pwc-archive/papers-with-abstracts huggingface dataset:
Attend in groups: a weakly-supervised deep learning framework for learning from web data
Authors: Bohan Zhuang, Lingqiao Liu, Yao Li, Chunhua Shen, Ian Reid
Date: 2016-11-30 00:00:00
Proceeding: CVPR 2017 7

Large-scale datasets have driven the rapid development of deep neural
networks for visual recognition. However, annotating a massive dataset is
expensive and time-consuming. Web images and their labels are, in comparison,
much easier to obtain, but direct training on such automatically harvested
images can lead to unsatisfactory performance, because the noisy labels of Web
images adversely affect the learned recognition models. To address this
drawback we propose an end-to-end weakly-supervised deep learning framework
which is robust to the label noise in Web images. The proposed framework relies
on two unified strategies -- random grouping and attention -- to effectively
reduce the negative impact of noisy web image annotations. Specifically, random
grouping stacks multiple images into a single training instance and thus
increases the labeling accuracy at the instance level. Attention, on the other
hand, suppresses the noisy signals from both incorrectly labeled images and
less discriminative image regions. By conducting intensive experiments on two
challenging datasets, including a newly collected fine-grained dataset with Web
images of different car models, the superior performance of the proposed
methods over competitive baselines is clearly demonstrated.
Efficient Likelihood Bayesian Constrained Local Model
Authors: Hailiang Li, Kin-Man Lam, Man-Yau Chiu, Kangheng Wu, Zhibin Lei
Date: 2016-11-30 00:00:00
Tasks: Bayesian Inference, Face Alignment, global-optimization, model

The constrained local model (CLM) proposes a paradigm that the locations of a
set of local landmark detectors are constrained to lie in a subspace, spanned
by a shape point distribution model (PDM). Fitting the model to an object
involves two steps. A response map, which represents the likelihood of the
location of a landmark, is first computed for each landmark using local-texture
detectors. Then, an optimal PDM is determined by jointly maximizing all the
response maps simultaneously, with a global shape constraint. This global
optimization can be considered as a Bayesian inference problem, where the
posterior distribution of the shape parameters, as well as the pose parameters,
can be inferred using maximum a posteriori (MAP). In this paper, we present a
cascaded face-alignment approach, which employs random-forest regressors to
estimate the positions of each landmark, as a likelihood term, efficiently in
the CLM model. Interpretation from CLM framework, this algorithm is named as an
efficient likelihood Bayesian constrained local model (elBCLM). Furthermore, in
each stage of the regressors, the PDM non-rigid parameters of previous stage
can work as shape clues for training each stage regressors. Experimental
results on benchmarks show our approach achieve about 3 to 5 times speed-up
compared with CLM models and improve around 10% on fitting quality compare with
the same setting regression models.
On Learning High Dimensional Structured Single Index Models
Authors: Nikhil Rao, Ravi Ganti, Laura Balzano, Rebecca Willett, Robert Nowak
Date: 2016-03-13 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Single Index Models (SIMs) are simple yet flexible semi-parametric models for
machine learning, where the response variable is modeled as a monotonic
function of a linear combination of features. Estimation in this context
requires learning both the feature weights and the nonlinear function that
relates features to observations. While methods have been described to learn
SIMs in the low dimensional regime, a method that can efficiently learn SIMs in
high dimensions, and under general structural assumptions, has not been
forthcoming. In this paper, we propose computationally efficient algorithms for
SIM inference in high dimensions with structural constraints. Our general
approach specializes to sparsity, group sparsity, and low-rank assumptions
among others. Experiments show that the proposed method enjoys superior
predictive performance when compared to generalized linear models, and achieves
results comparable to or better than single layer feedforward neural networks
with significantly less computational cost.
Choquet integral in decision analysis - lessons from the axiomatization
Authors: Mikhail Timonin
Date: 2016-11-29 00:00:00

The Choquet integral is a powerful aggregation operator which lists many
well-known models as its special cases. We look at these special cases and
provide their axiomatic analysis. In cases where an axiomatization has been
previously given in the literature, we connect the existing results with the
framework that we have developed. Next we turn to the question of learning,
which is especially important for the practical applications of the model. So
far, learning of the Choquet integral has been mostly confined to the learning
of the capacity. Such an approach requires making a powerful assumption that
all dimensions (e.g. criteria) are evaluated on the same scale, which is rarely
justified in practice. Too often categorical data is given arbitrary numerical
labels (e.g. AHP), and numerical data is considered cardinally and ordinally
commensurate, sometimes after a simple normalization. Such approaches clearly
lack scientific rigour, and yet they are commonly seen in all kinds of
applications. We discuss the pros and cons of making such an assumption and
look at the consequences which axiomatization uniqueness results have for the
learning problems. Finally, we review some of the applications of the Choquet
integral in decision analysis. Apart from MCDA, which is the main area of
interest for our results, we also discuss how the model can be interpreted in
the social choice context. We look in detail at the state-dependent utility,
and show how comonotonicity, central to the previous axiomatizations, actually
implies state-independency in the Choquet integral model. We also discuss the
conditions required to have a meaningful state-dependent utility representation
and show the novelty of our results compared to the previous methods of
building state-dependent models.
Blocking and Other Enhancements for Bottom-Up Model Generation Methods
Authors: Peter Baumgartner, Renate A. Schmidt
Date: 2016-11-28 00:00:00
Tasks: Automated Theorem Proving, Blocking

Model generation is a problem complementary to theorem proving and is
important for fault analysis and debugging of formal specifications of security
protocols, programs and terminological definitions. This paper discusses
several ways of enhancing the paradigm of bottom-up model generation. The two
main contributions are new, generalized blocking techniques and a new
range-restriction transformation. The blocking techniques are based on simple
transformations of the input set together with standard equality reasoning and
redundancy elimination techniques. These provide general methods for finding
small, finite models. The range-restriction transformation refines existing
transformations to range-restricted clauses by carefully limiting the creation
of domain terms. All possible combinations of the introduced techniques and
classical range-restriction were tested on the clausal problems of the TPTP
Version 6.0.0 with an implementation based on the SPASS theorem prover using a
hyperresolution-like refinement. Unrestricted domain blocking gave best results
for satisfiable problems showing it is a powerful technique indispensable for
bottom-up model generation methods. Both in combination with the new
range-restricting transformation, and the classical range-restricting
transformation, good results have been obtained. Limiting the creation of terms
during the inference process by using the new range restricting transformation
has paid off, especially when using it together with a shifting transformation.
The experimental results also show that classical range restriction with
unrestricted blocking provides a useful complementary method. Overall, the
results showed bottom-up model generation methods were good for disproving
theorems and generating models for satisfiable problems, but less efficient
than SPASS in auto mode for unsatisfiable problems.
C-RNN-GAN: Continuous recurrent neural networks with adversarial training
Authors: Olof Mogren
Date: 2016-11-29 00:00:00

Generative adversarial networks have been proposed as a way of efficiently
training deep generative neural networks. We propose a generative adversarial
model that works on continuous sequential data, and apply it by training it on
a collection of classical music. We conclude that it generates music that
sounds better and better as the model is trained, report statistics on
generated music, and let the reader judge the quality by downloading the
generated songs.
Context-aware Natural Language Generation with Recurrent Neural Networks
Authors: Jian Tang, Yifan Yang, Sam Carton, Ming Zhang, Qiaozhu Mei
Date: 2016-11-29 00:00:00
Tasks: Text Generation

This paper studied generating natural languages at particular contexts or
situations. We proposed two novel approaches which encode the contexts into a
continuous semantic representation and then decode the semantic representation
into text sequences with recurrent neural networks. During decoding, the
context information are attended through a gating mechanism, addressing the
problem of long-range dependency caused by lengthy sequences. We evaluate the
effectiveness of the proposed approaches on user review data, in which rich
contexts are available and two informative contexts, sentiments and products,
are selected for evaluation. Experiments show that the fake reviews generated
by our approaches are very natural. Results of fake review detection with human
judges show that more than 50\% of the fake reviews are misclassified as the
real reviews, and more than 90\% are misclassified by existing state-of-the-art
fake review detection algorithm.
Autism Spectrum Disorder Classification using Graph Kernels on Multidimensional Time Series
Authors: Rushil Anirudh, Jayaraman J. Thiagarajan, Irene Kim, Wolfgang Polonik
Date: 2016-11-29 00:00:00
Tasks: General Classification, Time Series, Time Series Analysis

We present an approach to model time series data from resting state fMRI for
autism spectrum disorder (ASD) severity classification. We propose to adopt
kernel machines and employ graph kernels that define a kernel dot product
between two graphs. This enables us to take advantage of spatio-temporal
information to capture the dynamics of the brain network, as opposed to
aggregating them in the spatial or temporal dimension. In addition to the
conventional similarity graphs, we explore the use of L1 graph using sparse
coding, and the persistent homology of time delay embeddings, in the proposed
pipeline for ASD classification. In our experiments on two datasets from the
ABIDE collection, we demonstrate a consistent and significant advantage in
using graph kernels over traditional linear or non linear kernels for a variety
of time series features.
Exploration for Multi-task Reinforcement Learning with Deep Generative Models
Authors: Sai Praveen Bangaru, JS Suhas, Balaraman Ravindran
Date: 2016-11-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Thompson Sampling

Exploration in multi-task reinforcement learning is critical in training
agents to deduce the underlying MDP. Many of the existing exploration
frameworks such as $E^3$, $R_{max}$, Thompson sampling assume a single
stationary MDP and are not suitable for system identification in the multi-task
setting. We present a novel method to facilitate exploration in multi-task
reinforcement learning using deep generative models. We supplement our method
with a low dimensional energy model to learn the underlying MDP distribution
and provide a resilient and adaptive exploration signal to the agent. We
evaluate our method on a new set of environments and provide intuitive
interpretation of our results.
Solving Marginal MAP Problems with NP Oracles and Parity Constraints
Authors: Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P. Gomes, Bart Selman
Date: 2016-10-08 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: BIG-bench Machine Learning, Decision Making

Arising from many applications at the intersection of decision making and
machine learning, Marginal Maximum A Posteriori (Marginal MAP) Problems unify
the two main classes of inference, namely maximization (optimization) and
marginal inference (counting), and are believed to have higher complexity than
both of them. We propose XOR_MMAP, a novel approach to solve the Marginal MAP
Problem, which represents the intractable counting subproblem with queries to
NP oracles, subject to additional parity constraints. XOR_MMAP provides a
constant factor approximation to the Marginal MAP Problem, by encoding it as a
single optimization in polynomial size of the original problem. We evaluate our
approach in several machine learning and decision making applications, and show
that our approach outperforms several state-of-the-art Marginal MAP solvers.
Identity-sensitive Word Embedding through Heterogeneous Networks
Authors: Jian Tang, Meng Qu, Qiaozhu Mei
Date: 2016-11-29 00:00:00
Tasks: Network Embedding, text-classification, Text Classification, Word Embeddings, Word Similarity

Most existing word embedding approaches do not distinguish the same words in
different contexts, therefore ignoring their contextual meanings. As a result,
the learned embeddings of these words are usually a mixture of multiple
meanings. In this paper, we acknowledge multiple identities of the same word in
different contexts and learn the \textbf{identity-sensitive} word embeddings.
Based on an identity-labeled text corpora, a heterogeneous network of words and
word identities is constructed to model different-levels of word
co-occurrences. The heterogeneous network is further embedded into a
low-dimensional space through a principled network embedding approach, through
which we are able to obtain the embeddings of words and the embeddings of word
identities. We study three different types of word identities including topics,
sentiments and categories. Experimental results on real-world data sets show
that the identity-sensitive word embeddings learned by our approach indeed
capture different meanings of words and outperforms competitive methods on
tasks including text classification and word similarity computation.
Multi-objective Active Control Policy Design for Commensurate and Incommensurate Fractional Order Chaotic Financial Systems
Authors: Indranil Pan, Saptarshi Das, Shantanu Das
Date: 2016-11-29 00:00:00

In this paper, an active control policy design for a fractional order (FO)
financial system is attempted, considering multiple conflicting objectives. An
active control template as a nonlinear state feedback mechanism is developed
and the controller gains are chosen within a multi-objective optimization (MOO)
framework to satisfy the conditions of asymptotic stability, derived
analytically. The MOO gives a set of solutions on the Pareto optimal front for
the multiple conflicting objectives that are considered. It is shown that there
is a trade-off between the multiple design objectives and a better performance
in one objective can only be obtained at the cost of performance deterioration
in the other objectives. The multi-objective controller design has been
compared using three different MOO techniques viz. Non Dominated Sorting
Genetic Algorithm-II (NSGA-II), epsilon variable Multi-Objective Genetic
Algorithm (ev-MOGA), and Multi Objective Evolutionary Algorithm with
Decomposition (MOEA/D). The robustness of the same control policy designed with
the nominal system settings have been investigated also for gradual decrease in
the commensurate and incommensurate fractional orders of the financial system.
Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text
Authors: Subhashini Venugopalan, Lisa Anne Hendricks, Raymond Mooney, Kate Saenko
Date: 2016-04-06 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Descriptive, Language Modeling, Language Modelling, Video Description

This paper investigates how linguistic knowledge mined from large text
corpora can aid the generation of natural language descriptions of videos.
Specifically, we integrate both a neural language model and distributional
semantics trained on large text corpora into a recent LSTM-based architecture
for video description. We evaluate our approach on a collection of Youtube
videos as well as two large movie description datasets showing significant
improvements in grammaticality while modestly improving descriptive quality.
Exploring Strategies for Classification of External Stimuli Using Statistical Features of the Plant Electrical Response
Authors: Shre Kumar Chatterjee, Saptarshi Das, Koushik Maharatna, Elisa Masi, Luisa Santopolo, Stefano Mancuso, Andrea Vitaletti
Date: 2016-11-29 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis

Plants sense their environment by producing electrical signals which in
essence represent changes in underlying physiological processes. These
electrical signals, when monitored, show both stochastic and deterministic
dynamics. In this paper, we compute 11 statistical features from the raw
non-stationary plant electrical signal time series to classify the stimulus
applied (causing the electrical signal). By using different discriminant
analysis based classification techniques, we successfully establish that there
is enough information in the raw electrical signal to classify the stimuli. In
the process, we also propose two standard features which consistently give good
classification results for three types of stimuli - Sodium Chloride (NaCl),
Sulphuric Acid (H2SO4) and Ozone (O3). This may facilitate reduction in the
complexity involved in computing all the features for online classification of
similar external stimuli in future.
Measuring and modeling the perception of natural and unconstrained gaze in humans and machines
Authors: Daniel Harari, Tao Gao, Nancy Kanwisher, Joshua Tenenbaum, Shimon Ullman
Date: 2016-11-29 00:00:00

Humans are remarkably adept at interpreting the gaze direction of other
individuals in their surroundings. This skill is at the core of the ability to
engage in joint visual attention, which is essential for establishing social
interactions. How accurate are humans in determining the gaze direction of
others in lifelike scenes, when they can move their heads and eyes freely, and
what are the sources of information for the underlying perceptual processes?
These questions pose a challenge from both empirical and computational
perspectives, due to the complexity of the visual input in real-life
situations. Here we measure empirically human accuracy in perceiving the gaze
direction of others in lifelike scenes, and study computationally the sources
of information and representations underlying this cognitive capacity. We show
that humans perform better in face-to-face conditions compared with recorded
conditions, and that this advantage is not due to the availability of input
dynamics. We further show that humans are still performing well when only the
eyes-region is visible, rather than the whole face. We develop a computational
model, which replicates the pattern of human performance, including the finding
that the eyes-region contains on its own, the required information for
estimating both head orientation and direction of gaze. Consistent with
neurophysiological findings on task-specific face regions in the brain, the
learned computational representations reproduce perceptual effects such as the
Wollaston illusion, when trained to estimate direction of gaze, but not when
trained to recognize objects or faces.
3D Ultrasound image segmentation: A Survey
Authors: Mohammad Hamed Mozaffari, Won-Sook Lee
Date: 2016-11-29 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Survey

Three-dimensional Ultrasound image segmentation methods are surveyed in this
paper. The focus of this report is to investigate applications of these
techniques and a review of the original ideas and concepts. Although many
two-dimensional image segmentation in the literature have been considered as a
three-dimensional approach by mistake but we review them as a three-dimensional
technique. We select the studies that have addressed the problem of medical
three-dimensional Ultrasound image segmentation utilizing their proposed
techniques. The evaluation methods and comparison between them are presented
and tabulated in terms of evaluation techniques, interactivity, and robustness.
Fractional Order Fuzzy Control of Hybrid Power System with Renewable Generation Using Chaotic PSO
Authors: Indranil Pan, Saptarshi Das
Date: 2016-11-29 00:00:00

This paper investigates the operation of a hybrid power system through a
novel fuzzy control scheme. The hybrid power system employs various autonomous
generation systems like wind turbine, solar photovoltaic, diesel engine,
fuel-cell, aqua electrolyzer etc. Other energy storage devices like the
battery, flywheel and ultra-capacitor are also present in the network. A novel
fractional order (FO) fuzzy control scheme is employed and its parameters are
tuned with a particle swarm optimization (PSO) algorithm augmented with two
chaotic maps for achieving an improved performance. This FO fuzzy controller
shows better performance over the classical PID, and the integer order fuzzy
PID controller in both linear and nonlinear operating regimes. The FO fuzzy
controller also shows stronger robustness properties against system parameter
variation and rate constraint nonlinearity, than that with the other controller
structures. The robustness is a highly desirable property in such a scenario
since many components of the hybrid power system may be switched on/off or may
run at lower/higher power output, at different time instants.
Fractional Order Load-Frequency Control of Interconnected Power Systems Using Chaotic Multi-objective Optimization
Authors: Indranil Pan, Saptarshi Das
Date: 2016-11-29 00:00:00

Fractional order proportional-integral-derivative (FOPID) controllers are
designed for load frequency control (LFC) of two interconnected power systems.
Conflicting time domain design objectives are considered in a multi objective
optimization (MOO) based design framework to design the gains and the
fractional differ-integral orders of the FOPID controllers in the two areas.
Here, we explore the effect of augmenting two different chaotic maps along with
the uniform random number generator (RNG) in the popular MOO algorithm - the
Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Different measures of
quality for MOO e.g. hypervolume indicator, moment of inertia based diversity
metric, total Pareto spread, spacing metric are adopted to select the best set
of controller parameters from multiple runs of all the NSGA-II variants (i.e.
nominal and chaotic versions). The chaotic versions of the NSGA-II algorithm
are compared with the standard NSGA-II in terms of solution quality and
computational time. In addition, the Pareto optimal fronts showing the
trade-off between the two conflicting time domain design objectives are
compared to show the advantage of using the FOPID controller over that with
simple PID controller. The nature of fast/slow and high/low noise amplification
effects of the FOPID structure or the four quadrant operation in the two
inter-connected areas of the power system is also explored. A fuzzy logic based
method has been adopted next to select the best compromise solution from the
best Pareto fronts corresponding to each MOO comparison criteria. The time
domain system responses are shown for the fuzzy best compromise solutions under
nominal operating conditions. Comparative analysis on the merits and de-merits
of each controller structure is reported then. A robustness analysis is also
done for the PID and the FOPID controllers.