pwc-archive/papers-with-abstracts huggingface dataset:
Rethinking Convolutional Semantic Segmentation Learning
Authors: Mrinal Haloi
Date: 2017-10-22 00:00:00
Tasks: Classification, General Classification, Segmentation, Semantic Segmentation

Deep convolutional semantic segmentation (DCSS) learning doesn't converge to
an optimal local minimum with random parameters initializations; a pre-trained
model on the same domain becomes necessary to achieve convergence.In this work,
we propose a joint cooperative end-to-end learning method for DCSS. It
addresses many drawbacks with existing deep semantic segmentation learning; the
proposed approach simultaneously learn both segmentation and classification;
taking away the essential need of the pre-trained model for learning
convergence. We present an improved inception based architecture with partial
attention gating (PAG) over encoder information. The PAG also adds to achieve
faster convergence and better accuracy for segmentation task. We will show the
effectiveness of this learning on a diabetic retinopathy classification and
segmentation dataset.
Hierarchical State Abstractions for Decision-Making Problems with Computational Constraints
Authors: Daniel T. Larsson, Daniel Braun, Panagiotis Tsiotras
Date: 2017-10-22 00:00:00
Tasks: Decision Making, Sequential Decision Making

In this semi-tutorial paper, we first review the information-theoretic
approach to account for the computational costs incurred during the search for
optimal actions in a sequential decision-making problem. The traditional (MDP)
framework ignores computational limitations while searching for optimal
policies, essentially assuming that the acting agent is perfectly rational and
aims for exact optimality. Using the free-energy, a variational principle is
introduced that accounts not only for the value of a policy alone, but also
considers the cost of finding this optimal policy. The solution of the
variational equations arising from this formulation can be obtained using
familiar Bellman-like value iterations from dynamic programming (DP) and the
Blahut-Arimoto (BA) algorithm from rate distortion theory. Finally, we
demonstrate the utility of the approach for generating hierarchies of state
abstractions that can be used to best exploit the available computational
resources. A numerical example showcases these concepts for a path-planning
problem in a grid world environment.
An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory
Authors: Mehmet Eren Ahsen, Mathukumalli Vidyasagar
Date: 2017-10-22 00:00:00
Tasks: 2k, compressed sensing, Learning Theory, PAC learning

In this paper, the problem of one-bit compressed sensing (OBCS) is formulated
as a problem in probably approximately correct (PAC) learning. It is shown that
the Vapnik-Chervonenkis (VC-) dimension of the set of half-spaces in
$\mathbb{R}^n$ generated by $k$-sparse vectors is bounded below by $k \lg
(n/k)$ and above by $2k \lg (n/k)$, plus some round-off terms. By coupling this
estimate with well-established results in PAC learning theory, we show that a
consistent algorithm can recover a $k$-sparse vector with $O(k \lg (n/k))$
measurements, given only the signs of the measurement vector. This result holds
for \textit{all} probability measures on $\mathbb{R}^n$. It is further shown
that random sign-flipping errors result only in an increase in the constant in
the $O(k \lg (n/k))$ estimate. Because constructing a consistent algorithm is
not straight-forward, we present a heuristic based on the $\ell_1$-norm support
vector machine, and illustrate that its computational performance is superior
to a currently popular method.
Backtracking Regression Forests for Accurate Camera Relocalization
Authors: Lili Meng, Jianhui Chen, Frederick Tung, James J. Little, Julien Valentin, Clarence W. de Silva
Date: 2017-10-22 00:00:00
Tasks: Camera Relocalization, Loop Closure Detection, regression, Simultaneous Localization and Mapping

Camera relocalization plays a vital role in many robotics and computer vision
tasks, such as global localization, recovery from tracking failure, and loop
closure detection. Recent random forests based methods directly predict 3D
world locations for 2D image locations to guide the camera pose optimization.
During training, each tree greedily splits the samples to minimize the spatial
variance. However, these greedy splits often produce uneven sub-trees in
training or incorrect 2D-3D correspondences in testing. To address these
problems, we propose a sample-balanced objective to encourage equal numbers of
samples in the left and right sub-trees, and a novel backtracking scheme to
remedy the incorrect 2D-3D correspondence predictions. Furthermore, we extend
the regression forests based methods to use local features in both training and
testing stages for outdoor RGB-only applications. Experimental results on
publicly available indoor and outdoor datasets demonstrate the efficacy of our
approach, which shows superior or on-par accuracy with several state-of-the-art
methods.
How big is big enough? Unsupervised word sense disambiguation using a very large corpus
Authors: Piotr Przybyła
Date: 2017-10-22 00:00:00
Tasks: Word Sense Disambiguation

In this paper, the problem of disambiguating a target word for Polish is
approached by searching for related words with known meaning. These relatives
are used to build a training corpus from unannotated text. This technique is
improved by proposing new rich sources of replacements that substitute the
traditional requirement of monosemy with heuristics based on wordnet relations.
The na\"ive Bayesian classifier has been modified to account for an unknown
distribution of senses. A corpus of 600 million web documents (594 billion
tokens), gathered by the NEKST search engine allows us to assess the
relationship between training set size and disambiguation accuracy. The
classifier is evaluated using both a wordnet baseline and a corpus with 17,314
manually annotated occurrences of 54 ambiguous words.
WristAuthen: A Dynamic Time Wrapping Approach for User Authentication by Hand-Interaction through Wrist-Worn Devices
Authors: Qi Lyu, Zhifeng Kong, Chao Shen, Tianwei Yue
Date: 2017-10-22 00:00:00

The growing trend of using wearable devices for context-aware computing and
pervasive sensing systems has raised its potentials for quick and reliable
authentication techniques. Since personal writing habitats differ from each
other, it is possible to realize user authentication through writing. This is
of great significance as sensible information is easily collected by these
devices. This paper presents a novel user authentication system through
wrist-worn devices by analyzing the interaction behavior with users, which is
both accurate and efficient for future usage. The key feature of our approach
lies in using much more effective Savitzky-Golay filter and Dynamic Time
Wrapping method to obtain fine-grained writing metrics for user authentication.
These new metrics are relatively unique from person to person and independent
of the computing platform. Analyses are conducted on the wristband-interaction
data collected from 50 users with diversity in gender, age, and height.
Extensive experimental results show that the proposed approach can identify
users in a timely and accurate manner, with a false-negative rate of 1.78\%,
false-positive rate of 6.7\%, and Area Under ROC Curve of 0.983 . Additional
examination on robustness to various mimic attacks, tolerance to training data,
and comparisons to further analyze the applicability.
Elliptical modeling and pattern analysis for perturbation models and classfication
Authors: Shan Suthaharan, Weining Shen
Date: 2017-10-22 00:00:00
Tasks: blind source separation, Dimensionality Reduction, General Classification

The characteristics (or numerical patterns) of a feature vector in the
transform domain of a perturbation model differ significantly from those of its
corresponding feature vector in the input domain. These differences - caused by
the perturbation techniques used for the transformation of feature patterns -
degrade the performance of machine learning techniques in the transform domain.
In this paper, we proposed a nonlinear parametric perturbation model that
transforms the input feature patterns to a set of elliptical patterns, and
studied the performance degradation issues associated with random forest
classification technique using both the input and transform domain features.
Compared with the linear transformation such as Principal Component Analysis
(PCA), the proposed method requires less statistical assumptions and is highly
suitable for the applications such as data privacy and security due to the
difficulty of inverting the elliptical patterns from the transform domain to
the input domain. In addition, we adopted a flexible block-wise dimensionality
reduction step in the proposed method to accommodate the possible
high-dimensional data in modern applications. We evaluated the empirical
performance of the proposed method on a network intrusion data set and a
biological data set, and compared the results with PCA in terms of
classification performance and data privacy protection (measured by the blind
source separation attack and signal interference ratio). Both results confirmed
the superior performance of the proposed elliptical transformation.
Joint Distribution Optimal Transportation for Domain Adaptation
Authors: Nicolas Courty, Rémi Flamary, Amaury Habrard, Alain Rakotomamonjy
Date: 2017-05-24 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

This paper deals with the unsupervised domain adaptation problem, where one
wants to estimate a prediction function $f$ in a given target domain without
any labeled sample by exploiting the knowledge available from a source domain
where labels are known. Our work makes the following assumption: there exists a
non-linear transformation between the joint feature/label space distributions
of the two domain $\mathcal{P}_s$ and $\mathcal{P}_t$. We propose a solution of
this problem with optimal transport, that allows to recover an estimated target
$\mathcal{P}^f_t=(X,f(X))$ by optimizing simultaneously the optimal coupling
and $f$. We show that our method corresponds to the minimization of a bound on
the target error, and provide an efficient algorithmic solution, for which
convergence is proved. The versatility of our approach, both in terms of class
of hypothesis or loss functions is demonstrated with real world classification
and regression problems, for which we reach or surpass state-of-the-art
results.
A Simple Yet Efficient Rank One Update for Covariance Matrix Adaptation
Authors: Zhenhua Li, Qingfu Zhang
Date: 2017-10-11 00:00:00

In this paper, we propose an efficient approximated rank one update for
covariance matrix adaptation evolution strategy (CMA-ES). It makes use of two
evolution paths as simple as that of CMA-ES, while avoiding the computational
matrix decomposition. We analyze the algorithms' properties and behaviors. We
experimentally study the proposed algorithm's performances. It generally
outperforms or performs competitively to the Cholesky CMA-ES.
Online Learning with Automata-based Expert Sequences
Authors: Mehryar Mohri, Scott Yang
Date: 2017-04-29 00:00:00

We consider a general framework of online learning with expert advice where
regret is defined with respect to sequences of experts accepted by a weighted
automaton. Our framework covers several problems previously studied, including
competing against k-shifting experts. We give a series of algorithms for this
problem, including an automata-based algorithm extending weighted-majority and
more efficient algorithms based on the notion of failure transitions. We
further present efficient algorithms based on an approximation of the
competitor automaton, in particular n-gram models obtained by minimizing the
\infty-R\'{e}nyi divergence, and present an extensive study of the
approximation properties of such models. Finally, we also extend our algorithms
and results to the framework of sleeping experts.
Strategic Classification from Revealed Preferences
Authors: Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, Zhiwei Steven Wu
Date: 2017-10-22 00:00:00
Tasks: Classification, General Classification

We study an online linear classification problem, in which the data is
generated by strategic agents who manipulate their features in an effort to
change the classification outcome. In rounds, the learner deploys a classifier,
and an adversarially chosen agent arrives, possibly manipulating her features
to optimally respond to the learner. The learner has no knowledge of the
agents' utility functions or "real" features, which may vary widely across
agents. Instead, the learner is only able to observe their "revealed
preferences" --- i.e. the actual manipulated feature vectors they provide. For
a broad family of agent cost functions, we give a computationally efficient
learning algorithm that is able to obtain diminishing "Stackelberg regret" ---
a form of policy regret that guarantees that the learner is obtaining loss
nearly as small as that of the best classifier in hindsight, even allowing for
the fact that agents will best-respond differently to the optimal classifier.
Hidden Two-Stream Convolutional Networks for Action Recognition
Authors: Yi Zhu, Zhenzhong Lan, Shawn Newsam, Alexander G. Hauptmann
Date: 2017-04-02 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization, Vocal Bursts Valence Prediction

Analyzing videos of human actions involves understanding the temporal
relationships among video frames. State-of-the-art action recognition
approaches rely on traditional optical flow estimation methods to pre-compute
motion information for CNNs. Such a two-stage approach is computationally
expensive, storage demanding, and not end-to-end trainable. In this paper, we
present a novel CNN architecture that implicitly captures motion information
between adjacent frames. We name our approach hidden two-stream CNNs because it
only takes raw video frames as input and directly predicts action classes
without explicitly computing optical flow. Our end-to-end approach is 10x
faster than its two-stage baseline. Experimental results on four challenging
action recognition datasets: UCF101, HMDB51, THUMOS14 and ActivityNet v1.2 show
that our approach significantly outperforms the previous best real-time
approaches.
When Point Process Meets RNNs: Predicting Fine-Grained User Interests with Mutual Behavioral Infectivity
Authors: Tong Chen, Lin Wu, Yang Wang, Jun Zhang, Hongxu Chen, Xue Li
Date: 2017-10-14 00:00:00
Tasks: Prediction

Predicting fine-grained interests of users with temporal behavior is
important to personalization and information filtering applications. However,
existing interest prediction methods are incapable of capturing the subtle
degreed user interests towards particular items, and the internal time-varying
drifting attention of individuals is not studied yet. Moreover, the prediction
process can also be affected by inter-personal influence, known as behavioral
mutual infectivity. Inspired by point process in modeling temporal point
process, in this paper we present a deep prediction method based on two
recurrent neural networks (RNNs) to jointly model each user's continuous
browsing history and asynchronous event sequences in the context of inter-user
behavioral mutual infectivity. Our model is able to predict the fine-grained
interest from a user regarding a particular item and corresponding timestamps
when an occurrence of event takes place. The proposed approach is more flexible
to capture the dynamic characteristic of event sequences by using the temporal
point process to model event data and timely update its intensity function by
RNNs. Furthermore, to improve the interpretability of the model, the attention
mechanism is introduced to emphasize both intra-personal and inter-personal
behavior influence over time. Experiments on real datasets demonstrate that our
model outperforms the state-of-the-art methods in fine-grained user interest
prediction.
Optimal Rates for Multi-pass Stochastic Gradient Methods
Authors: Junhong Lin, Lorenzo Rosasco
Date: 2016-05-28 00:00:00

We analyze the learning properties of the stochastic gradient method when
multiple passes over the data and mini-batches are allowed. We study how
regularization properties are controlled by the step-size, the number of passes
and the mini-batch size. In particular, we consider the square loss and show
that for a universal step-size choice, the number of passes acts as a
regularization parameter, and optimal finite sample bounds can be achieved by
early-stopping. Moreover, we show that larger step-sizes are allowed when
considering mini-batches. Our analysis is based on a unifying approach,
encompassing both batch and stochastic gradient methods as special cases. As a
byproduct, we derive optimal convergence results for batch gradient methods
(even in the non-attainable cases).
Insulin Regimen ML-based control for T2DM patients
Authors: Mark Shifrin, Hava Siegelmann
Date: 2017-10-21 00:00:00
Tasks: Model-based Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

\begin{abstract} We model individual T2DM patient blood glucose level (BGL)
by stochastic process with discrete number of states mainly but not solely
governed by medication regimen (e.g. insulin injections). BGL states change
otherwise according to various physiological triggers which render a
stochastic, statistically unknown, yet assumed to be quasi-stationary, nature
of the process. In order to express incentive for being in desired healthy BGL
we heuristically define a reward function which returns positive values for
desirable BG levels and negative values for undesirable BG levels. The state
space consists of sufficient number of states in order to allow for memoryless
assumption. This, in turn, allows to formulate Markov Decision Process (MDP),
with an objective to maximize the total reward, summarized over a long run. The
probability law is found by model-based reinforcement learning (RL) and the
optimal insulin treatment policy is retrieved from MDP solution.
Attentive Semantic Video Generation using Captions
Authors: Tanya Marwah, Gaurav Mittal, Vineeth N. Balasubramanian
Date: 2017-08-20 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Recognition, Style Transfer, Temporal Action Localization, Video Generation

This paper proposes a network architecture to perform variable length
semantic video generation using captions. We adopt a new perspective towards
video generation where we allow the captions to be combined with the long-term
and short-term dependencies between video frames and thus generate a video in
an incremental manner. Our experiments demonstrate our network architecture's
ability to distinguish between objects, actions and interactions in a video and
combine them to generate videos for unseen captions. The network also exhibits
the capability to perform spatio-temporal style transfer when asked to generate
videos for a sequence of captions. We also show that the network's ability to
learn a latent representation allows it generate videos in an unsupervised
manner and perform other tasks such as action recognition. (Accepted in
International Conference in Computer Vision (ICCV) 2017)
Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures
Authors: Gaurav Mittal, Tanya Marwah, Vineeth N. Balasubramanian
Date: 2016-11-30 00:00:00
Tasks: Text-to-Video Generation, Video Generation

This paper introduces a novel approach for generating videos called
Synchronized Deep Recurrent Attentive Writer (Sync-DRAW). Sync-DRAW can also
perform text-to-video generation which, to the best of our knowledge, makes it
the first approach of its kind. It combines a Variational Autoencoder~(VAE)
with a Recurrent Attention Mechanism in a novel manner to create a temporally
dependent sequence of frames that are gradually formed over time. The recurrent
attention mechanism in Sync-DRAW attends to each individual frame of the video
in sychronization, while the VAE learns a latent distribution for the entire
video at the global level. Our experiments with Bouncing MNIST, KTH and UCF-101
suggest that Sync-DRAW is efficient in learning the spatial and temporal
information of the videos and generates frames with high structural integrity,
and can generate videos from simple captions on these datasets. (Accepted as
oral paper in ACM-Multimedia 2017)
Deep Neural Network Approximation using Tensor Sketching
Authors: Shiva Prasad Kasiviswanathan, Nina Narodytska, Hongxia Jin
Date: 2017-10-21 00:00:00

Deep neural networks are powerful learning models that achieve
state-of-the-art performance on many computer vision, speech, and language
processing tasks. In this paper, we study a fundamental question that arises
when designing deep network architectures: Given a target network architecture
can we design a smaller network architecture that approximates the operation of
the target network? The question is, in part, motivated by the challenge of
parameter reduction (compression) in modern deep neural networks, as the ever
increasing storage and memory requirements of these networks pose a problem in
resource constrained environments.
  In this work, we focus on deep convolutional neural network architectures,
and propose a novel randomized tensor sketching technique that we utilize to
develop a unified framework for approximating the operation of both the
convolutional and fully connected layers. By applying the sketching technique
along different tensor dimensions, we design changes to the convolutional and
fully connected layers that substantially reduce the number of effective
parameters in a network. We show that the resulting smaller network can be
trained directly, and has a classification accuracy that is comparable to the
original network.
Heat Kernel Smoothing in Irregular Image Domains
Authors: Moo. K. Chung, Yanli Wang, Gurong Wu
Date: 2017-10-21 00:00:00

We present the discrete version of heat kernel smoothing on graph data
structure. The method is used to smooth data in an irregularly shaped domains
in 3D images.
  New statistical properties are derived. As an application, we show how to
filter out data in the lung blood vessel trees obtained from computed
tomography. The method can be further used in representing the complex vessel
trees parametrically and extracting the skeleton representation of the trees.
A Generative Restricted Boltzmann Machine Based Method for High-Dimensional Motion Data Modeling
Authors: Siqi Nie, Ziheng Wang, Qiang Ji
Date: 2017-10-21 00:00:00
Tasks: Action Recognition, Facial Expression Recognition, Facial Expression Recognition (FER), General Classification, Multi-class Classification, Temporal Action Localization

Many computer vision applications involve modeling complex spatio-temporal
patterns in high-dimensional motion data. Recently, restricted Boltzmann
machines (RBMs) have been widely used to capture and represent spatial patterns
in a single image or temporal patterns in several time slices. To model global
dynamics and local spatial interactions, we propose to theoretically extend the
conventional RBMs by introducing another term in the energy function to
explicitly model the local spatial interactions in the input data. A learning
method is then proposed to perform efficient learning for the proposed model.
We further introduce a new method for multi-class classification that can
effectively estimate the infeasible partition functions of different RBMs such
that RBM is treated as a generative model for classification purpose. The
improved RBM model is evaluated on two computer vision applications: facial
expression recognition and human action recognition. Experimental results on
benchmark databases demonstrate the effectiveness of the proposed algorithm.