pwc-archive/papers-with-abstracts huggingface dataset:
DeepVO: A Deep Learning approach for Monocular Visual Odometry
Authors: Vikram Mohanty, Shubh Agrawal, Shaswat Datta, Arna Ghosh, Vishnu Dutt Sharma, Debashish Chakravarty
Date: 2016-11-18 00:00:00
Tasks: Autonomous Navigation, Deep Learning, General Classification, image-classification, Image Classification, Monocular Visual Odometry, object-detection, Object Detection, Simultaneous Localization and Mapping, Visual Odometry

Deep Learning based techniques have been adopted with precision to solve a
lot of standard computer vision problems, some of which are image
classification, object detection and segmentation. Despite the widespread
success of these approaches, they have not yet been exploited largely for
solving the standard perception related problems encountered in autonomous
navigation such as Visual Odometry (VO), Structure from Motion (SfM) and
Simultaneous Localization and Mapping (SLAM). This paper analyzes the problem
of Monocular Visual Odometry using a Deep Learning-based framework, instead of
the regular 'feature detection and tracking' pipeline approaches. Several
experiments were performed to understand the influence of a known/unknown
environment, a conventional trackable feature and pre-trained activations tuned
for object classification on the network's ability to accurately estimate the
motion trajectory of the camera (or the vehicle). Based on these observations,
we propose a Convolutional Neural Network architecture, best suited for
estimating the object's pose under known environment conditions, and displays
promising results when it comes to inferring the actual scale using just a
single camera in real-time.
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
Authors: Sijie Song, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Jiaying Liu
Date: 2016-11-18 00:00:00
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Human action recognition is an important task in computer vision. Extracting
discriminative spatial and temporal features to model the spatial and temporal
evolutions of different actions plays a key role in accomplishing this task. In
this work, we propose an end-to-end spatial and temporal attention model for
human action recognition from skeleton data. We build our model on top of the
Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM), which
learns to selectively focus on discriminative joints of skeleton within each
frame of the inputs and pays different levels of attention to the outputs of
different frames. Furthermore, to ensure effective training of the network, we
propose a regularized cross-entropy loss to drive the model learning process
and develop a joint training strategy accordingly. Experimental results
demonstrate the effectiveness of the proposed model,both on the small human
action recognition data set of SBU and the currently largest NTU dataset.
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
Authors: Alexandre Abraham, Michael Milham, Adriana Di Martino, R. Cameron Craddock, Dimitris Samaras, Bertrand Thirion, Gaël Varoquaux
Date: 2016-11-18 00:00:00
Tasks: Diagnostic, General Classification

Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the
promise to reveal functional biomarkers of neuropsychiatric disorders. However,
extracting such biomarkers is challenging for complex multi-faceted
neuropatholo-gies, such as autism spectrum disorders. Large multi-site datasets
increase sample sizes to compensate for this complexity, at the cost of
uncontrolled heterogeneity. This heterogeneity raises new challenges, akin to
those face in realistic diagnostic applications. Here, we demonstrate the
feasibility of inter-site classification of neuropsychiatric status, with an
application to the Autism Brain Imaging Data Exchange (ABIDE) database, a large
(N=871) multi-site autism dataset. For this purpose, we investigate pipelines
that extract the most predictive biomarkers from the data. These R-fMRI
pipelines build participant-specific connectomes from functionally-defined
brain areas. Connectomes are then compared across participants to learn
patterns of connectivity that differentiate typical controls from individuals
with autism. We predict this neuropsychiatric status for participants from the
same acquisition sites or different, unseen, ones. Good choices of methods for
the various steps of the pipeline lead to 67% prediction accuracy on the full
ABIDE data, which is significantly better than previously reported results. We
perform extensive validation on multiple subsets of the data defined by
different inclusion criteria. These enables detailed analysis of the factors
contributing to successful connectome-based prediction. First, prediction
accuracy improves as we include more subjects, up to the maximum amount of
subjects available. Second, the definition of functional brain areas is of
paramount importance for biomarker discovery: brain areas extracted from large
R-fMRI datasets outperform reference atlases in the classification tasks.
Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity
Authors: Eugene Belilovsky, Gaël Varoquaux, Matthew B. Blaschko
Date: 2015-12-29 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Functional Connectivity

Functional brain networks are well described and estimated from data with
Gaussian Graphical Models (GGMs), e.g. using sparse inverse covariance
estimators. Comparing functional connectivity of subjects in two populations
calls for comparing these estimated GGMs. Our goal is to identify differences
in GGMs known to have similar structure. We characterize the uncertainty of
differences with confidence intervals obtained using a parametric distribution
on parameters of a sparse estimator. Sparse penalties enable statistical
guarantees and interpretable models even in high-dimensional and low-sample
settings. Characterizing the distributions of sparse models is inherently
challenging as the penalties produce a biased estimator. Recent work invokes
the sparsity assumptions to effectively remove the bias from a sparse estimator
such as the lasso. These distributions can be used to give confidence intervals
on edges in GGMs, and by extension their differences. However, in the case of
comparing GGMs, these estimators do not make use of any assumed joint structure
among the GGMs. Inspired by priors from brain functional connectivity we derive
the distribution of parameter differences under a joint penalty when parameters
are known to be sparse in the difference. This leads us to introduce the
debiased multi-task fused lasso, whose distribution can be characterized in an
efficient manner. We then show how the debiased lasso and multi-task fused
lasso can be used to obtain confidence intervals on edge differences in GGMs.
We validate the techniques proposed on a set of synthetic examples as well as
neuro-imaging dataset created for the study of autism.
A Novel Architecture for Computing Approximate Radon Transform
Authors: M. A. Khorsandi, N. Karimi, S. Samavi
Date: 2016-11-18 00:00:00

Radon transform is a type of transform which is used in image processing to
transfer the image into intercept-slope coordinate. Its diagonal properties
made it appropriate for some applications which need processes in different
degrees. Radon transform computation needs a lot of arithmetic operations which
makes it a compute-intensive algorithm. In literature an approximate algorithm
for computing Radon transform is introduces which reduces the complexity of
computations. But this algorithm is complex and need arbitrary accesses to
memory. In this paper we proposed an algorithm which accesses to memory
sequentially. In the following an architecture is introduced which uses
pipeline to reduce the time complexity of algorithm.
Probabilistic Failure Analysis in Model Validation & Verification
Authors: Ning Ge, Marc Pantel, Xavier Crégut
Date: 2016-11-15 00:00:00
Tasks: Fault localization, model

Automated fault localization is an important issue in model validation and
verification. It helps the end users in analyzing the origin of failure. In
this work, we show the early experiments with probabilistic analysis approaches
in fault localization. Inspired by the Kullback-Leibler Divergence from
Bayesian probabilistic theory, we propose a suspiciousness factor to compute
the fault contribution for the transitions in the reachability graph of model
checking, using which to rank the potential faulty transitions. To
automatically locate design faults in the simulation model of detailed design,
we propose to use the statistical model Hidden Markov Model (HMM), which
provides statistically identical information to component's real behavior. The
core of this method is a fault localization algorithm that gives out the set of
suspicious ranked faulty components and a backward algorithm that computes the
matching degree between the HMM and the simulation model to evaluate the
confidence degree of the localization conclusion.
Exploration Potential
Authors: Jan Leike
Date: 2016-09-16 00:00:00
Tasks: Multi-Armed Bandits, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce exploration potential, a quantity that measures how much a
reinforcement learning agent has explored its environment class. In contrast to
information gain, exploration potential takes the problem's reward structure
into account. This leads to an exploration criterion that is both necessary and
sufficient for asymptotic optimality (learning to act optimally across the
entire environment class). Our experiments in multi-armed bandits use
exploration potential to illustrate how different algorithms make the tradeoff
between exploration and exploitation.
Cross Domain Knowledge Transfer for Person Re-identification
Authors: Qiqi Xiao, Kelei Cao, Haonan Chen, Fangyue Peng, Chi Zhang
Date: 2016-11-18 00:00:00
Tasks: Attribute, Classification, General Classification, Person Re-Identification, Transfer Learning

Person Re-Identification (re-id) is a challenging task in computer vision,
especially when there are limited training data from multiple camera views. In
this paper, we pro- pose a deep learning based person re-identification method
by transferring knowledge of mid-level attribute features and high-level
classification features. Building on the idea that identity classification,
attribute recognition and re- identification share the same mid-level semantic
representations, they can be trained sequentially by fine-tuning one based on
another. In our framework, we train identity classification and attribute
recognition tasks from deep Convolutional Neural Network (dCNN) to learn person
information. The information can be transferred to the person re-id task and
improves its accuracy by a large margin. Further- more, a Long Short Term
Memory(LSTM) based Recurrent Neural Network (RNN) component is extended by a
spacial gate. This component is used in the re-id model to pay attention to
certain spacial parts in each recurrent unit. Experimental results show that
our method achieves 78.3% of rank-1 recognition accuracy on the CUHK03
benchmark.
Fuzzy Statistical Matrices for Cell Classification
Authors: Guillaume Thibault, Izhak Shafran
Date: 2016-11-18 00:00:00
Tasks: Classification, General Classification

In this paper, we generalize image (texture) statistical descriptors and
propose algorithms that improve their efficacy. Recently, a new method showed
how the popular Co-Occurrence Matrix (COM) can be modified into a fuzzy version
(FCOM) which is more effective and robust to noise. Here, we introduce new
fuzzy versions of two additional higher order statistical matrices: the Run
Length Matrix (RLM) and the Size Zone Matrix (SZM). We define the fuzzy zones
and propose an efficient algorithm to compute the descriptors. We demonstrate
the advantage of the proposed improvements over several state-of-the-art
methods on three tasks from quantitative cell biology: analyzing and
classifying Human Epithelial type 2 (HEp-2) cells using Indirect
Immunofluorescence protocol (IFF).
Monte Carlo Tableau Proof Search
Authors: Michael Färber, Cezary Kaliszyk, Josef Urban
Date: 2016-11-18 00:00:00
Tasks: Automated Theorem Proving

We study Monte Carlo Tree Search to guide proof search in tableau calculi. This includes proposing a number of proof-state evaluation heuristics, some of which are learnt from previous proofs. We present an implementation based on the leanCoP prover. The system is trained and evaluated on a large suite of related problems coming from the Mizar proof assistant, showing that it is capable to find new and different proofs.
Incremental Truncated LSTD
Authors: Clement Gehring, Yangchen Pan, Martha White
Date: 2015-11-26 00:00:00
Tasks: Computational Efficiency, Reinforcement Learning

Balancing between computational efficiency and sample efficiency is an
important goal in reinforcement learning. Temporal difference (TD) learning
algorithms stochastically update the value function, with a linear time
complexity in the number of features, whereas least-squares temporal difference
(LSTD) algorithms are sample efficient but can be quadratic in the number of
features. In this work, we develop an efficient incremental low-rank
LSTD({\lambda}) algorithm that progresses towards the goal of better balancing
computation and sample efficiency. The algorithm reduces the computation and
storage complexity to the number of features times the chosen rank parameter
while summarizing past samples efficiently to nearly obtain the sample
complexity of LSTD. We derive a simulation bound on the solution given by
truncated low-rank approximation, illustrating a bias- variance trade-off
dependent on the choice of rank. We demonstrate that the algorithm effectively
balances computational complexity and sample efficiency for policy evaluation
in a benchmark task and a high-dimensional energy allocation domain.
Robust and Scalable Column/Row Sampling from Corrupted Big Data
Authors: Mostafa Rahmani, George Atia
Date: 2016-11-18 00:00:00
Tasks: Descriptive

Conventional sampling techniques fall short of drawing descriptive sketches
of the data when the data is grossly corrupted as such corruptions break the
low rank structure required for them to perform satisfactorily. In this paper,
we present new sampling algorithms which can locate the informative columns in
presence of severe data corruptions. In addition, we develop new scalable
randomized designs of the proposed algorithms. The proposed approach is
simultaneously robust to sparse corruption and outliers and substantially
outperforms the state-of-the-art robust sampling algorithms as demonstrated by
experiments conducted using both real and synthetic data.
Statistical Properties of European Languages and Voynich Manuscript Analysis
Authors: Andronik Arutyunov, Leonid Borisov, Sergey Fedorov, Anastasiya Ivchenko, Elizabeth Kirina-Lilinskaya, Yurii Orlov, Konstantin Osminin, Sergey Shilin, Dmitriy Zeniuk
Date: 2016-11-18 00:00:00

The statistical properties of letters frequencies in European literature
texts are investigated. The determination of logarithmic dependence of letters
sequence for one-language and two-language texts are examined. The pare of
languages is suggested for Voynich Manuscript. The internal structure of
Manuscript is considered. The spectral portraits of two-letters distribution
are constructed.
Word and Document Embeddings based on Neural Network Approaches
Authors: Siwei Lai
Date: 2016-11-18 00:00:00
Tasks: Feature Engineering, Part-Of-Speech Tagging, Representation Learning, Word Embeddings

Data representation is a fundamental task in machine learning. The
representation of data affects the performance of the whole machine learning
system. In a long history, the representation of data is done by feature
engineering, and researchers aim at designing better features for specific
tasks. Recently, the rapid development of deep learning and representation
learning has brought new inspiration to various domains.
  In natural language processing, the most widely used feature representation
is the Bag-of-Words model. This model has the data sparsity problem and cannot
keep the word order information. Other features such as part-of-speech tagging
or more complex syntax features can only fit for specific tasks in most cases.
This thesis focuses on word representation and document representation. We
compare the existing systems and present our new model.
  First, for generating word embeddings, we make comprehensive comparisons
among existing word embedding models. In terms of theory, we figure out the
relationship between the two most important models, i.e., Skip-gram and GloVe.
In our experiments, we analyze three key points in generating word embeddings,
including the model construction, the training corpus and parameter design. We
evaluate word embeddings with three types of tasks, and we argue that they
cover the existing use of word embeddings. Through theory and practical
experiments, we present some guidelines for how to generate a good word
embedding.
  Second, in Chinese character or word representation. We introduce the joint
training of Chinese character and word. ...
  Third, for document representation, we analyze the existing document
representation models, including recursive NNs, recurrent NNs and convolutional
NNs. We point out the drawbacks of these models and present our new model, the
recurrent convolutional neural networks. ...
A Characterization of Prediction Errors
Authors: Christopher Meek
Date: 2016-11-18 00:00:00
Tasks: Prediction

Understanding prediction errors and determining how to fix them is critical
to building effective predictive systems. In this paper, we delineate four
types of prediction errors and demonstrate that these four types characterize
all prediction errors. In addition, we describe potential remedies and tools
that can be used to reduce the uncertainty when trying to determine the source
of a prediction error and when trying to take action to remove a prediction
errors.
Associative Adversarial Networks
Authors: Tarik Arici, Asli Celikyilmaz
Date: 2016-11-18 00:00:00
Tasks: Generative Adversarial Network

We propose a higher-level associative memory for learning adversarial
networks. Generative adversarial network (GAN) framework has a discriminator
and a generator network. The generator (G) maps white noise (z) to data samples
while the discriminator (D) maps data samples to a single scalar. To do so, G
learns how to map from high-level representation space to data space, and D
learns to do the opposite. We argue that higher-level representation spaces
need not necessarily follow a uniform probability distribution. In this work,
we use Restricted Boltzmann Machines (RBMs) as a higher-level associative
memory and learn the probability distribution for the high-level features
generated by D. The associative memory samples its underlying probability
distribution and G learns how to map these samples to data space. The proposed
associative adversarial networks (AANs) are generative models in the
higher-levels of the learning, and use adversarial non-stochastic models D and
G for learning the mapping between data and higher-level representation spaces.
Experiments show the potential of the proposed networks.
Analysis of a Design Pattern for Teaching with Features and Labels
Authors: Christopher Meek, Patrice Simard, Xiaojin Zhu
Date: 2016-11-18 00:00:00

We study the task of teaching a machine to classify objects using features
and labels. We introduce the Error-Driven-Featuring design pattern for teaching
using features and labels in which a teacher prefers to introduce features only
if they are needed. We analyze the potential risks and benefits of this
teaching pattern through the use of teaching protocols, illustrative examples,
and by providing bounds on the effort required for an optimal machine teacher
using a linear learning algorithm, the most commonly used type of learners in
interactive machine learning systems. Our analysis provides a deeper
understanding of potential trade-offs of using different learning algorithms
and between the effort required for featuring (creating new features) and
labeling (providing labels for objects).
Minimal Problems for the Calibrated Trifocal Variety
Authors: Joe Kileel
Date: 2016-11-18 00:00:00

We determine the algebraic degree of minimal problems for the calibrated
trifocal variety in computer vision. We rely on numerical algebraic geometry
and the homotopy continuation software Bertini.
Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation
Authors: Mohammad Sadegh Aliakbarian, Fatemehsadat Saleh, Basura Fernando, Mathieu Salzmann, Lars Petersson, Lars Andersson
Date: 2016-11-17 00:00:00
Tasks: Action Recognition, Activity Recognition, Temporal Action Localization

Action recognition and anticipation are key to the success of many computer
vision applications. Existing methods can roughly be grouped into those that
extract global, context-aware representations of the entire image or sequence,
and those that aim at focusing on the regions where the action occurs. While
the former may suffer from the fact that context is not always reliable, the
latter completely ignore this source of information, which can nonetheless be
helpful in many situations. In this paper, we aim at making the best of both
worlds by developing an approach that leverages both context-aware and
action-aware features. At the core of our method lies a novel multi-stage
recurrent architecture that allows us to effectively combine these two sources
of information throughout a video. This architecture first exploits the global,
context-aware features, and merges the resulting representation with the
localized, action-aware ones. Our experiments on standard datasets evidence the
benefits of our approach over methods that use each information type
separately. We outperform the state-of-the-art methods that, as us, rely only
on RGB frames as input for both action recognition and anticipation.
Universum Prescription: Regularization using Unlabeled Data
Authors: Xiang Zhang, Yann Lecun
Date: 2015-11-11 00:00:00
Tasks: Image Classification

This paper shows that simply prescribing "none of the above" labels to
unlabeled data has a beneficial regularization effect to supervised learning.
We call it universum prescription by the fact that the prescribed labels cannot
be one of the supervised labels. In spite of its simplicity, universum
prescription obtained competitive results in training deep convolutional
networks for CIFAR-10, CIFAR-100, STL-10 and ImageNet datasets. A qualitative
justification of these approaches using Rademacher complexity is presented. The
effect of a regularization parameter -- probability of sampling from unlabeled
data -- is also studied empirically.
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models
Authors: Viktoriya Krakovna, Finale Doshi-Velez
Date: 2016-11-18 00:00:00
Tasks: speech-recognition, Speech Recognition, Time Series, Time Series Analysis, Translation

As deep neural networks continue to revolutionize various application
domains, there is increasing interest in making these powerful models more
understandable and interpretable, and narrowing down the causes of good and bad
predictions. We focus on recurrent neural networks, state of the art models in
speech recognition and translation. Our approach to increasing interpretability
is by combining a long short-term memory (LSTM) model with a hidden Markov
model (HMM), a simpler and more transparent model. We add the HMM state
probabilities to the output layer of the LSTM, and then train the HMM and LSTM
either sequentially or jointly. The LSTM can make use of the information from
the HMM, and fill in the gaps when the HMM is not performing well. A small
hybrid model usually performs better than a standalone LSTM of the same size,
especially on smaller data sets. We test the algorithms on text data and
medical time series data, and find that the LSTM and HMM learn complementary
information about the features in the text.