pwc-archive/papers-with-abstracts huggingface dataset:
Learning Efficient Representations for Reinforcement Learning
Authors: Yanping Huang
Date: 2015-08-28 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Markov decision processes (MDPs) are a well studied framework for solving
sequential decision making problems under uncertainty. Exact methods for
solving MDPs based on dynamic programming such as policy iteration and value
iteration are effective on small problems. In problems with a large discrete
state space or with continuous state spaces, a compact representation is
essential for providing an efficient approximation solutions to MDPs. Commonly
used approximation algorithms involving constructing basis functions for
projecting the value function onto a low dimensional subspace, and building a
factored or hierarchical graphical model to decompose the transition and reward
functions. However, hand-coding a good compact representation for a given
reinforcement learning (RL) task can be quite difficult and time consuming.
Recent approaches have attempted to automatically discover efficient
representations for RL.
  In this thesis proposal, we discuss the problems of automatically
constructing structured kernel for kernel based RL, a popular approach to
learning non-parametric approximations for value function. We explore a space
of kernel structures which are built compositionally from base kernels using a
context-free grammar. We examine a greedy algorithm for searching over the
structure space. To demonstrate how the learned structure can represent and
approximate the original RL problem in terms of compactness and efficiency, we
plan to evaluate our method on a synthetic problem and compare it to other RL
baselines.
Partitioning Large Scale Deep Belief Networks Using Dropout
Authors: Yanping Huang, Sai Zhang
Date: 2015-08-28 00:00:00
Tasks: BIG-bench Machine Learning, GPU, Object Recognition, speech-recognition, Speech Recognition

Deep learning methods have shown great promise in many practical
applications, ranging from speech recognition, visual object recognition, to
text processing. However, most of the current deep learning methods suffer from
scalability problems for large-scale applications, forcing researchers or users
to focus on small-scale problems with fewer parameters.
  In this paper, we consider a well-known machine learning model, deep belief
networks (DBNs) that have yielded impressive classification performance on a
large number of benchmark machine learning tasks. To scale up DBN, we propose
an approach that can use the computing clusters in a distributed environment to
train large models, while the dense matrix computations within a single machine
are sped up using graphics processors (GPU). When training a DBN, each machine
randomly drops out a portion of neurons in each hidden layer, for each training
case, making the remaining neurons only learn to detect features that are
generally helpful for producing the correct answer. Within our approach, we
have developed four methods to combine outcomes from each machine to form a
unified model. Our preliminary experiment on the mnst handwritten digit
database demonstrates that our approach outperforms the state of the art test
error rate.
Understanding confounding effects in linguistic coordination: an information-theoretic approach
Authors: Shuyang Gao, Greg Ver Steeg, Aram Galstyan
Date: 2014-12-01 00:00:00

We suggest an information-theoretic approach for measuring stylistic
coordination in dialogues. The proposed measure has a simple predictive
interpretation and can account for various confounding factors through proper
conditioning. We revisit some of the previous studies that reported strong
signatures of stylistic accommodation, and find that a significant part of the
observed coordination can be attributed to a simple confounding effect - length
coordination. Specifically, longer utterances tend to be followed by longer
responses, which gives rise to spurious correlations in the other stylistic
features. We propose a test to distinguish correlations in length due to
contextual factors (topic of conversation, user verbosity, etc.) and
turn-by-turn coordination. We also suggest a test to identify whether stylistic
coordination persists even after accounting for length coordination and
contextual factors.
Validation of neural spike sorting algorithms without ground-truth information
Authors: Alex H. Barnett, Jeremy F. Magland, Leslie F. Greengard
Date: 2015-08-27 00:00:00
Tasks: Benchmarking, Spike Sorting

We describe a suite of validation metrics that assess the credibility of a
given automatic spike sorting algorithm applied to a given electrophysiological
recording, when ground-truth is unavailable. By rerunning the spike sorter two
or more times, the metrics measure stability under various perturbations
consistent with variations in the data itself, making no assumptions about the
noise model, nor about the internal workings of the sorting algorithm. Such
stability is a prerequisite for reproducibility of results. We illustrate the
metrics on standard sorting algorithms for both in vivo and ex vivo recordings.
We believe that such metrics could reduce the significant human labor currently
spent on validation, and should form an essential part of large-scale automated
spike sorting and systematic benchmarking of algorithms.
Compressive Sensing via Low-Rank Gaussian Mixture Models
Authors: Xin Yuan, Hong Jiang, Gang Huang, Paul A. Wilford
Date: 2015-08-27 00:00:00
Tasks: Compressive Sensing

We develop a new compressive sensing (CS) inversion algorithm by utilizing
the Gaussian mixture model (GMM). While the compressive sensing is performed
globally on the entire image as implemented in our lensless camera, a low-rank
GMM is imposed on the local image patches. This low-rank GMM is derived via
eigenvalue thresholding of the GMM trained on the projection of the measurement
data, thus learned {\em in situ}. The GMM and the projection of the measurement
data are updated iteratively during the reconstruction. Our GMM algorithm
degrades to the piecewise linear estimator (PLE) if each patch is represented
by a single Gaussian model. Inspired by this, a low-rank PLE algorithm is also
developed for CS inversion, constituting an additional contribution of this
paper. Extensive results on both simulation data and real data captured by the
lensless camera demonstrate the efficacy of the proposed algorithm.
Furthermore, we compare the CS reconstruction results using our algorithm with
the JPEG compression. Simulation results demonstrate that when limited
bandwidth is available (a small number of measurements), our algorithm can
achieve comparable results as JPEG.
Shopper Analytics: a customer activity recognition system using a distributed RGB-D camera network
Authors: Daniele Liciotti, Marco Contigiani, Emanuele Frontoni, Adriano Mancini, Primo Zingaretti, Valerio Placidi
Date: 2015-08-27 00:00:00
Tasks: Activity Recognition

The aim of this paper is to present an integrated system consisted of a RGB-D
camera and a software able to monitor shoppers in intelligent retail
environments. We propose an innovative low cost smart system that can
understand the shoppers' behavior and, in particular, their interactions with
the products in the shelves, with the aim to develop an automatic RGB-D
technique for video analysis. The system of cameras detects the presence of
people and univocally identifies them. Through the depth frames, the system
detects the interactions of the shoppers with the products on the shelf and
determines if a product is picked up or if the product is taken and then put
back and finally, if there is not contact with the products. The system is low
cost and easy to install, and experimental results demonstrated that its
performances are satisfactory also in real environments.
Encrypted statistical machine learning: new privacy preserving methods
Authors: Louis J. M. Aslett, Pedro M. Esperança, Chris C. Holmes
Date: 2015-08-27 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Privacy Preserving

We present two new statistical machine learning methods designed to learn on
fully homomorphic encrypted (FHE) data. The introduction of FHE schemes
following Gentry (2009) opens up the prospect of privacy preserving statistical
machine learning analysis and modelling of encrypted data without compromising
security constraints. We propose tailored algorithms for applying extremely
random forests, involving a new cryptographic stochastic fraction estimator,
and na\"{i}ve Bayes, involving a semi-parametric model for the class decision
boundary, and show how they can be used to learn and predict from encrypted
data. We demonstrate that these techniques perform competitively on a variety
of classification data sets and provide detailed information about the
computational practicalities of these and other FHE methods.
Introducing Elitist Black-Box Models: When Does Elitist Selection Weaken the Performance of Evolutionary Algorithms?
Authors: Carola Doerr, Johannes Lengler
Date: 2015-08-27 00:00:00
Tasks: Evolutionary Algorithms

Black-box complexity theory provides lower bounds for the runtime of
black-box optimizers like evolutionary algorithms and serves as an inspiration
for the design of new genetic algorithms. Several black-box models covering
different classes of algorithms exist, each highlighting a different aspect of
the algorithms under considerations. In this work we add to the existing
black-box notions a new \emph{elitist black-box model}, in which algorithms are
required to base all decisions solely on (a fixed number of) the best search
points sampled so far. Our model combines features of the ranking-based and the
memory-restricted black-box models with elitist selection.
  We provide several examples for which the elitist black-box complexity is
exponentially larger than that the respective complexities in all previous
black-box models, thus showing that the elitist black-box complexity can be
much closer to the runtime of typical evolutionary algorithms.
  We also introduce the concept of $p$-Monte Carlo black-box complexity, which
measures the time it takes to optimize a problem with failure probability at
most $p$. Even for small~$p$, the $p$-Monte Carlo black-box complexity of a
function class $\mathcal F$ can be smaller by an exponential factor than its
typically regarded Las Vegas complexity (which measures the \emph{expected}
time it takes to optimize $\mathcal F$).
Computing Stable Coalitions: Approximation Algorithms for Reward Sharing
Authors: Elliot Anshelevich, Shreyas Sekar
Date: 2015-08-27 00:00:00

Consider a setting where selfish agents are to be assigned to coalitions or
projects from a fixed set P. Each project k is characterized by a valuation
function; v_k(S) is the value generated by a set S of agents working on project
k. We study the following classic problem in this setting: "how should the
agents divide the value that they collectively create?". One traditional
approach in cooperative game theory is to study core stability with the
implicit assumption that there are infinite copies of one project, and agents
can partition themselves into any number of coalitions. In contrast, we
consider a model with a finite number of non-identical projects; this makes
computing both high-welfare solutions and core payments highly non-trivial.
  The main contribution of this paper is a black-box mechanism that reduces the
problem of computing a near-optimal core stable solution to the purely
algorithmic problem of welfare maximization; we apply this to compute an
approximately core stable solution that extracts one-fourth of the optimal
social welfare for the class of subadditive valuations. We also show much
stronger results for several popular sub-classes: anonymous, fractionally
subadditive, and submodular valuations, as well as provide new approximation
algorithms for welfare maximization with anonymous functions. Finally, we
establish a connection between our setting and the well-studied simultaneous
auctions with item bidding; we adapt our results to compute approximate pure
Nash equilibria for these auctions.
Nested Hierarchical Dirichlet Processes for Multi-Level Non-Parametric Admixture Modeling
Authors: Lavanya Sita Tekumalla, Priyanka Agrawal, Indrajit Bhattacharya
Date: 2015-08-26 00:00:00
Tasks: Clustering

Dirichlet Process(DP) is a Bayesian non-parametric prior for infinite mixture
modeling, where the number of mixture components grows with the number of data
items. The Hierarchical Dirichlet Process (HDP), is an extension of DP for
grouped data, often used for non-parametric topic modeling, where each group is
a mixture over shared mixture densities. The Nested Dirichlet Process (nDP), on
the other hand, is an extension of the DP for learning group level
distributions from data, simultaneously clustering the groups. It allows group
level distributions to be shared across groups in a non-parametric setting,
leading to a non-parametric mixture of mixtures. The nCRF extends the nDP for
multilevel non-parametric mixture modeling, enabling modeling topic
hierarchies. However, the nDP and nCRF do not allow sharing of distributions as
required in many applications, motivating the need for multi-level
non-parametric admixture modeling. We address this gap by proposing multi-level
nested HDPs (nHDP) where the base distribution of the HDP is itself a HDP at
each level thereby leading to admixtures of admixtures at each level. Because
of couplings between various HDP levels, scaling up is naturally a challenge
during inference. We propose a multi-level nested Chinese Restaurant Franchise
(nCRF) representation for the nested HDP, with which we outline an inference
algorithm based on Gibbs Sampling. We evaluate our model with the two level
nHDP for non-parametric entity topic modeling where an inner HDP creates a
countably infinite set of topic mixtures and associates them with author
entities, while an outer HDP associates documents with these author entities.
In our experiments on two real world research corpora, the nHDP is able to
generalize significantly better than existing models and detect missing author
entities with a reasonable level of accuracy.
A biologically constrained model of the whole basal ganglia addressing the paradoxes of connections and selection
Authors: Jean Liénard, Benoît Girard
Date: 2015-08-27 00:00:00

The basal ganglia nuclei form a complex network of nuclei often assumed to
perform selection, yet their individual roles and how they influence each other
is still largely unclear. In particular, the ties between the external and
internal parts of the globus pallidus are paradoxical, as anatomical data
suggest a potent inhibitory projection between them while electrophys-iological
recordings indicate that they have similar activities. Here we introduce a
theoretical study that reconciles both views on the intra-pallidal projection,
by providing a plausible characterization of the relationship between the
external and internal globus pallidus. Specifically, we developed a mean-field
model of the whole basal ganglia, whose parameterization is optimized to
respect best a collection of numerous anatomical and electrophysiological data.
We first obtained models respecting all our constraints, hence anatomical and
electrophysiological data on the intrapallidal projection are globally
consistent. This model furthermore predicts that both aforementioned views
about the intra-pallidal projection may be reconciled when this projection is
weakly inhibitory, thus making it possible to support similar neural activity
in both nuclei and for the entire basal ganglia to select between actions.
Second, we predicts that afferent projections are substantially unbalanced
towards the external segment, as it receives the strongest excitation from STN
and the weakest inhibition from the striatum. Finally, our study strongly
suggest that the intrapallidal connection pattern is not focused but diffuse,
as this latter pattern is more efficient for the overall selection performed in
the basal ganglia.
A fully data-driven method to identify (correlated) changes in diachronic corpora
Authors: Alexander Koplenig
Date: 2015-08-26 00:00:00
Tasks: POS, POS Tagging

In this paper, a method for measuring synchronic corpus (dis-)similarity put
forward by Kilgarriff (2001) is adapted and extended to identify trends and
correlated changes in diachronic text data, using the Corpus of Historical
American English (Davies 2010a) and the Google Ngram Corpora (Michel et al.
2010a). This paper shows that this fully data-driven method, which extracts
word types that have undergone the most pronounced change in frequency in a
given period of time, is computationally very cheap and that it allows
interpretations of diachronic trends that are both intuitively plausible and
motivated from the perspective of information theory. Furthermore, it
demonstrates that the method is able to identify correlated linguistic changes
and diachronic shifts that can be linked to historical events. Finally, it can
help to improve diachronic POS tagging and complement existing NLP approaches.
This indicates that the approach can facilitate an improved understanding of
diachronic processes in language change.
A Comparative Analysis of Retrieval Techniques In Content Based Image Retrieval
Authors: Mohini P. Sardey, G. K. Kharate
Date: 2015-08-27 00:00:00
Tasks: Content-Based Image Retrieval, Edge Detection, Image Retrieval, Retrieval

Basic group of visual techniques such as color, shape, texture are used in
Content Based Image Retrievals (CBIR) to retrieve query image or subregion of
image to find similar images in image database. To improve query result,
relevance feedback is used many times in CBIR to help user to express their
preference and improve query results.In this paper, a new approach for image
retrieval is proposed which is based on the features such as Color Histogram,
Eigen Values and Match Point. Images from various types of database are first
identified by using edge detection techniques.Once the image is identified,
then the image is searched in the particular database, then all related images
are displayed. This will save the retrieval time. Further to retrieve the
precise query image, any of the three techniques are used and comparison is
done w.r.t. average retrieval time. Eigen value technique found to be the best
as compared with other two techniques
Image Type Water Meter Character Recognition Based on Embedded DSP
Authors: Ying Liu, Yan-bin Han, Yu-lin Zhang
Date: 2015-08-27 00:00:00
Tasks: Meter Reading, Vocal Bursts Type Prediction

In the paper, we combined DSP processor with image processing algorithm and
studied the method of water meter character recognition. We collected water
meter image through camera at a fixed angle, and the projection method is used
to recognize those digital images. The experiment results show that the method
can recognize the meter characters accurately and artificial meter reading is
replaced by automatic digital recognition, which improves working efficiency.
Online Anomaly Detection via Class-Imbalance Learning
Authors: Chandresh Kumar Maurya, Durga Toshniwal, Gopalan Vijendran Venkoparao
Date: 2015-08-27 00:00:00
Tasks: Action Detection, Activity Detection, Anomaly Detection, Fraud Detection

Anomaly detection is an important task in many real world applications such
as fraud detection, suspicious activity detection, health care monitoring etc.
In this paper, we tackle this problem from supervised learning perspective in
online learning setting. We maximize well known \emph{Gmean} metric for
class-imbalance learning in online learning framework. Specifically, we show
that maximizing \emph{Gmean} is equivalent to minimizing a convex surrogate
loss function and based on that we propose novel online learning algorithm for
anomaly detection. We then show, by extensive experiments, that the performance
of the proposed algorithm with respect to $sum$ metric is as good as a recently
proposed Cost-Sensitive Online Classification(CSOC) algorithm for
class-imbalance learning over various benchmarked data sets while keeping
running time close to the perception algorithm. Our another conclusion is that
other competitive online algorithms do not perform consistently over data sets
of varying size. This shows the potential applicability of our proposed
approach.
Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation
Authors: Sijin Li, Weichen Zhang, Antoni B. Chan
Date: 2015-08-27 00:00:00
Proceeding: ICCV 2015 12
Tasks: 3D Human Pose Estimation, Pose Estimation

This paper focuses on structured-output learning using deep neural networks
for 3D human pose estimation from monocular images. Our network takes an image
and 3D pose as inputs and outputs a score value, which is high when the
image-pose pair matches and low otherwise. The network structure consists of a
convolutional neural network for image feature extraction, followed by two
sub-networks for transforming the image features and pose into a joint
embedding. The score function is then the dot-product between the image and
pose embeddings. The image-pose embedding and score function are jointly
trained using a maximum-margin cost function. Our proposed framework can be
interpreted as a special form of structured support vector machines where the
joint feature space is discriminatively learned using deep neural networks. We
test our framework on the Human3.6m dataset and obtain state-of-the-art results
compared to other recent methods. Finally, we present visualizations of the
image-pose embedding space, demonstrating the network has learned a high-level
embedding of body-orientation and pose-configuration.
Using Genetic Algorithms to Benchmark the Cloud
Authors: Jeff Kinnison, Sekou L. Remy
Date: 2015-08-27 00:00:00

This paper presents a novel application of Genetic Algorithms(GAs) to
quantify the performance of Platform as a Service (PaaS), a cloud service model
that plays a critical role in both industry and academia. While Cloud
benchmarks are not new, in this novel concept, the authors use a GA to take
advantage of the elasticity in Cloud services in a graceful manner that was not
previously possible. Using Google App Engine, Heroku, and Python Anywhere with
three distinct classes of client computers running our GA codebase, we
quantified the completion time for application of the GA to search for the
parameters of controllers for dynamical systems. Our results show statistically
significant differences in PaaS performance by vendor, and also that the
performance of the PaaS performance is dependent upon the client that uses it.
Results also show the effectiveness of our GA in determining the level of
service of PaaS providers, and for determining if the level of service of one
PaaS vendor is repeatable with another. Such a concept could then increase the
appeal of PaaS Cloud services by making them more financially appealing.
Component-Enhanced Chinese Character Embeddings
Authors: Yan-ran Li, Wenjie Li, Fei Sun, Sujian Li
Date: 2015-08-26 00:00:00
Proceeding: EMNLP 2015 9
Tasks: General Classification, text-classification, Text Classification, Word Embeddings, Word Similarity

Distributed word representations are very useful for capturing semantic
information and have been successfully applied in a variety of NLP tasks,
especially on English. In this work, we innovatively develop two
component-enhanced Chinese character embedding models and their bigram
extensions. Distinguished from English word embeddings, our models explore the
compositions of Chinese characters, which often serve as semantic indictors
inherently. The evaluations on both word similarity and text classification
demonstrate the effectiveness of our models.