pwc-archive/papers-with-abstracts huggingface dataset:
MLPnP - A Real-Time Maximum Likelihood Solution to the Perspective-n-Point Problem
Authors: Steffen Urban, Jens Leitloff, Stefan Hinz
Date: 2016-07-27 00:00:00
Tasks: Translation

In this paper, a statistically optimal solution to the Perspective-n-Point
(PnP) problem is presented. Many solutions to the PnP problem are geometrically
optimal, but do not consider the uncertainties of the observations. In
addition, it would be desirable to have an internal estimation of the accuracy
of the estimated rotation and translation parameters of the camera pose. Thus,
we propose a novel maximum likelihood solution to the PnP problem, that
incorporates image observation uncertainties and remains real-time capable at
the same time. Further, the presented method is general, as is works with 3D
direction vectors instead of 2D image points and is thus able to cope with
arbitrary central camera models. This is achieved by projecting (and thus
reducing) the covariance matrices of the observations to the corresponding
vector tangent space.
Automatically Reinforcing a Game AI
Authors: David L. St-Pierre, Jean-Baptiste Hoock, Jialin Liu, Fabien Teytaud, Olivier Teytaud
Date: 2016-07-27 00:00:00

A recent research trend in Artificial Intelligence (AI) is the combination of
several programs into one single, stronger, program; this is termed portfolio
methods. We here investigate the application of such methods to Game Playing
Programs (GPPs). In addition, we consider the case in which only one GPP is
available - by decomposing this single GPP into several ones through the use of
parameters or even simply random seeds. These portfolio methods are trained in
a learning phase. We propose two different offline approaches. The simplest
one, BestArm, is a straightforward optimization of seeds or parame- ters; it
performs quite well against the original GPP, but performs poorly against an
opponent which repeats games and learns. The second one, namely Nash-portfolio,
performs similarly in a "one game" test, and is much more robust against an
opponent who learns. We also propose an online learning portfolio, which tests
several of the GPP repeatedly and progressively switches to the best one -
using a bandit algorithm.
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification
Authors: Maxime Bucher, Stéphane Herbin, Frédéric Jurie
Date: 2016-07-27 00:00:00
Tasks: Attribute, General Classification, image-classification, Image Classification, Metric Learning, zero-shot-classification, Zero-Shot Image Classification, Zero-Shot Learning

This paper addresses the task of zero-shot image classification. The key
contribution of the proposed approach is to control the semantic embedding of
images -- one of the main ingredients of zero-shot learning -- by formulating
it as a metric learning problem. The optimized empirical criterion associates
two types of sub-task constraints: metric discriminating capacity and accurate
attribute prediction. This results in a novel expression of zero-shot learning
not requiring the notion of class in the training phase: only pairs of
image/attributes, augmented with a consistency indicator, are given as ground
truth. At test time, the learned model can predict the consistency of a test
image with a given set of attributes , allowing flexible ways to produce
recognition inferences. Despite its simplicity, the proposed approach gives
state-of-the-art results on four challenging datasets used for zero-shot
recognition evaluation.
Harmonization of conflicting medical opinions using argumentation protocols and textual entailment - a case study on Parkinson disease
Authors: Adrian Groza, Madalina Mand Nagy
Date: 2016-07-27 00:00:00
Tasks: Articles, Management, Natural Language Inference

Parkinson's disease is the second most common neurodegenerative disease,
affecting more than 1.2 million people in Europe. Medications are available for
the management of its symptoms, but the exact cause of the disease is unknown
and there is currently no cure on the market. To better understand the
relations between new findings and current medical knowledge, we need tools
able to analyse published medical papers based on natural language processing
and tools capable to identify various relationships of new findings with the
current medical knowledge. Our work aims to fill the above technological gap.
  To identify conflicting information in medical documents, we enact textual
entailment technology. To encapsulate existing medical knowledge, we rely on
ontologies. To connect the formal axioms in ontologies with natural text in
medical articles, we exploit ontology verbalisation techniques. To assess the
level of disagreement between human agents with respect to a medical issue, we
rely on fuzzy aggregation. To harmonize this disagreement, we design mediation
protocols within a multi-agent framework.
Mining Arguments from Cancer Documents Using Natural Language Processing and Ontologies
Authors: Adrian Groza, Oana Popa
Date: 2016-07-27 00:00:00

In the medical domain, the continuous stream of scientific research contains
contradictory results supported by arguments and counter-arguments. As medical
expertise occurs at different levels, part of the human agents have
difficulties to face the huge amount of studies, but also to understand the
reasons and pieces of evidences claimed by the proponents and the opponents of
the debated topic. To better understand the supporting arguments for new
findings related to current state of the art in the medical domain we need
tools able to identify arguments in scientific papers. Our work here aims to
fill the above technological gap.
  Quite aware of the difficulty of this task, we embark to this road by relying
on the well-known interleaving of domain knowledge with natural language
processing. To formalise the existing medical knowledge, we rely on ontologies.
To structure the argumentation model we use also the expressivity and reasoning
capabilities of Description Logics. To perform argumentation mining we
formalise various linguistic patterns in a rule-based language. We tested our
solution against a corpus of scientific papers related to breast cancer. The
run experiments show a F-measure between 0.71 and 0.86 for identifying
conclusions of an argument and between 0.65 and 0.86 for identifying premises
of an argument.
Assisting Drivers During Overtaking Using Car-2-Car Communication and Multi-Agent Systems
Authors: Adrian Groza, Calin Cara, Sergiu Zaporojan, Igor Calmicov
Date: 2016-07-27 00:00:00

A warning system for assisting drivers during overtaking maneuvers is
proposed. The system relies on Car-2-Car communication technologies and
multi-agent systems. A protocol for safety overtaking is proposed based on ACL
communicative acts. The mathematical model for safety overtaking used Kalman
filter to minimize localization error.
Encoding Prior Knowledge with Eigenword Embeddings
Authors: Dominique Osborne, Shashi Narayan, Shay B. Cohen
Date: 2015-09-03 00:00:00
Proceeding: TACL 2016 1
Tasks: Word Embeddings

Canonical correlation analysis (CCA) is a method for reducing the dimension
of data represented using two views. It has been previously used to derive word
embeddings, where one view indicates a word, and the other view indicates its
context. We describe a way to incorporate prior knowledge into CCA, give a
theoretical justification for it, and test it by deriving word embeddings and
evaluating them on a myriad of datasets.
Channel Vector Subspace Estimation from Low-Dimensional Projections
Authors: Saeid Haghighatshoar, Giuseppe Caire
Date: 2015-09-24 00:00:00

Massive MIMO is a variant of multiuser MIMO where the number of base-station
antennas $M$ is very large (typically 100), and generally much larger than the
number of spatially multiplexed data streams (typically 10). Unfortunately, the
front-end A/D conversion necessary to drive hundreds of antennas, with a signal
bandwidth of the order of 10 to 100 MHz, requires very large sampling bit-rate
and power consumption.
  In order to reduce such implementation requirements, Hybrid Digital-Analog
architectures have been proposed. In particular, our work in this paper is
motivated by one of such schemes named Joint Spatial Division and Multiplexing
(JSDM), where the downlink precoder (resp., uplink linear receiver) is split
into the product of a baseband linear projection (digital) and an RF
reconfigurable beamforming network (analog), such that only a reduced number $m
\ll M$ of A/D converters and RF modulation/demodulation chains is needed. In
JSDM, users are grouped according to the similarity of their channel dominant
subspaces, and these groups are separated by the analog beamforming stage,
where the multiplexing gain in each group is achieved using the digital
precoder. Therefore, it is apparent that extracting the channel subspace
information of the $M$-dim channel vectors from snapshots of $m$-dim
projections, with $m \ll M$, plays a fundamental role in JSDM implementation.
  In this paper, we develop novel efficient algorithms that require sampling
only $m = O(2\sqrt{M})$ specific array elements according to a coprime sampling
scheme, and for a given $p \ll M$, return a $p$-dim beamformer that has a
performance comparable with the best p-dim beamformer that can be designed from
the full knowledge of the exact channel covariance matrix. We assess the
performance of our proposed estimators both analytically and empirically via
numerical simulations.
Visual Tracking via Shallow and Deep Collaborative Model
Authors: Bohan Zhuang, Lijun Wang, Huchuan Lu
Date: 2016-07-27 00:00:00
Tasks: Incremental Learning, model, Visual Tracking

In this paper, we propose a robust tracking method based on the collaboration
of a generative model and a discriminative classifier, where features are
learned by shallow and deep architectures, respectively. For the generative
model, we introduce a block-based incremental learning scheme, in which a local
binary mask is constructed to deal with occlusion. The similarity degrees
between the local patches and their corresponding subspace are integrated to
formulate a more accurate global appearance model. In the discriminative model,
we exploit the advances of deep learning architectures to learn generic
features which are robust to both background clutters and foreground appearance
variations. To this end, we first construct a discriminative training set from
auxiliary video sequences. A deep classification neural network is then trained
offline on this training set. Through online fine-tuning, both the hierarchical
feature extractor and the classifier can be adapted to the appearance change of
the target for effective online tracking. The collaboration of these two models
achieves a good balance in handling occlusion and target appearance change,
which are two contradictory challenging factors in visual tracking. Both
quantitative and qualitative evaluations against several state-of-the-art
algorithms on challenging image sequences demonstrate the accuracy and the
robustness of the proposed tracker.
Behavior and path planning for the coalition of cognitive robots in smart relocation tasks
Authors: Aleksandr I. Panov, Konstantin Yakovlev
Date: 2016-07-27 00:00:00

In this paper we outline the approach of solving special type of navigation
tasks for robotic systems, when a coalition of robots (agents) acts in the 2D
environment, which can be modified by the actions, and share the same goal
location. The latter is originally unreachable for some members of the
coalition, but the common task still can be accomplished as the agents can
assist each other (e.g. by modifying the environment). We call such tasks smart
relocation tasks (as the can not be solved by pure path planning methods) and
study spatial and behavior interaction of robots while solving them. We use
cognitive approach and introduce semiotic knowledge representation - sign world
model which underlines behavioral planning methodology. Planning is viewed as a
recursive search process in the hierarchical state-space induced by sings with
path planning signs reside on the lowest level. Reaching this level triggers
path planning which is accomplished by state of the art grid-based planners
focused on producing smooth paths (e.g. LIAN) and thus indirectly guarantying
feasibility of that paths against agent's dynamic constraints.
Learning of Generalized Low-Rank Models: A Greedy Approach
Authors: Quanming Yao, James T. Kwok
Date: 2016-07-27 00:00:00
Tasks: BIG-bench Machine Learning, Matrix Completion

Learning of low-rank matrices is fundamental to many machine learning
applications. A state-of-the-art algorithm is the rank-one matrix pursuit
(R1MP). However, it can only be used in matrix completion problems with the
square loss. In this paper, we develop a more flexible greedy algorithm for
generalized low-rank models whose optimization objective can be smooth or
nonsmooth, general convex or strongly convex. The proposed algorithm has low
per-iteration time complexity and fast convergence rate. Experimental results
show that it is much faster than the state-of-the-art, with comparable or even
better prediction performance.
An Approximate Backpropagation Learning Rule for Memristor Based Neural Networks Using Synaptic Plasticity
Authors: D. V. Negrov, I. M. Karandashev, V. V. Shakirov, Yu. A. Matveyev, W. L. Dunin-Barkowski, A. V. Zenkevich
Date: 2015-11-22 00:00:00

We describe an approximation to backpropagation algorithm for training deep
neural networks, which is designed to work with synapses implemented with
memristors. The key idea is to represent the values of both the input signal
and the backpropagated delta value with a series of pulses that trigger
multiple positive or negative updates of the synaptic weight, and to use the
min operation instead of the product of the two signals. In computational
simulations, we show that the proposed approximation to backpropagation is well
converged and may be suitable for memristor implementations of multilayer
neural networks.
ATGV-Net: Accurate Depth Super-Resolution
Authors: Gernot Riegler, Matthias Rüther, Horst Bischof
Date: 2016-07-27 00:00:00
Tasks: Depth Map Super-Resolution, Image Super-Resolution, Rolling Shutter Correction, Super-Resolution

In this work we present a novel approach for single depth map
super-resolution. Modern consumer depth sensors, especially Time-of-Flight
sensors, produce dense depth measurements, but are affected by noise and have a
low lateral resolution. We propose a method that combines the benefits of
recent advances in machine learning based single image super-resolution, i.e.
deep convolutional networks, with a variational method to recover accurate
high-resolution depth maps. In particular, we integrate a variational method
that models the piecewise affine structures apparent in depth data via an
anisotropic total generalized variation regularization term on top of a deep
network. We call our method ATGV-Net and train it end-to-end by unrolling the
optimization procedure of the variational method. To train deep networks, a
large corpus of training data with accurate ground-truth is required. We
demonstrate that it is feasible to train our method solely on synthetic data
that we generate in large quantities for this task. Our evaluations show that
we achieve state-of-the-art results on three different benchmarks, as well as
on a challenging Time-of-Flight dataset, all without utilizing an additional
intensity image as guidance.
A Multiple Kernel Learning Approach for Human Behavioral Task Classification using STN-LFP Signal
Authors: Hosein M. Golshan, Adam O. Hebb, Sara J. Hanrahan, Joshua Nedrud, Mohammad H. Mahoor
Date: 2016-07-27 00:00:00
Tasks: Classification, General Classification

Deep Brain Stimulation (DBS) has gained increasing attention as an effective
method to mitigate Parkinsons disease (PD) disorders. Existing DBS systems are
open-loop such that the system parameters are not adjusted automatically based
on patients behavior. Classification of human behavior is an important step in
the design of the next generation of DBS systems that are closed-loop. This
paper presents a classification approach to recognize such behavioral tasks
using the subthalamic nucleus (STN) Local Field Potential (LFP) signals. In our
approach, we use the time-frequency representation (spectrogram) of the raw LFP
signals recorded from left and right STNs as the feature vectors. Then these
features are combined together via Support Vector Machines (SVM) with Multiple
Kernel Learning (MKL) formulation. The MKL-based classification method is
utilized to classify different tasks: button press, mouth movement, speech, and
arm movement. Our experiments show that the lp-norm MKL significantly
outperforms single kernel SVM-based classifiers in classifying behavioral tasks
of five subjects even using signals acquired with a low sampling rate of 10 Hz.
This leads to a lower computational cost.
A Continuous Optimization Approach for Efficient and Accurate Scene Flow
Authors: Zhaoyang Lv, Chris Beall, Pablo F. Alcantarilla, Fuxin Li, Zsolt Kira, Frank Dellaert
Date: 2016-07-27 00:00:00
Tasks: Position

We propose a continuous optimization method for solving dense 3D scene flow
problems from stereo imagery. As in recent work, we represent the dynamic 3D
scene as a collection of rigidly moving planar segments. The scene flow problem
then becomes the joint estimation of pixel-to-segment assignment, 3D position,
normal vector and rigid motion parameters for each segment, leading to a
complex and expensive discrete-continuous optimization problem. In contrast, we
propose a purely continuous formulation which can be solved more efficiently.
Using a fine superpixel segmentation that is fixed a-priori, we propose a
factor graph formulation that decomposes the problem into photometric,
geometric, and smoothing constraints. We initialize the solution with a novel,
high-quality initialization method, then independently refine the geometry and
motion of the scene, and finally perform a global non-linear refinement using
Levenberg-Marquardt. We evaluate our method in the challenging KITTI Scene Flow
benchmark, ranking in third position, while being 3 to 30 times faster than the
top competitors.
Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification
Authors: Yuezhang Li, Ronghuo Zheng, Tian Tian, Zhiting Hu, Rahul Iyer, Katia Sycara
Date: 2016-07-27 00:00:00
Proceeding: COLING 2016 12
Tasks: General Classification

Due to the lack of structured knowledge applied in learning distributed
representation of cate- gories, existing work cannot incorporate category
hierarchies into entity information. We propose a framework that embeds
entities and categories into a semantic space by integrating structured
knowledge and taxonomy hierarchy from large knowledge bases. The framework
allows to com- pute meaningful semantic relatedness between entities and
categories. Our framework can han- dle both single-word concepts and
multiple-word concepts with superior performance on concept categorization and
yield state of the art results on dataless hierarchical classification.
Seeing the Forest from the Trees in Two Looks: Matrix Sketching by Cascaded Bilateral Sampling
Authors: Kai Zhang, Chuanren Liu, Jie Zhang, Hui Xiong, Eric Xing, Jieping Ye
Date: 2016-07-25 00:00:00

Matrix sketching is aimed at finding close approximations of a matrix by
factors of much smaller dimensions, which has important applications in
optimization and machine learning. Given a matrix A of size m by n,
state-of-the-art randomized algorithms take O(m * n) time and space to obtain
its low-rank decomposition. Although quite useful, the need to store or
manipulate the entire matrix makes it a computational bottleneck for truly
large and dense inputs. Can we sketch an m-by-n matrix in O(m + n) cost by
accessing only a small fraction of its rows and columns, without knowing
anything about the remaining data? In this paper, we propose the cascaded
bilateral sampling (CABS) framework to solve this problem. We start from
demonstrating how the approximation quality of bilateral matrix sketching
depends on the encoding powers of sampling. In particular, the sampled rows and
columns should correspond to the code-vectors in the ground truth
decompositions. Motivated by this analysis, we propose to first generate a
pilot-sketch using simple random sampling, and then pursue more advanced,
"follow-up" sampling on the pilot-sketch factors seeking maximal encoding
powers. In this cascading process, the rise of approximation quality is shown
to be lower-bounded by the improvement of encoding powers in the follow-up
sampling step, thus theoretically guarantees the algorithmic boosting property.
Computationally, our framework only takes linear time and space, and at the
same time its performance rivals the quality of state-of-the-art algorithms
consuming a quadratic amount of resources. Empirical evaluations on benchmark
data fully demonstrate the potential of our methods in large scale matrix
sketching and related areas.
Is Faster R-CNN Doing Well for Pedestrian Detection?
Authors: Liliang Zhang, Liang Lin, Xiaodan Liang, Kaiming He
Date: 2016-07-24 00:00:00
Tasks: Object, object-detection, Object Detection, Pedestrian Detection, Region Proposal

Detecting pedestrian has been arguably addressed as a special topic beyond
general object detection. Although recent deep learning object detectors such
as Fast/Faster R-CNN [1, 2] have shown excellent performance for general object
detection, they have limited success for detecting pedestrian, and previous
leading pedestrian detectors were in general hybrid methods combining
hand-crafted and deep convolutional features. In this paper, we investigate
issues involving Faster R-CNN [2] for pedestrian detection. We discover that
the Region Proposal Network (RPN) in Faster R-CNN indeed performs well as a
stand-alone pedestrian detector, but surprisingly, the downstream classifier
degrades the results. We argue that two reasons account for the unsatisfactory
accuracy: (i) insufficient resolution of feature maps for handling small
instances, and (ii) lack of any bootstrapping strategy for mining hard negative
examples. Driven by these observations, we propose a very simple but effective
baseline for pedestrian detection, using an RPN followed by boosted forests on
shared, high-resolution convolutional feature maps. We comprehensively evaluate
this method on several benchmarks (Caltech, INRIA, ETH, and KITTI), presenting
competitive accuracy and good speed. Code will be made publicly available.
A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction
Authors: Ali Ghadirzadeh, Judith Bütepage, Atsuto Maki, Danica Kragic, Mårten Björkman
Date: 2016-07-27 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes, Position, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Modeling of physical human-robot collaborations is generally a challenging
problem due to the unpredictive nature of human behavior. To address this
issue, we present a data-efficient reinforcement learning framework which
enables a robot to learn how to collaborate with a human partner. The robot
learns the task from its own sensorimotor experiences in an unsupervised
manner. The uncertainty of the human actions is modeled using Gaussian
processes (GP) to implement action-value functions. Optimal action selection
given the uncertain GP model is ensured by Bayesian optimization. We apply the
framework to a scenario in which a human and a PR2 robot jointly control the
ball position on a plank based on vision and force/torque data. Our
experimental results show the suitability of the proposed method in terms of
fast and data-efficient model learning, optimal action selection under
uncertainties and equal role sharing between the partners.