pwc-archive/papers-with-abstracts huggingface dataset:
A Linear Extrinsic Calibration of Kaleidoscopic Imaging System from Single 3D Point
Authors: Kosuke Takahashi, Akihiro Miyata, Shohei Nobuhara, Takashi Matsuyama
Date: 2017-03-08 00:00:00
Proceeding: CVPR 2017 7

This paper proposes a new extrinsic calibration of kaleidoscopic imaging
system by estimating normals and distances of the mirrors. The problem to be
solved in this paper is a simultaneous estimation of all mirror parameters
consistent throughout multiple reflections. Unlike conventional methods
utilizing a pair of direct and mirrored images of a reference 3D object to
estimate the parameters on a per-mirror basis, our method renders the
simultaneous estimation problem into solving a linear set of equations. The key
contribution of this paper is to introduce a linear estimation of multiple
mirror parameters from kaleidoscopic 2D projections of a single 3D point of
unknown geometry. Evaluations with synthesized and real images demonstrate the
performance of the proposed algorithm in comparison with conventional methods.
Global hard thresholding algorithms for joint sparse image representation and denoising
Authors: Reza Borhani, Jeremy Watt, Aggelos Katsaggelos
Date: 2017-05-27 00:00:00
Tasks: All, Denoising

Sparse coding of images is traditionally done by cutting them into small
patches and representing each patch individually over some dictionary given a
pre-determined number of nonzero coefficients to use for each patch. In lack of
a way to effectively distribute a total number (or global budget) of nonzero
coefficients across all patches, current sparse recovery algorithms distribute
the global budget equally across all patches despite the wide range of
differences in structural complexity among them. In this work we propose a new
framework for joint sparse representation and recovery of all image patches
simultaneously. We also present two novel global hard thresholding algorithms,
based on the notion of variable splitting, for solving the joint sparse model.
Experimentation using both synthetic and real data shows effectiveness of the
proposed framework for sparse image representation and denoising tasks.
Additionally, time complexity analysis of the proposed algorithms indicate high
scalability of both algorithms, making them favorable to use on large megapixel
images.
Growth-Optimal Portfolio Selection under CVaR Constraints
Authors: Guy Uziel, Ran El-Yaniv
Date: 2017-05-27 00:00:00
Tasks: Decision Making

Online portfolio selection research has so far focused mainly on minimizing
regret defined in terms of wealth growth. Practical financial decision making,
however, is deeply concerned with both wealth and risk. We consider online
learning of portfolios of stocks whose prices are governed by arbitrary
(unknown) stationary and ergodic processes, where the goal is to maximize
wealth while keeping the conditional value at risk (CVaR) below a desired
threshold. We characterize the asymptomatically optimal risk-adjusted
performance and present an investment strategy whose portfolios are guaranteed
to achieve the asymptotic optimal solution while fulfilling the desired risk
constraint. We also numerically demonstrate and validate the viability of our
method on standard datasets.
LiDAR-Camera Calibration using 3D-3D Point correspondences
Authors: Ankit Dhall, Kunal Chelani, Vishnu Radhakrishnan, K. M. Krishna
Date: 2017-05-27 00:00:00
Tasks: Autonomous Vehicles, Camera Calibration, Translation

With the advent of autonomous vehicles, LiDAR and cameras have become an
indispensable combination of sensors. They both provide rich and complementary
data which can be used by various algorithms and machine learning to sense and
make vital inferences about the surroundings. We propose a novel pipeline and
experimental setup to find accurate rigid-body transformation for extrinsically
calibrating a LiDAR and a camera. The pipeling uses 3D-3D point correspondences
in LiDAR and camera frame and gives a closed form solution. We further show the
accuracy of the estimate by fusing point clouds from two stereo cameras which
align perfectly with the rotation and translation estimated by our method,
confirming the accuracy of our method's estimates both mathematically and
visually. Taking our idea of extrinsic LiDAR-camera calibration forward, we
demonstrate how two cameras with no overlapping field-of-view can also be
calibrated extrinsically using 3D point correspondences. The code has been made
available as open-source software in the form of a ROS package, more
information about which can be sought here:
https://github.com/ankitdhall/lidar_camera_calibration .
Deep Matching and Validation Network -- An End-to-End Solution to Constrained Image Splicing Localization and Detection
Authors: Yue Wu, Wael Abd-Almageed, Prem Natarajan
Date: 2017-05-27 00:00:00
Tasks: Image Manipulation

Image splicing is a very common image manipulation technique that is
sometimes used for malicious purposes. A splicing detec- tion and localization
algorithm usually takes an input image and produces a binary decision
indicating whether the input image has been manipulated, and also a
segmentation mask that corre- sponds to the spliced region. Most existing
splicing detection and localization pipelines suffer from two main
shortcomings: 1) they use handcrafted features that are not robust against
subsequent processing (e.g., compression), and 2) each stage of the pipeline is
usually optimized independently. In this paper we extend the formulation of the
underlying splicing problem to consider two input images, a query image and a
potential donor image. Here the task is to estimate the probability that the
donor image has been used to splice the query image, and obtain the splicing
masks for both the query and donor images. We introduce a novel deep
convolutional neural network architecture, called Deep Matching and Validation
Network (DMVN), which simultaneously localizes and detects image splicing. The
proposed approach does not depend on handcrafted features and uses raw input
images to create deep learned representations. Furthermore, the DMVN is
end-to-end op- timized to produce the probability estimates and the
segmentation masks. Our extensive experiments demonstrate that this approach
outperforms state-of-the-art splicing detection methods by a large margin in
terms of both AUC score and speed.
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning
Authors: Justin Fu, John D. Co-Reyes, Sergey Levine
Date: 2017-03-03 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Deep Reinforcement Learning, Density Estimation, Novelty Detection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning algorithms have been shown to learn complex tasks
using highly general policy classes. However, sparse reward problems remain a
significant challenge. Exploration methods based on novelty detection have been
particularly successful in such settings but typically require generative or
predictive models of the observations, which can be difficult to train when the
observations are very high-dimensional and complex, as in the case of raw
images. We propose a novelty detection algorithm for exploration that is based
entirely on discriminatively trained exemplar models, where classifiers are
trained to discriminate each visited state against all others. Intuitively,
novel states are easier to distinguish against other states seen during
training. We show that this kind of discriminative modeling corresponds to
implicit density estimation, and that it can be combined with count-based
exploration to produce competitive results on a range of popular benchmark
tasks, including state-of-the-art results on challenging egocentric
observations in the vizDoom benchmark.
Blind phoneme segmentation with temporal prediction errors
Authors: Paul Michel, Okko Räsänen, Roland Thiollière, Emmanuel Dupoux
Date: 2016-08-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Prediction, speech-recognition, Speech Recognition

Phonemic segmentation of speech is a critical step of speech recognition
systems. We propose a novel unsupervised algorithm based on sequence prediction
models such as Markov chains and recurrent neural network. Our approach
consists in analyzing the error profile of a model trained to predict speech
features frame-by-frame. Specifically, we try to learn the dynamics of speech
in the MFCC space and hypothesize boundaries from local maxima in the
prediction error. We evaluate our system on the TIMIT dataset, with
improvements over similar methods.
Stochastic Feedback Control of Systems with Unknown Nonlinear Dynamics
Authors: Dan Yu, Mohammadhussein Rafieisakhaei, Suman Chakravorty
Date: 2017-05-27 00:00:00

This paper studies the stochastic optimal control problem for systems with
unknown dynamics. First, an open-loop deterministic trajectory optimization
problem is solved without knowing the explicit form of the dynamical system.
Next, a Linear Quadratic Gaussian (LQG) controller is designed for the nominal
trajectory-dependent linearized system, such that under a small noise
assumption, the actual states remain close to the optimal trajectory. The
trajectory-dependent linearized system is identified using input-output
experimental data consisting of the impulse responses of the nominal system. A
computational example is given to illustrate the performance of the proposed
approach.
CASENet: Deep Category-Aware Semantic Edge Detection
Authors: Zhiding Yu, Chen Feng, Ming-Yu Liu, Srikumar Ramalingam
Date: 2017-05-27 00:00:00
Proceeding: CVPR 2017 7
Tasks: Edge Detection, Object Proposal Generation, Semantic Segmentation

Boundary and edge cues are highly beneficial in improving a wide variety of
vision tasks such as semantic segmentation, object recognition, stereo, and
object proposal generation. Recently, the problem of edge detection has been
revisited and significant progress has been made with deep learning. While
classical edge detection is a challenging binary problem in itself, the
category-aware semantic edge detection by nature is an even more challenging
multi-label problem. We model the problem such that each edge pixel can be
associated with more than one class as they appear in contours or junctions
belonging to two or more semantic classes. To this end, we propose a novel
end-to-end deep semantic edge learning architecture based on ResNet and a new
skip-layer architecture where category-wise edge activations at the top
convolution layer share and are fused with the same set of bottom layer
features. We then propose a multi-label loss function to supervise the fused
activations. We show that our proposed architecture benefits this problem with
better performance, and we outperform the current state-of-the-art semantic
edge detection methods by a large margin on standard data sets such as SBD and
Cityscapes.
word2vec Skip-Gram with Negative Sampling is a Weighted Logistic PCA
Authors: Andrew J. Landgraf, Jeremy Bellay
Date: 2017-05-27 00:00:00

We show that the skip-gram formulation of word2vec trained with negative
sampling is equivalent to a weighted logistic PCA. This connection allows us to
better understand the objective, compare it to other word embedding methods,
and extend it to higher dimensional models.
Cardinal Virtues: Extracting Relation Cardinalities from Text
Authors: Paramita Mirza, Simon Razniewski, Fariz Darari, Gerhard Weikum
Date: 2017-04-14 00:00:00
Proceeding: ACL 2017 7
Tasks: Relation

Information extraction (IE) from text has largely focused on relations
between individual entities, such as who has won which award. However, some
facts are never fully mentioned, and no IE method has perfect recall. Thus, it
is beneficial to also tap contents about the cardinalities of these relations,
for example, how many awards someone has won. We introduce this novel problem
of extracting cardinalities and discusses the specific challenges that set it
apart from standard IE. We present a distant supervision method using
conditional random fields. A preliminary evaluation results in precision
between 3% and 55%, depending on the difficulty of relations.
Improving Correlation with Human Judgments by Integrating Semantic Similarity with Second--Order Vectors
Authors: Bridget T. McInnes, Ted Pedersen
Date: 2016-09-02 00:00:00
Proceeding: WS 2017 8
Tasks: Semantic Similarity, Semantic Textual Similarity

Vector space methods that measure semantic similarity and relatedness often
rely on distributional information such as co--occurrence frequencies or
statistical measures of association to weight the importance of particular
co--occurrences. In this paper, we extend these methods by incorporating a
measure of semantic similarity based on a human curated taxonomy into a
second--order vector representation. This results in a measure of semantic
relatedness that combines both the contextual information available in a
corpus--based vector space representation with the semantic knowledge found in
a biomedical ontology. Our results show that incorporating semantic similarity
into a second order co--occurrence matrices improves correlation with human
judgments for both similarity and relatedness, and that our method compares
favorably to various different word embedding methods that have recently been
evaluated on the same reference standards we have used.
Multiplex lexical networks reveal patterns in early word acquisition in children
Authors: Massimo Stella, Nicole M. Beckage, Markus Brede
Date: 2016-09-11 00:00:00

Network models of language have provided a way of linking cognitive processes
to the structure and connectivity of language. However, one shortcoming of
current approaches is focusing on only one type of linguistic relationship at a
time, missing the complex multi-relational nature of language. In this work, we
overcome this limitation by modelling the mental lexicon of English-speaking
toddlers as a multiplex lexical network, i.e. a multi-layered network where
N=529 words/nodes are connected according to four types of relationships: (i)
free associations, (ii) feature sharing, (iii) co-occurrence, and (iv)
phonological similarity. We provide analysis of the topology of the resulting
multiplex and then proceed to evaluate single layers as well as the full
multiplex structure on their ability to predict empirically observed age of
acquisition data of English speaking toddlers. We find that the emerging
multiplex network topology is an important proxy of the cognitive processes of
acquisition, capable of capturing emergent lexicon structure. In fact, we show
that the multiplex topology is fundamentally more powerful than individual
layers in predicting the ordering with which words are acquired. Furthermore,
multiplex analysis allows for a quantification of distinct phases of lexical
acquisition in early learners: while initially all the multiplex layers
contribute to word learning, after about month 23 free associations take the
lead in driving word acquisition.
Direct Estimation of Regional Wall Thicknesses via Residual Recurrent Neural Network
Authors: Wufeng Xue, Ilanit Ben Nachum, Sachin Pandey, James Warrington, Stephanie Leung, Shuo Li
Date: 2017-05-26 00:00:00

Accurate estimation of regional wall thicknesses (RWT) of left ventricular
(LV) myocardium from cardiac MR sequences is of significant importance for
identification and diagnosis of cardiac disease. Existing RWT estimation still
relies on segmentation of LV myocardium, which requires strong prior
information and user interaction. No work has been devoted into direct
estimation of RWT from cardiac MR images due to the diverse shapes and
structures for various subjects and cardiac diseases, as well as the complex
regional deformation of LV myocardium during the systole and diastole phases of
the cardiac cycle. In this paper, we present a newly proposed Residual
Recurrent Neural Network (ResRNN) that fully leverages the spatial and temporal
dynamics of LV myocardium to achieve accurate frame-wise RWT estimation. Our
ResRNN comprises two paths: 1) a feed forward convolution neural network (CNN)
for effective and robust CNN embedding learning of various cardiac images and
preliminary estimation of RWT from each frame itself independently, and 2) a
recurrent neural network (RNN) for further improving the estimation by modeling
spatial and temporal dynamics of LV myocardium. For the RNN path, we design for
cardiac sequences a Circle-RNN to eliminate the effect of null hidden input for
the first time-step. Our ResRNN is capable of obtaining accurate estimation of
cardiac RWT with Mean Absolute Error of 1.44mm (less than 1-pixel error) when
validated on cardiac MR sequences of 145 subjects, evidencing its great
potential in clinical cardiac function assessment.
Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN
Authors: Hyeungill Lee, Sungyeob Han, Jungwoo Lee
Date: 2017-05-09 00:00:00
Tasks: Adversarial Defense, Generative Adversarial Network

We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network. We alternately train both classifier and generator networks. The generator network generates an adversarial perturbation that can easily fool the classifier network by using a gradient of each image. Simultaneously, the classifier network is trained to classify correctly both original and adversarial images generated by the generator. These procedures help the classifier network to become more robust to adversarial perturbations. Furthermore, our adversarial training framework efficiently reduces overfitting and outperforms other regularization methods such as Dropout. We applied our method to supervised learning for CIFAR datasets, and experimantal results show that our method significantly lowers the generalization error of the network. To the best of our knowledge, this is the first method which uses GAN to improve supervised learning.
Semi-Supervised Model Training for Unbounded Conversational Speech Recognition
Authors: Shane Walker, Morten Pedersen, Iroro Orife, Jason Flaks
Date: 2017-05-26 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

For conversational large-vocabulary continuous speech recognition (LVCSR)
tasks, up to about two thousand hours of audio is commonly used to train state
of the art models. Collection of labeled conversational audio however, is
prohibitively expensive, laborious and error-prone. Furthermore, academic
corpora like Fisher English (2004) or Switchboard (1992) are inadequate to
train models with sufficient accuracy in the unbounded space of conversational
speech. These corpora are also timeworn due to dated acoustic telephony
features and the rapid advancement of colloquial vocabulary and idiomatic
speech over the last decades. Utilizing the colossal scale of our unlabeled
telephony dataset, we propose a technique to construct a modern, high quality
conversational speech training corpus on the order of hundreds of millions of
utterances (or tens of thousands of hours) for both acoustic and language model
training. We describe the data collection, selection and training, evaluating
the results of our updated speech recognition system on a test corpus of 7K
manually transcribed utterances. We show relative word error rate (WER)
reductions of {35%, 19%} on {agent, caller} utterances over our seed model and
5% absolute WER improvements over IBM Watson STT on this conversational speech
task.
The Prediction Advantage: A Universally Meaningful Performance Measure for Classification and Regression
Authors: Ran El-Yaniv, Yonatan Geifman, Yair Wiener
Date: 2017-05-23 00:00:00
Tasks: General Classification, imbalanced classification, Prediction, regression

We introduce the Prediction Advantage (PA), a novel performance measure for
prediction functions under any loss function (e.g., classification or
regression). The PA is defined as the performance advantage relative to the
Bayesian risk restricted to knowing only the distribution of the labels. We
derive the PA for well-known loss functions, including 0/1 loss, cross-entropy
loss, absolute loss, and squared loss. In the latter case, the PA is identical
to the well-known R-squared measure, widely used in statistics. The use of the
PA ensures meaningful quantification of prediction performance, which is not
guaranteed, for example, when dealing with noisy imbalanced classification
problems. We argue that among several known alternative performance measures,
PA is the best (and only) quantity ensuring meaningfulness for all noise and
imbalance levels.
Systems of natural-language-facilitated human-robot cooperation: A review
Authors: Rui Liu, Xiaoli Zhang
Date: 2017-01-28 00:00:00

Natural-language-facilitated human-robot cooperation (NLC), in which natural
language (NL) is used to share knowledge between a human and a robot for
conducting intuitive human-robot cooperation (HRC), is continuously developing
in the recent decade. Currently, NLC is used in several robotic domains such as
manufacturing, daily assistance and health caregiving. It is necessary to
summarize current NLC-based robotic systems and discuss the future developing
trends, providing helpful information for future NLC research. In this review,
we first analyzed the driving forces behind the NLC research. Regarding to a
robot s cognition level during the cooperation, the NLC implementations then
were categorized into four types {NL-based control, NL-based robot training,
NL-based task execution, NL-based social companion} for comparison and
discussion. Last based on our perspective and comprehensive paper review, the
future research trends were discussed.
Multi-scale Online Learning and its Applications to Online Auctions
Authors: Sébastien Bubeck, Nikhil R. Devanur, Zhiyi Huang, Rad Niazadeh
Date: 2017-05-26 00:00:00

We consider revenue maximization in online auction/pricing problems. A seller
sells an identical item in each period to a new buyer, or a new set of buyers.
For the online posted pricing problem, we show regret bounds that scale with
the best fixed price, rather than the range of the values. We also show regret
bounds that are almost scale free, and match the offline sample complexity,
when comparing to a benchmark that requires a lower bound on the market share.
These results are obtained by generalizing the classical learning from experts
and multi-armed bandit problems to their multi-scale versions. In this version,
the reward of each action is in a different range, and the regret w.r.t. a
given action scales with its own range, rather than the maximum range.
FractalNet: Ultra-Deep Neural Networks without Residuals
Authors: Gustav Larsson, Michael Maire, Gregory Shakhnarovich
Date: 2016-05-24 00:00:00
Tasks: Image Classification

We introduce a design strategy for neural network macro-architecture based on
self-similarity. Repeated application of a simple expansion rule generates deep
networks whose structural layouts are precisely truncated fractals. These
networks contain interacting subpaths of different lengths, but do not include
any pass-through or residual connections; every internal signal is transformed
by a filter and nonlinearity before being seen by subsequent layers. In
experiments, fractal networks match the excellent performance of standard
residual networks on both CIFAR and ImageNet classification tasks, thereby
demonstrating that residual representations may not be fundamental to the
success of extremely deep convolutional neural networks. Rather, the key may be
the ability to transition, during training, from effectively shallow to deep.
We note similarities with student-teacher behavior and develop drop-path, a
natural extension of dropout, to regularize co-adaptation of subpaths in
fractal architectures. Such regularization allows extraction of
high-performance fixed-depth subnetworks. Additionally, fractal networks
exhibit an anytime property: shallow subnetworks provide a quick answer, while
deeper subnetworks, with higher latency, provide a more accurate answer.