pwc-archive/papers-with-abstracts huggingface dataset:
VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising
Authors: Bihan Wen, Saiprasad Ravishankar, Yoram Bresler
Date: 2017-10-03 00:00:00
Tasks: Denoising, Dictionary Learning, Image Denoising, Image Reconstruction, Video Denoising, Vocal Bursts Intensity Prediction

Techniques exploiting the sparsity of images in a transform domain have been
effective for various applications in image and video processing. Transform
learning methods involve cheap computations and have been demonstrated to
perform well in applications such as image denoising and medical image
reconstruction. Recently, we proposed methods for online learning of
sparsifying transforms from streaming signals, which enjoy good convergence
guarantees, and involve lower computational costs than online synthesis
dictionary learning. In this work, we apply online transform learning to video
denoising. We present a novel framework for online video denoising based on
high-dimensional sparsifying transform learning for spatio-temporal patches.
The patches are constructed either from corresponding 2D patches in successive
frames or using an online block matching technique. The proposed online video
denoising requires little memory, and offers efficient processing. Numerical
experiments compare the performance to the proposed video denoising scheme but
fixing the transform to be 3D DCT, as well as prior schemes such as dictionary
learning-based schemes, and the state-of-the-art VBM3D and VBM4D on several
video data sets, demonstrating the promising performance of the proposed
methods.
Identifying Nominals with No Head Match Co-references Using Deep Learning
Authors: M. Stone, R. Arora
Date: 2017-10-02 00:00:00
Tasks: coreference-resolution, Coreference Resolution, Deep Learning, regression

Identifying nominals with no head match is a long-standing challenge in
coreference resolution with current systems performing significantly worse than
humans. In this paper we present a new neural network architecture which
outperforms the current state-of-the-art system on the English portion of the
CoNLL 2012 Shared Task. This is done by using a logistic regression on features
produced by two submodels, one of which is has the architecture proposed in
[CM16a] while the other combines domain specific embeddings of the antecedent
and the mention. We also propose some simple additional features which seem to
improve performance for all models substantially, increasing F1 by almost 4% on
basic logistic regression and other complex models.
Optimal modularity and memory capacity of neural reservoirs
Authors: Nathaniel Rodriguez, Eduardo Izquierdo, Yong-Yeol Ahn
Date: 2017-06-20 00:00:00

The neural network is a powerful computing framework that has been exploited
by biological evolution and by humans for solving diverse problems. Although
the computational capabilities of neural networks are determined by their
structure, the current understanding of the relationships between a neural
network's architecture and function is still primitive. Here we reveal that
neural network's modular architecture plays a vital role in determining the
neural dynamics and memory performance of the network of threshold neurons. In
particular, we demonstrate that there exists an optimal modularity for memory
performance, where a balance between local cohesion and global connectivity is
established, allowing optimally modular networks to remember longer. Our
results suggest that insights from dynamical analysis of neural networks and
information spreading processes can be leveraged to better design neural
networks and may shed light on the brain's modular organization.
Minimal Dependency Translation: a Framework for Computer-Assisted Translation for Under-Resourced Languages
Authors: Michael Gasser
Date: 2017-10-02 00:00:00
Tasks: Machine Translation, Sentence, Translation

This paper introduces Minimal Dependency Translation (MDT), an ongoing
project to develop a rule-based framework for the creation of rudimentary
bilingual lexicon-grammars for machine translation and computer-assisted
translation into and out of under-resourced languages as well as initial steps
towards an implementation of MDT for English-to-Amharic translation. The basic
units in MDT, called groups, are headed multi-item sequences. In addition to
wordforms, groups may contain lexemes, syntactic-semantic categories, and
grammatical features. Each group is associated with one or more translations,
each of which is a group in a target language. During translation, constraint
satisfaction is used to select a set of source-language groups for the input
sentence and to sequence the words in the associated target-language groups.
Obstacle detection test in real-word traffic contexts for the purposes of motorcycle autonomous emergency braking (MAEB)
Authors: Giovanni Savino, Simone Piantini, Gustavo Gil, Marco Pierini
Date: 2017-06-25 00:00:00

Research suggests that a Motorcycle Autonomous Emergency Braking system
(MAEB) could influence 25% of the crashes involving powered two wheelers
(PTWs). By automatically slowing down a host PTW of up to 10 km/h in inevitable
collision scenarios, MAEB could potentially mitigate the crash severity for the
riders. The feasibility of automatic decelerations of motorcycles was shown via
field trials in controlled environment. However, the feasibility of correct
MAEB triggering in the real traffic context is still unclear. In particular,
MAEB requires an accurate obstacle detection, the feasibility of which from a
single track vehicle has not been confirmed yet. To address this issue, our
study presents obstacle detection tests in a real-world MAEB-sensitive crash
scenario.
Online and Distributed Robust Regressions under Adversarial Data Corruption
Authors: Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu
Date: 2017-10-02 00:00:00
Tasks: regression

In today's era of big data, robust least-squares regression becomes a more
challenging problem when considering the adversarial corruption along with
explosive growth of datasets. Traditional robust methods can handle the noise
but suffer from several challenges when applied in huge dataset including 1)
computational infeasibility of handling an entire dataset at once, 2) existence
of heterogeneously distributed corruption, and 3) difficulty in corruption
estimation when data cannot be entirely loaded. This paper proposes online and
distributed robust regression approaches, both of which can concurrently
address all the above challenges. Specifically, the distributed algorithm
optimizes the regression coefficients of each data block via heuristic hard
thresholding and combines all the estimates in a distributed robust
consolidation. Furthermore, an online version of the distributed algorithm is
proposed to incrementally update the existing estimates with new incoming data.
We also prove that our algorithms benefit from strong robustness guarantees in
terms of regression coefficient recovery with a constant upper bound on the
error of state-of-the-art batch methods. Extensive experiments on synthetic and
real datasets demonstrate that our approaches are superior to those of existing
methods in effectiveness, with competitive efficiency.
PIRVS: An Advanced Visual-Inertial SLAM System with Flexible Sensor Fusion and Hardware Co-Design
Authors: Zhe Zhang, Shaoshan Liu, Grace Tsai, Hongbing Hu, Chen-Chi Chu, Feng Zheng
Date: 2017-10-02 00:00:00
Tasks: Sensor Fusion, Simultaneous Localization and Mapping

In this paper, we present the PerceptIn Robotics Vision System (PIRVS)
system, a visual-inertial computing hardware with embedded simultaneous
localization and mapping (SLAM) algorithm. The PIRVS hardware is equipped with
a multi-core processor, a global-shutter stereo camera, and an IMU with precise
hardware synchronization. The PIRVS software features a novel and flexible
sensor fusion approach to not only tightly integrate visual measurements with
inertial measurements and also to loosely couple with additional sensor
modalities. It runs in real-time on both PC and the PIRVS hardware. We perform
a thorough evaluation of the proposed system using multiple public
visual-inertial datasets. Experimental results demonstrate that our system
reaches comparable accuracy of state-of-the-art visual-inertial algorithms on
PC, while being more efficient on the PIRVS hardware.
Rényi Differential Privacy Mechanisms for Posterior Sampling
Authors: Joseph Geumlek, Shuang Song, Kamalika Chaudhuri
Date: 2017-10-02 00:00:00
Tasks: regression

Using a recently proposed privacy definition of R\'enyi Differential Privacy
(RDP), we re-examine the inherent privacy of releasing a single sample from a
posterior distribution. We exploit the impact of the prior distribution in
mitigating the influence of individual data points. In particular, we focus on
sampling from an exponential family and specific generalized linear models,
such as logistic regression. We propose novel RDP mechanisms as well as
offering a new RDP analysis for an existing method in order to add value to the
RDP framework. Each method is capable of achieving arbitrary RDP privacy
guarantees, and we offer experimental results of their efficacy.
How is Distributed ADMM Affected by Network Topology?
Authors: Guilherme França, José Bento
Date: 2017-10-02 00:00:00
Tasks: valid

When solving consensus optimization problems over a graph, there is often an
explicit characterization of the convergence rate of Gradient Descent (GD)
using the spectrum of the graph Laplacian. The same type of problems under the
Alternating Direction Method of Multipliers (ADMM) are, however, poorly
understood. For instance, simple but important non-strongly-convex consensus
problems have not yet being analyzed, especially concerning the dependency of
the convergence rate on the graph topology. Recently, for a non-strongly-convex
consensus problem, a connection between distributed ADMM and lifted Markov
chains was proposed, followed by a conjecture that ADMM is faster than GD by a
square root factor in its convergence time, in close analogy to the mixing
speedup achieved by lifting several Markov chains. Nevertheless, a proof of
such a claim is is still lacking. Here we provide a full characterization of
the convergence of distributed over-relaxed ADMM for the same type of consensus
problem in terms of the topology of the underlying graph. Our results provide
explicit formulas for optimal parameter selection in terms of the second
largest eigenvalue of the transition matrix of the graph's random walk. Another
consequence of our results is a proof of the aforementioned conjecture, which
interestingly, we show it is valid for any graph, even the ones whose random
walks cannot be accelerated via Markov chain lifting.
Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight
Authors: Yen-Chen Lin, Ming-Yu Liu, Min Sun, Jia-Bin Huang
Date: 2017-10-02 00:00:00
Tasks: Autonomous Vehicles, Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Deep reinforcement learning has shown promising results in learning control
policies for complex sequential decision-making tasks. However, these neural
network-based policies are known to be vulnerable to adversarial examples. This
vulnerability poses a potentially serious threat to safety-critical systems
such as autonomous vehicles. In this paper, we propose a defense mechanism to
defend reinforcement learning agents from adversarial attacks by leveraging an
action-conditioned frame prediction module. Our core idea is that the
adversarial examples targeting at a neural network-based policy are not
effective for the frame prediction model. By comparing the action distribution
produced by a policy from processing the current observed frame to the action
distribution produced by the same policy from processing the predicted frame
from the action-conditioned frame prediction module, we can detect the presence
of adversarial examples. Beyond detecting the presence of adversarial examples,
our method allows the agent to continue performing the task using the predicted
frame when the agent is under attack. We evaluate the performance of our
algorithm using five games in Atari 2600. Our results demonstrate that the
proposed defense mechanism achieves favorable performance against baseline
algorithms in detecting adversarial examples and in earning rewards when the
agents are under attack.
It Takes Two to Tango: Towards Theory of AI's Mind
Authors: Arjun Chandrasekaran, Deshraj Yadav, Prithvijit Chattopadhyay, Viraj Prabhu, Devi Parikh
Date: 2017-04-03 00:00:00
Tasks: Attribute, Question Answering, Visual Question Answering, Visual Question Answering (VQA), Vocal Bursts Valence Prediction

Theory of Mind is the ability to attribute mental states (beliefs, intents,
knowledge, perspectives, etc.) to others and recognize that these mental states
may differ from one's own. Theory of Mind is critical to effective
communication and to teams demonstrating higher collective performance. To
effectively leverage the progress in Artificial Intelligence (AI) to make our
lives more productive, it is important for humans and AI to work well together
in a team. Traditionally, there has been much emphasis on research to make AI
more accurate, and (to a lesser extent) on having it better understand human
intentions, tendencies, beliefs, and contexts. The latter involves making AI
more human-like and having it develop a theory of our minds. In this work, we
argue that for human-AI teams to be effective, humans must also develop a
theory of AI's mind (ToAIM) - get to know its strengths, weaknesses, beliefs,
and quirks. We instantiate these ideas within the domain of Visual Question
Answering (VQA). We find that using just a few examples (50), lay people can be
trained to better predict responses and oncoming failures of a complex VQA
model. We further evaluate the role existing explanation (or interpretability)
modalities play in helping humans build ToAIM. Explainable AI has received
considerable scientific and popular attention in recent times. Surprisingly, we
find that having access to the model's internal states - its confidence in its
top-k predictions, explicit or implicit attention maps which highlight regions
in the image (and words in the question) the model is looking at (and listening
to) while answering a question about an image - do not help people better
predict its behavior.
Compiling and Processing Historical and Contemporary Portuguese Corpora
Authors: Marcos Zampieri
Date: 2017-10-02 00:00:00

This technical report describes the framework used for processing three large
Portuguese corpora. Two corpora contain texts from newspapers, one published in
Brazil and the other published in Portugal. The third corpus is Colonia, a
historical Portuguese collection containing texts written between the 16th and
the early 20th century. The report presents pre-processing methods,
segmentation, and annotation of the corpora as well as indexing and querying
methods. Finally, it presents published research papers using the corpora.
What Does Explainable AI Really Mean? A New Conceptualization of Perspectives
Authors: Derek Doran, Sarah Schulz, Tarek R. Besold
Date: 2017-10-02 00:00:00

We characterize three notions of explainable AI that cut across research
fields: opaque systems that offer no insight into its algo- rithmic mechanisms;
interpretable systems where users can mathemat- ically analyze its algorithmic
mechanisms; and comprehensible systems that emit symbols enabling user-driven
explanations of how a conclusion is reached. The paper is motivated by a corpus
analysis of NIPS, ACL, COGSCI, and ICCV/ECCV paper titles showing differences
in how work on explainable AI is positioned in various fields. We close by
introducing a fourth notion: truly explainable systems, where automated
reasoning is central to output crafted explanations without requiring human
post processing as final step of the generative process.
Real-Time Multiple Object Tracking - A Study on the Importance of Speed
Authors: Samuel Murray
Date: 2017-09-11 00:00:00
Tasks: Multiple Object Tracking, Object Tracking

In this project, we implement a multiple object tracker, following the
tracking-by-detection paradigm, as an extension of an existing method. It works
by modelling the movement of objects by solving the filtering problem, and
associating detections with predicted new locations in new frames using the
Hungarian algorithm. Three different similarity measures are used, which use
the location and shape of the bounding boxes. Compared to other trackers on the
MOTChallenge leaderboard, our method, referred to as C++SORT, is the fastest
non-anonymous submission, while also achieving decent score on other metrics.
By running our model on the Okutama-Action dataset, sampled at different
frame-rates, we show that the performance is greatly reduced when running the
model - including detecting objects - in real-time. In most metrics, the score
is reduced by 50%, but in certain cases as much as 90%. We argue that this
indicates that other, slower methods could not be used for tracking in
real-time, but that more research is required specifically on this.
Progressive Color Transfer with Dense Semantic Correspondences
Authors: Mingming He, Jing Liao, Dong-Dong Chen, Lu Yuan, Pedro V. Sander
Date: 2017-10-02 00:00:00

We propose a new algorithm for color transfer between images that have
perceptually similar semantic structures. We aim to achieve a more accurate
color transfer that leverages semantically-meaningful dense correspondence
between images. To accomplish this, our algorithm uses neural representations
for matching. Additionally, the color transfer should be spatially variant and
globally coherent. Therefore, our algorithm optimizes a local linear model for
color transfer satisfying both local and global constraints. Our proposed
approach jointly optimizes matching and color transfer, adopting a
coarse-to-fine strategy. The proposed method can be successfully extended from
one-to-one to one-to-many color transfer. The latter further addresses the
problem of mismatching elements of the input image. We validate our proposed
method by testing it on a large variety of image content.
Spinal cord gray matter segmentation using deep dilated convolutions
Authors: Christian S. Perone, Evan Calabrese, Julien Cohen-Adad
Date: 2017-10-02 00:00:00
Tasks: Medical Image Segmentation, Segmentation, Spinal Cord Gray Matter - Segmentation

Gray matter (GM) tissue changes have been associated with a wide range of
neurological disorders and was also recently found relevant as a biomarker for
disability in amyotrophic lateral sclerosis. The ability to automatically
segment the GM is, therefore, an important task for modern studies of the
spinal cord. In this work, we devise a modern, simple and end-to-end fully
automated human spinal cord gray matter segmentation method using Deep
Learning, that works both on in vivo and ex vivo MRI acquisitions. We evaluate
our method against six independently developed methods on a GM segmentation
challenge and report state-of-the-art results in 8 out of 10 different
evaluation metrics as well as major network parameter reduction when compared
to the traditional medical imaging architectures such as U-Nets.
Learning hard quantum distributions with variational autoencoders
Authors: Andrea Rocchetto, Edward Grant, Sergii Strelchuk, Giuseppe Carleo, Simone Severini
Date: 2017-10-02 00:00:00
Tasks: State Estimation

Studying general quantum many-body systems is one of the major challenges in
modern physics because it requires an amount of computational resources that
scales exponentially with the size of the system.Simulating the evolution of a
state, or even storing its description, rapidly becomes intractable for exact
classical algorithms. Recently, machine learning techniques, in the form of
restricted Boltzmann machines, have been proposed as a way to efficiently
represent certain quantum states with applications in state tomography and
ground state estimation. Here, we introduce a new representation of states
based on variational autoencoders. Variational autoencoders are a type of
generative model in the form of a neural network. We probe the power of this
representation by encoding probability distributions associated with states
from different classes. Our simulations show that deep networks give a better
representation for states that are hard to sample from, while providing no
benefit for random states. This suggests that the probability distributions
associated to hard quantum states might have a compositional structure that can
be exploited by layered neural networks. Specifically, we consider the
learnability of a class of quantum states introduced by Fefferman and Umans.
Such states are provably hard to sample for classical computers, but not for
quantum ones, under plausible computational complexity assumptions. The good
level of compression achieved for hard states suggests these methods can be
suitable for characterising states of the size expected in first generation
quantum hardware.
Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics
Authors: Martin Boyanov, Ivan Koychev, Preslav Nakov, Alessandro Moschitti, Giovanni Da San Martino
Date: 2017-10-02 00:00:00
Proceeding: RANLP 2017 9
Tasks: Model Selection, Question Answering

We propose to use question answering (QA) data from Web forums to train
chatbots from scratch, i.e., without dialog training data. First, we extract
pairs of question and answer sentences from the typically much longer texts of
questions and answers in a forum. We then use these shorter texts to train
seq2seq models in a more efficient way. We further improve the parameter
optimization using a new model selection strategy based on QA measures.
Finally, we propose to use extrinsic evaluation with respect to a QA task as an
automatic evaluation method for chatbots. The evaluation shows that the model
achieves a MAP of 63.5% on the extrinsic task. Moreover, it can answer
correctly 49.5% of the questions when they are similar to questions asked in
the forum, and 47.3% of the questions when they are more conversational in
style.
Deep Learning applied to Road Traffic Speed forecasting
Authors: Thomas Epelbaum, Fabrice Gamboa, Jean-Michel Loubes, Jessica Martin
Date: 2017-10-02 00:00:00
Tasks: Deep Learning, regression

In this paper, we propose deep learning architectures (FNN, CNN and LSTM) to
forecast a regression model for time dependent data. These algorithm's are
designed to handle Floating Car Data (FCD) historic speeds to predict road
traffic data. For this we aggregate the speeds into the network inputs in an
innovative way. We compare the RMSE thus obtained with the results of a simpler
physical model, and show that the latter achieves better RMSE accuracy. We also
propose a new indicator, which evaluates the algorithms improvement when
compared to a benchmark prediction. We conclude by questioning the interest of
using deep learning methods for this specific regression task.