explicitly modelling 3D avatars, sensing approaches that create person-specific
avatars are becoming popular. However, affordable solutions typically suffer
from a low visual quality and professional solution are often too expensive to
be deployed in nonprofit projects.
  We present an open-source project, BodyDigitizer, which aims at providing
both build instructions and configuration software for a high-resolution
photogrammetry-based 3D body scanner. Our system encompasses up to 96 Rasperry
PI cameras, active LED lighting, a sturdy frame construction and open-source
configuration software. %We demonstrate the applicability of the body scanner
in a nonprofit Mixed Reality health project. The detailed build instruction and
software are available at http://www.bodydigitizer.org.
Topic Based Sentiment Analysis Using Deep Learning
Authors: Sharath T. S., Shubhangi Tandon
Date: 2017-10-28 00:00:00
Tasks: Deep Learning, Sentiment Analysis, Word Embeddings

In this paper , we tackle Sentiment Analysis conditioned on a Topic in
Twitter data using Deep Learning . We propose a 2-tier approach : In the first
phase we create our own Word Embeddings and see that they do perform better
than state-of-the-art embeddings when used with standard classifiers. We then
perform inference on these embeddings to learn more about a word with respect
to all the topics being considered, and also the top n-influencing words for
each topic. In the second phase we use these embeddings to predict the
sentiment of the tweet with respect to a given topic, and all other topics
under discussion.
Block building programming for symbolic regression
Authors: Chen Chen, Changtong Luo, Zonglin Jiang
Date: 2017-05-22 00:00:00
Tasks: Computational Efficiency, regression, Symbolic Regression

Symbolic regression that aims to detect underlying data-driven models has
become increasingly important for industrial data analysis. For most existing
algorithms such as genetic programming (GP), the convergence speed might be too
slow for large-scale problems with a large number of variables. This situation
may become even worse with increasing problem size. The aforementioned
difficulty makes symbolic regression limited in practical applications.
Fortunately, in many engineering problems, the independent variables in target
models are separable or partially separable. This feature inspires us to
develop a new approach, block building programming (BBP). BBP divides the
original target function into several blocks, and further into factors. The
factors are then modeled by an optimization engine (e.g. GP). Under such
circumstances, BBP can make large reductions to the search space. The partition
of separability is based on a special method, block and factor detection. Two
different optimization engines are applied to test the performance of BBP on a
set of symbolic regression problems. Numerical results show that BBP has a good
capability of structure and coefficient optimization with high computational
efficiency.
Wasserstein Identity Testing
Authors: Shichuan Deng, Wenzheng Li, Xuan Wu
Date: 2017-10-28 00:00:00

Uniformity testing and the more general identity testing are well studied
problems in distributional property testing. Most previous work focuses on
testing under $L_1$-distance. However, when the support is very large or even
continuous, testing under $L_1$-distance may require a huge (even infinite)
number of samples. Motivated by such issues, we consider the identity testing
in Wasserstein distance (a.k.a. transportation distance and earthmover
distance) on a metric space (discrete or continuous).
  In this paper, we propose the Wasserstein identity testing problem (Identity
Testing in Wasserstein distance). We obtain nearly optimal worst-case sample
complexity for the problem. Moreover, for a large class of probability
distributions satisfying the so-called "Doubling Condition", we provide nearly
instance-optimal sample complexity.
Inducing Regular Grammars Using Recurrent Neural Networks
Authors: Mor Cohen, Avi Caciularu, Idan Rejwan, Jonathan Berant
Date: 2017-10-28 00:00:00

Grammar induction is the task of learning a grammar from a set of examples.
Recently, neural networks have been shown to be powerful learning machines that
can identify patterns in streams of data. In this work we investigate their
effectiveness in inducing a regular grammar from data, without any assumptions
about the grammar. We train a recurrent neural network to distinguish between
strings that are in or outside a regular language, and utilize an algorithm for
extracting the learned finite-state automaton. We apply this method to several
regular languages and find unexpected results regarding the connections between
the network's states that may be regarded as evidence for generalization.
An Ontology to support automated negotiation
Authors: Susel Fernandez, Takayuki Ito
Date: 2017-10-28 00:00:00

In this work we propose an ontology to support automated negotiation in
multiagent systems. The ontology can be connected with some domain-specific
ontologies to facilitate the negotiation in different domains, such as
Intelligent Transportation Systems (ITS), e-commerce, etc. The specific
negotiation rules for each type of negotiation strategy can also be defined as
part of the ontology, reducing the amount of knowledge hardcoded in the agents
and ensuring the interoperability. The expressiveness of the ontology was
proved in a multiagent architecture for the automatic traffic light setting
application on ITS.
Efficient Licence Plate Detection By Unique Edge Detection Algorithm and Smarter Interpretation Through IoT
Authors: Tejas K, Ashok Reddy K, Pradeep Reddy D, Rajesh Kumar M
Date: 2017-10-28 00:00:00
Tasks: Edge Detection

Vehicles play a vital role in modern day transportation systems. Number plate
provides a standard means of identification for any vehicle. To serve this
purpose, automatic licence plate recognition system was developed. This
consisted of four major steps: Pre-processing of the obtained image, extraction
of licence plate region, segmentation and character recognition. In earlier
research, direct application of Sobel edge detection algorithm or applying
threshold were used as key steps to extract the licence plate region, which
does not produce effective results when the captured image is subjected to the
high intensity of light. The use of morphological operations causes deformity
in the characters during segmentation. We propose a novel algorithm to tackle
the mentioned issues through a unique edge detection algorithm. It is also a
tedious task to create and update the database of required vehicles frequently.
This problem is solved by the use of Internet of things(IOT) where an online
database can be created and updated from any module instantly. Also, through
IoT, we connect all the cameras in a geographical area to one server to create
a universal eye which drastically increases the probability of tracing a
vehicle over having manual database attached to each camera for identification
purpose.
Exponentially vanishing sub-optimal local minima in multilayer neural networks
Authors: Daniel Soudry, Elad Hoffer
Date: 2017-02-19 00:00:00
Proceeding: ICLR 2018 1
Tasks: Binary Classification

Background: Statistical mechanics results (Dauphin et al. (2014); Choromanska
et al. (2015)) suggest that local minima with high error are exponentially rare
in high dimensions. However, to prove low error guarantees for Multilayer
Neural Networks (MNNs), previous works so far required either a heavily
modified MNN model or training method, strong assumptions on the labels (e.g.,
"near" linear separability), or an unrealistic hidden layer with
$\Omega\left(N\right)$ units.
  Results: We examine a MNN with one hidden layer of piecewise linear units, a
single output, and a quadratic loss. We prove that, with high probability in
the limit of $N\rightarrow\infty$ datapoints, the volume of differentiable
regions of the empiric loss containing sub-optimal differentiable local minima
is exponentially vanishing in comparison with the same volume of global minima,
given standard normal input of dimension
$d_{0}=\tilde{\Omega}\left(\sqrt{N}\right)$, and a more realistic number of
$d_{1}=\tilde{\Omega}\left(N/d_{0}\right)$ hidden units. We demonstrate our
results numerically: for example, $0\%$ binary classification training error on
CIFAR with only $N/d_{0}\approx 16$ hidden neurons.
Efficient Localized Inference for Large Graphical Models
Authors: Jinglin Chen, Jian Peng, Qiang Liu
Date: 2017-10-28 00:00:00

We propose a new localized inference algorithm for answering marginalization
queries in large graphical models with the correlation decay property. Given a
query variable and a large graphical model, we define a much smaller model in a
local region around the query variable in the target model so that the marginal
distribution of the query variable can be accurately approximated. We introduce
two approximation error bounds based on the Dobrushin's comparison theorem and
apply our bounds to derive a greedy expansion algorithm that efficiently guides
the selection of neighbor nodes for localized inference. We verify our
theoretical bounds on various datasets and demonstrate that our localized
inference algorithm can provide fast and accurate approximation for large
graphical models.
Trainable back-propagated functional transfer matrices
Authors: Cheng-Hao Cai, Yanyan Xu, Dengfeng Ke, Kaile Su, Jing Sun
Date: 2017-10-28 00:00:00

Connections between nodes of fully connected neural networks are usually
represented by weight matrices. In this article, functional transfer matrices
are introduced as alternatives to the weight matrices: Instead of using real
weights, a functional transfer matrix uses real functions with trainable
parameters to represent connections between nodes. Multiple functional transfer
matrices are then stacked together with bias vectors and activations to form
deep functional transfer neural networks. These neural networks can be trained
within the framework of back-propagation, based on a revision of the delta
rules and the error transmission rule for functional connections. In
experiments, it is demonstrated that the revised rules can be used to train a
range of functional connections: 20 different functions are applied to neural
networks with up to 10 hidden layers, and most of them gain high test
accuracies on the MNIST database. It is also demonstrated that a functional
transfer matrix with a memory function can roughly memorise a non-cyclical
sequence of 400 digits.
Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition
Authors: Chee Kheng Chng, Chee Seng Chan
Date: 2017-10-28 00:00:00
Tasks: Curved Text Detection, Scene Text Detection, Scene Text Recognition, Text Detection

Text in curve orientation, despite being one of the common text orientations
in real world environment, has close to zero existence in well received scene
text datasets such as ICDAR2013 and MSRA-TD500. The main motivation of
Total-Text is to fill this gap and facilitate a new research direction for the
scene text community. On top of the conventional horizontal and multi-oriented
texts, it features curved-oriented text. Total-Text is highly diversified in
orientations, more than half of its images have a combination of more than two
orientations. Recently, a new breed of solutions that casted text detection as
a segmentation problem has demonstrated their effectiveness against
multi-oriented text. In order to evaluate its robustness against curved text,
we fine-tuned DeconvNet and benchmark it on Total-Text. Total-Text with its
annotation is available at https://github.com/cs-chan/Total-Text-Dataset
Customized Routing Optimization Based on Gradient Boost Regressor Model
Authors: Chen Zheng, Clara Grzegorz Kasprowicz, Carol Saunders
Date: 2017-10-28 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we discussed limitation of current
electronic-design-automoation (EDA) tool and proposed a machine learning
framework to overcome the limitations and achieve better design quality. We
explored how to efficiently extract relevant features and leverage gradient
boost regressor (GBR) model to predict underestimated risky net (URN).
Customized routing optimizations are applied to the URNs and results show clear
timing improvement and trend to converge toward timing closure.
Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks
Authors: Xu Sun, Bingzhen Wei, Xuancheng Ren, Shuming Ma
Date: 2017-10-28 00:00:00
Proceeding: ICLR 2018 1

We propose a method, called Label Embedding Network, which can learn label
representation (label embedding) during the training process of deep networks.
With the proposed method, the label embedding is adaptively and automatically
learned through back propagation. The original one-hot represented loss
function is converted into a new loss function with soft distributions, such
that the originally unrelated labels have continuous interactions with each
other during the training process. As a result, the trained model can achieve
substantially higher accuracy and with faster convergence speed. Experimental
results based on competitive tasks demonstrate the effectiveness of the
proposed method, and the learned label embedding is reasonable and
interpretable. The proposed method achieves comparable or even better results
than the state-of-the-art systems. The source code is available at
\url{https://github.com/lancopku/LabelEmb}.
JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis
Authors: Ryosuke Sonobe, Shinnosuke Takamichi, Hiroshi Saruwatari
Date: 2017-10-28 00:00:00
Tasks: BIG-bench Machine Learning, Speech Synthesis

Thanks to improvements in machine learning techniques including deep
learning, a free large-scale speech corpus that can be shared between academic
institutions and commercial companies has an important role. However, such a
corpus for Japanese speech synthesis does not exist. In this paper, we designed
a novel Japanese speech corpus, named the "JSUT corpus," that is aimed at
achieving end-to-end speech synthesis. The corpus consists of 10 hours of
reading-style speech data and its transcription and covers all of the main
pronunciations of daily-use Japanese characters. In this paper, we describe how
we designed and analyzed the corpus. The corpus is freely available online.
Minimax Rates and Efficient Algorithms for Noisy Sorting
Authors: Cheng Mao, Jonathan Weed, Philippe Rigollet
Date: 2017-10-28 00:00:00

There has been a recent surge of interest in studying permutation-based
models for ranking from pairwise comparison data. Despite being structurally
richer and more robust than parametric ranking models, permutation-based models
are less well understood statistically and generally lack efficient learning
algorithms. In this work, we study a prototype of permutation-based ranking
models, namely, the noisy sorting model. We establish the optimal rates of
learning the model under two sampling procedures. Furthermore, we provide a
fast algorithm to achieve near-optimal rates if the observations are sampled
independently. Along the way, we discover properties of the symmetric group
which are of theoretical interest.
Partitioning Relational Matrices of Similarities or Dissimilarities using the Value of Information
Authors: Isaac J. Sledge, Jose C. Principe
Date: 2017-10-28 00:00:00
Tasks: Clustering

In this paper, we provide an approach to clustering relational matrices whose
entries correspond to either similarities or dissimilarities between objects.
Our approach is based on the value of information, a parameterized,
information-theoretic criterion that measures the change in costs associated
with changes in information. Optimizing the value of information yields a
deterministic annealing style of clustering with many benefits. For instance,
investigators avoid needing to a priori specify the number of clusters, as the
partitions naturally undergo phase changes, during the annealing process,
whereby the number of clusters changes in a data-driven fashion. The
global-best partition can also often be identified.
Lower Bounds for Two-Sample Structural Change Detection in Ising and Gaussian Models
Authors: Aditya Gangrade, Bobak Nazer, Venkatesh Saligrama
Date: 2017-10-28 00:00:00
Tasks: Change Detection

The change detection problem is to determine if the Markov network structures
of two Markov random fields differ from one another given two sets of samples
drawn from the respective underlying distributions. We study the trade-off
between the sample sizes and the reliability of change detection, measured as a
minimax risk, for the important cases of the Ising models and the Gaussian
Markov random fields restricted to the models which have network structures
with $p$ nodes and degree at most $d$, and obtain information-theoretic lower
bounds for reliable change detection over these models. We show that for the
Ising model, $\Omega\left(\frac{d^2}{(\log d)^2}\log p\right)$ samples are
required from each dataset to detect even the sparsest possible changes, and
that for the Gaussian, $\Omega\left( \gamma^{-2} \log(p)\right)$ samples are
required from each dataset to detect change, where $\gamma$ is the smallest
ratio of off-diagonal to diagonal terms in the precision matrices of the
distributions. These bounds are compared to the corresponding results in
structure learning, and closely match them under mild conditions on the model
parameters. Thus, our change detection bounds inherit partial tightness from
the structure learning schemes in previous literature, demonstrating that in
certain parameter regimes, the naive structure learning based approach to
change detection is minimax optimal up to constant factors.
Consistency of Lipschitz learning with infinite unlabeled data and finite labeled data
Authors: Jeff Calder
Date: 2017-10-28 00:00:00

We study the consistency of Lipschitz learning on graphs in the limit of infinite unlabeled data and finite labeled data. Previous work has conjectured that Lipschitz learning is well-posed in this limit, but is insensitive to the distribution of the unlabeled data, which is undesirable for semi-supervised learning. We first prove that this conjecture is true in the special case of a random geometric graph model with kernel-based weights. Then we go on to show that on a random geometric graph with self-tuning weights, Lipschitz learning is in fact highly sensitive to the distribution of the unlabeled data, and we show how the degree of sensitivity can be adjusted by tuning the weights. In both cases, our results follow from showing that the sequence of learned functions converges to the viscosity solution of an $\infty$-Laplace type equation, and studying the structure of the limiting equation.
Deep Residual Learning for Small-Footprint Keyword Spotting
Authors: Raphael Tang, Jimmy Lin
Date: 2017-10-28 00:00:00
Tasks: Keyword Spotting, Small-Footprint Keyword Spotting

We explore the application of deep residual learning and dilated convolutions
to the keyword spotting task, using the recently-released Google Speech
Commands Dataset as our benchmark. Our best residual network (ResNet)
implementation significantly outperforms Google's previous convolutional neural
networks in terms of accuracy. By varying model depth and width, we can achieve
compact models that also outperform previous small-footprint variants. To our
knowledge, we are the first to examine these approaches for keyword spotting,
and our results establish an open-source state-of-the-art reference to support
the development of future speech-based interfaces.