pwc-archive/papers-with-abstracts huggingface dataset:
Structure in the Value Function of Two-Player Zero-Sum Games of Incomplete Information
Authors: Auke J. Wiggers, Frans A. Oliehoek, Diederik M. Roijers
Date: 2016-06-22 00:00:00
Tasks: Decision Making

Zero-sum stochastic games provide a rich model for competitive decision
making. However, under general forms of state uncertainty as considered in the
Partially Observable Stochastic Game (POSG), such decision making problems are
still not very well understood. This paper makes a contribution to the theory
of zero-sum POSGs by characterizing structure in their value function. In
particular, we introduce a new formulation of the value function for zs-POSGs
as a function of the "plan-time sufficient statistics" (roughly speaking the
information distribution in the POSG), which has the potential to enable
generalization over such information distributions. We further delineate this
generalization capability by proving a structural result on the shape of value
function: it exhibits concavity and convexity with respect to appropriately
chosen marginals of the statistic space. This result is a key pre-cursor for
developing solution methods that may be able to exploit such structure.
Finally, we show how these results allow us to reduce a zs-POSG to a
"centralized" model with shared observations, thereby transferring results for
the latter, narrower class, to games with individual (private) observations.
Model-based Deep Hand Pose Estimation
Authors: Xingyi Zhou, Qingfu Wan, Wei zhang, xiangyang xue, Yichen Wei
Date: 2016-06-22 00:00:00
Tasks: Deep Learning, Hand Pose Estimation, model, Pose Estimation, valid

Previous learning based hand pose estimation methods does not fully exploit
the prior information in hand model geometry. Instead, they usually rely a
separate model fitting step to generate valid hand poses. Such a post
processing is inconvenient and sub-optimal. In this work, we propose a model
based deep learning approach that adopts a forward kinematics based layer to
ensure the geometric validity of estimated poses. For the first time, we show
that embedding such a non-linear generative process in deep learning is
feasible for hand pose estimation. Our approach is verified on challenging
public datasets and achieves state-of-the-art performance.
Evolutionary computation for multicomponent problems: opportunities and future directions
Authors: Mohammad Reza Bonyadi, Zbigniew Michalewicz, Frank Neumann, Markus Wagner
Date: 2016-06-22 00:00:00

Over the past 30 years many researchers in the field of evolutionary
computation have put a lot of effort to introduce various approaches for
solving hard problems. Most of these problems have been inspired by major
industries so that solving them, by providing either optimal or near optimal
solution, was of major significance. Indeed, this was a very promising
trajectory as advances in these problem-solving approaches could result in
adding values to major industries. In this paper we revisit this trajectory to
find out whether the attempts that started three decades ago are still aligned
with the same goal, as complexities of real-world problems increased
significantly. We present some examples of modern real-world problems, discuss
why they might be difficult to solve, and whether there is any mismatch between
these examples and the problems that are investigated in the evolutionary
computation area.
Sense Embedding Learning for Word Sense Induction
Authors: Linfeng Song, Zhiguo Wang, Haitao Mi, Daniel Gildea
Date: 2016-06-17 00:00:00
Proceeding: SEMEVAL 2016 8
Tasks: Word Sense Induction

Conventional word sense induction (WSI) methods usually represent each
instance with discrete linguistic features or cooccurrence features, and train
a model for each polysemous word individually. In this work, we propose to
learn sense embeddings for the WSI task. In the training stage, our method
induces several sense centroids (embedding) for each polysemous word. In the
testing stage, our method represents each instance as a contextual vector, and
induces its sense by finding the nearest sense centroid in the embedding space.
The advantages of our method are (1) distributed sense vectors are taken as the
knowledge representations which are trained discriminatively, and usually have
better performance than traditional count-based distributional models, and (2)
a general model for the whole vocabulary is jointly trained to induce sense
centroids under the mutlitask learning framework. Evaluated on SemEval-2010 WSI
dataset, our method outperforms all participants and most of the recent
state-of-the-art methods. We further verify the two advantages by comparing
with carefully designed baselines.
Where to Focus: Query Adaptive Matching for Instance Retrieval Using Convolutional Feature Maps
Authors: Jiewei Cao, Lingqiao Liu, Peng Wang, Zi Huang, Chunhua Shen, Heng Tao Shen
Date: 2016-06-22 00:00:00
Tasks: Reranking, Retrieval

Instance retrieval requires one to search for images that contain a
particular object within a large corpus. Recent studies show that using image
features generated by pooling convolutional layer feature maps (CFMs) of a
pretrained convolutional neural network (CNN) leads to promising performance
for this task. However, due to the global pooling strategy adopted in those
works, the generated image feature is less robust to image clutter and tends to
be contaminated by the irrelevant image patterns. In this article, we alleviate
this drawback by proposing a novel reranking algorithm using CFMs to refine the
retrieval result obtained by existing methods. Our key idea, called query
adaptive matching (QAM), is to first represent the CFMs of each image by a set
of base regions which can be freely combined into larger regions-of-interest.
Then the similarity between the query and a candidate image is measured by the
best similarity score that can be attained by comparing the query feature and
the feature pooled from a combined region. We show that the above procedure can
be cast as an optimization problem and it can be solved efficiently with an
off-the-shelf solver. Besides this general framework, we also propose two
practical ways to create the base regions. One is based on the property of the
CFM and the other one is based on a multi-scale spatial pyramid scheme. Through
extensive experiments, we show that our reranking approaches bring substantial
performance improvement and by applying them we can outperform the state of the
art on several instance retrieval benchmarks.
Variable Elimination in the Fourier Domain
Authors: Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla P. Gomes, Bart Selman
Date: 2015-08-17 00:00:00

The ability to represent complex high dimensional probability distributions
in a compact form is one of the key insights in the field of graphical models.
Factored representations are ubiquitous in machine learning and lead to major
computational advantages. We explore a different type of compact representation
based on discrete Fourier representations, complementing the classical approach
based on conditional independencies. We show that a large class of
probabilistic graphical models have a compact Fourier representation. This
theoretical result opens up an entirely new way of approximating a probability
distribution. We demonstrate the significance of this approach by applying it
to the variable elimination algorithm. Compared with the traditional bucket
representation and other approximate inference algorithms, we obtain
significant improvements.
Problems With Evaluation of Word Embeddings Using Word Similarity Tasks
Authors: Manaal Faruqui, Yulia Tsvetkov, Pushpendre Rastogi, Chris Dyer
Date: 2016-05-08 00:00:00
Proceeding: WS 2016 8
Tasks: Semantic Similarity, Semantic Textual Similarity, Word Embeddings, Word Similarity

Lacking standardized extrinsic evaluation methods for vector representations
of words, the NLP community has relied heavily on word similarity tasks as a
proxy for intrinsic evaluation of word vectors. Word similarity evaluation,
which correlates the distance between vectors and human judgments of semantic
similarity is attractive, because it is computationally inexpensive and fast.
In this paper we present several problems associated with the evaluation of
word vectors on word similarity datasets, and summarize existing solutions. Our
study suggests that the use of word similarity tasks for evaluation of word
vectors is not sustainable and calls for further research on evaluation
methods.
Étude de Problèmes d'Optimisation Combinatoire à Multiples Composantes Interdépendantes
Authors: Mohamed El Yafrani, Belaïd Ahiod
Date: 2016-06-22 00:00:00

This extended abstract presents an overview on NP-hard optimization problems
with multiple interdependent components. These problems occur in many
real-world applications: industrial applications, engineering, and logistics.
The fact that these problems are composed of many sub-problems that are NP-hard
makes them even more challenging to solve using exact algorithms. This is
mainly due to the high complexity of this class of algorithms and the hardness
of the problems themselves. The main source of difficulty of these problems is
the presence of internal dependencies between sub-problems. This aspect of
interdependence of components is presented, and some outlines on solving
approaches are briefly introduced from a (meta)heuristics and evolutionary
computation perspective.
Risk-consistency of cross-validation with lasso-type procedures
Authors: Darren Homrighausen, Daniel J. McDonald
Date: 2013-08-04 00:00:00
Tasks: Model Selection, Vocal Bursts Type Prediction

The lasso and related sparsity inducing algorithms have been the target of
substantial theoretical and applied research. Correspondingly, many results are
known about their behavior for a fixed or optimally chosen tuning parameter
specified up to unknown constants. In practice, however, this oracle tuning
parameter is inaccessible so one must use the data to select one. Common
statistical practice is to use a variant of cross-validation for this task.
However, little is known about the theoretical properties of the resulting
predictions with such data-dependent methods. We consider the high-dimensional
setting with random design wherein the number of predictors $p$ grows with the
number of observations $n$. Under typical assumptions on the data generating
process, similar to those in the literature, we recover oracle rates up to a
log factor when choosing the tuning parameter with cross-validation. Under
weaker conditions, when the true model is not necessarily linear, we show that
the lasso remains risk consistent relative to its linear oracle. We also
generalize these results to the group lasso and square-root lasso and
investigate the predictive and model selection performance of cross-validation
via simulation.
Data-driven HR - Résumé Analysis Based on Natural Language Processing and Machine Learning
Authors: Tim Zimmermann, Leo Kotschenreuther, Karsten Schmidt
Date: 2016-06-17 00:00:00
Tasks: BIG-bench Machine Learning

Recruiters usually spend less than a minute looking at each r\'esum\'e when
deciding whether it's worth continuing the recruitment process with the
candidate. Recruiters focus on keywords, and it's almost impossible to
guarantee a fair process of candidate selection. The main scope of this paper
is to tackle this issue by introducing a data-driven approach that shows how to
process r\'esum\'es automatically and give recruiters more time to only examine
promising candidates. Furthermore, we show how to leverage Machine Learning and
Natural Language Processing in order to extract all required information from
the r\'esum\'es. Once the information is extracted, a ranking score is
calculated. The score describes how well the candidates fit based on their
education, work experience and skills. Later this paper illustrates a prototype
application that shows how this novel approach can increase the productivity of
recruiters. The application enables them to filter and rank candidates based on
predefined job descriptions. Guided by the ranking, recruiters can get deeper
insights from candidate profiles and validate why and how the application
ranked them. This application shows how to improve the hiring process by giving
an unbiased hiring decision support.
Chinese Song Iambics Generation with Neural Attention-based Model
Authors: Qixin Wang, Tianyi Luo, Dong Wang, Chao Xing
Date: 2016-04-21 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

Learning and generating Chinese poems is a charming yet challenging task.
Traditional approaches involve various language modeling and machine
translation techniques, however, they perform not as well when generating poems
with complex pattern constraints, for example Song iambics, a famous type of
poems that involve variable-length sentences and strict rhythmic patterns. This
paper applies the attention-based sequence-to-sequence model to generate
Chinese Song iambics. Specifically, we encode the cue sentences by a
bi-directional Long-Short Term Memory (LSTM) model and then predict the entire
iambic with the information provided by the encoder, in the form of an
attention-based LSTM that can regularize the generation process by the fine
structure of the input cues. Several techniques are investigated to improve the
model, including global context integration, hybrid style training, character
vector initialization and adaptation. Both the automatic and subjective
evaluation results show that our model indeed can learn the complex structural
and rhythmic patterns of Song iambics, and the generation is rather successful.
A Theoretical Analysis of Deep Neural Networks for Texture Classification
Authors: Saikat Basu, Manohar Karki, Robert DiBiano, Supratik Mukhopadhyay, Sangram Ganguly, Ramakrishna Nemani, Shreekant Gayaka
Date: 2016-05-09 00:00:00
Tasks: Classification, General Classification, Object Recognition, Texture Classification

We investigate the use of Deep Neural Networks for the classification of
image datasets where texture features are important for generating
class-conditional discriminative representations. To this end, we first derive
the size of the feature space for some standard textural features extracted
from the input dataset and then use the theory of Vapnik-Chervonenkis dimension
to show that hand-crafted feature extraction creates low-dimensional
representations which help in reducing the overall excess error rate. As a
corollary to this analysis, we derive for the first time upper bounds on the VC
dimension of Convolutional Neural Network as well as Dropout and Dropconnect
networks and the relation between excess error rate of Dropout and Dropconnect
networks. The concept of intrinsic dimension is used to validate the intuition
that texture-based datasets are inherently higher dimensional as compared to
handwritten digits or other object recognition datasets and hence more
difficult to be shattered by neural networks. We then derive the mean distance
from the centroid to the nearest and farthest sampling points in an
n-dimensional manifold and show that the Relative Contrast of the sample data
vanishes as dimensionality of the underlying vector space tends to infinity.
Correlation-based Intrinsic Evaluation of Word Vector Representations
Authors: Yulia Tsvetkov, Manaal Faruqui, Chris Dyer
Date: 2016-06-21 00:00:00
Proceeding: WS 2016 8
Tasks: Word Similarity

We introduce QVEC-CCA--an intrinsic evaluation metric for word vector
representations based on correlations of learned vectors with features
extracted from linguistic resources. We show that QVEC-CCA scores are an
effective proxy for a range of extrinsic semantic and syntactic tasks. We also
show that the proposed evaluation obtains higher and more consistent
correlations with downstream tasks, compared to existing approaches to
intrinsic evaluation of word vectors that are based on word similarity.
Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning
Authors: Yulia Tsvetkov, Manaal Faruqui, Wang Ling, Brian MacWhinney, Chris Dyer
Date: 2016-05-12 00:00:00
Proceeding: ACL 2016 8
Tasks: Bayesian Optimization, Representation Learning

We use Bayesian optimization to learn curricula for word representation
learning, optimizing performance on downstream tasks that depend on the learned
representations as features. The curricula are modeled by a linear ranking
function which is the scalar product of a learned weight vector and an
engineered feature vector that characterizes the different aspects of the
complexity of each instance in the training corpus. We show that learning the
curriculum improves performance on a variety of downstream tasks over random
orders and in comparison to the natural corpus order.
Using Visual Analytics to Interpret Predictive Machine Learning Models
Authors: Josua Krause, Adam Perer, Enrico Bertini
Date: 2016-06-17 00:00:00
Tasks: BIG-bench Machine Learning

It is commonly believed that increasing the interpretability of a machine
learning model may decrease its predictive power. However, inspecting
input-output relationships of those models using visual analytics, while
treating them as black-box, can help to understand the reasoning behind
outcomes without sacrificing predictive quality. We identify a space of
possible solutions and provide two examples of where such techniques have been
successfully used in practice.
Persistence Lenses: Segmentation, Simplification, Vectorization, Scale Space and Fractal Analysis of Images
Authors: Martin Brooks
Date: 2016-04-25 00:00:00
Tasks: Vector Graphics

A persistence lens is a hierarchy of disjoint upper and lower level sets of a
continuous luminance image's Reeb graph. The boundary components of a
persistence lens's interior components are Jordan curves that serve as a
hierarchical segmentation of the image, and may be rendered as vector graphics.
A persistence lens determines a varilet basis for the luminance image, in which
image simplification is a realized by subspace projection. Image scale space,
and image fractal analysis, result from applying a scale measure to each basis
function.
Tracking Time-Vertex Propagation using Dynamic Graph Wavelets
Authors: Francesco Grassi, Nathanael Perraudin, Benjamin Ricaud
Date: 2016-06-21 00:00:00
Tasks: Compressive Sensing

Graph Signal Processing generalizes classical signal processing to signal or
data indexed by the vertices of a weighted graph. So far, the research efforts
have been focused on static graph signals. However numerous applications
involve graph signals evolving in time, such as spreading or propagation of
waves on a network. The analysis of this type of data requires a new set of
methods that fully takes into account the time and graph dimensions. We propose
a novel class of wavelet frames named Dynamic Graph Wavelets, whose time-vertex
evolution follows a dynamic process. We demonstrate that this set of functions
can be combined with sparsity based approaches such as compressive sensing to
reveal information on the dynamic processes occurring on a graph. Experiments
on real seismological data show the efficiency of the technique, allowing to
estimate the epicenter of earthquake events recorded by a seismic network.
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Authors: Özgün Çiçek, Ahmed Abdulkadir, Soeren S. Lienkamp, Thomas Brox, Olaf Ronneberger
Date: 2016-06-21 00:00:00
Tasks: 3D Instance Segmentation, Data Augmentation, Machine Translation, Segmentation

This paper introduces a network for volumetric segmentation that learns from
sparsely annotated volumetric images. We outline two attractive use cases of
this method: (1) In a semi-automated setup, the user annotates some slices in
the volume to be segmented. The network learns from these sparse annotations
and provides a dense 3D segmentation. (2) In a fully-automated setup, we assume
that a representative, sparsely annotated training set exists. Trained on this
data set, the network densely segments new volumetric images. The proposed
network extends the previous u-net architecture from Ronneberger et al. by
replacing all 2D operations with their 3D counterparts. The implementation
performs on-the-fly elastic deformations for efficient data augmentation during
training. It is trained end-to-end from scratch, i.e., no pre-trained network
is required. We test the performance of the proposed method on a complex,
highly variable 3D structure, the Xenopus kidney, and achieve good results for
both use cases.
Neural Morphological Tagging from Characters for Morphologically Rich Languages
Authors: Georg Heigold, Guenter Neumann, Josef van Genabith
Date: 2016-06-21 00:00:00
Tasks: Morphological Tagging, TAG, Word Embeddings

This paper investigates neural character-based morphological tagging for
languages with complex morphology and large tag sets. We systematically explore
a variety of neural architectures (DNN, CNN, CNNHighway, LSTM, BLSTM) to obtain
character-based word vectors combined with bidirectional LSTMs to model
across-word context in an end-to-end setting. We explore supplementary use of
word-based vectors trained on large amounts of unlabeled data. Our experiments
for morphological tagging suggest that for "simple" model configurations, the
choice of the network architecture (CNN vs. CNNHighway vs. LSTM vs. BLSTM) or
the augmentation with pre-trained word embeddings can be important and clearly
impact the accuracy. Increasing the model capacity by adding depth, for
example, and carefully optimizing the neural networks can lead to substantial
improvements, and the differences in accuracy (but not training time) become
much smaller or even negligible. Overall, our best morphological taggers for
German and Czech outperform the best results reported in the literature by a
large margin.
Leveraging Mid-Level Deep Representations For Predicting Face Attributes in the Wild
Authors: Yang Zhong, Josephine Sullivan, Hai-Bo Li
Date: 2016-02-04 00:00:00
Tasks: Attribute, Face Recognition, image-classification, Image Classification, Prediction

Predicting facial attributes from faces in the wild is very challenging due
to pose and lighting variations in the real world. The key to this problem is
to build proper feature representations to cope with these unfavourable
conditions. Given the success of Convolutional Neural Network (CNN) in image
classification, the high-level CNN feature, as an intuitive and reasonable
choice, has been widely utilized for this problem. In this paper, however, we
consider the mid-level CNN features as an alternative to the high-level ones
for attribute prediction. This is based on the observation that face attributes
are different: some of them are locally oriented while others are globally
defined. Our investigations reveal that the mid-level deep representations
outperform the prediction accuracy achieved by the (fine-tuned) high-level
abstractions. We empirically demonstrate that the midlevel representations
achieve state-of-the-art prediction performance on CelebA and LFWA datasets.
Our investigations also show that by utilizing the mid-level representations
one can employ a single deep network to achieve both face recognition and
attribute prediction.
Subspace Clustering Based Tag Sharing for Inductive Tag Matrix Refinement with Complex Errors
Authors: Yuqing Hou, Zhouchen Lin, Jin-Ge Yao
Date: 2016-01-12 00:00:00
Tasks: Clustering, Matrix Completion, TAG

Annotating images with tags is useful for indexing and retrieving images.
However, many available annotation data include missing or inaccurate
annotations. In this paper, we propose an image annotation framework which
sequentially performs tag completion and refinement. We utilize the subspace
property of data via sparse subspace clustering for tag completion. Then we
propose a novel matrix completion model for tag refinement, integrating visual
correlation, semantic correlation and the novelly studied property of complex
errors. The proposed method outperforms the state-of-the-art approaches on
multiple benchmark datasets even when they contain certain levels of annotation
noise.