pwc-archive/papers-with-abstracts huggingface dataset:
Divide and Recombine for Large and Complex Data: Model Likelihood Functions using MCMC
Authors: Qi Liu, Anindya Bhadra, William S. Cleveland
Date: 2018-01-15 00:00:00
Tasks: regression

In Divide & Recombine (D&R), big data are divided into subsets, each analytic
method is applied to subsets, and the outputs are recombined. This enables deep
analysis and practical computational performance. An innovate D\&R procedure is
proposed to compute likelihood functions of data-model (DM) parameters for big
data. The likelihood-model (LM) is a parametric probability density function of
the DM parameters. The density parameters are estimated by fitting the density
to MCMC draws from each subset DM likelihood function, and then the fitted
densities are recombined. The procedure is illustrated using normal and
skew-normal LMs for the logistic regression DM.
A Joint 3D-2D based Method for Free Space Detection on Roads
Authors: Suvam Patra, Pranjal Maheshwari, Shashank Yadav, Chetan Arora, Subhashis Banerjee
Date: 2017-11-06 00:00:00
Tasks: Autonomous Driving, General Classification, object-detection, Object Detection, Road Segmentation, Segmentation

In this paper, we address the problem of road segmentation and free space
detection in the context of autonomous driving. Traditional methods either use
3-dimensional (3D) cues such as point clouds obtained from LIDAR, RADAR or
stereo cameras or 2-dimensional (2D) cues such as lane markings, road
boundaries and object detection. Typical 3D point clouds do not have enough
resolution to detect fine differences in heights such as between road and
pavement. Image based 2D cues fail when encountering uneven road textures such
as due to shadows, potholes, lane markings or road restoration. We propose a
novel free road space detection technique combining both 2D and 3D cues. In
particular, we use CNN based road segmentation from 2D images and plane/box
fitting on sparse depth data obtained from SLAM as priors to formulate an
energy minimization using conditional random field (CRF), for road pixels
classification. While the CNN learns the road texture and is unaffected by
depth boundaries, the 3D information helps in overcoming texture based
classification failures. Finally, we use the obtained road segmentation with
the 3D depth data from monocular SLAM to detect the free space for the
navigation purposes. Our experiments on KITTI odometry dataset, Camvid dataset,
as well as videos captured by us, validate the superiority of the proposed
approach over the state of the art.
What Level of Quality can Neural Machine Translation Attain on Literary Text?
Authors: Antonio Toral, Andy Way
Date: 2018-01-15 00:00:00
Tasks: Machine Translation, NMT, Translation

Given the rise of a new approach to MT, Neural MT (NMT), and its promising
performance on different text types, we assess the translation quality it can
attain on what is perceived to be the greatest challenge for MT: literary text.
Specifically, we target novels, arguably the most popular type of literary
text. We build a literary-adapted NMT system for the English-to-Catalan
translation direction and evaluate it against a system pertaining to the
previous dominant paradigm in MT: statistical phrase-based MT (PBSMT). To this
end, for the first time we train MT systems, both NMT and PBSMT, on large
amounts of literary text (over 100 million words) and evaluate them on a set of
twelve widely known novels spanning from the the 1920s to the present day.
According to the BLEU automatic evaluation metric, NMT is significantly better
than PBSMT (p < 0.01) on all the novels considered. Overall, NMT results in a
11% relative improvement (3 points absolute) over PBSMT. A complementary human
evaluation on three of the books shows that between 17% and 34% of the
translations, depending on the book, produced by NMT (versus 8% and 20% with
PBSMT) are perceived by native speakers of the target language to be of
equivalent quality to translations produced by a professional human translator.
Topic Modeling on Health Journals with Regularized Variational Inference
Authors: Robert Giaquinto, Arindam Banerjee
Date: 2018-01-15 00:00:00
Tasks: Topic Models, Variational Inference

Topic modeling enables exploration and compact representation of a corpus.
The CaringBridge (CB) dataset is a massive collection of journals written by
patients and caregivers during a health crisis. Topic modeling on the CB
dataset, however, is challenging due to the asynchronous nature of multiple
authors writing about their health journeys. To overcome this challenge we
introduce the Dynamic Author-Persona topic model (DAP), a probabilistic
graphical model designed for temporal corpora with multiple authors. The
novelty of the DAP model lies in its representation of authors by a persona ---
where personas capture the propensity to write about certain topics over time.
Further, we present a regularized variational inference algorithm, which we use
to encourage the DAP model's personas to be distinct. Our results show
significant improvements over competing topic models --- particularly after
regularization, and highlight the DAP model's unique ability to capture common
journeys shared by different authors.
The Active Atlas: Combining 3D Anatomical Models with Texture Detectors
Authors: Yuncong Chen, Lauren McElvain, Alex Tolpygo, Daniel Ferrante, Harvey Karten, Partha Mitra, David Kleinfeld, Yoav Freund
Date: 2017-02-28 00:00:00
Tasks: Anatomy

While modern imaging technologies such as fMRI have opened exciting new
possibilities for studying the brain in vivo, histological sections remain the
best way to study the anatomy of the brain at the level of single neurons. The
histological atlas changed little since 1909 and localizing brain regions is a
still a labor intensive process performed only by experienced neuro-anatomists.
Existing digital atlases such as the Allen Brain atlas are limited to low
resolution images which cannot identify the detailed structure of the neurons.
We have developed a digital atlas methodology that combines information about
the 3D organization of the brain and the detailed texture of neurons in
different structures. Using the methodology we developed an atlas for the mouse
brainstem and mid-brain, two regions for which there are currently no good
atlases. Our atlas is "active" in that it can be used to automatically align a
histological stack to the atlas, thus reducing the work of the neuroanatomist.
Subspace Clustering with Missing and Corrupted Data
Authors: Zachary Charles, Amin Jalali, Rebecca Willett
Date: 2017-07-08 00:00:00
Tasks: Clustering

Given full or partial information about a collection of points that lie close
to a union of several subspaces, subspace clustering refers to the process of
clustering the points according to their subspace and identifying the
subspaces. One popular approach, sparse subspace clustering (SSC), represents
each sample as a weighted combination of the other samples, with weights of
minimal $\ell_1$ norm, and then uses those learned weights to cluster the
samples. SSC is stable in settings where each sample is contaminated by a
relatively small amount of noise. However, when there is a significant amount
of additive noise, or a considerable number of entries are missing, theoretical
guarantees are scarce. In this paper, we study a robust variant of SSC and
establish clustering guarantees in the presence of corrupted or missing data.
We give explicit bounds on amount of noise and missing data that the algorithm
can tolerate, both in deterministic settings and in a random generative model.
Notably, our approach provides guarantees for higher tolerance to noise and
missing data than existing analyses for this method. By design, the results
hold even when we do not know the locations of the missing data; e.g., as in
presence-only data.
Behavior Trees in Robotics and AI: An Introduction
Authors: Michele Colledanchise, Petter Ögren
Date: 2017-08-31 00:00:00

A Behavior Tree (BT) is a way to structure the switching between different tasks in an autonomous agent, such as a robot or a virtual entity in a computer game. BTs are a very efficient way of creating complex systems that are both modular and reactive. These properties are crucial in many applications, which has led to the spread of BT from computer game programming to many branches of AI and Robotics. In this book, we will first give an introduction to BTs, then we describe how BTs relate to, and in many cases generalize, earlier switching structures. These ideas are then used as a foundation for a set of efficient and easy to use design principles. Properties such as safety, robustness, and efficiency are important for an autonomous system, and we describe a set of tools for formally analyzing these using a state space description of BTs. With the new analysis tools, we can formalize the descriptions of how BTs generalize earlier approaches. We also show the use of BTs in automated planning and machine learning. Finally, we describe an extended set of tools to capture the behavior of Stochastic BTs, where the outcomes of actions are described by probabilities. These tools enable the computation of both success probabilities and time to completion.
Unsupervised Cipher Cracking Using Discrete GANs
Authors: Aidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, Lukasz Kaiser
Date: 2018-01-15 00:00:00
Proceeding: ICLR 2018 1

This work details CipherGAN, an architecture inspired by CycleGAN used for
inferring the underlying cipher mapping given banks of unpaired ciphertext and
plaintext. We demonstrate that CipherGAN is capable of cracking language data
enciphered using shift and Vigenere ciphers to a high degree of fidelity and
for vocabularies much larger than previously achieved. We present how CycleGAN
can be made compatible with discrete data and train in a stable way. We then
prove that the technique used in CipherGAN avoids the common problem of
uninformative discrimination associated with GANs applied to discrete data.
Classification of histopathological breast cancer images using iterative VMD aided Zernike moments & textural signatures
Authors: Subhankar Chattoraj, Karan Vishwakarma
Date: 2018-01-15 00:00:00
Tasks: General Classification

In this paper we present a novel method for an automated diagnosis of breast
carcinoma through multilevel iterative variational mode decomposition (VMD) and
textural features encompassing Zernaike moments, fractal dimension and entropy
features namely, Kapoor entropy, Renyi entropy, Yager entropy features are
extracted from VMD components. The proposed method considers the
histopathological image as a set of multidimensional spatially-evolving
signals. ReliefF algorithm is used to select the discriminatory features and
statistically most significant features are fed to squares support vector
machine (SVM) for classification. We evaluate the efficiency of the proposed
methodology on publicly available Breakhis dataset containing 7,909 breast
cancer histological images, collected from 82 patients, of both benign and
malignant cases. Experimental results shows the efficacy of the proposed method
in outperforming the state of the art while achieving an average classification
rates of 89.61% and 88:23% using three-fold and ten-fold cross-validation
strategies, respectively. This system can aid the pathologist in accurate and
reliable diagnosis of biopsy samples. BreaKHis, a publicly dataset available at
http://web.inf.ufpr.br/vri/breast-cancer-database.
Generalizing, Decoding, and Optimizing Support Vector Machine Classification
Authors: Mario Michael Krell
Date: 2018-01-15 00:00:00
Tasks: Classification, General Classification

The classification of complex data usually requires the composition of
processing steps. Here, a major challenge is the selection of optimal
algorithms for preprocessing and classification (including parameterizations).
Nowadays, parts of the optimization process are automized but expert knowledge
and manual work are still required. We present three steps to face this process
and ease the optimization. Namely, we take a theoretical view on classical
classifiers, provide an approach to interpret the classifier together with the
preprocessing, and integrate both into one framework which enables a
semiautomatic optimization of the processing chain and which interfaces
numerous algorithms.
Building a Conversational Agent Overnight with Dialogue Self-Play
Authors: Pararth Shah, Dilek Hakkani-Tür, Gokhan Tür, Abhinav Rastogi, Ankur Bapna, Neha Nayak, Larry Heck
Date: 2018-01-15 00:00:00
Tasks: Diversity

We propose Machines Talking To Machines (M2M), a framework combining
automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents
for goal-oriented dialogues in arbitrary domains. M2M scales to new tasks with
just a task schema and an API client from the dialogue system developer, but it
is also customizable to cater to task-specific interactions. Compared to the
Wizard-of-Oz approach for data collection, M2M achieves greater diversity and
coverage of salient dialogue flows while maintaining the naturalness of
individual utterances. In the first phase, a simulated user bot and a
domain-agnostic system bot converse to exhaustively generate dialogue
"outlines", i.e. sequences of template utterances and their semantic parses. In
the second phase, crowd workers provide contextual rewrites of the dialogues to
make the utterances more natural while preserving their meaning. The entire
process can finish within a few hours. We propose a new corpus of 3,000
dialogues spanning 2 domains collected with M2M, and present comparisons with
popular dialogue datasets on the quality and diversity of the surface forms and
dialogue flows.
Smooth Alternating Direction Methods for Nonsmooth Constrained Convex Optimization
Authors: Quoc Tran-Dinh, Volkan Cevher
Date: 2015-07-14 00:00:00

We propose two new alternating direction methods to solve "fully" nonsmooth
constrained convex problems. Our algorithms have the best known worst-case
iteration-complexity guarantee under mild assumptions for both the objective
residual and feasibility gap. Through theoretical analysis, we show how to
update all the algorithmic parameters automatically with clear impact on the
convergence performance. We also provide a representative numerical example
showing the advantages of our methods over the classical alternating direction
methods using a well-known feasibility problem.
Improving Orbit Prediction Accuracy through Supervised Machine Learning
Authors: Hao Peng, Xiaoli Bai
Date: 2018-01-15 00:00:00
Tasks: BIG-bench Machine Learning, Collision Avoidance, Prediction

Due to the lack of information such as the space environment condition and
resident space objects' (RSOs') body characteristics, current orbit predictions
that are solely grounded on physics-based models may fail to achieve required
accuracy for collision avoidance and have led to satellite collisions already.
This paper presents a methodology to predict RSOs' trajectories with higher
accuracy than that of the current methods. Inspired by the machine learning
(ML) theory through which the models are learned based on large amounts of
observed data and the prediction is conducted without explicitly modeling space
objects and space environment, the proposed ML approach integrates
physics-based orbit prediction algorithms with a learning-based process that
focuses on reducing the prediction errors. Using a simulation-based space
catalog environment as the test bed, the paper demonstrates three types of
generalization capability for the proposed ML approach: 1) the ML model can be
used to improve the same RSO's orbit information that is not available during
the learning process but shares the same time interval as the training data; 2)
the ML model can be used to improve predictions of the same RSO at future
epochs; and 3) the ML model based on a RSO can be applied to other RSOs that
share some common features.
Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up
Authors: Matej Hoffmann, Rolf Pfeifer
Date: 2018-01-15 00:00:00

A large body of compelling evidence has been accumulated demonstrating that embodiment - the agent's physical setup, including its shape, materials, sensors and actuators - is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe.
Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly
Authors: Michael Opitz, Georg Waltner, Horst Possegger, Horst Bischof
Date: 2018-01-15 00:00:00
Tasks: Diversity, Image Retrieval, Metric Learning, Retrieval

Learning similarity functions between image pairs with deep neural networks
yields highly correlated activations of embeddings. In this work, we show how
to improve the robustness of such embeddings by exploiting the independence
within ensembles. To this end, we divide the last embedding layer of a deep
network into an embedding ensemble and formulate training this ensemble as an
online gradient boosting problem. Each learner receives a reweighted training
sample from the previous learners. Further, we propose two loss functions which
increase the diversity in our ensemble. These loss functions can be applied
either for weight initialization or during training. Together, our
contributions leverage large embedding sizes more effectively by significantly
reducing correlation of the embedding and consequently increase retrieval
accuracy of the embedding. Our method works with any differentiable loss
function and does not introduce any additional parameters during test time. We
evaluate our metric learning method on image retrieval tasks and show that it
improves over state-of-the-art methods on the CUB 200-2011, Cars-196, Stanford
Online Products, In-Shop Clothes Retrieval and VehicleID datasets.
Predicting Movie Genres Based on Plot Summaries
Authors: Quan Hoang
Date: 2018-01-15 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, text-classification, Text Classification

This project explores several Machine Learning methods to predict movie
genres based on plot summaries. Naive Bayes, Word2Vec+XGBoost and Recurrent
Neural Networks are used for text classification, while K-binary
transformation, rank method and probabilistic classification with learned
probability threshold are employed for the multi-label problem involved in the
genre tagging task.Experiments with more than 250,000 movies show that
employing the Gated Recurrent Units (GRU) neural networks for the probabilistic
classification with learned probability threshold approach achieves the best
result on the test set. The model attains a Jaccard Index of 50.0%, a F-score
of 0.56, and a hit rate of 80.5%.
Eye-Movement behavior identification for AD diagnosis
Authors: Juan Biondi, Gerardo Fernandez, Silvia Castro, Osvaldo Agamennoni
Date: 2017-02-02 00:00:00
Tasks: Denoising

In the present work, we develop a deep-learning approach for differentiating
the eye-movement behavior of people with neurodegenerative diseases over
healthy control subjects during reading well-defined sentences. We define an
information compaction of the eye-tracking data of subjects without and with
probable Alzheimer's disease when reading a set of well-defined, previously
validated, sentences including high-, low-predictable sentences, and proverbs.
Using this information we train a set of denoising sparse-autoencoders and
build a deep neural network with these and a softmax classifier. Our results
are very promising and show that these models may help to understand the
dynamics of eye movement behavior and its relationship with underlying
neuropsychological correlates.
Artificial Intelligence (AI) Methods in Optical Networks: A Comprehensive Survey
Authors: Javier Mata, Ignacio de Miguel, Ramó n J. Durá n, Noemí Merayo, Sandeep Kumar Singh, Admela Jukan, Mohit Chamania
Date: 2018-01-05 00:00:00
Tasks: Management, Survey

Artificial intelligence (AI) is an extensive scientific discipline which
enables computer systems to solve problems by emulating complex biological
processes such as learning, reasoning and self-correction. This paper presents
a comprehensive review of the application of AI techniques for improving
performance of optical communication systems and networks. The use of AI-based
techniques is first studied in applications related to optical transmission,
ranging from the characterization and operation of network components to
performance monitoring, mitigation of nonlinearities, and quality of
transmission estimation. Then, applications related to optical network control
and management are also reviewed, including topics like optical network
planning and operation in both transport and access networks. Finally, the
paper also presents a summary of opportunities and challenges in optical
networking where AI is expected to play a key role in the near future.
SAR Image Despeckling Using Quadratic-Linear Approximated L1-Norm
Authors: Fatih Nar
Date: 2018-01-15 00:00:00
Tasks: Computational Efficiency, Sar Image Despeckling

Speckle noise, inherent in synthetic aperture radar (SAR) images, degrades
the performance of the various SAR image analysis tasks. Thus, speckle noise
reduction is a critical preprocessing step for smoothing homogeneous regions
while preserving details. This letter proposes a variational despeckling
approach where L1-norm total variation regularization term is approximated in a
quadratic and linear manner to increase accuracy while decreasing the
computation time. Despeckling performance and computational efficiency of the
proposed method are shown using synthetic and real-world SAR images.
FOTS: Fast Oriented Text Spotting with a Unified Network
Authors: Xuebo Liu, Ding Liang, Shi Yan, Dagui Chen, Yu Qiao, Junjie Yan
Date: 2018-01-05 00:00:00
Proceeding: CVPR 2018 6
Tasks: Scene Text Detection, Scene Text Recognition, Text Detection, Text Spotting

Incidental scene text spotting is considered one of the most difficult and
valuable challenges in the document analysis community. Most existing methods
treat text detection and recognition as separate tasks. In this work, we
propose a unified end-to-end trainable Fast Oriented Text Spotting (FOTS)
network for simultaneous detection and recognition, sharing computation and
visual information among the two complementary tasks. Specially, RoIRotate is
introduced to share convolutional features between detection and recognition.
Benefiting from convolution sharing strategy, our FOTS has little computation
overhead compared to baseline text detection network, and the joint training
method learns more generic features to make our method perform better than
these two-stage methods. Experiments on ICDAR 2015, ICDAR 2017 MLT, and ICDAR
2013 datasets demonstrate that the proposed method outperforms state-of-the-art
methods significantly, which further allows us to develop the first real-time
oriented text spotting system which surpasses all previous state-of-the-art
results by more than 5% on ICDAR 2015 text spotting task while keeping 22.6
fps.