pwc-archive/papers-with-abstracts huggingface dataset:
On the Diagnostic of Road Pathway Visibility
Authors: Pierre Charbonnier, Jean-Philippe Tarel, Francois Goulette
Date: 2016-01-21 00:00:00
Tasks: 3D Reconstruction, Diagnostic, Road Segmentation

Visibility distance on the road pathway plays a significant role in road
safety and in particular, has a clear impact on the choice of speed limits.
Visibility distance is thus of importance for road engineers and authorities.
While visibility distance criteria are routinely taken into account in road
design, only a few systems exist for estimating it on existing road networks.
Most existing systems comprise a target vehicle followed at a constant distance
by an observer vehicle, which only allows to check if a given, fixed visibility
distance is available. We propose two new approaches that allow estimating the
maximum available visibility distance, involving only one vehicle and based on
different sensor technologies, namely binocular stereovision and 3D range
sensing (LIDAR). The first approach is based on the processing of two views
taken by digital cameras onboard the diagnostic vehicle. The main stages of the
process are: road segmentation, edge registration between the two views, road
profile 3D reconstruction and finally, maximal road visibility distance
estimation. The second approach involves the use of a Terrestrial LIDAR Mobile
Mapping System. The triangulated 3D model of the road and its surroundings
provided by the system is used to simulate targets at different distances,
which allows estimating the maximum geometric visibility distance along the
pathway. These approaches were developed in the context of the SARI-VIZIR
PREDIT project. Both approaches are described, evaluated and compared. Their
pros and cons with respect to vehicle following systems are also discussed.
RGB-D-based Action Recognition Datasets: A Survey
Authors: Jing Zhang, Wanqing Li, Philip O. Ogunbona, Pichao Wang, Chang Tang
Date: 2016-01-21 00:00:00
Tasks: Action Recognition, Survey, Temporal Action Localization

Human action recognition from RGB-D (Red, Green, Blue and Depth) data has
attracted increasing attention since the first work reported in 2010. Over this
period, many benchmark datasets have been created to facilitate the development
and evaluation of new algorithms. This raises the question of which dataset to
select and how to use it in providing a fair and objective comparative
evaluation against state-of-the-art methods. To address this issue, this paper
provides a comprehensive review of the most commonly used action recognition
related RGB-D video datasets, including 27 single-view datasets, 10 multi-view
datasets, and 7 multi-person datasets. The detailed information and analysis of
these datasets is a useful resource in guiding insightful selection of datasets
for future research. In addition, the issues with current algorithm evaluation
vis-\'{a}-vis limitations of the available datasets and evaluation protocols
are also highlighted; resulting in a number of recommendations for collection
of new datasets and use of evaluation protocols.
Neural Enquirer: Learning to Query Tables with Natural Language
Authors: Pengcheng Yin, Zhengdong Lu, Hang Li, Ben Kao
Date: 2015-12-03 00:00:00
Tasks: Semantic Parsing

We proposed Neural Enquirer as a neural network architecture to execute a
natural language (NL) query on a knowledge-base (KB) for answers. Basically,
Neural Enquirer finds the distributed representation of a query and then
executes it on knowledge-base tables to obtain the answer as one of the values
in the tables. Unlike similar efforts in end-to-end training of semantic
parsers, Neural Enquirer is fully "neuralized": it not only gives
distributional representation of the query and the knowledge-base, but also
realizes the execution of compositional queries as a series of differentiable
operations, with intermediate results (consisting of annotations of the tables
at different levels) saved on multiple layers of memory. Neural Enquirer can be
trained with gradient descent, with which not only the parameters of the
controlling components and semantic parsing component, but also the embeddings
of the tables and query words can be learned from scratch. The training can be
done in an end-to-end fashion, but it can take stronger guidance, e.g., the
step-by-step supervision for complicated queries, and benefit from it. Neural
Enquirer is one step towards building neural network systems which seek to
understand language by executing it on real-world. Our experiments show that
Neural Enquirer can learn to execute fairly complicated NL queries on tables
with rich structures.
Bayesian model comparison with un-normalised likelihoods
Authors: Richard G. Everitt, Adam M. Johansen, Ellen Rowing, Melina Evdemon-Hogan
Date: 2015-04-01 00:00:00
Tasks: model

Models for which the likelihood function can be evaluated only up to a
parameter-dependent unknown normalising constant, such as Markov random field
models, are used widely in computer science, statistical physics, spatial
statistics, and network analysis. However, Bayesian analysis of these models
using standard Monte Carlo methods is not possible due to the intractability of
their likelihood functions. Several methods that permit exact, or close to
exact, simulation from the posterior distribution have recently been developed.
However, estimating the evidence and Bayes' factors (BFs) for these models
remains challenging in general. This paper describes new random weight
importance sampling and sequential Monte Carlo methods for estimating BFs that
use simulation to circumvent the evaluation of the intractable likelihood, and
compares them to existing methods. In some cases we observe an advantage in the
use of biased weight estimates. An initial investigation into the theoretical
and empirical properties of this class of methods is presented. Some support
for the use of biased estimates is presented, but we advocate caution in the
use of such estimates.
Hierarchical Latent Word Clustering
Authors: Halid Ziya Yerebakan, Fitsum Reda, Yiqiang Zhan, Yoshihisa Shinagawa
Date: 2016-01-20 00:00:00
Tasks: Clustering

This paper presents a new Bayesian non-parametric model by extending the
usage of Hierarchical Dirichlet Allocation to extract tree structured word
clusters from text data. The inference algorithm of the model collects words in
a cluster if they share similar distribution over documents. In our
experiments, we observed meaningful hierarchical structures on NIPS corpus and
radiology reports collected from public repositories.
Monotonic Calibrated Interpolated Look-Up Tables
Authors: Maya Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin Canini, Alexander Mangylov, Wojtek Moczydlowski, Alex van Esbroeck
Date: 2015-05-23 00:00:00
Tasks: BIG-bench Machine Learning

Real-world machine learning applications may require functions that are
fast-to-evaluate and interpretable. In particular, guaranteed monotonicity of
the learned function can be critical to user trust. We propose meeting these
goals for low-dimensional machine learning problems by learning flexible,
monotonic functions using calibrated interpolated look-up tables. We extend the
structural risk minimization framework of lattice regression to train monotonic
look-up tables by solving a convex problem with appropriate linear inequality
constraints. In addition, we propose jointly learning interpretable
calibrations of each feature to normalize continuous features and handle
categorical or missing data, at the cost of making the objective non-convex. We
address large-scale learning through parallelization, mini-batching, and
propose random sampling of additive regularizer terms. Case studies with
real-world problems with five to sixteen features and thousands to millions of
training samples demonstrate the proposed monotonic functions can achieve
state-of-the-art accuracy on practical problems while providing greater
transparency to users.
Matrix Product State for Feature Extraction of Higher-Order Tensors
Authors: Johann A. Bengua, Ho N. Phien, Hoang D. Tuan, Minh N. Do
Date: 2015-03-02 00:00:00
Tasks: General Classification

This paper introduces matrix product state (MPS) decomposition as a
computational tool for extracting features of multidimensional data represented
by higher-order tensors. Regardless of tensor order, MPS extracts its relevant
features to the so-called core tensor of maximum order three which can be used
for classification. Mainly based on a successive sequence of singular value
decompositions (SVD), MPS is quite simple to implement without any recursive
procedure needed for optimizing local tensors. Thus, it leads to substantial
computational savings compared to other tensor feature extraction methods such
as higher-order orthogonal iteration (HOOI) underlying the Tucker decomposition
(TD). Benchmark results show that MPS can reduce significantly the feature
space of data while achieving better classification performance compared to
HOOI.
Detecting Temporally Consistent Objects in Videos through Object Class Label Propagation
Authors: Subarna Tripathi, Serge Belongie, Youngbae Hwang, Truong Nguyen
Date: 2016-01-20 00:00:00
Tasks: Clustering, Object, object-detection, Object Detection

Object proposals for detecting moving or static video objects need to address
issues such as speed, memory complexity and temporal consistency. We propose an
efficient Video Object Proposal (VOP) generation method and show its efficacy
in learning a better video object detector. A deep-learning based video object
detector learned using the proposed VOP achieves state-of-the-art detection
performance on the Youtube-Objects dataset. We further propose a clustering of
VOPs which can efficiently be used for detecting objects in video in a
streaming fashion. As opposed to applying per-frame convolutional neural
network (CNN) based object detection, our proposed method called Objects in
Video Enabler thRough LAbel Propagation (OVERLAP) needs to classify only a
small fraction of all candidate proposals in every video frame through
streaming clustering of object proposals and class-label propagation. Source
code will be made available soon.
QUOTE: "Querying" Users as Oracles in Tag Engines - A Semi-Supervised Learning Approach to Personalized Image Tagging
Authors: Amandianeze O. Nwana, Tsuhan Chen
Date: 2016-01-20 00:00:00
Tasks: Retrieval, TAG

One common trend in image tagging research is to focus on visually relevant
tags, and this tends to ignore the personal and social aspect of tags,
especially on photoblogging websites such as Flickr. Previous work has
correctly identified that many of the tags that users provide on images are not
visually relevant (i.e. representative of the salient content in the image) and
they go on to treat such tags as noise, ignoring that the users chose to
provide those tags over others that could have been more visually relevant.
Another common assumption about user generated tags for images is that the
order of these tags provides no useful information for the prediction of tags
on future images. This assumption also tends to define usefulness in terms of
what is visually relevant to the image. For general tagging or labeling
applications that focus on providing visual information about image content,
these assumptions are reasonable, but when considering personalized image
tagging applications, these assumptions are at best too rigid, ignoring user
choice and preferences.
  We challenge the aforementioned assumptions, and provide a machine learning
approach to the problem of personalized image tagging with the following
contributions: 1.) We reformulate the personalized image tagging problem as a
search/retrieval ranking problem, 2.) We leverage the order of tags, which does
not always reflect visual relevance, provided by the user in the past as a cue
to their tag preferences, similar to click data, 3.) We propose a technique to
augment sparse user tag data (semi-supervision), and 4.) We demonstrate the
efficacy of our method on a subset of Flickr images, showing improvement over
previous state-of-art methods.
Selecting Efficient Features via a Hyper-Heuristic Approach
Authors: Mitra Montazeri, Mahdieh Soleymani Baghshah, Aliakbar Niknafs
Date: 2016-01-20 00:00:00
Tasks: feature selection

By Emerging huge databases and the need to efficient learning algorithms on
these datasets, new problems have appeared and some methods have been proposed
to solve these problems by selecting efficient features. Feature selection is a
problem of finding efficient features among all features in which the final
feature set can improve accuracy and reduce complexity. One way to solve this
problem is to evaluate all possible feature subsets. However, evaluating all
possible feature subsets is an exhaustive search and thus it has high
computational complexity. Until now many heuristic algorithms have been studied
for solving this problem. Hyper-heuristic is a new heuristic approach which can
search the solution space effectively by applying local searches appropriately.
Each local search is a neighborhood searching algorithm. Since each region of
the solution space can have its own characteristics, it should be chosen an
appropriate local search and apply it to current solution. This task is tackled
to a supervisor. The supervisor chooses a local search based on the functional
history of local searches. By doing this task, it can trade of between
exploitation and exploration. Since the existing heuristic cannot trade of
between exploration and exploitation appropriately, the solution space has not
been searched appropriately in these methods and thus they have low convergence
rate. For the first time, in this paper use a hyper-heuristic approach to find
an efficient feature subset. In the proposed method, genetic algorithm is used
as a supervisor and 16 heuristic algorithms are used as local searches.
Empirical study of the proposed method on several commonly used data sets from
UCI data sets indicates that it outperforms recent existing methods in the
literature for feature selection.
Semantic Word Clusters Using Signed Normalized Graph Cuts
Authors: João Sedoc, Jean Gallier, Lyle Ungar, Dean Foster
Date: 2016-01-20 00:00:00
Tasks: Clustering, Word Similarity

Vector space representations of words capture many aspects of word
similarity, but such methods tend to make vector spaces in which antonyms (as
well as synonyms) are close to each other. We present a new signed spectral
normalized graph cut algorithm, signed clustering, that overlays existing
thesauri upon distributionally derived vector representations of words, so that
antonym relationships between word pairs are represented by negative weights.
Our signed clustering algorithm produces clusters of words which simultaneously
capture distributional and synonym relations. We evaluate these clusters
against the SimLex-999 dataset (Hill et al.,2014) of human judgments of word
pair similarities, and also show the benefit of using our clusters to predict
the sentiment of a given text.
Disaggregation of Remotely Sensed Soil Moisture in Heterogeneous Landscapes using Holistic Structure based Models
Authors: Subit Chakrabarti, Jasmeet Judge, Anand Rangarajan, Sanjay Ranka
Date: 2015-01-30 00:00:00
Tasks: Clustering

In this study, a novel machine learning algorithm is presented for
disaggregation of satellite soil moisture (SM) based on self-regularized
regressive models (SRRM) using high-resolution correlated information from
auxiliary sources. It includes regularized clustering that assigns soft
memberships to each pixel at fine-scale followed by a kernel regression that
computes the value of the desired variable at all pixels. Coarse-scale remotely
sensed SM were disaggregated from 10km to 1km using land cover, precipitation,
land surface temperature, leaf area index, and in-situ observations of SM. This
algorithm was evaluated using multi-scale synthetic observations in NC Florida
for heterogeneous agricultural land covers. It was found that the root mean
square error (RMSE) for 96% of the pixels was less than 0.02 $m^3/m^3$. The
clusters generated represented the data well and reduced the RMSE by upto 40%
during periods of high heterogeneity in land-cover and meteorological
conditions. The Kullback Leibler divergence (KLD) between the true SM and the
disaggregated estimates is close to 0, for both vegetated and baresoil
landcovers. The disaggregated estimates were compared to those generated by the
Principle of Relevant Information (PRI) method. The RMSE for the PRI
disaggregated estimates is higher than the RMSE for the SRRM on each day of the
season. The KLD of the disaggregated estimates generated by the SRRM is at
least four orders of magnitude lower than those for the PRI disaggregated
estimates, while the computational time needed was reduced by three times. The
results indicate that the SRRM can be used for disaggregating SM with complex
non-linear correlations on a grid with high accuracy.
Binding via Reconstruction Clustering
Authors: Klaus Greff, Rupesh Kumar Srivastava, Jürgen Schmidhuber
Date: 2015-11-19 00:00:00
Tasks: Clustering, Denoising, Representation Learning

Disentangled distributed representations of data are desirable for machine
learning, since they are more expressive and can generalize from fewer
examples. However, for complex data, the distributed representations of
multiple objects present in the same input can interfere and lead to
ambiguities, which is commonly referred to as the binding problem. We argue for
the importance of the binding problem to the field of representation learning,
and develop a probabilistic framework that explicitly models inputs as a
composition of multiple objects. We propose an unsupervised algorithm that uses
denoising autoencoders to dynamically bind features together in multi-object
inputs through an Expectation-Maximization-like clustering process. The
effectiveness of this method is demonstrated on artificially generated datasets
of binary images, showing that it can even generalize to bind together new
objects never seen by the autoencoder during training.
Sub-Optimal Multi-Phase Path Planning: A Method for Solving Rubik's Revenge
Authors: Jared Weed
Date: 2016-01-20 00:00:00
Tasks: Rubik's Cube, Time Series, Time Series Analysis

Rubik's Revenge, a 4x4x4 variant of the Rubik's puzzles, remains to date as
an unsolved puzzle. That is to say, we do not have a method or successful
categorization to optimally solve every one of its approximately $7.401 \times
10^{45}$ possible configurations. Rubik's Cube, Rubik's Revenge's predecessor
(3x3x3), with its approximately $4.33 \times 10^{19}$ possible configurations,
has only recently been completely solved by Rokicki et. al, further finding
that any configuration requires no more than 20 moves. With the sheer dimension
of Rubik's Revenge and its total configuration space, a brute-force method of
finding all optimal solutions would be in vain. Similar to the methods used by
Rokicki et. al on Rubik's Cube, in this paper we develop a method for solving
arbitrary configurations of Rubik's Revenge in phases, using a combination of a
powerful algorithm known as IDA* and a useful definition of distance in the
cube space. While time-series results were not successfully gathered, it will
be shown that this method far outweighs current human-solving methods and can
be used to determine loose upper bounds for the cube space. Discussion will
suggest that this method can also be applied to other puzzles with the proper
transformations.
Political Speech Generation
Authors: Valentin Kassarnig
Date: 2016-01-13 00:00:00
Tasks: Language Modeling, Language Modelling, POS, Sentence, TAG

In this report we present a system that can generate political speeches for a
desired political party. Furthermore, the system allows to specify whether a
speech should hold a supportive or opposing opinion. The system relies on a
combination of several state-of-the-art NLP methods which are discussed in this
report. These include n-grams, Justeson & Katz POS tag filter, recurrent neural
networks, and latent Dirichlet allocation. Sequences of words are generated
based on probabilities obtained from two underlying models: A language model
takes care of the grammatical correctness while a topic model aims for textual
consistency. Both models were trained on the Convote dataset which contains
transcripts from US congressional floor debates. Furthermore, we present a
manual and an automated approach to evaluate the quality of generated speeches.
In an experimental evaluation generated speeches have shown very high quality
in terms of grammatical correctness and sentence transitions.
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies
Authors: Vincent François-Lavet, Raphael Fonteneau, Damien Ernst
Date: 2015-12-07 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Using deep neural nets as function approximator for reinforcement learning
tasks have recently been shown to be very powerful for solving problems
approaching real-world complexity. Using these results as a benchmark, we
discuss the role that the discount factor may play in the quality of the
learning process of a deep Q-network (DQN). When the discount factor
progressively increases up to its final value, we empirically show that it is
possible to significantly reduce the number of learning steps. When used in
conjunction with a varying learning rate, we empirically show that it
outperforms original DQN on several experiments. We relate this phenomenon with
the instabilities of neural networks when they are used in an approximate
Dynamic Programming setting. We also describe the possibility to fall within a
local optimum during the learning process, thus connecting our discussion with
the exploration/exploitation dilemma.
Improved Spoken Document Summarization with Coverage Modeling Techniques
Authors: Kuan-Yu Chen, Shih-Hung Liu, Berlin Chen, Hsin-Min Wang
Date: 2016-01-20 00:00:00
Tasks: Diversity, Document Summarization, Extractive Summarization, Sentence

Extractive summarization aims at selecting a set of indicative sentences from
a source document as a summary that can express the major theme of the
document. A general consensus on extractive summarization is that both
relevance and coverage are critical issues to address. The existing methods
designed to model coverage can be characterized by either reducing redundancy
or increasing diversity in the summary. Maximal margin relevance (MMR) is a
widely-cited method since it takes both relevance and redundancy into account
when generating a summary for a given document. In addition to MMR, there is
only a dearth of research concentrating on reducing redundancy or increasing
diversity for the spoken document summarization task, as far as we are aware.
Motivated by these observations, two major contributions are presented in this
paper. First, in contrast to MMR, which considers coverage by reducing
redundancy, we propose two novel coverage-based methods, which directly
increase diversity. With the proposed methods, a set of representative
sentences, which not only are relevant to the given document but also cover
most of the important sub-themes of the document, can be selected
automatically. Second, we make a step forward to plug in several
document/sentence representation methods into the proposed framework to further
enhance the summarization performance. A series of empirical evaluations
demonstrate the effectiveness of our proposed methods.