pwc-archive/papers-with-abstracts huggingface dataset:
Grounding of Textual Phrases in Images by Reconstruction
Authors: Anna Rohrbach, Marcus Rohrbach, Ronghang Hu, Trevor Darrell, Bernt Schiele
Date: 2015-11-12 00:00:00
Tasks: Language Modeling, Language Modelling, Natural Language Visual Grounding, Phrase Grounding, Visual Grounding

Grounding (i.e. localizing) arbitrary, free-form textual phrases in visual
content is a challenging problem with many applications for human-computer
interaction and image-text reference resolution. Few datasets provide the
ground truth spatial localization of phrases, thus it is desirable to learn
from data with no or little grounding supervision. We propose a novel approach
which learns grounding by reconstructing a given phrase using an attention
mechanism, which can be either latent or optimized directly. During training
our approach encodes the phrase using a recurrent network language model and
then learns to attend to the relevant image region in order to reconstruct the
input phrase. At test time, the correct attention, i.e., the grounding, is
evaluated. If grounding supervision is available it can be directly applied via
a loss over the attention mechanism. We demonstrate the effectiveness of our
approach on the Flickr 30k Entities and ReferItGame datasets with different
levels of supervision, ranging from no supervision over partial supervision to
full supervision. Our supervised variant improves by a large margin over the
state-of-the-art on both datasets.
Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios
Authors: Hang Ma, Sven Koenig, Nora Ayanian, Liron Cohen, Wolfgang Hoenig, T. K. Satish Kumar, Tansel Uras, Hong Xu, Craig Tovey, Guni Sharon
Date: 2017-02-17 00:00:00
Tasks: Multi-Agent Path Finding

Multi-agent path finding (MAPF) is well-studied in artificial intelligence,
robotics, theoretical computer science and operations research. We discuss
issues that arise when generalizing MAPF methods to real-world scenarios and
four research directions that address them. We emphasize the importance of
addressing these issues as opposed to developing faster methods for the
standard formulation of the MAPF problem.
An Unsupervised Approach for Overlapping Cervical Cell Cytoplasm Segmentation
Authors: Pranav Kumar, S. L. Happy, Swarnadip Chatterjee, Debdoot Sheet, Aurobinda Routray
Date: 2017-02-17 00:00:00
Tasks: Segmentation

The poor contrast and the overlapping of cervical cell cytoplasm are the
major issues in the accurate segmentation of cervical cell cytoplasm. This
paper presents an automated unsupervised cytoplasm segmentation approach which
can effectively find the cytoplasm boundaries in overlapping cells. The
proposed approach first segments the cell clumps from the cervical smear image
and detects the nuclei in each cell clump. A modified Otsu method with prior
class probability is proposed for accurate segmentation of nuclei from the cell
clumps. Using distance regularized level set evolution, the contour around each
nucleus is evolved until it reaches the cytoplasm boundaries. Promising results
were obtained by experimenting on ISBI 2015 challenge dataset.
Adversarial Discriminative Domain Adaptation
Authors: Eric Tzeng, Judy Hoffman, Kate Saenko, Trevor Darrell
Date: 2017-02-17 00:00:00
Proceeding: CVPR 2017 7
Tasks: Domain Adaptation, General Classification, Unsupervised Domain Adaptation, Unsupervised Image-To-Image Translation

Adversarial learning methods are a promising approach to training robust deep
networks, and can generate complex samples across diverse domains. They also
can improve recognition despite the presence of domain shift or dataset bias:
several adversarial approaches to unsupervised domain adaptation have recently
been introduced, which reduce the difference between the training and test
domain distributions and thus improve generalization performance. Prior
generative approaches show compelling visualizations, but are not optimal on
discriminative tasks and can be limited to smaller shifts. Prior discriminative
approaches could handle larger domain shifts, but imposed tied weights on the
model and did not exploit a GAN-based loss. We first outline a novel
generalized framework for adversarial adaptation, which subsumes recent
state-of-the-art approaches as special cases, and we use this generalized view
to better relate the prior approaches. We propose a previously unexplored
instance of our general framework which combines discriminative modeling,
untied weight sharing, and a GAN loss, which we call Adversarial Discriminative
Domain Adaptation (ADDA). We show that ADDA is more effective yet considerably
simpler than competing domain-adversarial methods, and demonstrate the promise
of our approach by exceeding state-of-the-art unsupervised adaptation results
on standard cross-domain digit classification tasks and a new more difficult
cross-modality object classification task.
How close are the eigenvectors and eigenvalues of the sample and actual covariance matrices?
Authors: Andreas Loukas
Date: 2017-02-17 00:00:00

How many samples are sufficient to guarantee that the eigenvectors and
eigenvalues of the sample covariance matrix are close to those of the actual
covariance matrix? For a wide family of distributions, including distributions
with finite second moment and distributions supported in a centered Euclidean
ball, we prove that the inner product between eigenvectors of the sample and
actual covariance matrices decreases proportionally to the respective
eigenvalue distance. Our findings imply non-asymptotic concentration bounds for
eigenvectors, eigenspaces, and eigenvalues. They also provide conditions for
distinguishing principal components based on a constant number of samples.
3D Cell Nuclei Segmentation with Balanced Graph Partitioning
Authors: Julian Arz, Peter Sanders, Johannes Stegmaier, Ralf Mikut
Date: 2017-02-17 00:00:00
Tasks: Binarization, graph partitioning, Image Segmentation, Segmentation, Semantic Segmentation

Cell nuclei segmentation is one of the most important tasks in the analysis
of biomedical images. With ever-growing sizes and amounts of three-dimensional
images to be processed, there is a need for better and faster segmentation
methods. Graph-based image segmentation has seen a rise in popularity in recent
years, but is seen as very costly with regard to computational demand. We
propose a new segmentation algorithm which overcomes these limitations. Our
method uses recursive balanced graph partitioning to segment foreground
components of a fast and efficient binarization. We construct a model for the
cell nuclei to guide the partitioning process. Our algorithm is compared to
other state-of-the-art segmentation algorithms in an experimental evaluation on
two sets of realistically simulated inputs. Our method is faster, has similar
or better quality and an acceptable memory overhead.
Experiment Segmentation in Scientific Discourse as Clause-level Structured Prediction using Recurrent Neural Networks
Authors: Pradeep Dasigi, Gully A. P. C. Burns, Eduard Hovy, Anita de Waard
Date: 2017-02-17 00:00:00
Tasks: Structured Prediction

We propose a deep learning model for identifying structure within experiment
narratives in scientific literature. We take a sequence labeling approach to
this problem, and label clauses within experiment narratives to identify the
different parts of the experiment. Our dataset consists of paragraphs taken
from open access PubMed papers labeled with rhetorical information as a result
of our pilot annotation. Our model is a Recurrent Neural Network (RNN) with
Long Short-Term Memory (LSTM) cells that labels clauses. The clause
representations are computed by combining word representations using a novel
attention mechanism that involves a separate RNN. We compare this model against
LSTMs where the input layer has simple or no attention and a feature rich CRF
model. Furthermore, we describe how our work could be useful for information
extraction from scientific literature.
Approximate Bayes learning of stochastic differential equations
Authors: Philipp Batz, Andreas Ruttor, Manfred Opper
Date: 2017-02-17 00:00:00
Tasks: Gaussian Processes, regression

We introduce a nonparametric approach for estimating drift and diffusion
functions in systems of stochastic differential equations from observations of
the state vector. Gaussian processes are used as flexible models for these
functions and estimates are calculated directly from dense data sets using
Gaussian process regression. We also develop an approximate expectation
maximization algorithm to deal with the unobserved, latent dynamics between
sparse observations. The posterior over states is approximated by a piecewise
linearized process of the Ornstein-Uhlenbeck type and the maximum a posteriori
estimation of the drift is facilitated by a sparse Gaussian process
approximation.
Towards a Unified Taxonomy of Biclustering Methods
Authors: Dmitry I. Ignatov, Bruce W. Watson
Date: 2017-02-17 00:00:00
Tasks: Attribute, Clustering, Survey

Being an unsupervised machine learning and data mining technique,
biclustering and its multimodal extensions are becoming popular tools for
analysing object-attribute data in different domains. Apart from conventional
clustering techniques, biclustering is searching for homogeneous groups of
objects while keeping their common description, e.g., in binary setting, their
shared attributes. In bioinformatics, biclustering is used to find genes, which
are active in a subset of situations, thus being candidates for biomarkers.
However, the authors of those biclustering techniques that are popular in gene
expression analysis, may overlook the existing methods. For instance, BiMax
algorithm is aimed at finding biclusters, which are well-known for decades as
formal concepts. Moreover, even if bioinformatics classify the biclustering
methods according to reasonable domain-driven criteria, their classification
taxonomies may be different from survey to survey and not full as well. So, in
this paper we propose to use concept lattices as a tool for taxonomy building
(in the biclustering domain) and attribute exploration as means for
cross-domain taxonomy completion.
Local minima in training of neural networks
Authors: Grzegorz Swirszcz, Wojciech Marian Czarnecki, Razvan Pascanu
Date: 2016-11-19 00:00:00

There has been a lot of recent interest in trying to characterize the error
surface of deep models. This stems from a long standing question. Given that
deep networks are highly nonlinear systems optimized by local gradient methods,
why do they not seem to be affected by bad local minima? It is widely believed
that training of deep models using gradient methods works so well because the
error surface either has no local minima, or if they exist they need to be
close in value to the global minimum. It is known that such results hold under
very strong assumptions which are not satisfied by real models. In this paper
we present examples showing that for such theorem to be true additional
assumptions on the data, initialization schemes and/or the model classes have
to be made. We look at the particular case of finite size datasets. We
demonstrate that in this scenario one can construct counter-examples (datasets
or initialization schemes) when the network does become susceptible to bad
local minima over the weight space.
DISCOMAX: A Proximity-Preserving Distance Correlation Maximization Algorithm
Authors: Praneeth Vepakomma, Ahmed Elgammal
Date: 2013-06-11 00:00:00
Tasks: regression

In a regression setting we propose algorithms that reduce the dimensionality
of the features while simultaneously maximizing a statistical measure of
dependence known as distance correlation between the low-dimensional features
and a response variable. This helps in solving the prediction problem with a
low-dimensional set of features. Our setting is different from subset-selection
algorithms where the problem is to choose the best subset of features for
regression. Instead, we attempt to generate a new set of low-dimensional
features as in a feature-learning setting. We attempt to keep our proposed
approach as model-free and our algorithm does not assume the application of any
specific regression model in conjunction with the low-dimensional features that
it learns. The algorithm is iterative and is fomulated as a combination of the
majorization-minimization and concave-convex optimization procedures. We also
present spectral radius based convergence results for the proposed iterations.
Wages of wins: could an amateur make money from match outcome predictions?
Authors: Albrecht Zimmermann
Date: 2017-02-17 00:00:00

Evaluating the accuracies of models for match outcome predictions is nice and
well but in the end the real proof is in the money to be made by betting. To
evaluate the question whether the models developed by us could be used easily
to make money via sports betting, we evaluate three cases: NCAAB post-season,
NBA season, and NFL season, and find that it is possible yet not without its
pitfalls. In particular, we illustrate that high accuracy does not
automatically equal high pay-out, by looking at the type of match-ups that are
predicted correctly by different models.
Benchmarking State-of-the-Art Deep Learning Software Tools
Authors: Shaohuai Shi, Qiang Wang, Pengfei Xu, Xiaowen Chu
Date: 2016-08-25 00:00:00
Tasks: Benchmarking, CPU, Deep Learning, GPU

Deep learning has been shown as a successful machine learning method for a
variety of tasks, and its popularity results in numerous open-source deep
learning software tools. Training a deep network is usually a very
time-consuming process. To address the computational challenge in deep
learning, many tools exploit hardware features such as multi-core CPUs and
many-core GPUs to shorten the training time. However, different tools exhibit
different features and running performance when training different types of
deep networks on different hardware platforms, which makes it difficult for end
users to select an appropriate pair of software and hardware. In this paper, we
aim to make a comparative study of the state-of-the-art GPU-accelerated deep
learning software tools, including Caffe, CNTK, MXNet, TensorFlow, and Torch.
We first benchmark the running performance of these tools with three popular
types of neural networks on two CPU platforms and three GPU platforms. We then
benchmark some distributed versions on multiple GPUs. Our contribution is
two-fold. First, for end users of deep learning tools, our benchmarking results
can serve as a guide to selecting appropriate hardware platforms and software
tools. Second, for software developers of deep learning tools, our in-depth
analysis points out possible future directions to further optimize the running
performance.
Stochastic Graphlet Embedding
Authors: Anjan Dutta, Hichem Sahbi
Date: 2017-02-01 00:00:00
Tasks: BIG-bench Machine Learning

Graph-based methods are known to be successful in many machine learning and
pattern classification tasks. These methods consider semi-structured data as
graphs where nodes correspond to primitives (parts, interest points, segments,
etc.) and edges characterize the relationships between these primitives.
However, these non-vectorial graph data cannot be straightforwardly plugged
into off-the-shelf machine learning algorithms without a preliminary step of --
explicit/implicit -- graph vectorization and embedding. This embedding process
should be resilient to intra-class graph variations while being highly
discriminant. In this paper, we propose a novel high-order stochastic graphlet
embedding (SGE) that maps graphs into vector spaces. Our main contribution
includes a new stochastic search procedure that efficiently parses a given
graph and extracts/samples unlimitedly high-order graphlets. We consider these
graphlets, with increasing orders, to model local primitives as well as their
increasingly complex interactions. In order to build our graph representation,
we measure the distribution of these graphlets into a given graph, using
particular hash functions that efficiently assign sampled graphlets into
isomorphic sets with a very low probability of collision. When combined with
maximum margin classifiers, these graphlet-based representations have positive
impact on the performance of pattern comparison and recognition as corroborated
through extensive experiments using standard benchmark databases.
Deep Hybrid Similarity Learning for Person Re-identification
Authors: Jianqing Zhu, Huanqiang Zeng, Shengcai Liao, Zhen Lei, Canhui Cai, Lixin Zheng
Date: 2017-02-16 00:00:00
Tasks: Metric Learning, Person Re-Identification

Person Re-IDentification (Re-ID) aims to match person images captured from
two non-overlapping cameras. In this paper, a deep hybrid similarity learning
(DHSL) method for person Re-ID based on a convolution neural network (CNN) is
proposed. In our approach, a CNN learning feature pair for the input image pair
is simultaneously extracted. Then, both the element-wise absolute difference
and multiplication of the CNN learning feature pair are calculated. Finally, a
hybrid similarity function is designed to measure the similarity between the
feature pair, which is realized by learning a group of weight coefficients to
project the element-wise absolute difference and multiplication into a
similarity score. Consequently, the proposed DHSL method is able to reasonably
assign parameters of feature learning and metric learning in a CNN so that the
performance of person Re-ID is improved. Experiments on three challenging
person Re-ID databases, QMUL GRID, VIPeR and CUHK03, illustrate that the
proposed DHSL method is superior to multiple state-of-the-art person Re-ID
methods.
Dynamic Multi-Objectives Optimization with a Changing Number of Objectives
Authors: Renzhi Chen, Ke Li, Xin Yao
Date: 2016-08-23 00:00:00

Existing studies on dynamic multi-objective optimization focus on problems
with time-dependent objective functions, while the ones with a changing number
of objectives have rarely been considered in the literature. Instead of
changing the shape or position of the Pareto-optimal front/set when having
time-dependent objective functions, increasing or decreasing the number of
objectives usually leads to the expansion or contraction of the dimension of
the Pareto-optimal front/set manifold. Unfortunately, most existing dynamic
handling techniques can hardly be adapted to this type of dynamics. In this
paper, we report our attempt toward tackling the dynamic multi-objective
optimization problems with a changing number of objectives. We implement a new
two-archive evolutionary algorithm which maintains two co-evolving populations
simultaneously. In particular, these two populations are complementary to each
other: one concerns more about the convergence while the other concerns more
about the diversity. The compositions of these two populations are adaptively
reconstructed once the environment changes. In addition, these two populations
interact with each other via a mating selection mechanism. Comprehensive
experiments are conducted on various benchmark problems with a time-dependent
number of objectives. Empirical results fully demonstrate the effectiveness of
our proposed algorithm.
Be Precise or Fuzzy: Learning the Meaning of Cardinals and Quantifiers from Vision
Authors: Sandro Pezzelle, Marco Marelli, Raffaella Bernardi
Date: 2017-02-17 00:00:00
Proceeding: EACL 2017 4

People can refer to quantities in a visual scene by using either exact
cardinals (e.g. one, two, three) or natural language quantifiers (e.g. few,
most, all). In humans, these two processes underlie fairly different cognitive
and neural mechanisms. Inspired by this evidence, the present study proposes
two models for learning the objective meaning of cardinals and quantifiers from
visual scenes containing multiple objects. We show that a model capitalizing on
a 'fuzzy' measure of similarity is effective for learning quantifiers, whereas
the learning of exact cardinals is better accomplished when information about
number is provided.
Achieving Human Parity in Conversational Speech Recognition
Authors: W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, G. Zweig
Date: 2016-10-17 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Conversational speech recognition has served as a flagship speech recognition
task since the release of the Switchboard corpus in the 1990s. In this paper,
we measure the human error rate on the widely used NIST 2000 test set, and find
that our latest automated system has reached human parity. The error rate of
professional transcribers is 5.9% for the Switchboard portion of the data, in
which newly acquainted pairs of people discuss an assigned topic, and 11.3% for
the CallHome portion where friends and family members have open-ended
conversations. In both cases, our automated system establishes a new state of
the art, and edges past the human benchmark, achieving error rates of 5.8% and
11.0%, respectively. The key to our system's performance is the use of various
convolutional and LSTM acoustic model architectures, combined with a novel
spatial smoothing method and lattice-free MMI acoustic training, multiple
recurrent neural network language modeling approaches, and a systematic use of
system combination.
Dynamic Key-Value Memory Networks for Knowledge Tracing
Authors: Jiani Zhang, Xingjian Shi, Irwin King, Dit-yan Yeung
Date: 2016-11-24 00:00:00
Tasks: Knowledge Tracing

Knowledge Tracing (KT) is a task of tracing evolving knowledge state of
students with respect to one or more concepts as they engage in a sequence of
learning activities. One important purpose of KT is to personalize the practice
sequence to help students learn knowledge concepts efficiently. However,
existing methods such as Bayesian Knowledge Tracing and Deep Knowledge Tracing
either model knowledge state for each predefined concept separately or fail to
pinpoint exactly which concepts a student is good at or unfamiliar with. To
solve these problems, this work introduces a new model called Dynamic Key-Value
Memory Networks (DKVMN) that can exploit the relationships between underlying
concepts and directly output a student's mastery level of each concept. Unlike
standard memory-augmented neural networks that facilitate a single memory
matrix or two static memory matrices, our model has one static matrix called
key, which stores the knowledge concepts and the other dynamic matrix called
value, which stores and updates the mastery levels of corresponding concepts.
Experiments show that our model consistently outperforms the state-of-the-art
model in a range of KT datasets. Moreover, the DKVMN model can automatically
discover underlying concepts of exercises typically performed by human
annotations and depict the changing knowledge state of a student.
Distinguishing Posed and Spontaneous Smiles by Facial Dynamics
Authors: Bappaditya Mandal, David Lee, Nizar Ouarti
Date: 2017-01-06 00:00:00
Tasks: Classification, Face Model, General Classification, Optical Flow Estimation, Quantization

Smile is one of the key elements in identifying emotions and present state of
mind of an individual. In this work, we propose a cluster of approaches to
classify posed and spontaneous smiles using deep convolutional neural network
(CNN) face features, local phase quantization (LPQ), dense optical flow and
histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for
micro-expression smile amplification along with three normalization procedures
for distinguishing posed and spontaneous smiles. Although the deep CNN face
model is trained with large number of face images, HOG features outperforms
this model for overall face smile classification task. Using EVM to amplify
micro-expressions did not have a significant impact on classification accuracy,
while the normalizing facial features improved classification accuracy. Unlike
many manual or semi-automatic methodologies, our approach aims to automatically
classify all smiles into either `spontaneous' or `posed' categories, by using
support vector machines (SVM). Experimental results on large UvA-NEMO smile
database show promising results as compared to other relevant methods.