pwc-archive/papers-with-abstracts huggingface dataset:
Fast, Precise Myelin Water Quantification using DESS MRI and Kernel Learning
Authors: Gopal Nataraj, Jon-Fredrik Nielsen, Mingjie Gao, Jeffrey A. Fessler
Date: 2018-09-24 00:00:00
Tasks: parameter estimation

Purpose: To investigate the feasibility of myelin water content
quantification using fast dual-echo steady-state (DESS) scans and machine
learning with kernels.
  Methods: We optimized combinations of steady-state (SS) scans for precisely
estimating the fast-relaxing signal fraction ff of a two-compartment signal
model, subject to a scan time constraint. We estimated ff from the optimized
DESS acquisition using a recently developed method for rapid parameter
estimation via regression with kernels (PERK). We compared DESS PERK ff
estimates to conventional myelin water fraction (MWF) estimates from a longer
multi-echo spin-echo (MESE) acquisition in simulation, in vivo, and ex vivo
studies.
  Results: Simulations demonstrate that DESS PERK ff estimators and MESE MWF
estimators achieve comparable error levels. In vivo and ex vivo experiments
demonstrate that MESE MWF and DESS PERK ff estimates are quantitatively
comparable measures of WM myelin water content. To our knowledge, these
experiments are the first to demonstrate myelin water images from a SS
acquisition that are quantitatively similar to conventional MESE MWF images.
  Conclusion: Combinations of fast DESS scans can be designed to enable precise
ff estimation. PERK is well-suited for ff estimation. DESS PERK ff and MESE MWF
estimates are quantitatively similar measures of WM myelin water content.
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
Authors: Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, Dragomir Radev
Date: 2018-09-24 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Semantic Parsing, Text to SQL, Text-To-SQL

We present Spider, a large-scale, complex and cross-domain semantic parsing
and text-to-SQL dataset annotated by 11 college students. It consists of 10,181
questions and 5,693 unique complex SQL queries on 200 databases with multiple
tables, covering 138 different domains. We define a new complex and
cross-domain semantic parsing and text-to-SQL task where different complex SQL
queries and databases appear in train and test sets. In this way, the task
requires the model to generalize well to both new SQL queries and new database
schemas. Spider is distinct from most of the previous semantic parsing tasks
because they all use a single database and the exact same programs in the train
set and the test set. We experiment with various state-of-the-art models and
the best model achieves only 12.4% exact matching accuracy on a database split
setting. This shows that Spider presents a strong challenge for future
research. Our dataset and task are publicly available at
https://yale-lily.github.io/spider
Robotics Rights and Ethics Rules
Authors: Tuncay Yigit, Utku Kose, Nilgun Sengoz
Date: 2018-09-24 00:00:00
Tasks: Ethics

It is very important to adhere strictly to ethical and social influences when
delivering most of our life to artificial intelligence systems. With industry
4.0, the internet of things, data analysis and automation have begun to be of
great importance in our lives. With the Yapanese version of Industry 5.0, it
has come to our attention that machine-human interaction and human intelligence
are working in harmony with the cognitive computer. In this context, robots
working on artificial intelligence algorithms co-ordinated with the development
of technology have begun to enter our lives. But the consequences of the recent
complaints of the Robots have been that important issues have arisen about how
to be followed in terms of intellectual property and ethics. Although there are
no laws regulating robots in our country at present, laws on robot ethics and
rights abroad have entered into force. This means that it is important that we
organize the necessary arrangements in the way that robots and artificial
intelligence are so important in the new world order. In this study, it was
aimed to examine the existing rules of machine and robot ethics and to set an
example for the arrangements to be made in our country, and various discussions
were given in this context.
Vision-based Control of a Quadrotor in User Proximity: Mediated vs End-to-End Learning Approaches
Authors: Dario Mantegazza, Jérôme Guzzi, Luca M. Gambardella, Alessandro Giusti
Date: 2018-09-24 00:00:00
Tasks: 3D Human Pose Estimation, State Estimation

We consider the task of controlling a quadrotor to hover in front of a freely
moving user, using input data from an onboard camera. On this specific task we
compare two widespread learning paradigms: a mediated approach, which learns an
high-level state from the input and then uses it for deriving control signals;
and an end-to-end approach, which skips high-level state estimation altogether.
We show that despite their fundamental difference, both approaches yield
equivalent performance on this task. We finally qualitatively analyze the
behavior of a quadrotor implementing such approaches.
A Probabilistic Semi-Supervised Approach to Multi-Task Human Activity Modeling
Authors: Judith Bütepage, Hedvig Kjellström, Danica Kragic
Date: 2018-09-24 00:00:00
Tasks: Action Classification, General Classification, motion prediction, Trajectory Prediction

Human behavior is a continuous stochastic spatio-temporal process which is
governed by semantic actions and affordances as well as latent factors.
Therefore, video-based human activity modeling is concerned with a number of
tasks such as inferring current and future semantic labels, predicting future
continuous observations as well as imagining possible future label and feature
sequences. In this paper we present a semi-supervised probabilistic deep latent
variable model that can represent both discrete labels and continuous
observations as well as latent dynamics over time. This allows the model to
solve several tasks at once without explicit fine-tuning. We focus here on the
tasks of action classification, detection, prediction and anticipation as well
as motion prediction and synthesis based on 3D human activity data recorded
with Kinect. We further extend the model to capture hierarchical label
structure and to model the dependencies between multiple entities, such as a
human and objects. Our experiments demonstrate that our principled approach to
human activity modeling can be used to detect current and anticipate future
semantic labels and to predict and synthesize future label and feature
sequences. When comparing our model to state-of-the-art approaches, which are
specifically designed for e.g. action classification, we find that our
probabilistic formulation outperforms or is comparable to these task specific
models.
Semi Parametric Estimations of rotating and scaling parameters for aeronautic loads
Authors: Edouard Fournier, Stéphane Grihon, Thierry Klein
Date: 2018-09-24 00:00:00

In this paper, we perform registration of noisy curves. We provide an
appropriate model in estimating the rotation and scaling parameters to adjust a
set of curves through a M-estimation procedure. We prove the consistency and
the asymptotic normality of our estimators. Numerical simulation and a real
life aeronautic example are given to illustrate our methodology.
A Comparative Study: Adaptive Fuzzy Inference Systems for Energy Prediction in Urban Buildings
Authors: Mainak Dan, Seshadhri Srinivasan
Date: 2018-09-24 00:00:00
Tasks: Time Series, Time Series Analysis

This investigation aims to study different adaptive fuzzy inference
algorithms capable of real-time sequential learning and prediction of
time-series data. A brief qualitative description of these algorithms namely
meta-cognitive fuzzy inference system (McFIS), sequential adaptive fuzzy
inference system (SAFIS) and evolving Takagi-Sugeno (ETS) model provide a
comprehensive comparison of their working principle, especially their unique
characteristics are discussed. These algorithms are then simulated with dataset
collected at one of the academic buildings at Nanyang Technological University,
Singapore. The performance are compared by means of the root mean squared error
(RMSE) and non-destructive error index (NDEI) of the predicted output. Analysis
shows that McFIS shows promising results either with lower RMSE and NDEI or
with lower architectural complexity over ETS and SAFIS. Statistical Analysis
also reveals the significance of the outcome of these algorithms.
A Framework towards Domain Specific Video Summarization
Authors: Vishal Kaushal, Sandeep Subramanian, Suraj Kothawade, Rishabh Iyer, Ganesh Ramakrishnan
Date: 2018-09-24 00:00:00
Tasks: Video Summarization

In the light of exponentially increasing video content, video summarization
has attracted a lot of attention recently due to its ability to optimize time
and storage. Characteristics of a good summary of a video depend on the
particular domain under question. We propose a novel framework for domain
specific video summarization. Given a video of a particular domain, our system
can produce a summary based on what is important for that domain in addition to
possessing other desired characteristics like representativeness, coverage,
diversity etc. as suitable to that domain. Past related work has focused either
on using supervised approaches for ranking the snippets to produce summary or
on using unsupervised approaches of generating the summary as a subset of
snippets with the above characteristics. We look at the joint problem of
learning domain specific importance of segments as well as the desired summary
characteristic for that domain. Our studies show that the more efficient way of
incorporating domain specific relevances into a summary is by obtaining ratings
of shots as opposed to binary inclusion/exclusion information. We also argue
that ratings can be seen as unified representation of all possible ground truth
summaries of a video, taking us one step closer in dealing with challenges
associated with multiple ground truth summaries of a video. We also propose a
novel evaluation measure which is more naturally suited in assessing the
quality of video summary for the task at hand than F1 like measures. It
leverages the ratings information and is richer in appropriately modeling
desirable and undesirable characteristics of a summary. Lastly, we release a
gold standard dataset for furthering research in domain specific video
summarization, which to our knowledge is the first dataset with long videos
across several domains with rating annotations.
Dynamical Isometry is Achieved in Residual Networks in a Universal Way for any Activation Function
Authors: Wojciech Tarnowski, Piotr Warchoł, Stanisław Jastrzębski, Jacek Tabor, Maciej A. Nowak
Date: 2018-09-24 00:00:00

We demonstrate that in residual neural networks (ResNets) dynamical isometry
is achievable irrespectively of the activation function used. We do that by
deriving, with the help of Free Probability and Random Matrix Theories, a
universal formula for the spectral density of the input-output Jacobian at
initialization, in the large network width and depth limit. The resulting
singular value spectrum depends on a single parameter, which we calculate for a
variety of popular activation functions, by analyzing the signal propagation in
the artificial neural network. We corroborate our results with numerical
simulations of both random matrices and ResNets applied to the CIFAR-10
classification problem. Moreover, we study the consequence of this universal
behavior for the initial and late phases of the learning processes. We conclude
by drawing attention to the simple fact, that initialization acts as a
confounding factor between the choice of activation function and the rate of
learning. We propose that in ResNets this can be resolved based on our results,
by ensuring the same level of dynamical isometry at initialization.
Vis-DSS: An Open-Source toolkit for Visual Data Selection and Summarization
Authors: Rishabh Iyer, Pratik Dubal, Kunal Dargan, Suraj Kothawade, Rohan Mahadev, Vishal Kaushal
Date: 2018-09-24 00:00:00
Tasks: Active Learning, Video Summarization

With increasing amounts of visual data being created in the form of videos
and images, visual data selection and summarization are becoming ever
increasing problems. We present Vis-DSS, an open-source toolkit for Visual Data
Selection and Summarization. Vis-DSS implements a framework of models for
summarization and data subset selection using submodular functions, which are
becoming increasingly popular today for these problems. We present several
classes of models, capturing notions of diversity, coverage, representation and
importance, along with optimization/inference and learning algorithms. Vis-DSS
is the first open source toolkit for several Data selection and summarization
tasks including Image Collection Summarization, Video Summarization, Training
Data selection for Classification and Diversified Active Learning. We
demonstrate state-of-the art performance on all these tasks, and also show how
we can scale to large problems. Vis-DSS allows easy integration for
applications to be built on it, also can serve as a general skeleton that can
be extended to several use cases, including video and image sharing platforms
for creating GIFs, image montage creation, or as a component to surveillance
systems and we demonstrate this by providing a graphical user-interface (GUI)
desktop app built over Qt framework. Vis-DSS is available at
https://github.com/rishabhk108/vis-dss
Dense neural networks as sparse graphs and the lightning initialization
Authors: Thomas Pircher, Dominik Haspel, Eberhard Schlücker
Date: 2018-09-24 00:00:00

Even though dense networks have lost importance today, they are still used as
final logic elements. It could be shown that these dense networks can be
simplified by the sparse graph interpretation. This in turn shows that the
information flow between input and output is not optimal with an initialization
common today. The lightning initialization sets the weights so that complete
information paths exist between input and output from the start. It turned out
that pure dense networks and also more complex networks with additional layers
benefit from this initialization. The networks accuracy increases faster. The
lightning initialization has two parameters which behaved robustly in the tests
carried out. However, especially with more complex networks, an improvement
effect only occurs at lower learning rates, which shows that the initialization
retains its positive effect over the epochs with learning rate reduction.
Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning
Authors: Changan Chen, Yuejiang Liu, Sven Kreiss, Alexandre Alahi
Date: 2018-09-24 00:00:00
Tasks: Deep Reinforcement Learning, Human Dynamics, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robot Navigation

Mobility in an effective and socially-compliant manner is an essential yet
challenging task for robots operating in crowded spaces. Recent works have
shown the power of deep reinforcement learning techniques to learn socially
cooperative policies. However, their cooperation ability deteriorates as the
crowd grows since they typically relax the problem as a one-way Human-Robot
interaction problem. In this work, we want to go beyond first-order Human-Robot
interaction and more explicitly model Crowd-Robot Interaction (CRI). We propose
to (i) rethink pairwise interactions with a self-attention mechanism, and (ii)
jointly model Human-Robot as well as Human-Human interactions in the deep
reinforcement learning framework. Our model captures the Human-Human
interactions occurring in dense crowds that indirectly affects the robot's
anticipation capability. Our proposed attentive pooling mechanism learns the
collective importance of neighboring humans with respect to their future
states. Various experiments demonstrate that our model can anticipate human
dynamics and navigate in crowds with time efficiency, outperforming
state-of-the-art methods.
Wasserstein Distributionally Robust Kalman Filtering
Authors: Soroosh Shafieezadeh-Abadeh, Viet Anh Nguyen, Daniel Kuhn, Peyman Mohajerin Esfahani
Date: 2018-09-24 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Form

We study a distributionally robust mean square error estimation problem over
a nonconvex Wasserstein ambiguity set containing only normal distributions. We
show that the optimal estimator and the least favorable distribution form a
Nash equilibrium. Despite the non-convex nature of the ambiguity set, we prove
that the estimation problem is equivalent to a tractable convex program. We
further devise a Frank-Wolfe algorithm for this convex program whose
direction-searching subproblem can be solved in a quasi-closed form. Using
these ingredients, we introduce a distributionally robust Kalman filter that
hedges against model risk.
Information-Weighted Neural Cache Language Models for ASR
Authors: Lyan Verwimp, Joris Pelemans, Hugo Van hamme, Patrick Wambacq
Date: 2018-09-24 00:00:00

Neural cache language models (LMs) extend the idea of regular cache language
models by making the cache probability dependent on the similarity between the
current context and the context of the words in the cache. We make an extensive
comparison of 'regular' cache models with neural cache models, both in terms of
perplexity and WER after rescoring first-pass ASR results. Furthermore, we
propose two extensions to this neural cache model that make use of the content
value/information weight of the word: firstly, combining the cache probability
and LM probability with an information-weighted interpolation and secondly,
selectively adding only content words to the cache. We obtain a 29.9%/32.1%
(validation/test set) relative improvement in perplexity with respect to a
baseline LSTM LM on the WikiText-2 dataset, outperforming previous work on
neural cache LMs. Additionally, we observe significant WER reductions with
respect to the baseline model on the WSJ ASR task.
Representing Sets as Summed Semantic Vectors
Authors: Douglas Summers-Stay, Peter Sutor, Dandan Li
Date: 2018-09-24 00:00:00

Representing meaning in the form of high dimensional vectors is a common and
powerful tool in biologically inspired architectures. While the meaning of a
set of concepts can be summarized by taking a (possibly weighted) sum of their
associated vectors, this has generally been treated as a one-way operation. In
this paper we show how a technique built to aid sparse vector decomposition
allows in many cases the exact recovery of the inputs and weights to such a
sum, allowing a single vector to represent an entire set of vectors from a
dictionary. We characterize the number of vectors that can be recovered under
various conditions, and explore several ways such a tool can be used for
vector-based reasoning.
Orthogonally Decoupled Variational Gaussian Processes
Authors: Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc Deisenroth
Date: 2018-09-24 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Gaussian Processes, Variational Inference

Gaussian processes (GPs) provide a powerful non-parametric framework for
reasoning over functions. Despite appealing theory, its superlinear
computational and memory complexities have presented a long-standing challenge.
State-of-the-art sparse variational inference methods trade modeling accuracy
against complexity. However, the complexities of these methods still scale
superlinearly in the number of basis functions, implying that that sparse GP
methods are able to learn from large datasets only when a small model is used.
Recently, a decoupled approach was proposed that removes the unnecessary
coupling between the complexities of modeling the mean and the covariance
functions of a GP. It achieves a linear complexity in the number of mean
parameters, so an expressive posterior mean function can be modeled. While
promising, this approach suffers from optimization difficulties due to
ill-conditioning and non-convexity. In this work, we propose an alternative
decoupled parametrization. It adopts an orthogonal basis in the mean function
to model the residues that cannot be learned by the standard coupled approach.
Therefore, our method extends, rather than replaces, the coupled approach to
achieve strictly better performance. This construction admits a straightforward
natural gradient update rule, so the structure of the information manifold that
is lost during decoupling can be leveraged to speed up learning. Empirically,
our algorithm demonstrates significantly faster convergence in multiple
experiments.
MobileFace: 3D Face Reconstruction with Efficient CNN Regression
Authors: Nikolai Chinaev, Alexander Chigorin, Ivan Laptev
Date: 2018-09-24 00:00:00
Tasks: 3D Face Reconstruction, Face Reconstruction, Face Transfer, regression

Estimation of facial shapes plays a central role for face transfer and
animation. Accurate 3D face reconstruction, however, often deploys iterative
and costly methods preventing real-time applications. In this work we design a
compact and fast CNN model enabling real-time face reconstruction on mobile
devices. For this purpose, we first study more traditional but slow morphable
face models and use them to automatically annotate a large set of images for
CNN training. We then investigate a class of efficient MobileNet CNNs and adapt
such models for the task of shape regression. Our evaluation on three datasets
demonstrates significant improvements in the speed and the size of our model
while maintaining state-of-the-art reconstruction accuracy.
Beyond Binomial and Negative Binomial: Adaptation in Bernoulli Parameter Estimation
Authors: Safa C. Medin, John Murray-Bruce, David Castañón, Vivek K Goyal
Date: 2018-09-24 00:00:00
Tasks: parameter estimation

Estimating the parameter of a Bernoulli process arises in many applications,
including photon-efficient active imaging where each illumination period is
regarded as a single Bernoulli trial. Motivated by acquisition efficiency when
multiple Bernoulli processes are of interest, we formulate the allocation of
trials under a constraint on the mean as an optimal resource allocation
problem. An oracle-aided trial allocation demonstrates that there can be a
significant advantage from varying the allocation for different processes and
inspires a simple trial allocation gain quantity. Motivated by realizing this
gain without an oracle, we present a trellis-based framework for representing
and optimizing stopping rules. Considering the convenient case of Beta priors,
three implementable stopping rules with similar performances are explored, and
the simplest of these is shown to asymptotically achieve the oracle-aided trial
allocation. These approaches are further extended to estimating functions of a
Bernoulli parameter. In simulations inspired by realistic active imaging
scenarios, we demonstrate significant mean-squared error improvements: up to
4.36 dB for the estimation of p and up to 1.80 dB for the estimation of log p.
Chargrid: Towards Understanding 2D Documents
Authors: Anoop Raveendra Katti, Christian Reisswig, Cordula Guder, Sebastian Brarda, Steffen Bickel, Johannes Höhne, Jean Baptiste Faddoul
Date: 2018-09-24 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, document understanding

We introduce a novel type of text representation that preserves the 2D layout
of a document. This is achieved by encoding each document page as a
two-dimensional grid of characters. Based on this representation, we present a
generic document understanding pipeline for structured documents. This pipeline
makes use of a fully convolutional encoder-decoder network that predicts a
segmentation mask and bounding boxes. We demonstrate its capabilities on an
information extraction task from invoices and show that it significantly
outperforms approaches based on sequential text or document images.