pwc-archive/papers-with-abstracts huggingface dataset:
Are we done with object recognition? The iCub robot's perspective
Authors: Giulia Pasquale, Carlo Ciliberto, Francesca Odone, Lorenzo Rosasco, Lorenzo Natale
Date: 2017-09-28 00:00:00
Tasks: Deep Learning, Image Retrieval, Object, Object Categorization, Object Recognition, Retrieval, Transfer Learning

We report on an extensive study of the benefits and limitations of current
deep learning approaches to object recognition in robot vision scenarios,
introducing a novel dataset used for our investigation. To avoid the biases in
currently available datasets, we consider a natural human-robot interaction
setting to design a data-acquisition protocol for visual object recognition on
the iCub humanoid robot. Analyzing the performance of off-the-shelf models
trained off-line on large-scale image retrieval datasets, we show the necessity
for knowledge transfer. We evaluate different ways in which this last step can
be done, and identify the major bottlenecks affecting robotic scenarios. By
studying both object categorization and identification problems, we highlight
key differences between object recognition in robotics applications and in
image retrieval tasks, for which the considered deep learning approaches have
been originally designed. In a nutshell, our results confirm the remarkable
improvements yield by deep learning in this setting, while pointing to specific
open challenges that need be addressed for seamless deployment in robotics.
Recognition of Documents in Braille
Authors: Jomy John
Date: 2017-09-28 00:00:00

Visually impaired people are integral part of the society and it has been a
must to provide them with means and system through which they may communicate
with the world. In this work, I would like to address how computers can be made
useful to read the scripts in Braille. The importance of this work is to reduce
communication gap between visually impaired people and the society. Braille
remains the most popular tactile reading code even in this century. There are
numerous amount of literature locked up in Braille. Braille recognition not
only reduces time in reading or extracting information from Braille document
but also helps people engaged in special education for correcting papers and
other school related works. The availability of such a system will enhance
communication and collaboration possibilities with visually impaired people.
Existing works supports only documents in white either bright or dull in
colour. Hardly any work could be traced on hand printed ordinary documents in
Braille.
A Generative Model for Score Normalization in Speaker Recognition
Authors: Albert Swart, Niko Brummer
Date: 2017-09-28 00:00:00
Tasks: Speaker Recognition

We propose a theoretical framework for thinking about score normalization,
which confirms that normalization is not needed under (admittedly fragile)
ideal conditions. If, however, these conditions are not met, e.g. under
data-set shift between training and runtime, our theory reveals dependencies
between scores that could be exploited by strategies such as score
normalization. Indeed, it has been demonstrated over and over experimentally,
that various ad-hoc score normalization recipes do work. We present a first
attempt at using probability theory to design a generative score-space
normalization model which gives similar improvements to ZT-norm on the
text-dependent RSR 2015 database.
Distance-based Confidence Score for Neural Network Classifiers
Authors: Amit Mandelbaum, Daphna Weinshall
Date: 2017-09-28 00:00:00
Tasks: Novelty Detection

The reliable measurement of confidence in classifiers' predictions is very
important for many applications and is, therefore, an important part of
classifier design. Yet, although deep learning has received tremendous
attention in recent years, not much progress has been made in quantifying the
prediction confidence of neural network classifiers. Bayesian models offer a
mathematically grounded framework to reason about model uncertainty, but
usually come with prohibitive computational costs. In this paper we propose a
simple, scalable method to achieve a reliable confidence score, based on the
data embedding derived from the penultimate layer of the network. We
investigate two ways to achieve desirable embeddings, by using either a
distance-based loss or Adversarial Training. We then test the benefits of our
method when used for classification error prediction, weighting an ensemble of
classifiers, and novelty detection. In all tasks we show significant
improvement over traditional, commonly used confidence scores.
Soft Correspondences in Multimodal Scene Parsing
Authors: Sarah Taghavi Namin, Mohammad Najafi, Mathieu Salzmann, Lars Petersson
Date: 2017-09-28 00:00:00
Tasks: Scene Parsing

Exploiting multiple modalities for semantic scene parsing has been shown to
improve accuracy over the singlemodality scenario. However multimodal datasets
often suffer from problems such as data misalignment and label inconsistencies,
where the existing methods assume that corresponding regions in two modalities
must have identical labels. We propose to address this issue, by formulating
multimodal semantic labeling as inference in a CRF and introducing latent nodes
to explicitly model inconsistencies between two modalities. These latent nodes
allow us not only to leverage information from both domains to improve their
labeling, but also to cut the edges between inconsistent regions. We propose to
learn intradomain and inter-domain potential functions from training data to
avoid hand-tuning of the model parameters. We evaluate our approach on two
publicly available datasets containing 2D and 3D data. Thanks to our latent
nodes and our learning strategy, our method outperforms the state-of-the-art in
both cases. Moreover, in order to highlight the benefits of the geometric
information and the potential of our method in simultaneous 2D/3D semantic and
geometric inference, we performed simultaneous inference of semantic and
geometric classes both in 2D and 3D that led to satisfactory improvements of
the labeling results in both datasets.
PSA: A novel optimization algorithm based on survival rules of porcellio scaber
Authors: Yinyan Zhang, Pei Zhang, Shuai Li
Date: 2017-09-28 00:00:00

Bio-inspired algorithms such as neural network algorithms and genetic algorithms have received a significant amount of attention in both academic and engineering societies. In this paper, based on the observation of two major survival rules of a species of woodlice, i.e., porcellio scaber, we present an algorithm called the porcellio scaber algorithm (PSA) for solving general unconstrained optimization problems, including differentiable and non-differential ones as well as the case with local optima. Numerical results based on benchmark problems are presented to validate the efficacy of PSA.
Heuristic Online Goal Recognition in Continuous Domains
Authors: Mor Vered, Gal A. Kaminka
Date: 2017-09-28 00:00:00

Goal recognition is the problem of inferring the goal of an agent, based on
its observed actions. An inspiring approach - plan recognition by planning
(PRP) - uses off-the-shelf planners to dynamically generate plans for given
goals, eliminating the need for the traditional plan library. However, existing
PRP formulation is inherently inefficient in online recognition, and cannot be
used with motion planners for continuous spaces. In this paper, we utilize a
different PRP formulation which allows for online goal recognition, and for
application in continuous spaces. We present an online recognition algorithm,
where two heuristic decision points may be used to improve run-time
significantly over existing work. We specify heuristics for continuous domains,
prove guarantees on their use, and empirically evaluate the algorithm over
hundreds of experiments in both a 3D navigational environment and a cooperative
robotic team task.
Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data
Authors: Paroma Varma, Bryan He, Dan Iter, Peng Xu, Rose Yu, Christopher De Sa, Christopher Ré
Date: 2016-10-25 00:00:00
Tasks: Relation Extraction

A challenge in training discriminative models like neural networks is
obtaining enough labeled training data. Recent approaches use generative models
to combine weak supervision sources, like user-defined heuristics or knowledge
bases, to label training data. Prior work has explored learning accuracies for
these sources even without ground truth labels, but they assume that a single
accuracy parameter is sufficient to model the behavior of these sources over
the entire training set. In particular, they fail to model latent subsets in
the training data in which the supervision sources perform differently than on
average. We present Socratic learning, a paradigm that uses feedback from a
corresponding discriminative model to automatically identify these subsets and
augments the structure of the generative model accordingly. Experimentally, we
show that without any ground truth labels, the augmented generative model
reduces error by up to 56.06% for a relation extraction task compared to a
state-of-the-art weak supervision technique that utilizes generative models.
Photorealistic Style Transfer with Screened Poisson Equation
Authors: Roey Mechrez, Eli Shechtman, Lihi Zelnik-Manor
Date: 2017-09-28 00:00:00
Tasks: Style Transfer

Recent work has shown impressive success in transferring painterly style to
images. These approaches, however, fall short of photorealistic style transfer.
Even when both the input and reference images are photographs, the output still
exhibits distortions reminiscent of a painting. In this paper we propose an
approach that takes as input a stylized image and makes it more photorealistic.
It relies on the Screened Poisson Equation, maintaining the fidelity of the
stylized image while constraining the gradients to those of the original input
image. Our method is fast, simple, fully automatic and shows positive progress
in making a stylized image photorealistic. Our results exhibit finer details
and are less prone to artifacts than the state-of-the-art.
Generative Adversarial Mapping Networks
Authors: Jianbo Guo, Guangxiang Zhu, Jian Li
Date: 2017-09-28 00:00:00

Generative Adversarial Networks (GANs) have shown impressive performance in
generating photo-realistic images. They fit generative models by minimizing
certain distance measure between the real image distribution and the generated
data distribution. Several distance measures have been used, such as
Jensen-Shannon divergence, $f$-divergence, and Wasserstein distance, and
choosing an appropriate distance measure is very important for training the
generative network. In this paper, we choose to use the maximum mean
discrepancy (MMD) as the distance metric, which has several nice theoretical
guarantees. In fact, generative moment matching network (GMMN) (Li, Swersky,
and Zemel 2015) is such a generative model which contains only one generator
network $G$ trained by directly minimizing MMD between the real and generated
distributions. However, it fails to generate meaningful samples on challenging
benchmark datasets, such as CIFAR-10 and LSUN. To improve on GMMN, we propose
to add an extra network $F$, called mapper. $F$ maps both real data
distribution and generated data distribution from the original data space to a
feature representation space $\mathcal{R}$, and it is trained to maximize MMD
between the two mapped distributions in $\mathcal{R}$, while the generator $G$
tries to minimize the MMD. We call the new model generative adversarial mapping
networks (GAMNs). We demonstrate that the adversarial mapper $F$ can help $G$
to better capture the underlying data distribution. We also show that GAMN
significantly outperforms GMMN, and is also superior to or comparable with
other state-of-the-art GAN based methods on MNIST, CIFAR-10 and LSUN-Bedrooms
datasets.
Input-to-Output Gate to Improve RNN Language Models
Authors: Sho Takase, Jun Suzuki, Masaaki Nagata
Date: 2017-09-26 00:00:00
Proceeding: IJCNLP 2017 11

This paper proposes a reinforcing method that refines the output layers of
existing Recurrent Neural Network (RNN) language models. We refer to our
proposed method as Input-to-Output Gate (IOG). IOG has an extremely simple
structure, and thus, can be easily combined with any RNN language models. Our
experiments on the Penn Treebank and WikiText-2 datasets demonstrate that IOG
consistently boosts the performance of several different types of current
topline RNN language models.
Edina: Building an Open Domain Socialbot with Self-dialogues
Authors: Ben Krause, Marco Damonte, Mihai Dobre, Daniel Duma, Joachim Fainberg, Federico Fancellu, Emmanuel Kahembwe, Jianpeng Cheng, Bonnie Webber
Date: 2017-09-28 00:00:00

We present Edina, the University of Edinburgh's social bot for the Amazon
Alexa Prize competition. Edina is a conversational agent whose responses
utilize data harvested from Amazon Mechanical Turk (AMT) through an innovative
new technique we call self-dialogues. These are conversations in which a single
AMT Worker plays both participants in a dialogue. Such dialogues are
surprisingly natural, efficient to collect and reflective of relevant and/or
trending topics. These self-dialogues provide training data for a generative
neural network as well as a basis for soft rules used by a matching score
component. Each match of a soft rule against a user utterance is associated
with a confidence score which we show is strongly indicative of reply quality,
allowing this component to self-censor and be effectively integrated with other
components. Edina's full architecture features a rule-based system backing off
to a matching score, backing off to a generative neural network. Our hybrid
data-driven methodology thus addresses both coverage limitations of a strictly
rule-based approach and the lack of guarantees of a strictly machine-learning
approach.
Robust nonparametric nearest neighbor random process clustering
Authors: Michael Tschannen, Helmut Bölcskei
Date: 2016-12-04 00:00:00
Tasks: Clustering

We consider the problem of clustering noisy finite-length observations of
stationary ergodic random processes according to their generative models
without prior knowledge of the model statistics and the number of generative
models. Two algorithms, both using the $L^1$-distance between estimated power
spectral densities (PSDs) as a measure of dissimilarity, are analyzed. The
first one, termed nearest neighbor process clustering (NNPC), relies on
partitioning the nearest neighbor graph of the observations via spectral
clustering. The second algorithm, simply referred to as $k$-means (KM),
consists of a single $k$-means iteration with farthest point initialization and
was considered before in the literature, albeit with a different dissimilarity
measure. We prove that both algorithms succeed with high probability in the
presence of noise and missing entries, and even when the generative process
PSDs overlap significantly, all provided that the observation length is
sufficiently large. Our results quantify the tradeoff between the overlap of
the generative process PSDs, the observation length, the fraction of missing
entries, and the noise variance. Finally, we provide extensive numerical
results for synthetic and real data and find that NNPC outperforms
state-of-the-art algorithms in human motion sequence clustering.
Spectral Method and Regularized MLE Are Both Optimal for Top-$K$ Ranking
Authors: Yuxin Chen, Jianqing Fan, Cong Ma, Kaizheng Wang
Date: 2017-07-31 00:00:00

This paper is concerned with the problem of top-$K$ ranking from pairwise
comparisons. Given a collection of $n$ items and a few pairwise comparisons
across them, one wishes to identify the set of $K$ items that receive the
highest ranks. To tackle this problem, we adopt the logistic parametric model
--- the Bradley-Terry-Luce model, where each item is assigned a latent
preference score, and where the outcome of each pairwise comparison depends
solely on the relative scores of the two items involved. Recent works have made
significant progress towards characterizing the performance (e.g. the mean
square error for estimating the scores) of several classical methods, including
the spectral method and the maximum likelihood estimator (MLE). However, where
they stand regarding top-$K$ ranking remains unsettled.
  We demonstrate that under a natural random sampling model, the spectral
method alone, or the regularized MLE alone, is minimax optimal in terms of the
sample complexity --- the number of paired comparisons needed to ensure exact
top-$K$ identification, for the fixed dynamic range regime. This is
accomplished via optimal control of the entrywise error of the score estimates.
We complement our theoretical studies by numerical experiments, confirming that
both methods yield low entrywise errors for estimating the underlying scores.
Our theory is established via a novel leave-one-out trick, which proves
effective for analyzing both iterative and non-iterative procedures. Along the
way, we derive an elementary eigenvector perturbation bound for probability
transition matrices, which parallels the Davis-Kahan $\sin\Theta$ theorem for
symmetric matrices. This also allows us to close the gap between the $\ell_2$
error upper bound for the spectral method and the minimax lower limit.
Parallelizable sparse inverse formulation Gaussian processes (SpInGP)
Authors: Alexander Grigorievskiy, Neil Lawrence, Simo Särkkä
Date: 2016-10-25 00:00:00
Tasks: Gaussian Processes

We propose a parallelizable sparse inverse formulation Gaussian process
(SpInGP) for temporal models. It uses a sparse precision GP formulation and
sparse matrix routines to speed up the computations. Due to the state-space
formulation used in the algorithm, the time complexity of the basic SpInGP is
linear, and because all the computations are parallelizable, the parallel form
of the algorithm is sublinear in the number of data points. We provide example
algorithms to implement the sparse matrix routines and experimentally test the
method using both simulated and real data.
Automatic skin lesion segmentation with fully convolutional-deconvolutional networks
Authors: Yading Yuan
Date: 2017-03-15 00:00:00
Tasks: Lesion Segmentation, Segmentation, Skin Lesion Segmentation

This paper summarizes our method and validation results for the ISBI
Challenge 2017 - Skin Lesion Analysis Towards Melanoma Detection - Part I:
Lesion Segmentation
A Deep Neural Network Approach To Parallel Sentence Extraction
Authors: Francis Grégoire, Philippe Langlais
Date: 2017-09-28 00:00:00
Tasks: Feature Engineering, Machine Translation, Sentence, Translation, Word Alignment

Parallel sentence extraction is a task addressing the data sparsity problem
found in multilingual natural language processing applications. We propose an
end-to-end deep neural network approach to detect translational equivalence
between sentences in two different languages. In contrast to previous
approaches, which typically rely on multiples models and various word alignment
features, by leveraging continuous vector representation of sentences we remove
the need of any domain specific feature engineering. Using a siamese
bidirectional recurrent neural networks, our results against a strong baseline
based on a state-of-the-art parallel sentence extraction system show a
significant improvement in both the quality of the extracted parallel sentences
and the translation performance of statistical machine translation systems. We
believe this study is the first one to investigate deep learning for the
parallel sentence extraction task.
Tweeting AI: Perceptions of AI-Tweeters (AIT) vs Expert AI-Tweeters (EAIT)
Authors: Lydia Manikonda, Cameron Dudley, Subbarao Kambhampati
Date: 2017-04-27 00:00:00

With the recent advancements in Artificial Intelligence (AI), various
organizations and individuals started debating about the progress of AI as a
blessing or a curse for the future of the society. This paper conducts an
investigation on how the public perceives the progress of AI by utilizing the
data shared on Twitter. Specifically, this paper performs a comparative
analysis on the understanding of users from two categories -- general
AI-Tweeters (AIT) and the expert AI-Tweeters (EAIT) who share posts about AI on
Twitter. Our analysis revealed that users from both the categories express
distinct emotions and interests towards AI. Users from both the categories
regard AI as positive and are optimistic about the progress of AI but the
experts are more negative than the general AI-Tweeters. Characterization of
users manifested that `London' is the popular location of users from where they
tweet about AI. Tweets posted by AIT are highly retweeted than posts made by
EAIT that reveals greater diffusion of information from AIT.
Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks
Authors: Yading Yuan, Yeh-Chi Lo
Date: 2017-09-28 00:00:00
Tasks: Image Segmentation, Lesion Segmentation, Segmentation, Semantic Segmentation, Skin Lesion Segmentation

Automatic skin lesion segmentation on dermoscopic images is an essential step
in computer-aided diagnosis of melanoma. However, this task is challenging due
to significant variations of lesion appearances across different patients. This
challenge is further exacerbated when dealing with a large amount of image
data. In this paper, we extended our previous work by developing a deeper
network architecture with smaller kernels to enhance its discriminant capacity.
In addition, we explicitly included color information from multiple color
spaces to facilitate network training and thus to further improve the
segmentation performance. We extensively evaluated our method on the ISBI 2017
skin lesion segmentation challenge. By training with the 2000 challenge
training images, our method achieved an average Jaccard Index (JA) of 0.765 on
the 600 challenge testing images, which ranked itself in the first place in the
challenge
Sampling Without Compromising Accuracy in Adaptive Data Analysis
Authors: Benjamin Fish, Lev Reyzin, Benjamin I. P. Rubinstein
Date: 2017-09-28 00:00:00

In this work, we study how to use sampling to speed up mechanisms for answering adaptive queries into datasets without reducing the accuracy of those mechanisms. This is important to do when both the datasets and the number of queries asked are very large. In particular, we describe a mechanism that provides a polynomial speed-up per query over previous mechanisms, without needing to increase the total amount of data required to maintain the same generalization error as before. We prove that this speed-up holds for arbitrary statistical queries. We also provide an even faster method for achieving statistically-meaningful responses wherein the mechanism is only allowed to see a constant number of samples from the data per query. Finally, we show that our general results yield a simple, fast, and unified approach for adaptively optimizing convex and strongly convex functions over a dataset.