pwc-archive/papers-with-abstracts huggingface dataset:
Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision
Authors: Marius Leordeanu, Alexandra Radu, Shumeet Baluja, Rahul Sukthankar
Date: 2015-12-01 00:00:00
Tasks: Clustering, feature selection, General Classification, Video Classification

Feature selection is essential for effective visual recognition. We propose
an efficient joint classifier learning and feature selection method that
discovers sparse, compact representations of input features from a vast sea of
candidates, with an almost unsupervised formulation. Our method requires only
the following knowledge, which we call the \emph{feature sign}---whether or not
a particular feature has on average stronger values over positive samples than
over negatives. We show how this can be estimated using as few as a single
labeled training sample per class. Then, using these feature signs, we extend
an initial supervised learning problem into an (almost) unsupervised clustering
formulation that can incorporate new data without requiring ground truth
labels. Our method works both as a feature selection mechanism and as a fully
competitive classifier. It has important properties, low computational cost and
excellent accuracy, especially in difficult cases of very limited training
data. We experiment on large-scale recognition in video and show superior speed
and performance to established feature selection approaches such as AdaBoost,
Lasso, greedy forward-backward selection, and powerful classifiers such as SVM.
Character-Aware Neural Language Models
Authors: Yoon Kim, Yacine Jernite, David Sontag, Alexander M. Rush
Date: 2015-08-26 00:00:00
Tasks: Language Modeling, Language Modelling

We describe a simple neural language model that relies only on
character-level inputs. Predictions are still made at the word-level. Our model
employs a convolutional neural network (CNN) and a highway network over
characters, whose output is given to a long short-term memory (LSTM) recurrent
neural network language model (RNN-LM). On the English Penn Treebank the model
is on par with the existing state-of-the-art despite having 60% fewer
parameters. On languages with rich morphology (Arabic, Czech, French, German,
Spanish, Russian), the model outperforms word-level/morpheme-level LSTM
baselines, again with fewer parameters. The results suggest that on many
languages, character inputs are sufficient for language modeling. Analysis of
word representations obtained from the character composition part of the model
reveals that the model is able to encode, from characters only, both semantic
and orthographic information.
Adaptive Sampling of RF Fingerprints for Fine-grained Indoor Localization
Authors: Xiao-Yang Liu, Shuchin Aeron, Vaneet Aggarwal, Xiaodong Wang, Min-You Wu
Date: 2015-08-10 00:00:00
Tasks: Indoor Localization

Indoor localization is a supporting technology for a broadening range of
pervasive wireless applications. One promis- ing approach is to locate users
with radio frequency fingerprints. However, its wide adoption in real-world
systems is challenged by the time- and manpower-consuming site survey process,
which builds a fingerprint database a priori for localization. To address this
problem, we visualize the 3-D RF fingerprint data as a function of locations
(x-y) and indices of access points (fingerprint), as a tensor and use tensor
algebraic methods for an adaptive tubal-sampling of this fingerprint space. In
particular using a recently proposed tensor algebraic framework in [1] we
capture the complexity of the fingerprint space as a low-dimensional
tensor-column space. In this formulation the proposed scheme exploits
adaptivity to identify reference points which are highly informative for
learning this low-dimensional space. Further, under certain incoherency
conditions we prove that the proposed scheme achieves bounded recovery error
and near-optimal sampling complexity. In contrast to several existing work that
rely on random sampling, this paper shows that adaptivity in sampling can lead
to significant improvements in localization accuracy. The approach is validated
on both data generated by the ray-tracing indoor model which accounts for the
floor plan and the impact of walls and the real world data. Simulation results
show that, while maintaining the same localization accuracy of existing
approaches, the amount of samples can be cut down by 71% for the high SNR case
and 55% for the low SNR case.
Efficient Edge Detection on Low-Cost FPGAs
Authors: Jamie Schiel, Andrew Bainbridge-Smith
Date: 2015-12-01 00:00:00
Tasks: Edge Detection

Improving the efficiency of edge detection in embedded applications, such as
UAV control, is critical for reducing system cost and power dissipation. Field
programmable gate arrays (FPGA) are a good platform for making improvements
because of their specialised internal structure. However, current FPGA edge
detectors do not exploit this structure well. A new edge detection architecture
is proposed that is better optimised for FPGAs. The basis of the architecture
is the Sobel edge kernels that are shown to be the most suitable because of
their separability and absence of multiplications. Edge intensities are
calculated with a new 4:2 compressor that consists of two custom-designed 3:2
compressors. Addition speed is increased by breaking carry propagation chains
with look-ahead logic. Testing of the design showed it gives a 28% increase in
speed and 4.4% reduction in area over previous equivalent designs, which
demonstrated that it will lower the cost of edge detection systems, dissipate
less power and still maintain high-speed control.
Information entropy as an anthropomorphic concept
Authors: Panteleimon Rodis
Date: 2015-03-06 00:00:00
Tasks: Diversity

According to E.T. Jaynes and E.P. Wigner, entropy is an anthropomorphic
concept in the sense that in a physical system correspond many thermodynamic
systems. The physical system can be examined from many points of view each time
examining different variables and calculating entropy differently. In this
paper we discuss how this concept may be applied in information entropy; how
Shannon's definition of entropy can fit in Jayne's and Wigner's statement. This
is achieved by generalizing Shannon's notion of information entropy and this is
the main contribution of the paper. Then we discuss how entropy under these
considerations may be used for the comparison of password complexity and as a
measure of diversity useful in the analysis of the behavior of genetic
algorithms.
Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex
Authors: Jeff Hawkins, Subutai Ahmad
Date: 2015-10-31 00:00:00

Neocortical neurons have thousands of excitatory synapses. It is a mystery
how neurons integrate the input from so many synapses and what kind of
large-scale network behavior this enables. It has been previously proposed that
non-linear properties of dendrites enable neurons to recognize multiple
patterns. In this paper we extend this idea by showing that a neuron with
several thousand synapses arranged along active dendrites can learn to
accurately and robustly recognize hundreds of unique patterns of cellular
activity, even in the presence of large amounts of noise and pattern variation.
We then propose a neuron model where some of the patterns recognized by a
neuron lead to action potentials and define the classic receptive field of the
neuron, whereas the majority of the patterns recognized by a neuron act as
predictions by slightly depolarizing the neuron without immediately generating
an action potential. We then present a network model based on neurons with
these properties and show that the network learns a robust model of time-based
sequences. Given the similarity of excitatory neurons throughout the neocortex
and the importance of sequence memory in inference and behavior, we propose
that this form of sequence memory is a universal property of neocortical
tissue. We further propose that cellular layers in the neocortex implement
variations of the same sequence memory algorithm to achieve different aspects
of inference and behavior. The neuron and network models we introduce are
robust over a wide range of parameters as long as the network uses a sparse
distributed code of cellular activations. The sequence capacity of the network
scales linearly with the number of synapses on each neuron. Thus neurons need
thousands of synapses to learn the many temporal patterns in sensory stimuli
and motor sequences.
Sharp Finite-Time Iterated-Logarithm Martingale Concentration
Authors: Akshay Balsubramani
Date: 2014-05-12 00:00:00

We give concentration bounds for martingales that are uniform over finite
times and extend classical Hoeffding and Bernstein inequalities. We also
demonstrate our concentration bounds to be optimal with a matching
anti-concentration inequality, proved using the same method. Together these
constitute a finite-time version of the law of the iterated logarithm, and shed
light on the relationship between it and the central limit theorem.
A New Approach for Scalable Analysis of Microbial Communities
Authors: Ehsaneddin Asgari, Kiavash Garakani, Mohammad R. K. Mofrad
Date: 2015-12-01 00:00:00
Tasks: Dimensionality Reduction, General Classification

Microbial communities play important roles in the function and maintenance of
various biosystems, ranging from human body to the environment. Current methods
for analysis of microbial communities are typically based on taxonomic
phylogenetic alignment using 16S rRNA metagenomic or Whole Genome Sequencing
data. In typical characterizations of microbial communities, studies deal with
billions of micobial sequences, aligning them to a phylogenetic tree. We
introduce a new approach for the efficient analysis of microbial communities.
Our new reference-free analysis tech- nique is based on n-gram sequence
analysis of 16S rRNA data and reduces the processing data size dramatically (by
105 fold), without requiring taxonomic alignment. The proposed approach is
applied to characterize phenotypic microbial community differ- ences in
different settings. Specifically, we applied this approach in classification of
microbial com- munities across different body sites, characterization of oral
microbiomes associated with healthy and diseased individuals, and
classification of microbial communities longitudinally during the develop- ment
of infants. Different dimensionality reduction methods are introduced that
offer a more scalable analysis framework, while minimizing the loss in
classification accuracies. Among dimensionality re- duction techniques, we
propose a continuous vector representation for microbial communities, which can
widely be used for deep learning applications in microbial informatics.
Beyond OWL 2 QL in OBDA: Rewritings and Approximations (Extended Version)
Authors: Elena Botoeva, Diego Calvanese, Valerio Santarelli, Domenico Fabio Savo, Alessandro Solimando, Guohui Xiao
Date: 2015-11-26 00:00:00

Ontology-based data access (OBDA) is a novel paradigm facilitating access to
relational data, realized by linking data sources to an ontology by means of
declarative mappings. DL-Lite_R, which is the logic underpinning the W3C
ontology language OWL 2 QL and the current language of choice for OBDA, has
been designed with the goal of delegating query answering to the underlying
database engine, and thus is restricted in expressive power. E.g., it does not
allow one to express disjunctive information, and any form of recursion on the
data. The aim of this paper is to overcome these limitations of DL-Lite_R, and
extend OBDA to more expressive ontology languages, while still leveraging the
underlying relational technology for query answering. We achieve this by
relying on two well-known mechanisms, namely conservative rewriting and
approximation, but significantly extend their practical impact by bringing into
the picture the mapping, an essential component of OBDA. Specifically, we
develop techniques to rewrite OBDA specifications with an expressive ontology
to "equivalent" ones with a DL-Lite_R ontology, if possible, and to approximate
them otherwise. We do so by exploiting the high expressive power of the mapping
layer to capture part of the domain semantics of rich ontology languages. We
have implemented our techniques in the prototype system OntoProx, making use of
the state-of-the-art OBDA system Ontop and the query answering system Clipper,
and we have shown their feasibility and effectiveness with experiments on
synthetic and real-world data.
Taxonomy grounded aggregation of classifiers with different label sets
Authors: Amrita Saha, Sathish Indurthi, Shantanu Godbole, Subendhu Rongali, Vikas C. Raykar
Date: 2015-12-01 00:00:00
Tasks: General Classification, text-classification, Text Classification

We describe the problem of aggregating the label predictions of diverse
classifiers using a class taxonomy. Such a taxonomy may not have been available
or referenced when the individual classifiers were designed and trained, yet
mapping the output labels into the taxonomy is desirable to integrate the
effort spent in training the constituent classifiers. A hierarchical taxonomy
representing some domain knowledge may be different from, but partially
mappable to, the label sets of the individual classifiers. We present a
heuristic approach and a principled graphical model to aggregate the label
predictions by grounding them into the available taxonomy. Our model aggregates
the labels using the taxonomy structure as constraints to find the most likely
hierarchically consistent class. We experimentally validate our proposed method
on image and text classification tasks.
Discriminative and Efficient Label Propagation on Complementary Graphs for Multi-Object Tracking
Authors: Amit Kumar K. C., Laurent Jacques, Christophe De Vleeschouwer
Date: 2015-04-05 00:00:00
Tasks: graph construction, Multi-Object Tracking, Object Tracking

Given a set of detections, detected at each time instant independently, we
investigate how to associate them across time. This is done by propagating
labels on a set of graphs, each graph capturing how either the spatio-temporal
or the appearance cues promote the assignment of identical or distinct labels
to a pair of detections. The graph construction is motivated by a locally
linear embedding of the detection features. Interestingly, the neighborhood of
a node in appearance graph is defined to include all the nodes for which the
appearance feature is available (even if they are temporally distant). This
gives our framework the uncommon ability to exploit the appearance features
that are available only sporadically. Once the graphs have been defined,
multi-object tracking is formulated as the problem of finding a label
assignment that is consistent with the constraints captured each graph, which
results into a difference of convex (DC) program. We propose to decompose the
global objective function into node-wise sub-problems. This not only allows a
computationally efficient solution, but also supports an incremental and
scalable construction of the graph, thereby making the framework applicable to
large graphs and practical tracking scenarios. Moreover, it opens the
possibility of parallel implementation.
Highly Scalable Tensor Factorization for Prediction of Drug-Protein Interaction Type
Authors: Adam Arany, Jaak Simm, Pooya Zakeri, Tom Haber, Jörg K. Wegner, Vladimir Chupakhin, Hugo Ceulemans, Yves Moreau
Date: 2015-12-01 00:00:00
Tasks: Drug Design, Vocal Bursts Type Prediction

The understanding of the type of inhibitory interaction plays an important
role in drug design. Therefore, researchers are interested to know whether a
drug has competitive or non-competitive interaction to particular protein
targets.
  Method: to analyze the interaction types we propose factorization method
Macau which allows us to combine different measurement types into a single
tensor together with proteins and compounds. The compounds are characterized by
high dimensional 2D ECFP fingerprints. The novelty of the proposed method is
that using a specially designed noise injection MCMC sampler it can incorporate
high dimensional side information, i.e., millions of unique 2D ECFP compound
features, even for large scale datasets of millions of compounds. Without the
side information, in this case, the tensor factorization would be practically
futile.
  Results: using public IC50 and Ki data from ChEMBL we trained a model from
where we can identify the latent subspace separating the two measurement types
(IC50 and Ki). The results suggest the proposed method can detect the
competitive inhibitory activity between compounds and proteins.
A Hybrid Intelligent Model for Software Cost Estimation
Authors: Wei Lin Du, Luiz Fernando Capretz, Ali Bou Nassif, Danny Ho
Date: 2015-12-01 00:00:00
Tasks: model

Accurate software development effort estimation is critical to the success of
software projects. Although many techniques and algorithmic models have been
developed and implemented by practitioners, accurate software development
effort prediction is still a challenging endeavor in the field of software
engineering, especially in handling uncertain and imprecise inputs and
collinear characteristics. In this paper, a hybrid in-telligent model combining
a neural network model integrated with fuzzy model (neuro-fuzzy model) has been
used to improve the accuracy of estimating software cost. The performance of
the proposed model is assessed by designing and conducting evaluation with
published project and industrial data. Results have shown that the proposed
model demonstrates the ability of improving the estimation accuracy by 18%
based on the Mean Magnitude of Relative Error (MMRE) criterion.
On Optical Flow Models for Variational Motion Estimation
Authors: Martin Burger, Hendrik Dirks, Lena Frerking
Date: 2015-12-01 00:00:00
Tasks: Motion Estimation, Optical Flow Estimation

The aim of this paper is to discuss and evaluate total variation based
regularization methods for motion estimation, with particular focus on optical
flow models. In addition to standard $L^2$ and $L^1$ data fidelities we give an
overview of different variants of total variation regularization obtained from
combination with higher order models and a unified computational optimization
approach based on primal-dual methods. Moreover, we extend the models by
Bregman iterations and provide an inverse problems perspective to the analysis
of variational optical flow models. A particular focus of the paper is the
quantitative evaluation of motion estimation, which is a difficult and often
underestimated task. We discuss several approaches for quality measures of
motion estimation and apply them to compare the previously discussed
regularization approaches.
Towards Dropout Training for Convolutional Neural Networks
Authors: Haibing Wu, Xiaodong Gu
Date: 2015-12-01 00:00:00
Tasks: Data Augmentation

Recently, dropout has seen increasing use in deep learning. For deep
convolutional neural networks, dropout is known to work well in fully-connected
layers. However, its effect in convolutional and pooling layers is still not
clear. This paper demonstrates that max-pooling dropout is equivalent to
randomly picking activation based on a multinomial distribution at training
time. In light of this insight, we advocate employing our proposed
probabilistic weighted pooling, instead of commonly used max-pooling, to act as
model averaging at test time. Empirical evidence validates the superiority of
probabilistic weighted pooling. We also empirically show that the effect of
convolutional dropout is not trivial, despite the dramatically reduced
possibility of over-fitting due to the convolutional architecture. Elaborately
designing dropout training simultaneously in max-pooling and fully-connected
layers, we achieve state-of-the-art performance on MNIST, and very competitive
results on CIFAR-10 and CIFAR-100, relative to other approaches without data
augmentation. Finally, we compare max-pooling dropout and stochastic pooling,
both of which introduce stochasticity based on multinomial distributions at
pooling stage.
Fast and High Quality Highlight Removal from A Single Image
Authors: Dongsheng An, Jinli Suo, Xiangyang Ji, Haoqian Wang, Qionghai Dai
Date: 2015-12-01 00:00:00
Tasks: Clustering, Diversity, highlight removal, Vocal Bursts Intensity Prediction

Specular reflection exists widely in photography and causes the recorded
color deviating from its true value, so fast and high quality highlight removal
from a single nature image is of great importance. In spite of the progress in
the past decades in highlight removal, achieving wide applicability to the
large diversity of nature scenes is quite challenging. To handle this problem,
we propose an analytic solution to highlight removal based on an L2
chromaticity definition and corresponding dichromatic model. Specifically, this
paper derives a normalized dichromatic model for the pixels with identical
diffuse color: a unit circle equation of projection coefficients in two
subspaces that are orthogonal to and parallel with the illumination,
respectively. In the former illumination orthogonal subspace, which is
specular-free, we can conduct robust clustering with an explicit criterion to
determine the cluster number adaptively. In the latter illumination parallel
subspace, a property called pure diffuse pixels distribution rule (PDDR) helps
map each specular-influenced pixel to its diffuse component. In terms of
efficiency, the proposed approach involves few complex calculation, and thus
can remove highlight from high resolution images fast. Experiments show that
this method is of superior performance in various challenging cases.
Distributionally Robust Logistic Regression
Authors: Soroosh Shafieezadeh-Abadeh, Peyman Mohajerin Esfahani, Daniel Kuhn
Date: 2015-09-30 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: regression

This paper proposes a distributionally robust approach to logistic
regression. We use the Wasserstein distance to construct a ball in the space of
probability distributions centered at the uniform distribution on the training
samples. If the radius of this ball is chosen judiciously, we can guarantee
that it contains the unknown data-generating distribution with high confidence.
We then formulate a distributionally robust logistic regression model that
minimizes a worst-case expected logloss function, where the worst case is taken
over all distributions in the Wasserstein ball. We prove that this optimization
problem admits a tractable reformulation and encapsulates the classical as well
as the popular regularized logistic regression problems as special cases. We
further propose a distributionally robust approach based on Wasserstein balls
to compute upper and lower confidence bounds on the misclassification
probability of the resulting classifier. These bounds are given by the optimal
values of two highly tractable linear programs. We validate our theoretical
out-of-sample guarantees through simulated and empirical experiments.
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
Authors: Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltan Szabo, Lars Buesing, Maneesh Sahani
Date: 2014-10-24 00:00:00
Proceeding: NeurIPS 2015 12

We introduce the Locally Linear Latent Variable Model (LL-LVM), a
probabilistic model for non-linear manifold discovery that describes a joint
distribution over observations, their manifold coordinates and locally linear
maps conditioned on a set of neighbourhood relationships. The model allows
straightforward variational optimisation of the posterior distribution on
coordinates and locally linear maps from the latent space to the observation
space given the data. Thus, the LL-LVM encapsulates the local-geometry
preserving intuitions that underlie non-probabilistic methods such as locally
linear embedding (LLE). Its probabilistic semantics make it easy to evaluate
the quality of hypothesised neighbourhood relationships, select the intrinsic
dimensionality of the manifold, construct out-of-sample extensions and to
combine the manifold model with additional probabilistic models that capture
the structure of coordinates within the manifold.