pwc-archive/papers-with-abstracts huggingface dataset:
Reinforcement and Imitation Learning via Interactive No-Regret Learning
Authors: Stephane Ross, J. Andrew Bagnell
Date: 2014-06-23 00:00:00
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Structured Prediction

Recent work has demonstrated that problems-- particularly imitation learning
and structured prediction-- where a learner's predictions influence the
input-distribution it is tested on can be naturally addressed by an interactive
approach and analyzed using no-regret online learning. These approaches to
imitation learning, however, neither require nor benefit from information about
the cost of actions. We extend existing results in two directions: first, we
develop an interactive imitation learning approach that leverages cost
information; second, we extend the technique to address reinforcement learning.
The results provide theoretical support to the commonly observed successes of
online approximate policy iteration. Our approach suggests a broad new family
of algorithms and provides a unifying view of existing techniques for imitation
and reinforcement learning.
Committees of deep feedforward networks trained with few data
Authors: Bogdan Miclut, Thomas Kaester, Thomas Martinetz, Erhardt Barth
Date: 2014-06-23 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Deep convolutional neural networks are known to give good results on image
classification tasks. In this paper we present a method to improve the
classification result by combining multiple such networks in a committee. We
adopt the STL-10 dataset which has very few training examples and show that our
method can achieve results that are better than the state of the art. The
networks are trained layer-wise and no backpropagation is used. We also explore
the effects of dataset augmentation by mirroring, rotation, and scaling.
Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions
Authors: Roman Shapovalov, Dmitry Vetrov, Anton Osokin, Pushmeet Kohli
Date: 2014-06-23 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Structured Prediction

Structured-output learning is a challenging problem; particularly so because
of the difficulty in obtaining large datasets of fully labelled instances for
training. In this paper we try to overcome this difficulty by presenting a
multi-utility learning framework for structured prediction that can learn from
training instances with different forms of supervision. We propose a unified
technique for inferring the loss functions most suitable for quantifying the
consistency of solutions with the given weak annotation. We demonstrate the
effectiveness of our framework on the challenging semantic image segmentation
problem for which a wide variety of annotations can be used. For instance, the
popular training datasets for semantic segmentation are composed of images with
hard-to-generate full pixel labellings, as well as images with easy-to-obtain
weak annotations, such as bounding boxes around objects, or image-level labels
that specify which object categories are present in an image. Experimental
evaluation shows that the use of annotation-specific loss functions
dramatically improves segmentation accuracy compared to the baseline system
where only one type of weak annotation is used.
Interactive Ant Colony Optimisation (iACO) for Early Lifecycle Software Design
Authors: Christopher L. Simons, Jim Smith, Paul White
Date: 2012-12-21 00:00:00
Tasks: Heuristic Search

Software design is crucial to successful software development, yet is a
demanding multi-objective problem for software engineers. In an attempt to
assist the software designer, interactive (i.e. human in-the-loop)
meta-heuristic search techniques such as evolutionary computing have been
applied and show promising results. Recent investigations have also shown that
Ant Colony Optimization (ACO) can outperform evolutionary computing as a
potential search engine for interactive software design. With a limited
computational budget, ACO produces superior candidate design solutions in a
smaller number of iterations. Building on these findings, we propose a novel
interactive ACO (iACO) approach to assist the designer in early lifecycle
software design, in which the search is steered jointly by subjective designer
evaluation as well as machine fitness functions relating the structural
integrity and surrogate elegance of software designs. Results show that iACO is
speedy, responsive and highly effective in enabling interactive, dynamic
multi-objective search in early lifecycle software design. Study participants
rate the iACO search experience as compelling. Results of machine learning of
fitness measure weightings indicate that software design elegance does indeed
play a significant role in designer evaluation of candidate software design. We
conclude that the evenness of the number of attributes and methods among
classes (NAC) is a significant surrogate elegance measure, which in turn
suggests that this evenness of distribution, when combined with structural
integrity, is an implicit but crucial component of effective early lifecycle
software design.
VideoSET: Video Summary Evaluation through Text
Authors: Serena Yeung, Alireza Fathi, Li Fei-Fei
Date: 2014-06-23 00:00:00

In this paper we present VideoSET, a method for Video Summary Evaluation
through Text that can evaluate how well a video summary is able to retain the
semantic information contained in its original video. We observe that semantics
is most easily expressed in words, and develop a text-based approach for the
evaluation. Given a video summary, a text representation of the video summary
is first generated, and an NLP-based metric is then used to measure its
semantic distance to ground-truth text summaries written by humans. We show
that our technique has higher agreement with human judgment than pixel-based
distance metrics. We also release text annotations and ground-truth text
summaries for a number of publicly available video datasets, for use by the
computer vision community.
A Unified Quantitative Model of Vision and Audition
Authors: Peilei Liu, Ting Wang
Date: 2014-06-23 00:00:00
Tasks: model

We have put forwards a unified quantitative framework of vision and audition,
based on existing data and theories. According to this model, the retina is a
feedforward network self-adaptive to inputs in a specific period. After fully
grown, cells become specialized detectors based on statistics of stimulus
history. This model has provided explanations for perception mechanisms of
colour, shape, depth and motion. Moreover, based on this ground we have put
forwards a bold conjecture that single ear can detect sound direction. This is
complementary to existing theories and has provided better explanations for
sound localization.
Further heuristics for $k$-means: The merge-and-split heuristic and the $(k,l)$-means
Authors: Frank Nielsen, Richard Nock
Date: 2014-06-23 00:00:00
Tasks: Clustering

Finding the optimal $k$-means clustering is NP-hard in general and many
heuristics have been designed for minimizing monotonically the $k$-means
objective. We first show how to extend Lloyd's batched relocation heuristic and
Hartigan's single-point relocation heuristic to take into account empty-cluster
and single-point cluster events, respectively. Those events tend to
increasingly occur when $k$ or $d$ increases, or when performing several
restarts. First, we show that those special events are a blessing because they
allow to partially re-seed some cluster centers while further minimizing the
$k$-means objective function. Second, we describe a novel heuristic,
merge-and-split $k$-means, that consists in merging two clusters and splitting
this merged cluster again with two new centers provided it improves the
$k$-means objective. This novel heuristic can improve Hartigan's $k$-means when
it has converged to a local minimum. We show empirically that this
merge-and-split $k$-means improves over the Hartigan's heuristic which is the
{\em de facto} method of choice. Finally, we propose the $(k,l)$-means
objective that generalizes the $k$-means objective by associating the data
points to their $l$ closest cluster centers, and show how to either directly
convert or iteratively relax the $(k,l)$-means into a $k$-means in order to
reach better local minima.
Environmental Sensing by Wearable Device for Indoor Activity and Location Estimation
Authors: Ming Jin, Han Zou, Kevin Weekly, Ruoxi Jia, Alexandre M. Bayen, Costas J. Spanos
Date: 2014-06-22 00:00:00
Tasks: energy management, Management, Sensor Fusion

We present results from a set of experiments in this pilot study to
investigate the causal influence of user activity on various environmental
parameters monitored by occupant carried multi-purpose sensors. Hypotheses with
respect to each type of measurements are verified, including temperature,
humidity, and light level collected during eight typical activities: sitting in
lab / cubicle, indoor walking / running, resting after physical activity,
climbing stairs, taking elevators, and outdoor walking. Our main contribution
is the development of features for activity and location recognition based on
environmental measurements, which exploit location- and activity-specific
characteristics and capture the trends resulted from the underlying
physiological process. The features are statistically shown to have good
separability and are also information-rich. Fusing environmental sensing
together with acceleration is shown to achieve classification accuracy as high
as 99.13%. For building applications, this study motivates a sensor fusion
paradigm for learning individualized activity, location, and environmental
preferences for energy management and user comfort.
Recovery of Images with Missing Pixels using a Gradient Compressive Sensing Algorithm
Authors: Isidora Stanković
Date: 2014-06-22 00:00:00
Tasks: Compressive Sensing

This paper investigates the possibility of reconstruction of images
considering that they are sparse in the DCT transformation domain. Two
approaches are considered. One when the image is pre-processed in the DCT
domain, using 8x8 blocks. The image is made sparse by setting the smallest DCT
coefficients to zero. In the other case the original image is considered
without pre-processing, assuming the sparsity as intrinsic property of the
analyzed image. A gradient based algorithm is used to recover a large number of
missing pixels in the image. The case of a salt-and-paper noise affecting a
large number of pixels is easily reduced to the case of missing pixels and
considered within the same framework. The reconstruction of images affected
with salt-and-paper impulsive is compared with the images filtered using a
median filter. The same algorithm can be used considering transformation of the
whole image. Reconstructions of black and white and colour images are
considered.
Divide-and-Conquer Learning by Anchoring a Conical Hull
Authors: Tianyi Zhou, Jeff Bilmes, Carlos Guestrin
Date: 2014-06-22 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Clustering

We reduce a broad class of machine learning problems, usually addressed by EM
or sampling, to the problem of finding the $k$ extremal rays spanning the
conical hull of a data point set. These $k$ "anchors" lead to a global solution
and a more interpretable model that can even outperform EM and sampling on
generalization error. To find the $k$ anchors, we propose a novel
divide-and-conquer learning scheme "DCA" that distributes the problem to
$\mathcal O(k\log k)$ same-type sub-problems on different low-D random
hyperplanes, each can be solved by any solver. For the 2D sub-problem, we
present a non-iterative solver that only needs to compute an array of cosine
values and its max/min entries. DCA also provides a faster subroutine for other
methods to check whether a point is covered in a conical hull, which improves
algorithm design in multiple dimensions and brings significant speedup to
learning. We apply our method to GMM, HMM, LDA, NMF and subspace clustering,
then show its competitive performance and scalability over other methods on
rich datasets.
Convex Optimization Learning of Faithful Euclidean Distance Representations in Nonlinear Dimensionality Reduction
Authors: Chao Ding, Hou-Duo Qi
Date: 2014-06-22 00:00:00
Tasks: Dimensionality Reduction

Classical multidimensional scaling only works well when the noisy distances
observed in a high dimensional space can be faithfully represented by Euclidean
distances in a low dimensional space. Advanced models such as Maximum Variance
Unfolding (MVU) and Minimum Volume Embedding (MVE) use Semi-Definite
Programming (SDP) to reconstruct such faithful representations. While those SDP
models are capable of producing high quality configuration numerically, they
suffer two major drawbacks. One is that there exist no theoretically guaranteed
bounds on the quality of the configuration. The other is that they are slow in
computation when the data points are beyond moderate size. In this paper, we
propose a convex optimization model of Euclidean distance matrices. We
establish a non-asymptotic error bound for the random graph model with
sub-Gaussian noise, and prove that our model produces a matrix estimator of
high accuracy when the order of the uniform sample size is roughly the degree
of freedom of a low-rank matrix up to a logarithmic factor. Our results
partially explain why MVU and MVE often work well. Moreover, we develop a fast
inexact accelerated proximal gradient method. Numerical experiments show that
the model can produce configurations of high quality on large data points that
the SDP approach would struggle to cope with.
Cognitive Surveillance: Why does it never appear among the AVSS Conferences topics?
Authors: Emanuel Diamant
Date: 2014-06-22 00:00:00

Video Surveillance is a fast evolving field of research and development (R&D)
driven by the urgent need for public security and safety (due to the growing
threats of terrorism, vandalism, and anti-social behavior). Traditionally,
surveillance systems are comprised of two components - video cameras
distributed over the guarded area and human observer watching and analyzing the
incoming video. Explosive growth of installed cameras and limited human
operator's ability to process the delivered video content raise an urgent
demand for developing surveillance systems with human like cognitive
capabilities, that is - Cognitive surveillance systems. The growing interest in
this issue is testified by the tens of workshops, symposiums and conferences
held over the world each year. The IEEE International Conference on Advanced
Video and Signal-Based Surveillance (AVSS) is certainly one of them. However,
for unknown reasons, the term Cognitive Surveillance does never appear among
its topics. As to me, the explanation for this is simple - the complexity and
the indefinable nature of the term "Cognition". In this paper, I am trying to
resolve the problem providing a novel definition of cognition equally suitable
for biological as well as technological applications. I hope my humble efforts
will be helpful.
Natural Color Image Enhancement based on Modified Multiscale Retinex Algorithm and Performance Evaluation usingWavelet Energy
Authors: M. C. Hanumantharaju, M. Ravishankar, D. R. Rameshbabu
Date: 2014-06-22 00:00:00
Tasks: Image Enhancement, Image Quality Assessment

This paper presents a new color image enhancement technique based on modified
MultiScale Retinex(MSR) algorithm and visual quality of the enhanced images are
evaluated using a new metric, namely, wavelet energy. The color image
enhancement is achieved by down sampling the value component of HSV color space
converted image into three scales (normal, medium and fine) following the
contrast stretching operation. These down sampled value components are enhanced
using the MSR algorithm. The value component is reconstructed by averaging each
pixels of the lower scale image with that of the upper scale image subsequent
to up sampling the lower scale image. This process replaces dark pixel by the
average pixels of both the lower scale and upper scale, while retaining the
bright pixels. The quality of the reconstructed images in the proposed method
is found to be good and far better then the other researchers method. The
performance of the proposed scheme is evaluated using new wavelet domain based
assessment criterion, referred as wavelet energy. This scheme computes the
energy of both original and enhanced image in wavelet domain. The number of
edge details as well as wavelet energy is less in a poor quality image compared
with naturally enhanced image. Experimental results presented confirms that the
proposed wavelet energy based color image quality assessment technique
efficiently characterizes both the local and global details of enhanced image.
A CNL for Contract-Oriented Diagrams
Authors: John J. Camilleri, Gabriele Paganelli, Gerardo Schneider
Date: 2014-06-22 00:00:00

We present a first step towards a framework for defining and manipulating
normative documents or contracts described as Contract-Oriented (C-O) Diagrams.
These diagrams provide a visual representation for such texts, giving the
possibility to express a signatory's obligations, permissions and prohibitions,
with or without timing constraints, as well as the penalties resulting from the
non-fulfilment of a contract. This work presents a CNL for verbalising C-O
Diagrams, a web-based tool allowing editing in this CNL, and another for
visualising and manipulating the diagrams interactively. We then show how these
proof-of-concept tools can be used by applying them to a small example.
Deep Fragment Embeddings for Bidirectional Image Sentence Mapping
Authors: Andrej Karpathy, Armand Joulin, Li Fei-Fei
Date: 2014-06-22 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Referring Expression Comprehension, Retrieval, Sentence, Sentence Retrieval

We introduce a model for bidirectional retrieval of images and sentences
through a multi-modal embedding of visual and natural language data. Unlike
previous models that directly map images or sentences into a common embedding
space, our model works on a finer level and embeds fragments of images
(objects) and fragments of sentences (typed dependency tree relations) into a
common space. In addition to a ranking objective seen in previous work, this
allows us to add a new fragment alignment objective that learns to directly
associate these fragments across modalities. Extensive experimental evaluation
shows that reasoning on both the global level of images and sentences and the
finer level of their respective fragments significantly improves performance on
image-sentence retrieval tasks. Additionally, our model provides interpretable
predictions since the inferred inter-modal fragment alignment is explicit.
Constant Factor Approximation for Balanced Cut in the PIE model
Authors: Konstantin Makarychev, Yury Makarychev, Aravindan Vijayaraghavan
Date: 2014-06-22 00:00:00

We propose and study a new semi-random semi-adversarial model for Balanced
Cut, a planted model with permutation-invariant random edges (PIE). Our model
is much more general than planted models considered previously. Consider a set
of vertices V partitioned into two clusters $L$ and $R$ of equal size. Let $G$
be an arbitrary graph on $V$ with no edges between $L$ and $R$. Let
$E_{random}$ be a set of edges sampled from an arbitrary permutation-invariant
distribution (a distribution that is invariant under permutation of vertices in
$L$ and in $R$). Then we say that $G + E_{random}$ is a graph with
permutation-invariant random edges.
  We present an approximation algorithm for the Balanced Cut problem that finds
a balanced cut of cost $O(|E_{random}|) + n \text{polylog}(n)$ in this model.
In the regime when $|E_{random}| = \Omega(n \text{polylog}(n))$, this is a
constant factor approximation with respect to the cost of the planted cut.
Interactively Test Driving an Object Detector: Estimating Performance on Unlabeled Data
Authors: Rushil Anirudh, Pavan Turaga
Date: 2014-06-21 00:00:00

In this paper, we study the problem of `test-driving' a detector, i.e.
allowing a human user to get a quick sense of how well the detector generalizes
to their specific requirement. To this end, we present the first system that
estimates detector performance interactively without extensive ground truthing
using a human in the loop. We approach this as a problem of estimating
proportions and show that it is possible to make accurate inferences on the
proportion of classes or groups within a large data collection by observing
only $5-10\%$ of samples from the data. In estimating the false detections (for
precision), the samples are chosen carefully such that the overall
characteristics of the data collection are preserved. Next, inspired by its use
in estimating disease propagation we apply pooled testing approaches to
estimate missed detections (for recall) from the dataset. The estimates thus
obtained are close to the ones obtained using ground truth, thus reducing the
need for extensive labeling which is expensive and time consuming.
Análisis e implementación de algoritmos evolutivos para la optimización de simulaciones en ingeniería civil. (draft)
Authors: José Alberto García Gutiérrez, Alejandro Mateo Hernández Díaz
Date: 2014-01-20 00:00:00
Tasks: Evolutionary Algorithms

This paper studies the applicability of evolutionary algorithms,
particularly, the evolution strategies family in order to estimate a
degradation parameter in the shear design of reinforced concrete members. This
problem represents a great computational task and is highly relevant in the
framework of the structural engineering that for the first time is solved using
genetic algorithms.
  You are viewing a draft, the authors appreciate corrections, comments and
suggestions to this work.
Minimax-optimal Inference from Partial Rankings
Authors: Bruce Hajek, Sewoong Oh, Jiaming Xu
Date: 2014-06-21 00:00:00
Proceeding: NeurIPS 2014 12

This paper studies the problem of inferring a global preference based on the
partial rankings provided by many users over different subsets of items
according to the Plackett-Luce model. A question of particular interest is how
to optimally assign items to users for ranking and how many item assignments
are needed to achieve a target estimation error. For a given assignment of
items to users, we first derive an oracle lower bound of the estimation error
that holds even for the more general Thurstone models. Then we show that the
Cram\'er-Rao lower bound and our upper bounds inversely depend on the spectral
gap of the Laplacian of an appropriately defined comparison graph. When the
system is allowed to choose the item assignment, we propose a random assignment
scheme. Our oracle lower bound and upper bounds imply that it is
minimax-optimal up to a logarithmic factor among all assignment schemes and the
lower bound can be achieved by the maximum likelihood estimator as well as
popular rank-breaking schemes that decompose partial rankings into pairwise
comparisons. The numerical experiments corroborate our theoretical findings.
Venn-Abers predictors
Authors: Vladimir Vovk, Ivan Petej
Date: 2012-10-31 00:00:00
Tasks: Prediction, regression

This paper continues study, both theoretical and empirical, of the method of
Venn prediction, concentrating on binary prediction problems. Venn predictors
produce probability-type predictions for the labels of test objects which are
guaranteed to be well calibrated under the standard assumption that the
observations are generated independently from the same distribution. We give a
simple formalization and proof of this property. We also introduce Venn-Abers
predictors, a new class of Venn predictors based on the idea of isotonic
regression, and report promising empirical results both for Venn-Abers
predictors and for their more computationally efficient simplified version.