pwc-archive/papers-with-abstracts huggingface dataset:
Probabilistic Label Relation Graphs with Ising Models
Authors: Nan Ding, Jia Deng, Kevin Murphy, Hartmut Neven
Date: 2015-03-04 00:00:00
Proceeding: ICCV 2015 12
Tasks: General Classification, Relation

We consider classification problems in which the label space has structure. A
common example is hierarchical label spaces, corresponding to the case where
one label subsumes another (e.g., animal subsumes dog). But labels can also be
mutually exclusive (e.g., dog vs cat) or unrelated (e.g., furry, carnivore). To
jointly model hierarchy and exclusion relations, the notion of a HEX (hierarchy
and exclusion) graph was introduced in [7]. This combined a conditional random
field (CRF) with a deep neural network (DNN), resulting in state of the art
results when applied to visual object classification problems where the
training labels were drawn from different levels of the ImageNet hierarchy
(e.g., an image might be labeled with the basic level category "dog", rather
than the more specific label "husky"). In this paper, we extend the HEX model
to allow for soft or probabilistic relations between labels, which is useful
when there is uncertainty about the relationship between two labels (e.g., an
antelope is "sort of" furry, but not to the same degree as a grizzly bear). We
call our new model pHEX, for probabilistic HEX. We show that the pHEX graph can
be converted to an Ising model, which allows us to use existing off-the-shelf
inference methods (in contrast to the HEX method, which needed specialized
inference algorithms). Experimental results show significant improvements in a
number of large-scale visual object classification tasks, outperforming the
previous HEX model.
Heuristic algorithms for finding distribution reducts in probabilistic rough set model
Authors: Xi'ao Ma, Guoyin Wang, Hong Yu
Date: 2015-12-22 00:00:00
Tasks: Attribute

Attribute reduction is one of the most important topics in rough set theory.
Heuristic attribute reduction algorithms have been presented to solve the
attribute reduction problem. It is generally known that fitness functions play
a key role in developing heuristic attribute reduction algorithms. The
monotonicity of fitness functions can guarantee the validity of heuristic
attribute reduction algorithms. In probabilistic rough set model, distribution
reducts can ensure the decision rules derived from the reducts are compatible
with those derived from the original decision table. However, there are few
studies on developing heuristic attribute reduction algorithms for finding
distribution reducts. This is partly due to the fact that there are no
monotonic fitness functions that are used to design heuristic attribute
reduction algorithms in probabilistic rough set model. The main objective of
this paper is to develop heuristic attribute reduction algorithms for finding
distribution reducts in probabilistic rough set model. For one thing, two
monotonic fitness functions are constructed, from which equivalence definitions
of distribution reducts can be obtained. For another, two modified monotonic
fitness functions are proposed to evaluate the significance of attributes more
effectively. On this basis, two heuristic attribute reduction algorithms for
finding distribution reducts are developed based on addition-deletion method
and deletion method. In particular, the monotonicity of fitness functions
guarantees the rationality of the proposed heuristic attribute reduction
algorithms. Results of experimental analysis are included to quantify the
effectiveness of the proposed fitness functions and distribution reducts.
Do Less and Achieve More: Training CNNs for Action Recognition Utilizing Action Images from the Web
Authors: Shugao Ma, Sarah Adel Bargal, Jianming Zhang, Leonid Sigal, Stan Sclaroff
Date: 2015-12-22 00:00:00
Tasks: Action Recognition, Action Recognition In Videos, Temporal Action Localization

Recently, attempts have been made to collect millions of videos to train CNN
models for action recognition in videos. However, curating such large-scale
video datasets requires immense human labor, and training CNNs on millions of
videos demands huge computational resources. In contrast, collecting action
images from the Web is much easier and training on images requires much less
computation. In addition, labeled web images tend to contain discriminative
action poses, which highlight discriminative portions of a video's temporal
progression. We explore the question of whether we can utilize web action
images to train better CNN models for action recognition in videos. We collect
23.8K manually filtered images from the Web that depict the 101 actions in the
UCF101 action video dataset. We show that by utilizing web action images along
with videos in training, significant performance boosts of CNN models can be
achieved. We then investigate the scalability of the process by leveraging
crawled web images (unfiltered) for UCF101 and ActivityNet. We replace 16.2M
video frames by 393K unfiltered images and get comparable performance.
A New Vision of Collaborative Active Learning
Authors: Adrian Calma, Tobias Reitmaier, Bernhard Sick, Paul Lukowicz, Mark Embrechts
Date: 2015-04-01 00:00:00
Tasks: Active Learning

Active learning (AL) is a learning paradigm where an active learner has to
train a model (e.g., a classifier) which is in principal trained in a
supervised way, but in AL it has to be done by means of a data set with
initially unlabeled samples. To get labels for these samples, the active
learner has to ask an oracle (e.g., a human expert) for labels. The goal is to
maximize the performance of the model and to minimize the number of queries at
the same time. In this article, we first briefly discuss the state of the art
and own, preliminary work in the field of AL. Then, we propose the concept of
collaborative active learning (CAL). With CAL, we will overcome some of the
harsh limitations of current AL. In particular, we envision scenarios where an
expert may be wrong for various reasons, there might be several or even many
experts with different expertise, the experts may label not only samples but
also knowledge at a higher level such as rules, and we consider that the
labeling costs depend on many conditions. Moreover, in a CAL process human
experts will profit by improving their own knowledge, too.
A Comprehensive Approach to Mode Clustering
Authors: Yen-Chi Chen, Christopher R. Genovese, Larry Wasserman
Date: 2014-06-06 00:00:00
Tasks: Clustering, Denoising

Mode clustering is a nonparametric method for clustering that defines
clusters using the basins of attraction of a density estimator's modes. We
provide several enhancements to mode clustering: (i) a soft variant of cluster
assignment, (ii) a measure of connectivity between clusters, (iii) a technique
for choosing the bandwidth, (iv) a method for denoising small clusters, and (v)
an approach to visualizing the clusters. Combining all these enhancements gives
us a complete procedure for clustering in multivariate problems. We also
compare mode clustering to other clustering methods in several examples
Diffusion Methods for Classification with Pairwise Relationships
Authors: Pedro F. Felzenszwalb, Benar F. Svaiter
Date: 2015-05-22 00:00:00
Tasks: Binary Classification, Classification, Depth Estimation, General Classification, Image Restoration, Stereo Depth Estimation

We define two algorithms for propagating information in classification problems with pairwise relationships. The algorithms are based on contraction maps and are related to non-linear diffusion and random walks on graphs. The approach is also related to message passing algorithms, including belief propagation and mean field methods. The algorithms we describe are guaranteed to converge on graphs with arbitrary topology. Moreover they always converge to a unique fixed point, independent of initialization. We prove that the fixed points of the algorithms under consideration define lower-bounds on the energy function and the max-marginals of a Markov random field. The theoretical results also illustrate a relationship between message passing algorithms and value iteration for an infinite horizon Markov decision process. We illustrate the practical application of the algorithms under study with numerical experiments in image restoration, stereo depth estimation and binary classification on a grid.
DeepWriterID: An End-to-end Online Text-independent Writer Identification System
Authors: Weixin Yang, Lianwen Jin, Manfei Liu
Date: 2015-08-20 00:00:00
Tasks: Data Augmentation

Owing to the rapid growth of touchscreen mobile terminals and pen-based
interfaces, handwriting-based writer identification systems are attracting
increasing attention for personal authentication, digital forensics, and other
applications. However, most studies on writer identification have not been
satisfying because of the insufficiency of data and difficulty of designing
good features under various conditions of handwritings. Hence, we introduce an
end-to-end system, namely DeepWriterID, employed a deep convolutional neural
network (CNN) to address these problems. A key feature of DeepWriterID is a new
method we are proposing, called DropSegment. It designs to achieve data
augmentation and improve the generalized applicability of CNN. For sufficient
feature representation, we further introduce path signature feature maps to
improve performance. Experiments were conducted on the NLPR handwriting
database. Even though we only use pen-position information in the pen-down
state of the given handwriting samples, we achieved new state-of-the-art
identification rates of 95.72% for Chinese text and 98.51% for English text.
The Multiverse Loss for Robust Transfer Learning
Authors: Etai Littwin, Lior Wolf
Date: 2015-11-29 00:00:00
Proceeding: CVPR 2016 6
Tasks: Transfer Learning

Deep learning techniques are renowned for supporting effective transfer
learning. However, as we demonstrate, the transferred representations support
only a few modes of separation and much of its dimensionality is unutilized. In
this work, we suggest to learn, in the source domain, multiple orthogonal
classifiers. We prove that this leads to a reduced rank representation, which,
however, supports more discriminative directions. Interestingly, the softmax
probabilities produced by the multiple classifiers are likely to be identical.
Experimental results, on CIFAR-100 and LFW, further demonstrate the
effectiveness of our method.
Cost-based Feature Transfer for Vehicle Occupant Classification
Authors: Toby Perrett, Majid Mirmehdi, Eduardo Dias
Date: 2015-12-22 00:00:00
Tasks: Classification, General Classification, Transfer Learning

Knowledge of human presence and interaction in a vehicle is of growing
interest to vehicle manufacturers for design and safety purposes. We present a
framework to perform the tasks of occupant detection and occupant
classification for automatic child locks and airbag suppression. It operates
for all passenger seats, using a single overhead camera. A transfer learning
technique is introduced to make full use of training data from all seats whilst
still maintaining some control over the bias, necessary for a system designed
to penalize certain misclassifications more than others. An evaluation is
performed on a challenging dataset with both weighted and unweighted
classifiers, demonstrating the effectiveness of the transfer process.
Move from Perturbed scheme to exponential weighting average
Authors: Chunyang Xiao
Date: 2015-12-22 00:00:00

In an online decision problem, one makes decisions often with a pool of
decision sequence called experts but without knowledge of the future. After
each step, one pays a cost based on the decision and observed rate. One
reasonal goal would be to perform as well as the best expert in the pool. The
modern and well-known way to attain this goal is the algorithm of exponential
weighting. However, recently, another algorithm called follow the perturbed
leader is developed and achieved about the same performance. In our work, we
first show the properties shared in common by the two algorithms which explain
the similarities on the performance. Next we will show that for a specific
perturbation, the two algorithms are identical. Finally, we show with some
examples that follow-the-leader style algorithms extend naturally to a large
class of structured online problems for which the exponential algorithms are
inefficient.
Partial Functional Correspondence
Authors: Emanuele Rodolà, Luca Cosmo, Michael M. Bronstein, Andrea Torsello, Daniel Cremers
Date: 2015-06-17 00:00:00

In this paper, we propose a method for computing partial functional
correspondence between non-rigid shapes. We use perturbation analysis to show
how removal of shape parts changes the Laplace-Beltrami eigenfunctions, and
exploit it as a prior on the spectral representation of the correspondence.
Corresponding parts are optimization variables in our problem and are used to
weight the functional correspondence; we are looking for the largest and most
regular (in the Mumford-Shah sense) parts that minimize correspondence
distortion. We show that our approach can cope with very challenging
correspondence settings.
News Across Languages - Cross-Lingual Document Similarity and Event Tracking
Authors: Jan Rupnik, Andrej Muhic, Gregor Leban, Primoz Skraba, Blaz Fortuna, Marko Grobelnik
Date: 2015-12-22 00:00:00
Tasks: Articles

In today's world, we follow news which is distributed globally. Significant
events are reported by different sources and in different languages. In this
work, we address the problem of tracking of events in a large multilingual
stream. Within a recently developed system Event Registry we examine two
aspects of this problem: how to compare articles in different languages and how
to link collections of articles in different languages which refer to the same
event. Taking a multilingual stream and clusters of articles from each
language, we compare different cross-lingual document similarity measures based
on Wikipedia. This allows us to compute the similarity of any two articles
regardless of language. Building on previous work, we show there are methods
which scale well and can compute a meaningful similarity between articles from
languages with little or no direct overlap in the training data. Using this
capability, we then propose an approach to link clusters of articles across
languages which represent the same event. We provide an extensive evaluation of
the system as a whole, as well as an evaluation of the quality and robustness
of the similarity measure and the linking algorithm.
Implementation of deep learning algorithm for automatic detection of brain tumors using intraoperative IR-thermal mapping data
Authors: A. V. Makarenko, M. G. Volovik
Date: 2015-12-22 00:00:00
Tasks: Diagnostic

The efficiency of deep machine learning for automatic delineation of tumor
areas has been demonstrated for intraoperative neuronavigation using active
IR-mapping with the use of the cold test. The proposed approach employs a
matrix IR-imager to remotely register the space-time distribution of surface
temperature pattern, which is determined by the dynamics of local cerebral
blood flow. The advantages of this technique are non-invasiveness, zero risks
for the health of patients and medical staff, low implementation and
operational costs, ease and speed of use. Traditional IR-diagnostic technique
has a crucial limitation - it involves a diagnostician who determines the
boundaries of tumor areas, which gives rise to considerable uncertainty, which
can lead to diagnosis errors that are difficult to control. The current study
demonstrates that implementing deep learning algorithms allows to eliminate the
explained drawback.
Deep Learning with S-shaped Rectified Linear Activation Units
Authors: Xiaojie Jin, Chunyan Xu, Jiashi Feng, Yunchao Wei, Junjun Xiong, Shuicheng Yan
Date: 2015-12-22 00:00:00
Tasks: Deep Learning

Rectified linear activation units are important components for
state-of-the-art deep convolutional networks. In this paper, we propose a novel
S-shaped rectified linear activation unit (SReLU) to learn both convex and
non-convex functions, imitating the multiple function forms given by the two
fundamental laws, namely the Webner-Fechner law and the Stevens law, in
psychophysics and neural sciences. Specifically, SReLU consists of three
piecewise linear functions, which are formulated by four learnable parameters.
The SReLU is learned jointly with the training of the whole deep network
through back propagation. During the training phase, to initialize SReLU in
different layers, we propose a "freezing" method to degenerate SReLU into a
predefined leaky rectified linear unit in the initial several training epochs
and then adaptively learn the good initial values. SReLU can be universally
used in the existing deep networks with negligible additional parameters and
computation cost. Experiments with two popular CNN architectures, Network in
Network and GoogLeNet on scale-various benchmarks including CIFAR10, CIFAR100,
MNIST and ImageNet demonstrate that SReLU achieves remarkable improvement
compared to other activation functions.
Towards automating the generation of derivative nouns in Sanskrit by simulating Panini
Authors: Amrith Krishna, Pawan Goyal
Date: 2015-12-17 00:00:00

About 1115 rules in Astadhyayi from A.4.1.76 to A.5.4.160 deal with
generation of derivative nouns, making it one of the largest topical sections
in Astadhyayi, called as the Taddhita section owing to the head rule A.4.1.76.
This section is a systematic arrangement of rules that enumerates various
affixes that are used in the derivation under specific semantic relations. We
propose a system that automates the process of generation of derivative nouns
as per the rules in Astadhyayi. The proposed system follows a completely object
oriented approach, that models each rule as a class of its own and then groups
them as rule groups. The rule groups are decided on the basis of selective
grouping of rules by virtue of anuvrtti. The grouping of rules results in an
inheritance network of rules which is a directed acyclic graph. Every rule
group has a head rule and the head rule notifies all the direct member rules of
the group about the environment which contains all the details about data
entities, participating in the derivation process. The system implements this
mechanism using multilevel inheritance and observer design patterns. The system
focuses not only on generation of the desired final form, but also on the
correctness of sequence of rules applied to make sure that the derivation has
taken place in strict adherence to Astadhyayi. The proposed system's design
allows to incorporate various conflict resolution methods mentioned in
authentic texts and hence the effectiveness of those rules can be validated
with the results from the system. We also present cases where we have checked
the applicability of the system with the rules which are not specifically
applicable to derivation of derivative nouns, in order to see the effectiveness
of the proposed schema as a generic system for modeling Astadhyayi.
FAASTA: A fast solver for total-variation regularization of ill-conditioned problems with application to brain imaging
Authors: Gaël Varoquaux, Michael Eickenberg, Elvis Dohmatob, Bertand Thirion
Date: 2015-12-22 00:00:00
Tasks: Brain Decoding

The total variation (TV) penalty, as many other analysis-sparsity problems,
does not lead to separable factors or a proximal operatorwith a closed-form
expression, such as soft thresholding for the $\ell\_1$ penalty. As a result,
in a variational formulation of an inverse problem or statisticallearning
estimation, it leads to challenging non-smooth optimization problemsthat are
often solved with elaborate single-step first-order methods. When thedata-fit
term arises from empirical measurements, as in brain imaging, it isoften very
ill-conditioned and without simple structure. In this situation, in proximal
splitting methods, the computation cost of thegradient step can easily dominate
each iteration. Thus it is beneficialto minimize the number of gradient
steps.We present fAASTA, a variant of FISTA, that relies on an internal solver
forthe TV proximal operator, and refines its tolerance to balance
computationalcost of the gradient and the proximal steps. We give benchmarks
andillustrations on "brain decoding": recovering brain maps from
noisymeasurements to predict observed behavior. The algorithm as well as
theempirical study of convergence speed are valuable for any non-exact
proximaloperator, in particular analysis-sparsity problems.
On the Differential Privacy of Bayesian Inference
Authors: Zuhe Zhang, Benjamin Rubinstein, Christos Dimitrakakis
Date: 2015-12-22 00:00:00
Tasks: Bayesian Inference

We study how to communicate findings of Bayesian inference to third parties,
while preserving the strong guarantee of differential privacy. Our main
contributions are four different algorithms for private Bayesian inference on
proba-bilistic graphical models. These include two mechanisms for adding noise
to the Bayesian updates, either directly to the posterior parameters, or to
their Fourier transform so as to preserve update consistency. We also utilise a
recently introduced posterior sampling mechanism, for which we prove bounds for
the specific but general case of discrete Bayesian networks; and we introduce a
maximum-a-posteriori private mechanism. Our analysis includes utility and
privacy bounds, with a novel focus on the influence of graph structure on
privacy. Worked examples and experiments with Bayesian na{\"i}ve Bayes and
Bayesian linear regression illustrate the application of our mechanisms.
Multi-Instance Visual-Semantic Embedding
Authors: Zhou Ren, Hailin Jin, Zhe Lin, Chen Fang, Alan Yuille
Date: 2015-12-22 00:00:00
Tasks: General Classification, image-classification, Image Classification, Zero-Shot Learning

Visual-semantic embedding models have been recently proposed and shown to be
effective for image classification and zero-shot learning, by mapping images
into a continuous semantic label space. Although several approaches have been
proposed for single-label embedding tasks, handling images with multiple labels
(which is a more general setting) still remains an open problem, mainly due to
the complex underlying corresponding relationship between image and its labels.
In this work, we present Multi-Instance visual-semantic Embedding model (MIE)
for embedding images associated with either single or multiple labels. Our
model discovers and maps semantically-meaningful image subregions to their
corresponding labels. And we demonstrate the superiority of our method over the
state-of-the-art on two tasks, including multi-label image annotation and
zero-shot learning.
Action-Conditional Video Prediction using Deep Networks in Atari Games
Authors: Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard Lewis, Satinder Singh
Date: 2015-07-31 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Atari Games, Reinforcement Learning, Reinforcement Learning (RL), Video Prediction

Motivated by vision-based reinforcement learning (RL) problems, in particular
Atari games from the recent benchmark Aracade Learning Environment (ALE), we
consider spatio-temporal prediction problems where future (image-)frames are
dependent on control variables or actions as well as previous frames. While not
composed of natural scenes, frames in Atari games are high-dimensional in size,
can involve tens of objects with one or more objects being controlled by the
actions directly and many other objects being influenced indirectly, can
involve entry and departure of objects, and can involve deep partial
observability. We propose and evaluate two deep neural network architectures
that consist of encoding, action-conditional transformation, and decoding
layers based on convolutional neural networks and recurrent neural networks.
Experimental results show that the proposed architectures are able to generate
visually-realistic frames that are also useful for control over approximately
100-step action-conditional futures in some games. To the best of our
knowledge, this paper is the first to make and evaluate long-term predictions
on high-dimensional video conditioned by control inputs.