pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive matching pursuit for sparse signal recovery
Authors: Tiep H. Vu, Hojjat S. Mousavi, Vishal Monga
Date: 2016-09-12 00:00:00
Tasks: Bayesian Inference

Spike and Slab priors have been of much recent interest in signal processing
as a means of inducing sparsity in Bayesian inference. Applications domains
that benefit from the use of these priors include sparse recovery, regression
and classification. It is well-known that solving for the sparse coefficient
vector to maximize these priors results in a hard non-convex and mixed integer
programming problem. Most existing solutions to this optimization problem
either involve simplifying assumptions/relaxations or are computationally
expensive. We propose a new greedy and adaptive matching pursuit (AMP)
algorithm to directly solve this hard problem. Essentially, in each step of the
algorithm, the set of active elements would be updated by either adding or
removing one index, whichever results in better improvement. In addition, the
intermediate steps of the algorithm are calculated via an inexpensive Cholesky
decomposition which makes the algorithm much faster. Results on simulated data
sets as well as real-world image recovery challenges confirm the benefits of
the proposed AMP, particularly in providing a superior cost-quality trade-off
over existing alternatives.
Hyperspectral Unmixing with Endmember Variability using Partial Membership Latent Dirichlet Allocation
Authors: Sheng Zou, Alina Zare
Date: 2016-09-12 00:00:00
Tasks: Hyperspectral image analysis, Hyperspectral Unmixing

The application of Partial Membership Latent Dirichlet Allocation(PM-LDA) for
hyperspectral endmember estimation and spectral unmixing is presented. PM-LDA
provides a model for a hyperspectral image analysis that accounts for spectral
variability and incorporates spatial information through the use of
superpixel-based 'documents.' In our application of PM-LDA, we employ the
Normal Compositional Model in which endmembers are represented as Normal
distributions to account for spectral variability and proportion vectors are
modeled as random variables governed by a Dirichlet distribution. The use of
the Dirichlet distribution enforces positivity and sum-to-one constraints on
the proportion values. Algorithm results on real hyperspectral data indicate
that PM-LDA produces endmember distributions that represent the ground truth
classes and their associated variability.
Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction
Authors: Ritambhara Singh, Jack Lanchantin, Gabriel Robins, Yanjun Qi
Date: 2016-09-12 00:00:00
Tasks: Transfer Learning

Through sequence-based classification, this paper tries to accurately predict
the DNA binding sites of transcription factors (TFs) in an unannotated cellular
context. Related methods in the literature fail to perform such predictions
accurately, since they do not consider sample distribution shift of sequence
segments from an annotated (source) context to an unannotated (target) context.
We, therefore, propose a method called "Transfer String Kernel" (TSK) that
achieves improved prediction of transcription factor binding site (TFBS) using
knowledge transfer via cross-context sample adaptation. TSK maps sequence
segments to a high-dimensional feature space using a discriminative mismatch
string kernel framework. In this high-dimensional space, labeled examples of
the source context are re-weighted so that the revised sample distribution
matches the target context more closely. We have experimentally verified TSK
for TFBS identifications on fourteen different TFs under a cross-organism
setting. We find that TSK consistently outperforms the state-of the-art TFBS
tools, especially when working with TFs whose binding sequences are not
conserved across contexts. We also demonstrate the generalizability of TSK by
showing its cutting-edge performance on a different set of cross-context tasks
for the MHC peptide binding predictions.
Policy Networks with Two-Stage Training for Dialogue Systems
Authors: Mehdi Fatemi, Layla El Asri, Hannes Schulz, Jing He, Kaheer Suleman
Date: 2016-06-10 00:00:00
Proceeding: WS 2016 9
Tasks: Deep Reinforcement Learning, Dialogue State Tracking, Gaussian Processes, Reinforcement Learning, Reinforcement Learning (RL), Vocal Bursts Valence Prediction

In this paper, we propose to use deep policy networks which are trained with
an advantage actor-critic method for statistically optimised dialogue systems.
First, we show that, on summary state and action spaces, deep Reinforcement
Learning (RL) outperforms Gaussian Processes methods. Summary state and action
spaces lead to good performance but require pre-engineering effort, RL
knowledge, and domain expertise. In order to remove the need to define such
summary spaces, we show that deep RL can also be trained efficiently on the
original state and action spaces. Dialogue systems based on partially
observable Markov decision processes are known to require many dialogues to
train, which makes them unappealing for practical deployment. We show that a
deep RL method based on an actor-critic architecture can exploit a small amount
of data very efficiently. Indeed, with only a few hundred dialogues collected
with a handcrafted policy, the actor-critic deep learner is considerably
bootstrapped from a combination of supervised and batch RL. In addition,
convergence to an optimal policy is significantly sped up compared to other
deep RL methods initialized on the data with batch RL. All experiments are
performed on a restaurant domain derived from the Dialogue State Tracking
Challenge 2 (DSTC2) dataset.
Learning Sparse Graphs Under Smoothness Prior
Authors: Sundeep Prabhakar Chepuri, Sijia Liu, Geert Leus, Alfred O. Hero III
Date: 2016-09-12 00:00:00
Tasks: Denoising, Graph Learning

In this paper, we are interested in learning the underlying graph structure
behind training data. Solving this basic problem is essential to carry out any
graph signal processing or machine learning task. To realize this, we assume
that the data is smooth with respect to the graph topology, and we parameterize
the graph topology using an edge sampling function. That is, the graph
Laplacian is expressed in terms of a sparse edge selection vector, which
provides an explicit handle to control the sparsity level of the graph. We
solve the sparse graph learning problem given some training data in both the
noiseless and noisy settings. Given the true smooth data, the posed sparse
graph learning problem can be solved optimally and is based on simple rank
ordering. Given the noisy data, we show that the joint sparse graph learning
and denoising problem can be simplified to designing only the sparse edge
selection vector, which can be solved using convex optimization.
First-Order Bayesian Network Specifications Capture the Complexity Class PP
Authors: Fabio Gagliardi Cozman
Date: 2016-09-12 00:00:00
Tasks: valid

The point of this note is to prove that a language is in the complexity class
PP if and only if the strings of the language encode valid inferences in a
Bayesian network defined using function-free first-order logic with equality.
Active Canny: Edge Detection and Recovery with Open Active Contour Models
Authors: Muhammet Bastan, S. Saqib Bukhari, Thomas M. Breuel
Date: 2016-09-12 00:00:00
Tasks: Edge Detection

We introduce an edge detection and recovery framework based on open active
contour models (snakelets). This is motivated by the noisy or broken edges
output by standard edge detection algorithms, like Canny. The idea is to
utilize the local continuity and smoothness cues provided by strong edges and
grow them to recover the missing edges. This way, the strong edges are used to
recover weak or missing edges by considering the local edge structures, instead
of blindly linking them if gradient magnitudes are above some threshold. We
initialize short snakelets on the gradient magnitudes or binary edges
automatically and then deform and grow them under the influence of gradient
vector flow. The output snakelets are able to recover most of the breaks or
weak edges, and they provide a smooth edge representation of the image; they
can also be used for higher level analysis, like contour segmentation.
Morphological Constraints for Phrase Pivot Statistical Machine Translation
Authors: Ahmed El Kholy, Nizar Habash
Date: 2016-09-12 00:00:00
Tasks: Machine Translation, Translation

The lack of parallel data for many language pairs is an important challenge
to statistical machine translation (SMT). One common solution is to pivot
through a third language for which there exist parallel corpora with the source
and target languages. Although pivoting is a robust technique, it introduces
some low quality translations especially when a poor morphology language is
used as the pivot between rich morphology languages. In this paper, we examine
the use of synchronous morphology constraint features to improve the quality of
phrase pivot SMT. We compare hand-crafted constraints to those learned from
limited parallel data between source and target languages. The learned
morphology constraints are based on projected align- ments between the source
and target phrases in the pivot phrase table. We show positive results on
Hebrew-Arabic SMT (pivoting on English). We get 1.5 BLEU points over a phrase
pivot baseline and 0.8 BLEU points over a system combination baseline with a
direct model built from parallel data.
Modelling Creativity: Identifying Key Components through a Corpus-Based Approach
Authors: Anna Jordanous, Bill Keller
Date: 2016-09-12 00:00:00

Creativity is a complex, multi-faceted concept encompassing a variety of
related aspects, abilities, properties and behaviours. If we wish to study
creativity scientifically, then a tractable and well-articulated model of
creativity is required. Such a model would be of great value to researchers
investigating the nature of creativity and in particular, those concerned with
the evaluation of creative practice. This paper describes a unique approach to
developing a suitable model of how creative behaviour emerges that is based on
the words people use to describe the concept. Using techniques from the field
of statistical natural language processing, we identify a collection of
fourteen key components of creativity through an analysis of a corpus of
academic papers on the topic. Words are identified which appear significantly
often in connection with discussions of the concept. Using a measure of lexical
similarity to help cluster these words, a number of distinct themes emerge,
which collectively contribute to a comprehensive and multi-perspective model of
creativity. The components provide an ontology of creativity: a set of building
blocks which can be used to model creative practice in a variety of domains.
The components have been employed in two case studies to evaluate the
creativity of computational systems and have proven useful in articulating
achievements of this work and directions for further research.
Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks
Authors: Pichao Wang, Wanqing Li, Song Liu, Yuyao Zhang, Zhimin Gao, Philip Ogunbona
Date: 2016-08-22 00:00:00
Tasks: General Classification, Gesture Recognition

This paper addresses the problem of continuous gesture recognition from
sequences of depth maps using convolutional neutral networks (ConvNets). The
proposed method first segments individual gestures from a depth sequence based
on quantity of movement (QOM). For each segmented gesture, an Improved Depth
Motion Map (IDMM), which converts the depth sequence into one image, is
constructed and fed to a ConvNet for recognition. The IDMM effectively encodes
both spatial and temporal information and allows the fine-tuning with existing
ConvNet models for classification without introducing millions of parameters to
learn. The proposed method is evaluated on the Large-scale Continuous Gesture
Recognition of the ChaLearn Looking at People (LAP) challenge 2016. It achieved
the performance of 0.2655 (Mean Jaccard Index) and ranked $3^{rd}$ place in
this challenge.
On Generation of Time-based Label Refinements
Authors: Niek Tax, Emin Alasgarov, Natalia Sidorova, Reinder Haakma
Date: 2016-09-12 00:00:00
Tasks: Attribute

Process mining is a research field focused on the analysis of event data with
the aim of extracting insights in processes. Applying process mining techniques
on data from smart home environments has the potential to provide valuable
insights in (un)healthy habits and to contribute to ambient assisted living
solutions. Finding the right event labels to enable application of process
mining techniques is however far from trivial, as simply using the triggering
sensor as the label for sensor events results in uninformative models that
allow for too much behavior (overgeneralizing). Refinements of sensor level
event labels suggested by domain experts have shown to enable discovery of more
precise and insightful process models. However, there exist no automated
approach to generate refinements of event labels in the context of process
mining. In this paper we propose a framework for automated generation of label
refinements based on the time attribute of events. We show on a case study with
real life smart home event data that behaviorally more specific, and therefore
more insightful, process models can be found by using automatically generated
refined labels in process discovery.
Distributed Representations for Biological Sequence Analysis
Authors: Dhananjay Kimothi, Akshay Soni, Pravesh Biyani, James M. Hogan
Date: 2016-08-21 00:00:00
Tasks: Document Embedding, Retrieval

Biological sequence comparison is a key step in inferring the relatedness of
various organisms and the functional similarity of their components. Thanks to
the Next Generation Sequencing efforts, an abundance of sequence data is now
available to be processed for a range of bioinformatics applications. Embedding
a biological sequence over a nucleotide or amino acid alphabet in a lower
dimensional vector space makes the data more amenable for use by current
machine learning tools, provided the quality of embedding is high and it
captures the most meaningful information of the original sequences. Motivated
by recent advances in the text document embedding literature, we present a new
method, called seq2vec, to represent a complete biological sequence in an
Euclidean space. The new representation has the potential to capture the
contextual information of the original sequence necessary for sequence
comparison tasks. We test our embeddings with protein sequence classification
and retrieval tasks and demonstrate encouraging outcomes.
Fast Algorithm of High-resolution Microwave Imaging Using the Non-parametric Generalized Reflectivity Model
Authors: Long Gang Wang, Lianlin Li, Tie Jun Cui
Date: 2016-09-12 00:00:00

This paper presents an efficient algorithm of high-resolution microwave
imaging based on the concept of generalized reflectivity. The contribution made
in this paper is two-fold. We introduce the concept of non-parametric
generalized reflectivity (GR, for short) as a function of operational
frequencies and view angles, etc. The GR extends the conventional Born-based
imaging model, i.e., single-scattering model, into that accounting for more
realistic interaction between the electromagnetic wavefield and imaged scene.
Afterwards, the GR-based microwave imaging is formulated in the convex of
sparsity-regularized optimization. Typically, the sparsity-regularized
optimization requires the implementation of iterative strategy, which is
computationally expensive, especially for large-scale problems. To break this
bottleneck, we convert the imaging problem into the problem of physics-driven
image processing by introducing a dual transformation. Moreover, this image
processing is performed over overlapping patches, which can be efficiently
solved in the parallel or distributed manner. In this way, the proposed
high-resolution imaging methodology could be applicable to large-scale
microwave imaging problems. Selected simulation results are provided to
demonstrate the state-of-art performance of proposed methodology.
Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks
Authors: Yun-Nung Chen, Dilek Hakkani-Tur, Gokhan Tur, Asli Celikyilmaz, Jianfeng Gao, Li Deng
Date: 2016-09-12 00:00:00
Tasks: Natural Language Understanding

Natural language understanding (NLU) is a core component of a spoken dialogue
system. Recently recurrent neural networks (RNN) obtained strong results on NLU
due to their superior ability of preserving sequential information over time.
Traditionally, the NLU module tags semantic slots for utterances considering
their flat structures, as the underlying RNN structure is a linear chain.
However, natural language exhibits linguistic properties that provide rich,
structured information for better understanding. This paper introduces a novel
model, knowledge-guided structural attention networks (K-SAN), a generalization
of RNN to additionally incorporate non-flat network topologies guided by prior
knowledge. There are two characteristics: 1) important substructures can be
captured from small training data, allowing the model to generalize to
previously unseen test data; 2) the model automatically figures out the salient
substructures that are essential to predict the semantic tags of the given
sentences, so that the understanding performance can be improved. The
experiments on the benchmark Air Travel Information System (ATIS) data show
that the proposed K-SAN architecture can effectively extract salient knowledge
from substructures with an attention mechanism, and outperform the performance
of the state-of-the-art neural network based frameworks.
Segmentation and Classification of Skin Lesions for Disease Diagnosis
Authors: Sumithra R, Mahamad Suhil, D. S. Guru
Date: 2016-09-12 00:00:00
Tasks: Classification, General Classification, Segmentation

In this paper, a novel approach for automatic segmentation and classification
of skin lesions is proposed. Initially, skin images are filtered to remove
unwanted hairs and noise and then the segmentation process is carried out to
extract lesion areas. For segmentation, a region growing method is applied by
automatic initialization of seed points. The segmentation performance is
measured with different well known measures and the results are appreciable.
Subsequently, the extracted lesion areas are represented by color and texture
features. SVM and k-NN classifiers are used along with their fusion for the
classification using the extracted features. The performance of the system is
tested on our own dataset of 726 samples from 141 images consisting of 5
different classes of diseases. The results are very promising with 46.71% and
34% of F-measure using SVM and k-NN classifier respectively and with 61% of
F-measure for fusion of SVM and k-NN.
Towards Miss Universe Automatic Prediction: The Evening Gown Competition
Authors: Johanna Carvajal, Arnold Wiliem, Conrad Sanderson, Brian Lovell
Date: 2016-04-26 00:00:00
Tasks: Prediction

Can we predict the winner of Miss Universe after watching how they stride
down the catwalk during the evening gown competition? Fashion gurus say they
can! In our work, we study this question from the perspective of computer
vision. In particular, we want to understand whether existing computer vision
approaches can be used to automatically extract the qualities exhibited by the
Miss Universe winners during their catwalk. This study can pave the way towards
new vision-based applications for the fashion industry. To this end, we propose
a novel video dataset, called the Miss Universe dataset, comprising 10 years of
the evening gown competition selected between 1996-2010. We further propose two
ranking-related problems: (1) Miss Universe Listwise Ranking and (2) Miss
Universe Pairwise Ranking. In addition, we also develop an approach that
simultaneously addresses the two proposed problems. To describe the videos we
employ the recently proposed Stacked Fisher Vectors in conjunction with robust
local spatio-temporal features. From our evaluation we found that although the
addressed problems are extremely challenging, the proposed system is able to
rank the winner in the top 3 best predicted scores for 5 out of 10 Miss
Universe competitions.
Dynamic Parallel and Distributed Graph Cuts
Authors: Miao Yu, Shuhan Shen, Zhanyi Hu
Date: 2015-12-01 00:00:00

Graph-cuts are widely used in computer vision. In order to speed up the
optimization process and improve the scalability for large graphs, Strandmark
and Kahl introduced a splitting method to split a graph into multiple subgraphs
for parallel computation in both shared and distributed memory models. However,
this parallel algorithm (parallel BK-algorithm) does not have a polynomial
bound on the number of iterations and is found non-convergent in some cases due
to the possible multiple optimal solutions of its sub-problems.
  To remedy this non-convergence problem, in this work we first introduce a
merging method capable of merging any number of those adjacent sub-graphs which
could hardly reach an agreement on their overlapped region in the parallel BK
algorithm. Based on the pseudo-boolean representations of graph cuts,our
merging method is shown able to effectively reuse all the computed flows in
these sub-graphs. Through both the splitting and merging, we further propose a
dynamic parallel and distributed graph-cuts algorithm with guaranteed
convergence to the globally optimal solutions within a predefined number of
iterations. In essence, this work provides a general framework to allow more
sophisticated splitting and merging strategies to be employed to further boost
performance. Our dynamic parallel algorithm is validated with extensive
experimental results.
Reduced Space and Faster Convergence in Imperfect-Information Games via Regret-Based Pruning
Authors: Noam Brown, Tuomas Sandholm
Date: 2016-09-12 00:00:00
Proceeding: ICML 2017
Tasks: counterfactual

Counterfactual Regret Minimization (CFR) is the most popular iterative
algorithm for solving zero-sum imperfect-information games. Regret-Based
Pruning (RBP) is an improvement that allows poorly-performing actions to be
temporarily pruned, thus speeding up CFR. We introduce Total RBP, a new form of
RBP that reduces the space requirements of CFR as actions are pruned. We prove
that in zero-sum games it asymptotically prunes any action that is not part of
a best response to some Nash equilibrium. This leads to provably faster
convergence and lower space requirements. Experiments show that Total RBP
results in an order of magnitude reduction in space, and the reduction factor
increases with game size.
Adaptive Probabilistic Trajectory Optimization via Efficient Approximate Inference
Authors: Yunpeng Pan, Xinyan Yan, Evangelos Theodorou, Byron Boots
Date: 2016-08-22 00:00:00
Tasks: Model Predictive Control, Reinforcement Learning, Reinforcement Learning (RL)

Robotic systems must be able to quickly and robustly make decisions when
operating in uncertain and dynamic environments. While Reinforcement Learning
(RL) can be used to compute optimal policies with little prior knowledge about
the environment, it suffers from slow convergence. An alternative approach is
Model Predictive Control (MPC), which optimizes policies quickly, but also
requires accurate models of the system dynamics and environment. In this paper
we propose a new approach, adaptive probabilistic trajectory optimization, that
combines the benefits of RL and MPC. Our method uses scalable approximate
inference to learn and updates probabilistic models in an online incremental
fashion while also computing optimal control policies via successive local
approximations. We present two variations of our algorithm based on the Sparse
Spectrum Gaussian Process (SSGP) model, and we test our algorithm on three
learning tasks, demonstrating the effectiveness and efficiency of our approach.