pwc-archive/papers-with-abstracts huggingface dataset:
Spaceprint: a Mobility-based Fingerprinting Scheme for Public Spaces
Authors: Mitra Baratchi, Geert Heijenk, Maarten van Steen
Date: 2017-03-29 00:00:00

In this paper, we address the problem of how automated situation-awareness
can be achieved by learning real-world situations from ubiquitously generated
mobility data. Without semantic input about the time and space where situations
take place, this turns out to be a fundamental challenging problem.
Uncertainties also introduce technical challenges when data is generated in
irregular time intervals, being mixed with noise, and errors. Purely relying on
temporal patterns observable in mobility data, in this paper, we propose
Spaceprint, a fully automated algorithm for finding the repetitive pattern of
similar situations in spaces. We evaluate this technique by showing how the
latent variables describing the category, and the actual identity of a space
can be discovered from the extracted situation patterns. Doing so, we use
different real-world mobility datasets with data about the presence of mobile
entities in a variety of spaces. We also evaluate the performance of this
technique by showing its robustness against uncertainties.
Marginal likelihood based model comparison in Fuzzy Bayesian Learning
Authors: Indranil Pan, Dirk Bester
Date: 2017-03-29 00:00:00

In a recent paper [1] we introduced the Fuzzy Bayesian Learning (FBL)
paradigm where expert opinions can be encoded in the form of fuzzy rule bases
and the hyper-parameters of the fuzzy sets can be learned from data using a
Bayesian approach. The present paper extends this work for selecting the most
appropriate rule base among a set of competing alternatives, which best
explains the data, by calculating the model evidence or marginal likelihood. We
explain why this is an attractive alternative over simply minimizing a mean
squared error metric of prediction and show the validity of the proposition
using synthetic examples and a real world case study in the financial services
sector.
Evolving Boxes for Fast Vehicle Detection
Authors: Li Wang, Yao Lu, Hong Wang, Yingbin Zheng, Hao Ye, xiangyang xue
Date: 2017-02-01 00:00:00
Tasks: Fast Vehicle Detection, GPU, vehicle detection

We perform fast vehicle detection from traffic surveillance cameras. A novel
deep learning framework, namely Evolving Boxes, is developed that proposes and
refines the object boxes under different feature representations. Specifically,
our framework is embedded with a light-weight proposal network to generate
initial anchor boxes as well as to early discard unlikely regions; a
fine-turning network produces detailed features for these candidate boxes. We
show intriguingly that by applying different feature fusion techniques, the
initial boxes can be refined for both localization and recognition. We evaluate
our network on the recent DETRAC benchmark and obtain a significant improvement
over the state-of-the-art Faster RCNN by 9.5% mAP. Further, our network
achieves 9-13 FPS detection speed on a moderate commercial GPU.
Sentiment Recognition in Egocentric Photostreams
Authors: Estefania Talavera, Nicola Strisciuglio, Nicolai Petkov, Petia Radeva
Date: 2017-03-29 00:00:00
Tasks: General Classification, Sentiment Analysis

Lifelogging is a process of collecting rich source of information about daily
life of people. In this paper, we introduce the problem of sentiment analysis
in egocentric events focusing on the moments that compose the images recalling
positive, neutral or negative feelings to the observer. We propose a method for
the classification of the sentiments in egocentric pictures based on global and
semantic image features extracted by Convolutional Neural Networks. We carried
out experiments on an egocentric dataset, which we organized in 3 classes on
the basis of the sentiment that is recalled to the user (positive, negative or
neutral).
Exploiting Symmetry and/or Manhattan Properties for 3D Object Structure Estimation from Single and Multiple Images
Authors: Yuan Gao, Alan L. Yuille
Date: 2016-07-25 00:00:00
Proceeding: CVPR 2017 7

Many man-made objects have intrinsic symmetries and Manhattan structure. By
assuming an orthographic projection model, this paper addresses the estimation
of 3D structures and camera projection using symmetry and/or Manhattan
structure cues, which occur when the input is single- or multiple-image from
the same category, e.g., multiple different cars. Specifically, analysis on the
single image case implies that Manhattan alone is sufficient to recover the
camera projection, and then the 3D structure can be reconstructed uniquely
exploiting symmetry. However, Manhattan structure can be difficult to observe
from a single image due to occlusion. To this end, we extend to the
multiple-image case which can also exploit symmetry but does not require
Manhattan axes. We propose a novel rigid structure from motion method,
exploiting symmetry and using multiple images from the same category as input.
Experimental results on the Pascal3D+ dataset show that our method
significantly outperforms baseline methods.
Hierarchical Surrogate Modeling for Illumination Algorithms
Authors: Alexander Hagg
Date: 2017-03-29 00:00:00

Evolutionary illumination is a recent technique that allows producing many
diverse, optimal solutions in a map of manually defined features. To support
the large amount of objective function evaluations, surrogate model assistance
was recently introduced. Illumination models need to represent many more,
diverse optimal regions than classical surrogate models. In this PhD thesis, we
propose to decompose the sample set, decreasing model complexity, by
hierarchically segmenting the training set according to their coordinates in
feature space. An ensemble of diverse models can then be trained to serve as a
surrogate to illumination.
A Comprehensive Study of Deep Bidirectional LSTM RNNs for Acoustic Modeling in Speech Recognition
Authors: Albert Zeyer, Patrick Doetsch, Paul Voigtlaender, Ralf Schlüter, Hermann Ney
Date: 2016-06-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

We present a comprehensive study of deep bidirectional long short-term memory
(LSTM) recurrent neural network (RNN) based acoustic models for automatic
speech recognition (ASR). We study the effect of size and depth and train
models of up to 8 layers. We investigate the training aspect and study
different variants of optimization methods, batching, truncated
backpropagation, different regularization techniques such as dropout and $L_2$
regularization, and different gradient clipping variants.
  The major part of the experimental analysis was performed on the Quaero
corpus. Additional experiments also were performed on the Switchboard corpus.
Our best LSTM model has a relative improvement in word error rate of over 14\%
compared to our best feed-forward neural network (FFNN) baseline on the Quaero
task. On this task, we get our best result with an 8 layer bidirectional LSTM
and we show that a pretraining scheme with layer-wise construction helps for
deep LSTMs.
  Finally we compare the training calculation time of many of the presented
experiments in relation with recognition performance.
  All the experiments were done with RETURNN, the RWTH extensible training
framework for universal recurrent neural networks in combination with RASR, the
RWTH ASR toolkit.
On Convergence Property of Implicit Self-paced Objective
Authors: Zilu Ma, Shiqi Liu, Deyu Meng
Date: 2017-03-29 00:00:00

Self-paced learning (SPL) is a new methodology that simulates the learning
principle of humans/animals to start learning easier aspects of a learning
task, and then gradually take more complex examples into training. This
new-coming learning regime has been empirically substantiated to be effective
in various computer vision and pattern recognition tasks. Recently, it has been
proved that the SPL regime has a close relationship to a implicit self-paced
objective function. While this implicit objective could provide helpful
interpretations to the effectiveness, especially the robustness, insights under
the SPL paradigms, there are still no theoretical results strictly proved to
verify such relationship. To this issue, in this paper, we provide some
convergence results on this implicit objective of SPL. Specifically, we prove
that the learning process of SPL always converges to critical points of this
implicit objective under some mild conditions. This result verifies the
intrinsic relationship between SPL and this implicit objective, and makes the
previous robustness analysis on SPL complete and theoretically rational.
Towards thinner convolutional neural networks through Gradually Global Pruning
Authors: Zhengtao Wang, Ce Zhu, Zhiqiang Xia, Qi Guo, Yipeng Liu
Date: 2017-03-29 00:00:00
Tasks: Network Pruning

Deep network pruning is an effective method to reduce the storage and
computation cost of deep neural networks when applying them to resource-limited
devices. Among many pruning granularities, neuron level pruning will remove
redundant neurons and filters in the model and result in thinner networks. In
this paper, we propose a gradually global pruning scheme for neuron level
pruning. In each pruning step, a small percent of neurons were selected and
dropped across all layers in the model. We also propose a simple method to
eliminate the biases in evaluating the importance of neurons to make the scheme
feasible. Compared with layer-wise pruning scheme, our scheme avoid the
difficulty in determining the redundancy in each layer and is more effective
for deep networks. Our scheme would automatically find a thinner sub-network in
original network under a given performance.
Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks
Authors: Luca Caltagirone, Samuel Scheidegger, Lennart Svensson, Mattias Wahde
Date: 2017-03-10 00:00:00
Tasks: Semantic Segmentation

In this work, a deep learning approach has been developed to carry out road
detection using only LIDAR data. Starting from an unstructured point cloud,
top-view images encoding several basic statistics such as mean elevation and
density are generated. By considering a top-view representation, road detection
is reduced to a single-scale problem that can be addressed with a simple and
fast fully convolutional neural network (FCN). The FCN is specifically designed
for the task of pixel-wise semantic segmentation by combining a large receptive
field with high-resolution feature maps. The proposed system achieved excellent
performance and it is among the top-performing algorithms on the KITTI road
benchmark. Its fast inference makes it particularly suitable for real-time
applications.
One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models
Authors: J. H. Rick Chang, Chun-Liang Li, Barnabas Poczos, B. V. K. Vijaya Kumar, Aswin C. Sankaranarayanan
Date: 2017-03-29 00:00:00
Tasks: All, Compressive Sensing, Image Inpainting, Super-Resolution

While deep learning methods have achieved state-of-the-art performance in
many challenging inverse problems like image inpainting and super-resolution,
they invariably involve problem-specific training of the networks. Under this
approach, different problems require different networks. In scenarios where we
need to solve a wide variety of problems, e.g., on a mobile camera, it is
inefficient and costly to use these specially-trained networks. On the other
hand, traditional methods using signal priors can be used in all linear inverse
problems but often have worse performance on challenging tasks. In this work,
we provide a middle ground between the two kinds of methods --- we propose a
general framework to train a single deep neural network that solves arbitrary
linear inverse problems. The proposed network acts as a proximal operator for
an optimization algorithm and projects non-image signals onto the set of
natural images defined by the decision boundary of a classifier. In our
experiments, the proposed framework demonstrates superior performance over
traditional methods using a wavelet sparsity prior and achieves comparable
performance of specially-trained networks on tasks including compressive
sensing and pixel-wise inpainting.
Learning with Privileged Information for Multi-Label Classification
Authors: Shiyu Chen, Shangfei Wang, Tanfang Chen, Xiaoxiao Shi
Date: 2017-03-29 00:00:00
Tasks: Action Unit Detection, Classification, Facial Action Unit Detection, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Object, Object Recognition

In this paper, we propose a novel approach for learning multi-label
classifiers with the help of privileged information. Specifically, we use
similarity constraints to capture the relationship between available
information and privileged information, and use ranking constraints to capture
the dependencies among multiple labels. By integrating similarity constraints
and ranking constraints into the learning process of classifiers, the
privileged information and the dependencies among multiple labels are exploited
to construct better classifiers during training. A maximum margin classifier is
adopted, and an efficient learning algorithm of the proposed method is also
developed. We evaluate the proposed method on two applications: multiple object
recognition from images with the help of implicit information about object
importance conveyed by the list of manually annotated image tags; and multiple
facial action unit detection from low-resolution images augmented by
high-resolution images. Experimental results demonstrate that the proposed
method can effectively take full advantage of privileged information and
dependencies among multiple labels for better object recognition and better
facial action unit detection.
LabelBank: Revisiting Global Perspectives for Semantic Segmentation
Authors: Hexiang Hu, Zhiwei Deng, Guang-Tong Zhou, Fei Sha, Greg Mori
Date: 2017-03-29 00:00:00
Tasks: Segmentation, Semantic Segmentation

Semantic segmentation requires a detailed labeling of image pixels by object
category. Information derived from local image patches is necessary to describe
the detailed shape of individual objects. However, this information is
ambiguous and can result in noisy labels. Global inference of image content can
instead capture the general semantic concepts present. We advocate that
holistic inference of image concepts provides valuable information for detailed
pixel labeling. We propose a generic framework to leverage holistic information
in the form of a LabelBank for pixel-level segmentation.
  We show the ability of our framework to improve semantic segmentation
performance in a variety of settings. We learn models for extracting a holistic
LabelBank from visual cues, attributes, and/or textual descriptions. We
demonstrate improvements in semantic segmentation accuracy on standard datasets
across a range of state-of-the-art segmentation architectures and holistic
inference approaches.
Novel Structured Low-rank algorithm to recover spatially smooth exponential image time series
Authors: Arvind Balachandrasekaran, Mathews Jacob
Date: 2017-03-29 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion, Relation, Time Series, Time Series Analysis

We propose a structured low rank matrix completion algorithm to recover a
time series of images consisting of linear combination of exponential
parameters at every pixel, from under-sampled Fourier measurements. The spatial
smoothness of these parameters is exploited along with the exponential
structure of the time series at every pixel, to derive an annihilation relation
in the $k-t$ domain. This annihilation relation translates into a structured
low rank matrix formed from the $k-t$ samples. We demonstrate the algorithm in
the parameter mapping setting and show significant improvement over state of
the art methods.
Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations
Authors: Michael Laskey, Caleb Chuck, Jonathan Lee, Jeffrey Mahler, Sanjay Krishnan, Kevin Jamieson, Anca Dragan, Ken Goldberg
Date: 2016-10-04 00:00:00

Motivated by recent advances in Deep Learning for robot control, this paper
considers two learning algorithms in terms of how they acquire demonstrations.
"Human-Centric" (HC) sampling is the standard supervised learning algorithm,
where a human supervisor demonstrates the task by teleoperating the robot to
provide trajectories consisting of state-control pairs. "Robot-Centric" (RC)
sampling is an increasingly popular alternative used in algorithms such as
DAgger, where a human supervisor observes the robot executing a learned policy
and provides corrective control labels for each state visited. RC sampling can
be challenging for human supervisors and prone to mislabeling. RC sampling can
also induce error in policy performance because it repeatedly visits areas of
the state space that are harder to learn. Although policies learned with RC
sampling can be superior to HC sampling for standard learning models such as
linear SVMs, policies learned with HC sampling may be comparable with
highly-expressive learning models such as deep learning and hyper-parametric
decision trees, which have little model error. We compare HC and RC using a
grid world and a physical robot singulation task, where in the latter the input
is a binary image of a connected set of objects on a planar worksurface and the
policy generates a motion of the gripper to separate one object from the rest.
We observe in simulation that for linear SVMs, policies learned with RC
outperformed those learned with HC but that with deep models this advantage
disappears. We also find that with RC, the corrective control labels provided
by humans can be highly inconsistent. We prove there exists a class of examples
where in the limit, HC is guaranteed to converge to an optimal policy while RC
may fail to converge.
Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks
Authors: Joseph Antony, Kevin McGuinness, Kieran Moran, Noel E. O'Connor
Date: 2017-03-29 00:00:00
Tasks: General Classification, Multi-class Classification

This paper introduces a new approach to automatically quantify the severity
of knee OA using X-ray images. Automatically quantifying knee OA severity
involves two steps: first, automatically localizing the knee joints; next,
classifying the localized knee joint images. We introduce a new approach to
automatically detect the knee joints using a fully convolutional neural network
(FCN). We train convolutional neural networks (CNN) from scratch to
automatically quantify the knee OA severity optimizing a weighted ratio of two
loss functions: categorical cross-entropy and mean-squared loss. This joint
training further improves the overall quantification of knee OA severity, with
the added benefit of naturally producing simultaneous multi-class
classification and regression outputs. Two public datasets are used to evaluate
our approach, the Osteoarthritis Initiative (OAI) and the Multicenter
Osteoarthritis Study (MOST), with extremely promising results that outperform
existing approaches.
Solar Power Forecasting Using Support Vector Regression
Authors: Mohamed Abuella, Badrul Chowdhury
Date: 2017-03-29 00:00:00
Tasks: regression, Scheduling

Generation and load balance is required in the economic scheduling of
generating units in the smart grid. Variable energy generations, particularly
from wind and solar energy resources, are witnessing a rapid boost, and, it is
anticipated that with a certain level of their penetration, they can become
noteworthy sources of uncertainty. As in the case of load demand, energy
forecasting can also be used to mitigate some of the challenges that arise from
the uncertainty in the resource. While wind energy forecasting research is
considered mature, solar energy forecasting is witnessing a steadily growing
attention from the research community. This paper presents a support vector
regression model to produce solar power forecasts on a rolling basis for 24
hours ahead over an entire year, to mimic the practical business of energy
forecasting. Twelve weather variables are considered from a high-quality
benchmark dataset and new variables are extracted. The added value of the heat
index and wind speed as additional variables to the model is studied across
different seasons. The support vector regression model performance is compared
with artificial neural networks and multiple linear regression models for
energy forecasting.
Multi-fidelity Gaussian Process Bandit Optimisation
Authors: Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff Schneider, Barnabas Poczos
Date: 2016-03-20 00:00:00

In many scientific and engineering applications, we are tasked with the
maximisation of an expensive to evaluate black box function $f$. Traditional
settings for this problem assume just the availability of this single function.
However, in many cases, cheap approximations to $f$ may be obtainable. For
example, the expensive real world behaviour of a robot can be approximated by a
cheap computer simulation. We can use these approximations to eliminate low
function value regions cheaply and use the expensive evaluations of $f$ in a
small but promising region and speedily identify the optimum. We formalise this
task as a \emph{multi-fidelity} bandit problem where the target function and
its approximations are sampled from a Gaussian process. We develop MF-GP-UCB, a
novel method based on upper confidence bound techniques. In our theoretical
analysis we demonstrate that it exhibits precisely the above behaviour, and
achieves better regret than strategies which ignore multi-fidelity information.
Empirically, MF-GP-UCB outperforms such naive strategies and other
multi-fidelity methods on several synthetic and real experiments.
Stoic Ethics for Artificial Agents
Authors: Gabriel Murray
Date: 2017-01-09 00:00:00
Tasks: Ethics, Philosophy, Position

We present a position paper advocating the notion that Stoic philosophy and
ethics can inform the development of ethical A.I. systems. This is in sharp
contrast to most work on building ethical A.I., which has focused on
Utilitarian or Deontological ethical theories. We relate ethical A.I. to
several core Stoic notions, including the dichotomy of control, the four
cardinal virtues, the ideal Sage, Stoic practices, and Stoic perspectives on
emotion or affect. More generally, we put forward an ethical view of A.I. that
focuses more on internal states of the artificial agent rather than on external
actions of the agent. We provide examples relating to near-term A.I. systems as
well as hypothetical superintelligent agents.
Semi-Supervised Affective Meaning Lexicon Expansion Using Semantic and Distributed Word Representations
Authors: Areej Alhothali, Jesse Hoey
Date: 2017-03-28 00:00:00

In this paper, we propose an extension to graph-based sentiment lexicon
induction methods by incorporating distributed and semantic word
representations in building the similarity graph to expand a three-dimensional
sentiment lexicon. We also implemented and evaluated the label propagation
using four different word representations and similarity metrics. Our
comprehensive evaluation of the four approaches was performed on a single data
set, demonstrating that all four methods can generate a significant number of
new sentiment assignments with high accuracy. The highest correlations
(tau=0.51) and the lowest error (mean absolute error < 1.1%), obtained by
combining both the semantic and the distributional features, outperformed the
distributional-based and semantic-based label-propagation models and approached
a supervised algorithm.